JP2018054348A - Earthquake forecasting method and earthquake forecasting system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To make a wide range of earthquakes predictable at low cost. An earthquake prediction method includes a data collection step of collecting data on the spatial radiation amount at the measurement point, which is periodically measured by an spatial radiation amount measuring device installed on the ground at the measurement point, and periodic measurement. The abnormal value detection step of detecting the abnormal value of the deviation from the average in the space radiation amount as a precursor of an earthquake, and the abnormal value that can occur after the abnormal value occurs based on the detected abnormal value. It has a scale prediction stage for predicting the distance to the center of the earthquake corresponding to and the scale of the earthquake. [Selection diagram] Fig. 2

Description

  The present invention relates to an earthquake prediction method and an earthquake prediction system.

In recent years, it has been proposed to predict various earthquakes by predicting various electromagnetic phenomena as earthquake precursors.
For example, the system disclosed in Patent Document 1 detects disturbances generated in the ionosphere using, for example, very long wave (VHF) radio waves emitted from the ground. For example, in a normal case where no disturbance occurs in the E layer of the ionosphere, the VHF radio wave does not pass through the E layer and return to the ground. However, when disturbance occurs in the E layer, the VHF radio wave that reaches the E layer is scattered (reflected) by the disturbance of the E layer, and the VHF radio wave propagates out of line of sight (usually far away from the VHF radio wave). Is done. In the system shown in Patent Document 1, a VHF radio wave is emitted from the ground, and when the VHF radio wave is reflected by disturbance of the E layer, the reflected VHF radio wave is detected on the ground.

  In addition, a method of observing disturbances occurring in the lower part of the ionosphere (for example, D layer) generated before an earthquake using VLF (Very Low Frequency) or LF (Low Frequency) radio waves transmitted from the ground is known. . For example, the system shown in Patent Document 2 predicts an earthquake by utilizing the fact that an abnormality occurs in the intensity and phase of a received radio wave when disturbance occurs in the ionosphere on the propagation path of the VLF / LF radio wave.

  In addition, it has been proposed to make an earthquake prediction by regarding the radon status of groundwater in wells and rivers as a sign of an earthquake. For example, Patent Document 3 shows that groundwater in a well, a river, or the like is to be measured, and the status of radon contained in the water is detected to predict an earthquake.

Japanese Patent No. 2875398 Japanese Patent No. 4,867,016 JP 2008-139114 A

However, in the techniques described in Patent Documents 1 and 2 described above, it is necessary to install an antenna for receiving radio waves in an area where VHF radio waves are propagated due to disturbance of the ionosphere. In other words, there is a limit to the area where earthquake prediction is performed using an antenna arranged in one place. For example, in order to predict the possibility of an earthquake occurring in any region over a wide area across the country, it is necessary to construct antennas and their associated facilities at various locations in the wide area.
Moreover, in the technique described in Patent Document 3, it is required to detect the status of radon in a well or a river. Even with this technology, in order to predict the possibility of an earthquake occurring in any region over a wide area of the country, it is necessary to construct detection facilities at various locations in a wide area.

  The present invention has been made in view of the above-described problems, and an object thereof is to make it possible to predict earthquakes over a wide range at a low cost.

The present invention employs the following configuration in order to solve the above-described problems.
(1) a data collection stage for collecting spatial radiation dose data of the measurement point periodically measured by a spatial radiation dose measurement device installed on the ground of the measurement point;
An abnormal value detection stage for detecting an abnormal value of deviation from the average of the spatial radiation dose measured periodically as a precursor of an earthquake;
Based on the detected abnormal value, a scale prediction step for predicting the distance to the epicenter of the earthquake corresponding to the abnormal value and the magnitude of the earthquake that may occur after the abnormal value has occurred,
An earthquake prediction method characterized by comprising:

The inventor thoroughly examined the relevance to earthquakes for various phenomena. As a result, the present inventor has found that, among various phenomena, the relationship between the spatial radiation dose measured by the spatial radiation dose measurement device and the earthquake is high. We also found that the radiation dose at the measurement point is highly related to the distance to the epicenter of the earthquake and the magnitude of the earthquake. The present invention is based on such knowledge.
In the method (1), a precursor of an earthquake is detected based on the abnormal value of the spatial radiation dose measured by the spatial radiation dose measuring device, and the distance to the epicenter of the earthquake and the magnitude of the earthquake are determined based on the abnormal value. is expected. In this method, a spatial radiation dose measuring device can be used for measuring the spatial radiation dose at a measurement point. Such space radiation dose measuring devices have already been constructed in a wide range. A wide range of earthquakes can be predicted at low cost by using the detection results of a space radiation dose measuring device constructed over a wide area.
The deviation means the difference between the measured value and the average value at the measurement target time point among the measured values represented by the data.

  (2) The earthquake prediction method according to (1), further comprising a time prediction step of predicting the occurrence time of the earthquake based on the time when the abnormal value detected in the abnormal value detection step occurs.

  The present inventor has found that there is a relation between the time when an abnormal value in the space radiation dose measured by the space radiation dose measuring apparatus is generated and the occurrence time of the earthquake. The present invention is based on such knowledge. According to (2), information on the location, scale, and timing of the earthquake can be obtained by using the detection result of the space radiation dose measuring device. Therefore, the earthquake can be predicted at low cost.

  (3) The outlier detection step is performed when the space radiation dose measured by the space radiation dose measuring device is lower than the dose per unit time of the normal public dose limit as recommended by the International Commission on Radiological Protection. (1) or (2) earthquake prediction method, wherein an abnormal value is detected based on a spatial radiation dose.

Existing air radiation dose measuring devices are usually installed mainly for the purpose of detecting air radiation doses exceeding those recommended by the International Commission on Radiological Protection. This is because existing space radiation dose measuring devices are installed mainly to detect the effects of radiation in various places when damage to nuclear facilities occurs due to, for example, the occurrence of an earthquake. is there. However, existing space radiation dose measuring devices usually measure the amount of space radiation even when it is lower than the recommended amount.
According to (3), when a significant result is not detected as the original installation purpose of the space radiation dose measuring apparatus, an abnormal value is detected based on this detection result. Thereby, the detection result of the space radiation dose measuring device can be used for prediction of an earthquake before the earthquake occurs.
In addition, the amount per unit time of the dose limit of the public in the normal time according to the recommendation of the International Commission on Radiological Protection is 0.19 μS / h.

(4) The data collection step collects data of a spatial radiation dose measured by a spatial radiation dose measurement device installed at a plurality of measurement points;
The abnormal value detection step detects the abnormal value for each of a plurality of measurement points,
The magnitude prediction step predicts the distance to the epicenter of the earthquake corresponding to the abnormal value and the magnitude of the earthquake for each of a plurality of measurement points. Prediction method.

  According to (4), the distance to the epicenter of the earthquake and the magnitude of the earthquake are predicted based on the spatial radiation dose measured by the spatial radiation dose measurement devices installed at a plurality of measurement points. From the prediction results for a plurality of measurement points, the position of the epicenter of the earthquake can be predicted with higher accuracy. Therefore, the position of the earthquake can be predicted with high accuracy.

  (5) The said space radiation dose measuring apparatus transmits the data showing the measured space radiation dose through a computer network, The earthquake prediction method of any one of (1) to (4) characterized by the above-mentioned.

  According to (5), data representing the spatial radiation dose measured by the spatial radiation dose measurement device is transmitted through the computer network. For this reason, even if the space radiation dose measuring apparatus is arrange | positioned in the area separated from the apparatus which performs the process about the earthquake prediction using data, the process about an earthquake prediction can be started rapidly. Therefore, earthquake prediction can be performed quickly at low cost.

  (6) The method for predicting earthquake according to any one of (1) to (5), wherein the space radiation dose measuring device is a monitoring post for periodically monitoring the radiation dose at the measurement point.

According to (6), the spatial radiation dose measured by the monitoring post is used. Monitoring posts are installed on the site of nuclear power plants by power companies. In addition, monitoring posts are set up mainly by local governments around nuclear power plants. Real-time measurement data is published on the web pages of local governments, the Ministry of Education, Culture, Sports, Science and Technology, the Nuclear Regulatory Commission, and power companies. In particular, a number of monitoring posts have been built nationwide in the wake of the accident at a nuclear power plant following the 2011 Tohoku Earthquake. According to (6), earthquake prediction can be performed by using public information of a large number of monitoring posts installed throughout the country.
The monitoring post is originally a facility for monitoring the effects of radiation leakage from nuclear facilities. In particular, in the event of an accident, the main purpose is to detect the amount of space radiation that exceeds the equivalent value recommended by the International Commission on Radiological Protection. On the other hand, according to (6), the monitoring post should be used for the purpose of catching a sign of an earthquake by detecting fluctuations in the amount of space radiation that does not exceed the equivalent value recommended by the International Commission on Radiological Protection. Can do.
The monitoring post is primarily intended to monitor whether there is a radioactive leak from a nuclear facility due to the earthquake after the earthquake. On the other hand, according to (6), the monitoring post can be used for the purpose of catching a sign of an earthquake before the occurrence of the earthquake.
According to (6), it is possible to accurately predict a wide range of earthquakes at low cost using an existing monitoring post.

  (7) In the abnormal value detection step, the abnormal value corresponding to the earthquake is detected based on a ratio of the deviation detected in the abnormal value detection step to a standard deviation of the spatial radiation dose measured periodically. The earthquake prediction method according to any one of (1) to (6), wherein:

  There is a variation in the amount of space radiation when no abnormal value is detected, and the range of variation varies depending on the measurement point. According to (7), the abnormal value is detected based on the ratio of the deviation detected in the abnormal value detection stage to the standard deviation of the spatial radiation dose measured periodically. Therefore, the difference in detection accuracy depending on the measurement point can be suppressed.

(8) a determination step of determining whether or not the radio wave received by the receiving device installed on the ground has an influence of an earthquake precursor,
A predetermined region setting step for setting a predetermined region for predicting an earthquake based on the prediction result of the scale prediction step and the determination result of the determination step;
(1) to (7), including a prediction step of predicting that the probability that an earthquake will occur in the predetermined area is increased when it is determined that there is an influence in both the scale prediction step and the determination step. ) Any one earthquake prediction method.

  According to (8), the earthquake precursor verification determination by the radio wave received by the receiving device installed on the ground is combined with the prediction based on the space radiation dose. Therefore, the earthquake prediction accuracy can be increased at low cost.

(9) data collection means for collecting data of the spatial radiation dose at the measurement point periodically measured by a spatial radiation dose measurement device installed on the ground of the measurement point;
An abnormal value detecting means for detecting an abnormal value of deviation from the average of the spatial radiation dose measured periodically as a precursor of an earthquake;
Based on the detected abnormal value, a scale prediction step for predicting the distance to the epicenter of the earthquake corresponding to the abnormal value and the magnitude of the earthquake that may occur after the abnormal value has occurred,
An earthquake prediction system characterized by comprising:

  According to the earthquake prediction system of (9), a precursor of an earthquake is detected based on the abnormal value of the spatial radiation dose measured by the spatial radiation dose measuring device, and the distance to the epicenter of the earthquake based on the abnormal value and the above-mentioned The magnitude of the earthquake is predicted. In this system, a spatial radiation dose measuring device can be used for measuring the spatial radiation dose at a measurement point. Therefore, a wide range of earthquakes can be predicted at low cost.

  According to the present invention, a wide range of earthquakes can be predicted at low cost.

It is a figure which shows an example of an earthquake prediction system. It is the figure which showed the observation method typically. It is a graph which shows the change of the air dose in an example of the space radiation dose measuring apparatus. It is a graph which shows the relationship between the elapsed time after the abnormal value detection of an air dose, and the incidence of an earthquake. It is a graph which shows the relationship between the anomaly index in the past earthquake and the magnitude of an earthquake. It is a figure which shows the main characteristics of an observation method. It is a figure which shows an example of the system configuration | structure of an earthquake observation system. It is a figure which shows an example of the hardware constitutions of an analysis server. It is a figure which shows the determination result table. It is a figure which shows an earthquake prediction table. It is a figure which shows the table for a precursor abnormality period setting. It is a figure which shows the table for prediction hypocenter block setting. It is a figure which shows an example of an SRD observation network. It is a figure which shows an example of a VLF / LF observation network. It is a figure which shows an example of the flowchart which concerns on a main process. It is a figure which shows an example of the flowchart which concerns on an earthquake prediction process. It is a figure which shows an example of VLF observation data. It is a figure which shows an example of a VLF graph. It is a figure which shows an example of VOR observation data. It is a figure which shows an example of VOR observation data. It is a figure which shows an example of a VOR graph. It is a figure which shows an example of a VOR graph. It is a figure which shows an earthquake hazard map. It is a graph explaining the example of application of the earthquake prediction method using space radiation dose.

  Embodiments will be described below with reference to the drawings.

[First Embodiment]
<< Outline of earthquake prediction system (earthquake prediction method) >>
The outline of the earthquake prediction system (earthquake prediction method) will be described with reference to FIGS.
FIG. 1 is a diagram illustrating an example of an earthquake prediction system 100 according to the present embodiment. The earthquake prediction system 100 is a system that can improve the accuracy of earthquake prediction by collecting and analyzing a plurality of types of observation data related to the precursory phenomenon of an earthquake.

  The earthquake prediction system 100 includes a data processing unit 110 and a data storage unit 120.

  The data processing unit 110 performs data collection processing 111 (such as database construction). In the data collection process 111, the data processing unit 110 acquires the observation data 131 of each observation point and stores it as observation data in the data storage unit 120 (first database 121).

  The data processing unit 110 performs a data analysis process 112 (such as extraction of a precursor phenomenon). In the data analysis processing 112, the data processing unit 110 reads the observation data, performs data analysis (such as deviation analysis of air dose, VLF signal analysis, FFT, polarization analysis), writes the analysis result, and stores the data as the analysis result Store in the unit 120 (second database 122).

The data processing unit 110 performs prediction information creation processing 113 (determination of the degree of abnormality, etc.). In the prediction information creation process 113, the data processing unit 110 reads the analysis result and statistical basic data 132 (for example, statistical basic data for the past 10 to 20 years), determines the degree of abnormality, and calculates earthquake prediction information ( The information is stored in the data storage unit 120 (third database 123) as information including when, where, and how large an earthquake may occur.
The third database 123 includes information on earthquakes, weather, geology, age, and tide.

  The data processing unit 110 performs distribution information creation processing 114 (such as reflecting earthquake prediction information in map information). In the distribution information creation process 114, the data processing unit 110 reads the earthquake prediction information, reflects it in the map information, writes the distribution information, and stores it in the data storage unit 120 (fourth database 124) as the earthquake distribution information.

  The processing performed by the data processing unit 110 may be performed by a single information processing device or may be performed in a distributed manner by a plurality of information processing devices. About the process which the data processing part 110 performs, a person may take part or all the processes.

  The data stored in the data storage unit 120 may be stored in one storage device (storage medium) or may be stored in a distributed manner in a plurality of storage devices. Good. The data storage unit 120 may be a database or a recording medium such as paper.

<< Observation method (observation data) >>
FIG. 2 is a diagram schematically showing an observation method of observation data used in the earthquake prediction system 100. An observation method (observation data) will be described with reference to FIG. The observation data is data observed at observation points provided in various places.

  In the present embodiment, four precursor phenomena are used. The first precursor phenomenon is a variation in the amount of space radiation. The second to fourth precursor phenomena are fluctuations in radio waves.

<Observation data related to the first precursor phenomenon>
The first precursor phenomenon is a spatial radiation dose measured by the spatial radiation dose measurement device 212.
As a first precursor phenomenon, a change in space radiation dose (SRD) occurs in the epicenter area 201 (near the epicenter) before the earthquake occurs.
As a cause, it is presumed that in the epicenter area 201 (near the epicenter), a fine fracture (micro crack) occurs before the earthquake occurs, and radioactive elements are released into the air from countless cracks.
The amount of increase in the amount of space radiation is much smaller than the amount of radiation released due to, for example, damage to facilities in nuclear facilities such as nuclear power plants. For example, the monitoring post is a facility for monitoring whether or not radiation that may affect the human body due to an accident at a nuclear power plant is emitted. As the amount of radiation that can affect the human body, the International Commission on Radiological Protection recommends the amount per unit time of the normal dose limit for the public. The recommended value of the dose limit for the public in normal times is 1 mSv per year. Assuming a lifestyle pattern of staying outdoors for 8 hours and indoors for 16 hours in a day, the spatial radiation dose serving as an index for setting the annual exposure dose (additional exposure dose) to 1 mSv is 0.19 μSv / It corresponds to h. The fluctuation amount of the space radiation dose as a precursor of the earthquake is smaller than 0.19 μSv / h. In the earthquake prediction system 100 of the present embodiment, the dose per unit time of the normal public dose limit according to the recommendation of the International Radiation Protection Commission, that is, based on the spatial radiation dose when it is lower than 0.19 μSv / h. Detect outliers.
As a result of research by the inventors of the present application, the following has been found. The amount of increase in the amount of space radiation as a precursor of an earthquake tends to decrease as the distance from the epicenter of the earthquake increases. That is, the amount of increase in the amount of space radiation tends to increase as it approaches the epicenter of an earthquake. Also, the amount of increase in space radiation dose tends to increase as the magnitude of the earthquake increases. Also, an increase in the amount of space radiation tends to occur 7 to 13 days before the earthquake. The increase in the amount of space radiation is temporary, and often returns to the average value on the next day when the increase occurs.
By increasing the amount of space radiation, it is possible to predict an earthquake over a circular range with a radius of about 30 km from the first observation point.
Although details will be described later, the earthquake prediction system 100 can predict an earthquake in the vicinity of the space radiation dose measuring device 212 based on the air dose measurement data.

  The first precursor phenomenon using the space radiation dose (SRD) will be described in detail based on an example.

FIG. 3 is a graph showing changes in air dose at a monitoring post installed at a certain point in Hakodate city, which is an example of the space radiation dose measuring device 212.
As shown in FIG. 3, the air dose is extremely increased on June 9 compared with the others. However, the increased air dose is lower than 0.1 μSv / h. That is, the increased air dose is well below the dose limit recommended by the International Commission on Radiological Protection.
In Hakodate, an M5.3 earthquake was observed on June 16, seven days after the rise in air dose.

FIG. 4 is a graph showing the relationship between the elapsed time after detecting the abnormal value of the air dose and the earthquake occurrence rate.
Figure 4 shows the results of identifying outliers (increase in deviation from the average) of air doses at monitoring posts near the epicenter of major earthquakes of M5.0 or higher that occurred in the past 2015, going back from the date of the earthquake. As obtained. FIG. 4 shows the occurrence of an earthquake after detection of an abnormal value as a percentage of the total number of days from the day when the abnormal value occurs until the earthquake occurs.
As shown in FIG. 4, there is a high probability that an earthquake will occur in the period from 7 to 13 days after detection of an abnormal value due to an increase in the amount of space radiation.

  In the earthquake prediction system of the present embodiment, the occurrence time of the earthquake is predicted based on the time when the abnormal value of the space radiation dose occurs. More specifically, in the earthquake prediction system, after an abnormal value of the space radiation dose occurs, a period after 7 to 13 days is predicted as an earthquake occurrence time. In the case where the period is specified more narrowly, it is preferable to predict the period from 7 to 9 days after the occurrence of an abnormal value of the spatial radiation dose as the occurrence time of the earthquake.

Air radiation doses fluctuate during periods other than those associated with earthquakes. Therefore, in the earthquake prediction system of the present embodiment, an abnormal value of deviation with respect to the average of the periodically measured space radiation dose is detected as a precursor of the earthquake.
The abnormal value I of the space radiation dose is expressed by the following formula.

I = (x−m) / σ
here,
x: Air dose (μSv / h) (when abnormal)
m: Average value of air dose (average of air dose for the past 13 days) [μSv / h]
σ: Standard deviation [μSv / h]

  In the earthquake prediction system of this embodiment, when the abnormal value I exceeds a predetermined upper limit value, the prediction is performed assuming that an earthquake may occur after 7 to 9 days.

As mentioned above, assuming that the increase in space radiation dose is due to the release of radioactive materials due to microfracture in the epicenter region, the increase in space radiation dose depends on the magnitude of the earthquake and the distance to the epicenter Conceivable.
From this, the relationship between the magnitude of the earthquake and the distance to the epicenter is derived for an abnormal value of a certain amount of space radiation. Radiation from the release of radioactive material diffuses in all directions in a spherical shape. From this, an anomaly index Ip that takes into account the distance to the epicenter is derived for the above-mentioned anomaly value I.

Ip = I / 4πr ² = (x−m) / 4πr ² σ (I)
here,
r: Distance to the epicenter [km]

FIG. 5 is a graph showing the relationship between the anomaly index Ip and the magnitude of an earthquake in past earthquakes. Fig. 5 shows the relationship between the anomaly index Ip calculated based on the change in space radiation dose at the monitoring posts near the earthquake and the magnitude of the earthquake (magnitude M) for each earthquake. ing. The vertical axis of the graph of FIG. 5 expressed as the abnormal index Ip is logarithmic display.
From the graph of FIG. 5, an exponential approximation curve (a straight line in FIG. 8) that approximates the relationship between the anomaly index Ip and the magnitude of the earthquake M is obtained. Is guided. For example, the estimation formula is as follows.

Ip = 10 ⁻⁷ e ^1.7703M

From formula (I):
I / 4πr ² = 10 ⁻⁷ e ^{1.7703 M} (II)

Also,
1.7703M = ln ((x−m) / 4πr ² σ × 10 ⁷ ) (III)

  In the earthquake prediction system of this embodiment, the distance r from the abnormal value I to the epicenter of the earthquake corresponding to the abnormal value I and the magnitude M of the earthquake that can occur after the abnormal value I occurs are predicted. More specifically, the earthquake prediction system predicts the relationship between the distance r to the epicenter of the earthquake and the magnitude (scale) M of the earthquake. However, there is a lower limit to the magnitude of earthquakes that produce measurable abnormal values I. The lower limit is a range of magnitude 4.5 to 5.0. In this connection, the distance over which the anomalous value I is measured is also limited. The distance limit is about 30 km.

  For the earthquake plotted in the graph of FIG. 5, the relative error of the earthquake magnitude M calculated using the formula (III) with respect to the actual earthquake magnitude is 20% or less. Originally, a normal monitoring post has a dose detection error of about 20% nominally. Therefore, it can be said that the practical accuracy is estimated from the formula (III) itself.

The above formula (III) is an example of an estimation formula. Formula (III) is more generally expressed as:
aM = ln (b / r ² ) (IV)
a and b are values updated according to the result of the continuous measurement.

<Observation data related to the second precursor>
As a second precursor phenomenon, when an earthquake occurs, charge fluctuations due to microcracks occur, and when the charge fluctuations reach the ionosphere, the electron density (plasma density) of the ionosphere changes, and disturbance 202 occurs in the ionosphere. Has been reported. In addition, it has been known that the magnitude of the earthquake is determined according to the degree of the ionospheric disturbance 202.

(First radio wave)
At the receiving station 222 (second observation point) as a receiving device, observation related to the second precursor phenomenon is performed. More specifically, the receiving station 222 receives standard radio waves used for radio clocks and navigation radio waves used for navigation. If no special distinction is required, the standard radio wave and the navigation radio wave are referred to as a first radio wave 223 (VLF (Very Low Frequency) / LF (Low Frequency) radio wave). In addition, VLF / LF radio waves are referred to as VLF radio waves when no special distinction is required. When the first radio wave 223 hits the ionosphere, most of it is reflected by the lowermost surface of the ionosphere, and has a characteristic of propagating very far by being reflected between the ground and the ionosphere.

  Here, it is reported that the lower ionosphere (for example, D layer) above the epicenter 201 is lowered before the earthquake. In other words, an electric field is generated due to micro-cracks caused by seismic fault motion, and electromagnetic waves are radiated or charged particles are released into the atmosphere, which causes the lower ionosphere to fall, resulting in an earthquake. A propagation abnormality (variation abnormality) of the first radio wave 223 accompanying this is reported.

For example, when the first radio wave 223 hits the ionosphere, most of the first radio wave 223 is reflected from the lowermost surface of the ionosphere, so the propagation distance of the first radio wave 223 is short. Therefore, the arrival time at the observation point is earlier than normal.
In addition, for example, the energy loss increases as the light is reflected at a lower portion, and the reception intensity of the first radio wave 223 is smaller than normal.
In addition, for example, the sunrise time indicating the minimum phase is earlier than the several days before the earthquake, and the sunset time indicating the minimum phase is delayed.
Further, for example, the amount obtained by integrating the deviation from the average amplitude change at night over the night (the amount of fluctuation at night) increases.

For example, when there is no disturbance 202 in the ionosphere, if the VLF radio wave from the transmitting station 221 is received by the receiving station 222 after 1 second, if the ionosphere drops several kilometers before the earthquake, the path through which the radio wave passes becomes short. It reaches in 0.999 seconds (the phase advances). By accurately measuring the amplitude and phase at the receiving station 222, the ionospheric abnormality can be grasped.
That is, by detecting the propagation abnormality of the first radio wave 223, it is possible to predict an earthquake within a band (path) having a predetermined width (for example, about 200 km) connecting the transmission station 221 and the reception station 222 of the first radio wave 223. It becomes.

  In the earthquake prediction system 100, the receiving station 222 is configured to receive each radio wave transmitted from the plurality of transmitting stations 221. More specifically, the receiving station 222 includes a Seattle transmitting station (NLK: frequency 24.8 kHz), a Hawaii transmitting station (NPM: frequency 21.4 kHz), an Australian transmitting station (NWC: frequency 19.8 kHz), and a Chinese transmitting station. (BPC: frequency 20.6 kHz), Fukushima transmitter station (YYJ40: frequency 40 kHz), Saga transmitter station (YYJ60: frequency 60 kHz) and Miyazaki transmitter station (JJI: frequency 22.2 kHz) It is configured.

(Second radio wave)
At the receiving station 232 (third observation point), observation related to the second precursor phenomenon is performed. More specifically, the receiving station 232 can receive the second radio wave 233 (VHF (Very High Frequency) radio wave) used for the aviation radio sign. Normally, the second radio wave 233 is not reflected by the ionosphere and penetrates through the ionosphere. However, when a disturbance 202 (not shown) occurs in the ionosphere due to a precursor phenomenon of an earthquake, a part of the disturbance is caused by the disturbance. Radio waves are reflected, and they are received even in the distance (out of sight) where radio waves do not reach normally.
Note that the second radio wave 233 is different from FM radio and the like because it has neither interference nor stoppage. Therefore, the second radio wave 233 enables high-quality observation.

  That is, by grasping the propagation abnormality of the second radio wave 233, it is possible to predict an earthquake within a band (path) having a predetermined width (for example, about 150 km) connecting the transmission station 231 and the reception station 232 of the second radio wave 233. It becomes.

In the earthquake prediction system 100, the receiving station 232 is configured to be able to receive each radio wave transmitted from the plurality of transmitting stations 231. More specifically, the receiving station 232 has a VOR (frequency 112.2 MHz), a Narita transmitting station (frequency 117.9 MHz), a Yokosuka transmitting station (frequency 116.2 MHz), etc. (VHF Omnidirectional Radio Range) Each radio wave transmitted from the facility can be received. Hereinafter, each radio wave transmitted from the VOR facility is referred to as a VOR radio wave as appropriate.
The second radio wave 233 is not limited to the VHF radio wave, and may be an UHF (Ultra High Frequency) radio wave transmitted from a Hamamatsu transmission station (frequency: 998 MHz) or the like.

(Third radio wave)
At the receiving station 242 (fourth observation point), the observation relating to the third precursor phenomenon is performed. More specifically, the receiving station 242 receives the third radio wave 243 of two types of frequencies (L1: 1575.42 MHz, L2: 122.60 MHz) radiated from the transmitting station 241 (GPS satellite).
The speed of radio waves is generally the speed of light, but when passing through the ionosphere, the speed varies depending on the frequency. The transmitting station 241 emits a third radio wave 243 having a different frequency, and the number of electrons (electron density) in the ionosphere can be calculated from the time (delay difference) required for these to pass through the ionosphere. At 242, the electron density in the ionosphere is observed by receiving the third radio wave 243.

  In other words, it is possible to predict earthquakes by capturing abnormal fluctuations in electron density in the ionosphere. For earthquakes of “M6.0” or more, earthquakes within a radius of 1000 km from the receiving station 242 can be predicted.

  The space radiation dose measuring device 212, the receiving station 222, the receiving station 232, and the receiving station 242 may be installed in the same site or may be different.

(Main features of various observation methods)
FIG. 6 is a diagram showing the main characteristics of each observation method for observing the above-described spatial radiation 213 and the first radio wave 223 to the third radio wave 243. In addition, the numerical value shown in FIG. 6 is an example.

  As shown in FIG. 6, in the first observation method using the spatial radiation dose (SRD), as a spatial radiation dose measurement device, for example, measurement measured by existing radiation monitoring posts arranged at 3,000 or more locations nationwide. The result can be used. For this reason, the cost of the measurement itself can be suppressed. The radiation monitoring posts are connected via a computer network. Measurement results can be obtained without going to radiation monitoring posts in various places. Measurement results are usually available within one day. Specifically, the measurement result is published on a web page by a local government or the like.

  According to the first observation method using the spatial radiation dose (SRD), it is possible to predict the occurrence of an earthquake within a radius of about 30 km from the measurement point. Moreover, it is possible to grasp the occurrence location of the earthquake more accurately based on the measurement results of a plurality of adjacent measurement points.

  Also, since earthquakes are natural phenomena, they may occur in a certain pattern, but they do not necessarily occur in a certain pattern. In the prediction, there is a limit to the accuracy of the prediction. Therefore, it is possible to further improve the accuracy of earthquake prediction by observing the precursor phenomenon in combination using a plurality of observation methods, that is, by combining a plurality of types of observation methods.

  Various observation methods include a relatively wide range (detection range) in which a precursory phenomenon of an earthquake can be detected and a narrow range. Combining a method with a wide detection range with a method with a narrow detection range makes it possible to grasp the location of the earthquake more accurately.

As a combination of a method with a narrow detection range and a method with a wide detection range, the first observation method using the spatial radiation dose (SRD) and the second observation method to the fourth observation method can be appropriately combined. Of the second observation method to the fourth observation method, one may be combined, two may be combined, or three may be combined. As one combination with respect to the first observation method, for example, the first observation method and the second observation method may be combined, or another combination may be used. Moreover, as two combinations with respect to the first observation method, for example, the first observation method, the second observation method, and the fourth observation method may be combined, or other combinations may be used.
Note that the second observation method to the fourth observation method may be able to more accurately grasp the location of the earthquake by overlapping the detection ranges of the same observation method.

  In addition, various observation methods include a relatively early period and a late period in which the precursory phenomenon of an earthquake can be detected (precursor abnormality period). Combining the method with the early anomaly period and the method with the late anomaly period, the anomaly (influence of the precursory phenomenon of the earthquake) is detected with the early method and the anomaly is detected in the normal order with the slow method. If an error is detected, it is possible to determine that the probability of an earthquake is relatively high (than when an abnormality is detected in an irregular order, or when an abnormality is detected on either side). It becomes.

  As a combination of a method with an early precursory period and a method with a late precursory period, the first observation method using the spatial radiation dose (SRD) and the second to fourth observation methods may be appropriately combined. it can. Of the second observation method to the fourth observation method, one may be combined, two may be combined, or three may be combined. As one combination with respect to the first observation method, for example, the first observation method and the second observation method may be combined, or another combination may be used. Moreover, as two combinations with respect to the first observation method, for example, the first observation method, the second observation method, and the fourth observation method may be combined, or other combinations may be used.

  The first observation method to the fourth observation method are different in the location where the abnormality occurs (abnormality occurrence location), merit and demerit, so the combination of observation methods and the location of the observation point should be determined in consideration of each. It is preferable to determine.

<< System configuration of earthquake observation system >>
FIG. 7 is a diagram illustrating an example of a system configuration of the earthquake observation system 300 including the earthquake prediction system 100.
The earthquake observation system 300 includes an analysis server 101, a backup server 102, a plurality of space radiation dose measuring devices 212, a plurality of receiving stations 222, a plurality of receiving stations 232 and a receiving station 242.
Each of the space radiation dose measuring device 212 and the receiving stations 222 to 232 is not limited to a plurality, and may be one. The receiving station 242 is not limited to one and may be a plurality. The backup server 102 is not limited to one, and may be a plurality.

<Analysis server>
The analysis server 101 is an example of the earthquake prediction system 100, and is connected to the one or a plurality of spatial radiation dose measuring devices 212, the receiving stations 222 to 242 and the backup server 102 via the cloud 301 so as to be communicable.

  The analysis server 101 performs the above-described data collection processing 111, data analysis processing 112, prediction information creation processing 113, distribution information creation processing 114, and the like. The analysis server that performs the data collection process 111 and the analysis server that performs the data analysis process 112, the prediction information creation process 113, and the distribution information creation process 114 are separately connected via the cloud 301. You may be comprised with a server. The analysis server 101 is an example of an information processing apparatus, and the hardware configuration of the analysis server 101 will be described later.

<Backup server>
The backup server 102 backs up observation data, data such as analysis results from the analysis server 101, and the like. The backup server 102 is an example of an information processing apparatus, and the hardware configuration of the backup server 102 is the same as that of the analysis server 101, and thus the description thereof is omitted.

<Spatial radiation dose (SRD) measuring device>
The space radiation dose measuring device 212 includes a detector 2121, a controller 2122, and a computer 2123. The space radiation dose measuring device 212 is, for example, a monitoring post. In the spatial radiation dose measuring device 212, the spatial radiation dose at the measurement point where the spatial radiation dose measuring device 212 is installed is continuously measured and stored as observation data in a predetermined storage area.

  The detector 2121 is, for example, a γ ray sensor. The detector 2121 is connected to the controller 2122. The space radiation dose measuring device 212 is installed on the ground. The detector 2121 is arranged at a position above the ground and away from the ground. The detector 2121 outputs the space radiation dose to the controller 2122 as an analog voltage (measurement data signal). In other words, the detector 2121 is configured to measure the spatial radiation dose 213. Furthermore, the detector 2121 can measure a spatial radiation dose that is lower than the dose per unit time of the normal public dose limit according to the recommendations of the International Commission on Radiological Protection.

The detector 2121 of the spatial radiation dose measuring device 212 is a γ-ray sensor, and is different from the detector of the radon measuring device that detects α-rays, for example. The space radiation dose measuring device 212 is different from, for example, a device that measures radon in groundwater or air. Unlike the device that measures radon in groundwater or air, the space radiation dose measuring device 212 does not have a pipe or a pump for introducing water or outside air to the detector.
For example, α rays used for radon measurement have a short propagation distance (flying distance) in the air, and thus are easily affected by local influence in the basement of the measurement point. That is, the measured value is easily changed only by shifting the observation point by several meters. Even if a precursor is observed, the region where the precursor is observed is considered to be local.
On the other hand, the γ-rays used for the measurement of the present embodiment propagate farther than the α-rays. For this reason, the radiation dose tends to form a gentle spatial distribution over the range in which the precursor is observed. Therefore, the measured value and deviation as the precursor fluctuation can be parameterized and expressed up to distance and magnitude estimation.
Further, unlike the device that measures radon in the air, the space radiation dose measurement device 212 does not have a device that introduces water or outside air to the detector. For this reason, installation and maintenance are easy.

  The controller 2122 is communicably connected to the detector 2121 and the computer 2123. The controller 2122 amplifies the received signal output from the detector 2121, processes the waveform of the received signal, removes noise, or performs AD conversion (received signal (analog signal) can be processed by the computer 2123. Data), and AD-converted received data (measurement data) is transmitted to the computer 2123.

The computer 2123 is an example of an information processing apparatus, and is communicably connected to the controller 2122 and the cloud 301.
The computer 2123 transmits a command for transmitting the received data to the controller 2122. The controller 2122 transmits received data to the computer 2123 in response to the command. The computer 2123 receives the received data transmitted from the controller 2122, stores it in its predetermined storage area, and uploads it to a predetermined storage area on the cloud 301.

The analysis server 101 and the backup server 102 download the received data stored in a predetermined storage area at an appropriate timing.
In this way, the analysis server 101 can collect (acquire) received data from the spatial radiation dose measuring devices 212 installed in various places.
For example, among the functions of the analysis server 101, a server responsible for data collection may be provided in a local government, a power company, or the like. Data released by this server is received by a server having functions other than data collection among the functions of the analysis server 101.
The computer 2123 may be configured to be communicably connected to the analysis server 101 via a network (cloud 301). In this case, for example, the analysis server 101 transmits a transmission command for transmitting received data to the computer 2123. When the computer 2123 receives the transmission command, the computer 2123 transmits the reception data stored in the HDD or the like to the analysis server 101.

<Receiving station (VLF)>
The receiving station 222 includes an antenna 2221, a receiver 2222, and a computer 2223. At the receiving station 222, radio waves having a predetermined frequency set in advance are measured and stored as observation data in a predetermined storage area.

The antenna 2221 is a dipole antenna or the like and is connected to the receiver 2222. The antenna 2221 converts (receives) radio waves (electromagnetic waves) in space into high frequency energy and outputs the received signals to the receiver 2222 as reception signals.
The antenna 2221 is configured to receive radio waves in a wide band and receive the first radio wave 223. Note that the antenna 2221 may be provided for each frequency when receiving each radio wave transmitted from the plurality of transmission stations 221.

The receiver 2222 is connected to the antenna 2221 and the computer 2223 so as to communicate with each other. The receiver 2222 amplifies the reception signal output from the antenna 2221, processes the waveform of the reception signal, removes noise, and performs AD conversion (reception signal (analog signal) by the computer 2223). Or the received data (observation data) subjected to AD conversion is transmitted to the computer 2223.
Note that the receiver 2222 includes devices that can specify time, such as an antenna that can receive a standard radio wave used in a radio timepiece, and a GPS antenna that can receive a GPS signal that specifies time.

The computer 2223 is an example of an information processing device, and is communicably connected to the receiver 2222 and the cloud 301.
The computer 2223 transmits an instruction to transmit the reception data to the receiver 2222. The receiver 2222 transmits received data to the computer 2223 in response to the command. The computer 2223 receives the reception data (time, phase, amplitude, etc.) transmitted from the receiver 2222, stores it in its predetermined storage area, and uploads it to a predetermined storage area on the cloud 301.

The analysis server 101 and the backup server 102 download the received data stored in a predetermined storage area at an appropriate timing.
In this way, the analysis server 101 can collect (acquire) received data from the receiving stations 222 installed in various places.
The computer 2223 may be configured to be communicably connected to the analysis server 101 via a network (cloud 301). In this case, for example, the analysis server 101 transmits a transmission command for transmitting received data to the computer 2223. When the computer 2223 receives the transmission command, the computer 2223 transmits the reception data stored in the HDD or the like to the analysis server 101.

<Receiving station (VOR)>
The receiving station 232 includes an antenna 2321, a receiver 2322, and a computer 2323. At the receiving station 232, radio waves having a predetermined frequency set in advance are measured and stored as observation data in a predetermined storage area.

The antenna 2321 is a Yagi antenna or the like and is connected to the receiver 2322. The antenna 2321 converts (receives) radio waves (electromagnetic waves) in the space into high-frequency energy and outputs the received signals to the receiver 2322 as reception signals.
The antenna 2321 is configured to receive a radio wave in a wide band and receive the second radio wave 233. Note that the antenna 2321 may be provided for each frequency when receiving each radio wave transmitted from the plurality of transmission stations 231.

The receiver 2322 is connected to the antenna 2321 and the computer 2323 so as to communicate with each other. The receiver 2322 amplifies the reception signal output from the antenna 2321, processes the waveform of the reception signal, removes noise, and performs AD conversion (received signal (analog signal) with the computer 2323). Or reception data (observation data) subjected to AD conversion is transmitted to the computer 2323.
Note that the receiver 2322 includes devices that can specify time, such as an antenna that can receive a standard radio wave used in a radio clock, and a GPS antenna that can receive a GPS signal that specifies time.

The computer 2323 is an example of an information processing apparatus, and is connected so as to be communicable with the receiver 2322 and the cloud 301.
The computer 2323 transmits an instruction to transmit the reception data to the receiver 2322. The receiver 2322 transmits received data to the computer 2323 in response to the command. The computer 2323 receives the received data (time, phase, amplitude, etc.) transmitted from the receiver 2322, stores it in its own predetermined storage area, and uploads it to a predetermined storage area on the cloud 301.

The analysis server 101 and the backup server 102 download the received data stored in a predetermined storage area at an appropriate timing.
In this way, the analysis server 101 can collect (acquire) received data from the receiving stations 232 installed in various places.
The computer 2323 may be configured to be communicably connected to the analysis server 101 via a network (cloud 301). In this case, for example, the analysis server 101 transmits a transmission command for transmitting received data to the computer 2323. When the computer 2323 receives the transmission command, the computer 2323 transmits the reception data stored in the HDD or the like to the analysis server 101.

<Receiving station (GPS)>
The receiving station 242 includes an antenna 2421, a receiver 2422, and a computer 2423. In the receiving station 242, radio waves having a predetermined frequency set in advance are measured and stored as observation data in a predetermined storage area.

The antenna 2421 is a parabolic antenna or the like, and is connected to the receiver 2422. The antenna 2421 converts (receives) radio waves (electromagnetic waves) in the space into high-frequency energy and outputs the received signals to the receiver 2422 as reception signals.
The antenna 2421 is configured to receive the third radio wave 243.

The receiver 2422 is connected to the antenna 2421 and the computer 2423 so as to communicate with each other. The receiver 2422 amplifies the reception signal output from the antenna 2421, processes the waveform of the reception signal, removes noise, and performs AD conversion (reception signal (analog signal) by the computer 2423). Or received data (observation data) subjected to AD conversion is transmitted to the computer 2423.
Note that the receiver 2422 includes devices that can specify time, such as an antenna that can receive standard radio waves used in radio timepieces, and a GPS antenna that can receive GPS signals that specify time.

The computer 2423 is an example of an information processing apparatus, and is connected to the receiver 2422 and the cloud 301 so as to communicate with each other.
The computer 2423 transmits an instruction to transmit the reception data to the receiver 2422. The receiver 2422 transmits received data to the computer 2423 in response to the command. The computer 2423 receives the reception data (time, phase, amplitude, etc.) transmitted from the receiver 2422, stores it in its predetermined storage area, and uploads it to a predetermined storage area on the cloud 301.

The analysis server 101 and the backup server 102 download the received data stored in a predetermined storage area at an appropriate timing.
In this way, the analysis server 101 can collect (acquire) received data from the receiving stations 242 installed in various places.
Note that the computer 2423 may be configured to be communicably connected to the analysis server 101 via a network (cloud 301). In this case, for example, the analysis server 101 transmits a transmission command for transmitting received data to the computer 2423. When the computer 2423 receives the transmission command, the computer 2423 transmits the reception data stored in the HDD or the like to the analysis server 101.

<< Hardware configuration of analysis server >>
FIG. 8 is a diagram illustrating an example of a hardware configuration of the analysis server 101.
The analysis server 101 includes a CPU 1010 (Central Processing Unit), a ROM 1011 (Read Only Memory), a RAM 1012 (Random Access Memory), an external storage device 1013, a graphic board 1014, an input control device 1015, and a network I / F 1016 (interface).

  The CPU 1010 performs execution control of each component of the analysis server 101 and executes various programs stored in the ROM 1011 to perform various calculations.

  The ROM 1011 includes a memory device such as a flash memory, and stores permanent data executed by the CPU 1010. For example, a program related to the control of the earthquake prediction system 100 is stored.

  The RAM 1012 temporarily stores data necessary for executing various programs stored in the ROM 1011.

  The external storage device 1013 is a storage device such as a hard disk device, and stores programs executed by the CPU 1010 and data (tables, databases, etc.) used by the programs executed by the CPU 1010.

  The graphic board 1014 controls the LCD 1017 (Liquid Crystal Display) to display various information.

  The input control device 1015 converts the input from the keyboard 1018 and the input from the mouse 1019 into signals and transmits them to the CPU 1010.

  The network I / F 1016 realizes data communication such as downloading received data (observation data) from the cloud 301.

  In the analysis server 101, for example, the CPU 1010 reads out the program and observation data stored in the ROM 1011 or the like to the RAM 1012 and executes them, thereby realizing various functions (data processing unit, data storage unit, etc.) related to the analysis server 101. .

<< Various data >>
<Abnormality judgment result (abnormality judgment data)>
FIG. 9 is a diagram illustrating an example (determination result table) of an abnormality determination result (abnormality determination data). The abnormality determination result is data generated by determining the degree of abnormality in the prediction information creation process 113, and is basically stored in the external storage device 1013 (third database 123) once a day. .
The determination result table stores information indicating an abnormality determination result for each DOY (Day-Of-Year) corresponding to the observation data type.

  The observation data type is information that is provided corresponding to one piece of measurement information measured by each spatial radiation dose measuring device and each receiving station, and that can identify the type of measurement information. The DOY 2015 is information that can identify the date.

  The abnormality determination result has information (“◯” in this example) indicating the abnormality determination result for each DOY corresponding to the observation data type. For example, if the current DOY is “290” and an abnormality is detected in the radio waves of the observation data types “SRD2,” “SRD3,” “VOR6,” and “GPS”, information indicating the abnormality determination result at the corresponding location “O” is set.

For example, the abnormality determination result by the first observation method described above is information (magnitude: “M”) indicating the magnitude of the energy (earthquake magnitude) generated by the earthquake for each DOY corresponding to the observation data type “SRD”. Abbreviated). For the observation data type “SRD”, since the relationship between the distance and the scale is obtained from the abnormal value, the size at a certain virtual distance is provisionally defined.
Data indicating the correspondence between the abnormal value and the magnitude is stored in the external storage device 1013 for each observation data type “SRD”. In addition, the past performance is fed back to the data indicating the correspondence, the accuracy is gradually improved, and the magnitude of the earthquake can be predicted more accurately.

  In addition, for example, the abnormality determination result by the second observation method described above is information (magnitude: abbreviated as “M”) indicating the magnitude of the energy generated by the earthquake for each DOY corresponding to the observation data type “VLF”. Have. For the observation data type “VLF”, the magnitude depends on the abnormal value, such as “M5.0” when the abnormal value is “−2.5” and “M6.0” when the abnormal value is “−3.0”. Is identified. That is, if there is an observation data type “VLF” that specifies “M6.0” when the abnormal value is “−3.0”, “M6.5” is specified when the abnormal value is “−3.0”. There is an observation data type “VLF”, and for each observation data type “VLF”, data indicating the correspondence between an abnormal value and a magnitude is stored in the external storage device 1013.

<Earthquake forecast calendar (earthquake forecast data)>
FIG. 10 is a diagram illustrating an example (earthquake prediction table) of an earthquake prediction calendar (earthquake prediction data). The earthquake prediction calendar is stored in the third database 123 and updated based on the precursor abnormal period shown in FIG.
The earthquake prediction table stores information indicating that an earthquake is predicted to occur for each DOY corresponding to the observation data type.

<Precursor abnormal period (abnormal period data)>
FIG. 11 is a diagram illustrating an example of a precursor abnormality period (abnormal period data) (a precursor abnormality period setting table). The precursor abnormality period is data set in advance and is stored in the external storage device 1013. The precursor abnormal period will be reviewed as appropriate.
In the precursor abnormal period setting table, information indicating the start and end of the precursor abnormal period is defined for each observation method.

For example, as shown in FIG. 9, when an abnormality is detected in the determination of the degree of abnormality in the observation data type “SRD2” in the DOY “284”, the precursor abnormality period is referred to, and the start “7” and the end “13” Is identified and an earthquake is predicted to occur two to thirteen days later, and information “◯” indicating that an earthquake is predicted is set in DOY “291” to “297” of the earthquake prediction calendar shown in FIG. The
For example, as shown in FIG. 9, when an abnormality is detected in the determination of the degree of abnormality in the observation data type “VLF3” in the DOY “284”, the precursor abnormality period is referred to, and the start “2” and the end “ 10 ”is specified, and it is predicted that an earthquake will occur after 2 to 10 days. Information“ O ”indicating that an earthquake is predicted to occur in DOY“ 286 ”to“ 295 ”of the earthquake prediction calendar shown in FIG. Is set. Also, for example, when an abnormality is detected in the DOY “285” of the next day, information “O” indicating that an earthquake is predicted to occur in the DOY “287” to “296” of the earthquake prediction calendar is set (overwrite and Added). The same applies to the other observation methods “VOR” and “GPS”.

<Predicted epicenter block (block data)>
FIG. 12 is a diagram illustrating an example of a predicted seismic block (block data) (a predicted seismic block setting table). The predicted seismic block is stored in the external storage device 1013.

  The block classification is information that can identify each block that has been divided into a plurality of blocks in advance throughout Japan. The area (earthquake detection area) in which the earthquake can be detected is an observation data type in which the block can detect an earthquake. For example, the block classification “B” includes an earthquake detection area “B1” in which the observation data types “SRD2”, “SRD3”, “VLF3”, “VLF15”, “VOR6”, and “GPS” can detect an earthquake. And the seismic detection area “B2” where the seismic data can be detected by the observation data types “SRD2”, “SRD4”, “VLF4” and “GPS”, and the observation data types “SRD3”, “SRD4” and “VLF5” And an earthquake detection area “B3” in which an earthquake can be detected by “GPS”. The block image is information (file name, URL, etc.) that can identify the image corresponding to the block classification.

  In the present embodiment, the earthquake detection area is narrowed down based on the observed data type in which the abnormality is detected, and the block classification is specified as a place (region) for predicting the epicenter of the earthquake (may be the epicenter). For example, as shown in FIG. 10, when it is predicted that an earthquake will occur in the observation data types “SRD2”, “SRD3”, “VLF3”, “VLF15”, and “VOR6” in the DOY “290”, the area “ The block classification “B” is specified by narrowing down to “B1”.

<< Installation example of space radiation dose measuring device and receiving station >>
<Installation example of space radiation dose measuring device>
FIG. 13 is a diagram illustrating an example of an installation location (an SRD observation network) of the space radiation dose measuring device 212.

The space radiation dose measuring device 212 is installed at a plurality of measurement points.
For example, in a condition where a specific abnormal value and an earthquake having a magnitude of “M5.2” or larger are predicted, a radius of 30 km from the spatial radiation dose measuring device 212 is an observation area.
In order to capture the precursory phenomenon, for example, a monitoring post that overlaps measurement areas is selected from among already installed monitoring posts, and is certified as the spatial radiation dose measuring device 212 in the present embodiment, and the certified spatial radiation dose measurement is performed. It is preferred to collect measurement data from the device 212. In this case, selection and certification of the monitoring post corresponds to installation of the space radiation dose measuring device 212.

  For example, when a precursor phenomenon is confirmed in each observation data of the spatial radiation dose measurement device 212A, the spatial radiation dose measurement device 212D, and the spatial radiation dose measurement device 212F, the observation areas of the spatial radiation dose measurement devices 212A, 212D, and 212F overlap. It can be determined that an earthquake having an epicenter near the region 401 occurs.

  Further, for example, in the earthquake of “M6.0”, the radius of the observation area is 30 km, and therefore the (same) precursor phenomenon is confirmed at the same time in the spatial radiation dose measuring device 212D and the spatial radiation dose measuring device 212E. No (almost), but for earthquakes of “M7.0” or greater, the radius of the observation area is 100 km, so the precursory phenomenon was confirmed at the same time by the spatial radiation dose measurement device 212D and the spatial radiation dose measurement device 212E Can be done. In other words, when the same abnormality (abnormality showing substantially the same pattern) is detected by the space radiation dose measuring device 212D and the space radiation dose measuring device 212E, it can be predicted that an earthquake of “M7.0” or more will occur. .

  In addition, monitoring posts for measuring the radiation dose are installed throughout Japan. The space radiation dose measuring device 212 can be set all over Japan.

  Moreover, about the space radiation dose measuring apparatus 212, you may install uniquely, for example, you may utilize what was installed by the university or the company.

<Installation example of VLF radio wave receiving station>
FIG. 14 is a diagram illustrating an example of an installation location (VLF / LF observation network) of the receiving station 222.
In the earthquake prediction system 100, ten receiving stations 222 are provided so as to cover the whole of Japan, that is, the observation areas (paths) overlap.

  Each receiving station 222 can receive each radio wave from 7 transmitting stations including China transmitting station and Saga transmitting station selected independently, so the path overlap becomes denser and the accuracy of earthquake prediction is improved Can be achieved.

  For example, when a precursor phenomenon is confirmed in the first path and the second path, it can be determined that an earthquake having an epicenter near the area where the first path and the second path overlap occurs.

  The installation location of the receiving station 222 is not limited to that shown in FIG. For example, the receiving station 222 may be further installed such that the receiving station 222 is installed in Wakkanai so as to further cover the whole of Japan.

  About the receiving station 222, you may install uniquely, the thing installed by the university may be used, and the thing installed by the country may be used.

  The VOR receiving station 232 can be installed in the same manner as the VLF receiving station 222. Further, the GPS receiving station 242 is installed in Niigata and the like that can cover most of Japan because the observation area is as wide as 1000 km.

Here, as described above, by combining the SRD observation method with a narrow detection range with the VLF observation method with a wide detection range, it is possible to grasp the occurrence location of the earthquake more accurately.
More specifically, if an abnormality in the spatial radiation dose is detected and further an abnormality in the VLF is detected, the epicenter is narrowed down.
For example, when a spatial radiation dose measurement device is installed in the VLF observation area, or when a spatial radiation dose measurement device is installed in the vicinity of the VLF observation area, an abnormality in the VLF is detected and predicted When the magnitude of is 6.0 and the abnormality of the spatial radiation dose is detected, it is possible to predict that an earthquake will occur within the distance range corresponding to the abnormal value of the spatial radiation dose.

Further, for example, by combining the SRD observation method with a narrow detection range with the VOR observation method with a wide detection range, it is possible to more accurately grasp the place where the earthquake occurred.
In the case of this combination, when installing the spatial radiation dose measuring device, it is preferable to install the spatial radiation dose measuring device according to how large an earthquake is predicted.

Further, for example, by combining an SRD observation method with a narrow detection range with a GPS observation method with a wide detection range, it is possible to more accurately grasp the place where the earthquake occurred.
In the case of this combination, it is difficult to accurately predict the magnitude with the GPS observation method, the observation area is wide, and further, it is difficult to specify the observation area with one spatial radiation dose measuring device. It is preferable to install two or more devices.
For example, when an abnormality is detected for one spatial radiation dose measuring device and an abnormality is detected for another spatial radiation dose measuring device, the observation area of the one spatial radiation dose measuring device and another spatial radiation dose measuring device It is possible to predict that an earthquake will occur in an area that overlaps the observation area.

<< Processing related to earthquake prediction system (process related to earthquake prediction method) >>
In the earthquake prediction system, the analysis server 101 executes processing shown in the flowcharts of FIGS. 15 and 16.

<Main processing>
FIG. 15 is a diagram illustrating an example of a flowchart according to a main process of the earthquake prediction system. The main process is executed once a day at a predetermined time (for example, 9 o'clock).

  In step S11, the analysis server 101 downloads observation data uploaded on the cloud from each receiving station (collects observation data). As for the space radiation dose, for example, local government and electric power company servers collect data from the space radiation dose measurement device as a partial function of the analysis server 101 and publish it on a web page. In this case, the analysis server 101 downloads data on the amount of space radiation released by the local government and the power company from the web page. The analysis server 101 stores the downloaded observation data in the first database 121. The process in step S12 is an example of a data collection stage process. When the analysis server 101 ends this process, the process proceeds to step S12.

  In step S12, the analysis server 101 performs data analysis of each observation data. The analysis server 101 stores the analysis result (analysis result data) in the second database 122. The process in step S12 is an example of an abnormal value detection stage process. When the analysis server 101 ends this process, the process proceeds to step S13.

<Analysis of spatial radiation dose measurement data>
(1) The analysis server 101 extracts spatial radiation dose (SRD) measurement data observed at regular intervals during the day. In addition, the average value of the data measured several times a day may be used as space radiation dose (SRD) measurement data. Specifically, the analysis server 101 reads out measurement data representing the measurement result of the air dose at the monitoring post from the web page of the server disclosed by the local government or the like.
Servers operated by local governments collect data from monitoring posts and output measurement data on a web page. Data collection from the monitoring post is executed several times a day. In this embodiment, measurement data is used once a day.

(2) The analysis server 101 plots a waveform using the measurement data. The plotted waveform can be output to a display, paper, etc., and the analyst refers to the output waveform, for example, extremely large fluctuations and missing measurements due to radioactivity from nuclear facilities, etc. Are checked visually to eliminate inappropriate data. Thereby, appropriate data is used in data analysis (earthquake prediction).
An example of spatial radiation dose measurement data is shown in FIG.

(3) The analysis server 101 calculates the past average value m and the standard deviation σ of the spatial radiation dose in the past predetermined period. Specifically, the analysis server 101 calculates the arithmetic average value m of the spatial radiation dose from the measurement date to 13 days before the measurement date. The analysis server 101 calculates the standard deviation σ of the spatial radiation dose from the measurement date to 13 days before the measurement date.

(4) The analysis server 101 calculates the deviation x−m with respect to the average of the measurement data x on the measurement date. Further, the analysis server 101 calculates the ratio (xm) / σ of the deviation xm to the standard deviation σ.
When the ratio (x−m) / σ exceeds a predetermined upper limit value, it is determined that an abnormal value as a sign of an earthquake has been detected in a later step. Further, the analysis server 101 sets the ratio (x−m) / σ itself as an abnormal value.

<Data analysis of VLF>
(1) The analysis server 101 extracts VLF observation data observed at night (for example, from 21:00 to 3). The nighttime observation data is mainly used to avoid the influence of the sun.

(2) The analysis server 101 plots a waveform using the observation data. The plotted waveform can be output on a display, paper, etc., and the analyst refers to the output waveform and visually confirms missing measurement, wave stop, lightning strike, etc., and eliminates inappropriate data To do. Thereby, appropriate data is used in data analysis (earthquake prediction).
An example of VLF observation data is shown in FIG. FIG. 17 shows an example of observation data observed at the receiving station 222 installed in Nemuro.

(3) The analysis server 101 calculates the difference from the average intensity during the previous 15 days.

(4) The analysis server 101 calculates the total night time of the difference calculated in (3).

(5) The analysis server 101 plots the analysis result data in a graph (VLF graph) and stores it in the second database 122. An example of the VLF graph is shown in FIG.

<VOR data analysis>
(1) The analysis server 101 extracts VOR observation data.

(2) The analysis server 101 plots a waveform using the observation data. The plotted waveform can be output to a display, paper, etc., and the analyst can refer to the output waveform to check for missing, stopped, lightning, meteor echo, meteorological factors (eg, wind, surface temperature). ) Etc. by visual inspection to eliminate inappropriate data. Thereby, appropriate data is used in data analysis (earthquake prediction).
Here, an example of VOR observation data is shown in FIG. 19 and FIG. FIG. 19 shows an example of normal observation data. FIG. 20 shows an example of observation data at the time of abnormality.

(3) The analysis server 101 calculates “average (m) + three times standard deviation (3σ)” of the intensity of the VOR radio wave in the previous 15 days.

(4) The analysis server 101 plots the time exceeding “m + 3σ” in one day as analysis result data in a graph (VOR graph) and stores it in the second database 122.
An example of the VOR graph is shown in FIG. 21 and FIG. FIG. 21 shows observation data, average (m), calculation result “average (m) + three times standard deviation (3σ)”, and observation data exceeds “average (m) + three times standard deviation (3σ)”. It is a figure which shows an example of the relationship with a value. FIG. 22 is a diagram illustrating an example of accumulated time (every day) exceeding “average (m) + three times standard deviation (3σ)”.

<GPS data analysis>
(1) The analysis server 101 calculates ionospheric electron density fluctuation (TEC) from the arrival time difference between radio waves having different frequencies of L1 and L2.

(2) The analysis server 101 confirms that there is no influence of the magnetic field and the solar activity based on the magnetic field index (Kp, Dst [nT], etc.) and the solar activity index (IMF, F10.7, etc.). , Eliminate inappropriate data. Thereby, appropriate data is used in data analysis (earthquake prediction).

(3) The analysis server 101 plots the analysis result data on a graph (GPS graph) and stores it in the second database 122. The GPS graph is not shown.

  In step S13, the analysis server 101 performs earthquake prediction. Although details will be described later, in earthquake prediction, the occurrence / non-occurrence of an earthquake, occurrence location, occurrence period, and earthquake level are predicted based on analysis result data and the like. When the analysis server 101 ends this process, the process proceeds to step S14.

In step S <b> 14, the analysis server 101 performs distribution processing (performs distribution of prediction results). For example, as shown in FIG. 23, the analysis server 101 is a file in which a block image is mapped to a map of Japan (map information), and an occurrence period (predicted period) and an earthquake level can be displayed on an arbitrary display for each block category. (Screen information) is generated. Further, for example, the analysis server 101 generates a file (screen information) that can display text information of an occurrence location, an occurrence period (prediction period), and an earthquake level on an arbitrary display without mapping a block image on a map of Japan. To do.
The file can adopt any format such as PDF (Portable Document Format), HTML (HyperText Markup Language).

  Subsequently, the analysis server 101 stores the generated file in the fourth database 124. The analysis server 101 reads a file from the fourth database 124 at a predetermined timing, attaches the file to the email, and transmits the email to a pre-registered email address.

However, the distribution process is not limited to the above-described contents.
For example, the distribution may be performed every day, or may be performed on a specified day of the week, such as Wednesday and Friday every week. Further, for example, it may be distributed according to the earthquake level. When the earthquake level is “(No abnormality)” or “Caution”, delivery is performed on the specified day of the week. When it is “Warning”, delivery is performed on a temporary basis (for example, immediately after the earthquake prediction). Also good.

  Moreover, as a delivery destination, for example, it may be configured to transmit to a predetermined dedicated terminal (contract target display terminal, digital signage, etc.), and the predetermined dedicated terminal may display a prediction result. Further, for example, the prediction result may be transmitted to a facsimile having a number registered in advance. Further, for example, a configuration in which information including a prediction result is transmitted to a predetermined URL and displayed on a WEB site may be used.

<Earthquake prediction processing>
FIG. 16 is a diagram illustrating an example of a flowchart according to the earthquake prediction process.

  In step S <b> 101, the analysis server 101 reads the analysis result from the second database 122. If this process ends, the process moves to step S102.

  In step S102, the analysis server 101 determines the degree of abnormality of the analysis result (abnormality determination). An example of abnormality determination for each observation method is shown below. The processing in step S102 is an example of processing in the determination stage and the scale prediction stage. If this process ends, the process moves to step S103.

(Abnormality judgment based on space radiation dose SRD)
The analysis server 101 determines whether or not the ratio (xm) / σ of the deviation xm detected in the abnormal value detection stage to the standard deviation σ calculated in step S12 exceeds a predetermined upper limit value. Determine.
When the ratio (x−m) / σ exceeds a predetermined upper limit value, it is determined that an abnormal value as a sign of an earthquake has been detected. Further, the analysis server 101 sets the ratio (x−m) / σ itself as an abnormal value. If the ratio (x−m) / σ does not exceed a predetermined upper limit value, it is predicted that no earthquake will occur.
The analysis server 101 obtains the relationship between the distance to the epicenter of the corresponding earthquake and the magnitude of the earthquake using the above-described estimation formula (III).

(Abnormality judgment based on VLF radio waves)
The analysis server 101 determines whether or not the total night time calculated in step S12 has exceeded a predetermined value (“−2.5”, “−3”, etc.). When it is determined that the predetermined value is exceeded, an earthquake is predicted to occur, and when it is determined that the predetermined value is not exceeded, it is predicted that no earthquake will occur.

(Abnormality judgment based on VOR radio waves)
The analysis server 101 determines whether or not the time exceeding “m + 3σ” calculated in step S12 exceeds a predetermined value (for example, 120 minutes). When it is determined that the predetermined value is exceeded, an earthquake is predicted to occur, and when it is determined that the predetermined value is not exceeded, it is predicted that no earthquake will occur.
In addition, about the predetermined value, you may employ | adopt the structure by which the past performance is fed back, In that case, the data which show a predetermined value for every VOR observation data type are memorize | stored in the external storage device 1013, and predetermined value May differ for each VOR observation data type.

(Abnormality judgment based on GPS radio waves)
The analysis server 101 determines whether or not the time exceeding the threshold (for example, + 2σ with respect to the previous 15 days) has exceeded a predetermined time (for example, 10 hours) for the ionospheric electron density fluctuation calculated in step S12. When it is determined that the time exceeding the predetermined time has exceeded the predetermined time, it is predicted that an earthquake will occur. When it is determined that the time exceeding the threshold has not continued for the predetermined time or longer, it is predicted that no earthquake will occur.

  The abnormality determination is not limited to the above contents. Based on the characteristics of the above-described abnormality (propagation abnormality, URL radiation), the abnormality can be determined by an appropriate method.

  In other words, a part or all of the abnormality determination may be performed by a person with reference to an output graph or the like.

  In step S <b> 103, the analysis server 101 stores the determination result of the abnormality degree of the analysis result in the third database 123. For example, as illustrated in FIG. 9, the analysis server 101 stores information indicating an abnormality determination result for each observation data type. If this process ends, the process moves to step S104.

  In step S104, the analysis server 101 updates the earthquake prediction calendar. More specifically, the earthquake prediction calendar is updated with reference to the abnormality determination result and the warning abnormality period for each observation data type. The process in step S104 is an example of a process in a time prediction stage for predicting the occurrence time of an earthquake. If this process ends, the process moves to step S105.

  In step S105, the analysis server 101 determines whether an earthquake is predicted. More specifically, the analysis server 101 refers to the earthquake prediction calendar and sets information (occurrence date prediction information) indicating that an earthquake is predicted to occur within a predetermined number of days (for example, 10 days) from today. It is determined whether or not it has been done. If the analysis server 101 determines that the occurrence date prediction information is set, the analysis server 101 moves the process to step S106. If the analysis server 101 determines that the occurrence date prediction information is not set, the analysis server 101 moves the process to step S111.

  In step S106, the analysis server 101 extracts prediction data (observation data type, date on which the earthquake is predicted, magnitude) from which the earthquake is predicted from the earthquake prediction calendar. If this process ends, the process moves to a step S107.

  In step S107, the analysis server 101 collates each prediction data and narrows down the earthquake occurrence area. More specifically, the analysis server 101 narrows down (extracts) an earthquake detection area including an observation data type for which an earthquake is predicted. Subsequently, the analysis server 101 specifies (sets) a block classification corresponding to the narrowed-down earthquake detection area. Also, the analysis server 101 reads out a block image corresponding to the specified block classification from the external storage device 1013 (temporarily stores the file name of the block image). The process in step S107 is an example of a process in a predetermined area setting stage.

  In step S108, the analysis server 101 collates each prediction data and narrows down the earthquake occurrence period. The analysis server 101 narrows down the earthquake occurrence period for each block category. More specifically, the union of each earthquake occurrence period (the date on which the earthquake is predicted) of the observation data type associated with the block classification is obtained. If the last day of the calculated earthquake occurrence period exceeds a predetermined number of days (for example, 10 days), specify (set) the period from the next day to the predetermined number of days as the earthquake occurrence period (predicted period). Until the last day of the earthquake occurrence period is specified as the earthquake occurrence period (prediction period).

  In step S109, the analysis server 101 collates each prediction data and sets an earthquake level. As the earthquake level, “no abnormality”, “attention level (first level)” indicating caution, and “warning level (second level)” indicating caution are provided.

The analysis server 101 sets an earthquake level for each block category.
More specifically, when it is predicted that an earthquake will occur in all of the observation data types associated with the block classification (when occurrence date prediction information is set), a warning level is set.
For example, in the block classification “B” shown in FIG. 12, the observation data types are “SRD2”, “SRD3”, “SRD4”, “VLF3”, “VLF4”, “VLF5”, “VLF15” in all. , “VOR6”, and “GPS”, and the occurrence date prediction information is set for all the observation data types, the warning level is set for the block classification “B”. In this way, it is determined that there is an influence in both the scale prediction stage for predicting the distance to the epicenter and the magnitude (magnitude) of the earthquake based on the space radiation dose, and the determination stage for determining the abnormality based on the radio wave. The probability of an earthquake occurring in the corresponding area is predicted to increase.

On the other hand, if it is predicted that no earthquake will occur in all of the observation data types associated with the block classification, no abnormality is set. If it is predicted that an earthquake will occur only in the observation data type “SRD”, there is no abnormality. Set the caution level except for the above.
For example, regarding the block classification “B” shown in FIG. 12, when the occurrence date prediction information is set only for the observation data types “SRD2”, “SRD3”, and “SRD4”, the block classification “B” No abnormality is set. For example, when the block classification “B” shown in FIG. 12 is viewed, the occurrence date prediction information is set only in the observation data types “SRD2”, “SRD3”, “SRD4”, “VLF5”, and “VLF15”. The attention level is set for the block classification “B”.

  In addition, the setting of an earthquake level is not restricted to the above-mentioned content. For example, adopt a configuration in which a warning level is set when an earthquake is predicted to occur in all of the observation data types associated with any observation area in the block category, and a caution level is set in other cases Also good.

  In step S110, the analysis server 101 generates a prediction result indicating that an earthquake will occur. More specifically, the analysis server 101 generates a prediction result (earthquake prediction information) including the predicted earthquake occurrence location, occurrence period, and alert level.

  In step S111, the analysis server 101 generates a prediction result indicating that no earthquake occurs. When this process ends, the process returns to the main process.

  In addition, a person may be responsible for some or all of steps S103 to S110.

  For example, without storing the determination result (step S103) and updating the earthquake prediction calendar (step S104), it is determined whether or not an earthquake is predicted by referring to each graph on which analysis result data is plotted. May be.

  Further, for example, the earthquake occurrence area may be narrowed down with reference to each graph in which analysis result data is plotted, an earthquake prediction calendar, and the like. At this time, the SDR, VLF, and VOR observation areas where the abnormality is detected are color-coded and written on the map to narrow down the earthquake occurrence area (specify the epicenter).

  Further, for example, the earthquake occurrence period may be narrowed down with reference to each graph on which analysis result data is plotted, an earthquake prediction calendar, and the like.

  Further, for example, the earthquake level may be set with reference to each graph on which analysis result data is plotted, an earthquake prediction calendar, and the like.

  Further, for example, a prediction result indicating that an earthquake will occur (prediction result indicating that no earthquake will occur) may be created with reference to each graph in which analysis result data is plotted, an earthquake prediction calendar, and the like.

<< Distribution Information >>
FIG. 23 is a diagram illustrating an example of distribution information (earthquake hazard map: earthquake warning level map).
The earthquake hazard map includes a map image 500, block images 501, 502, 503, and 504 corresponding to the block classification, and earthquake prediction details 505.

  The map image 500 generally shows the whole of Japan. However, the map image 500 may be a map showing a part of Japan (for example, each frame in the figure) such as Hokkaido / Tohoku area, Tokyo area, Chubu area, Kinki area, Kyushu area, etc. Some combinations (configurations showing all but part) are also possible.

Block images 501, 502, 503, and 504 indicate the epicenter area of the earthquake.
Block images 501, 502, 503, and 504 also show earthquake levels. The block image 501 is an image in which the earthquake level indicates a warning level, and is displayed in red. The block images 502, 503, and 504 are images in which the earthquake level indicates the attention level, and are displayed in green.

  The earthquake prediction details 505 indicate the prediction period and the earthquake level for each block category. For example, block classification No. 3 shows that since a warning level is set from December 9, 2015 to December 12, 2015, it is understood that the probability of an earthquake occurring in the block segment during the period is relatively high.

  The earthquake hazard map is not limited to the contents described above.

  For example, the earthquake prediction details 505 may include an analyst's commentary in addition to or instead of the above-described content, and may include magnitude (value, scale level). For example, as the magnitude level of the magnitude, “M5.0” or more and less than “M6.5” is a medium scale level, “M6.5” or more and less than “M7.5” is a large scale level, and “M7.5” or more “ Less than M8.5 "may be provided with a huge scale level, and" M8.5 "or more may be provided with a large scale level.

  In addition, for example, information such as a predicted earthquake occurrence point (seismic source), an extent and extent of damage, an evacuation route, and an evacuation place may be shown on the map.

  In addition, this embodiment is not restricted to the above-mentioned content.

  In the present embodiment, four observation methods are shown and described, but all four may not be employed. For example, only the observation method of the space radiation dose (SRD) may be adopted alone. Further, for example, the SRD observation method may be used in combination with the VLF observation method, or the SRD observation method may be used in combination with the VLF observation method and the GPS observation method. That is, two arbitrary observation methods may be combined, or three arbitrary observation methods may be combined.

For example, the analysis server 101 may be configured to predict an earthquake having a predetermined magnitude (for example, “M6.0”) or more. More specifically, the analysis server 101 refers to the abnormality determination result and determines whether or not an earthquake having a predetermined magnitude or more is predicted for each block category.
According to the above-described configuration, since earthquakes with a magnitude greater than or equal to a predetermined magnitude are targeted for prediction, it is possible to eliminate small-scale earthquakes of “M4.0” or less that occur every day. Can be raised.

At this time, the magnitude of the earthquake of each block may be determined according to the priority order associated with the observation method (configuration in which the magnitude is determined by weighting the observation method).
As a combination with the SRD observation method, the observation method with the highest priority is the observation related to the VLF in which the past performance data is reflected in the magnitude prediction, and the observation method with the lowest priority is to specify the magnitude. This is an observation technique related to GPS that is constant.

On the other hand, a configuration in which the observation method is not weighted may be employed. In this case, a value indicating the highest magnitude is adopted.
According to the above-described configuration, it is possible to make a prediction assuming the worst case.

In addition to or instead of the four observation methods described above, an earthquake based on the result of being able to detect a radio wave called an ultra-long wave (ULF) generated from a minute crack generated before the earthquake with an ULF sensor. An observation method for predicting the above may be adopted.
Further, a method of observing galactic radio waves that can determine the critical frequency of the ionosphere with respect to the F layer, judge the level of the obtained critical frequency, and predict an earthquake may be adopted.
According to the configuration described above, it is possible to realize earthquake prediction with high probability by capturing the precursory phenomenon of the earthquake from various angles.

FIG. 24 is a graph for explaining an application example of the earthquake prediction method using the space radiation dose. The graph of FIG. 24 has shown the space radiation dose of the monitoring post installed in Kumamoto-shi in April, 2016.
On April 7, a spatial radiation dose of 0.083 μSv / h was observed. The deviation (xm) from the average value was 0.0454, and the ratio (xm) / σ from the standard deviation was 12.201002123, which was detected as an abnormal value. The following estimation formula was calculated using outliers.
1.7703 M = ln (122010102123 / (4πr ² ))
As a result, the relationship between the distance r to the epicenter and the magnitude M of the earthquake was predicted as follows.

Distance r: 5, 10, 15, 20, 25, 30
Size M: 7.3, 6.4, 6.0, 5.7, 5.4, 5.2

  In addition, it was predicted that an earthquake would occur between April 7th and April 13th. Anomalies were observed in the region corresponding to Saga-Ohito on April 7 by VLF observation. For this reason, the accuracy of earthquake occurrence in this area was predicted to be high.

  On April 16th, the Kumamoto earthquake, whose main shock was the M7.3 earthquake, occurred as a corresponding earthquake. The distance from the measurement point to the epicenter was about 4.6 km.

  On April 21, an abnormal value of the spatial radiation dose was detected. However, at this time, no abnormality was detected due to the observation of VLF. As a result, the earthquake corresponding to the abnormal value of the space radiation dose on April 21 did not occur.

  Based on the detection of anomalous values of space radiation dose, it was found that earthquakes can be detected with a significant probability.

100 Earthquake Prediction System 110 Data Processing Unit 120 Data Storage Unit 131 Observation Data 132 Statistical Basic Data

Claims (9)

A data collection stage for collecting data of the spatial radiation dose at the measurement point periodically measured by a spatial radiation dose measurement device installed on the ground of the measurement point;
An abnormal value detection stage for detecting an abnormal value of deviation from the average of the spatial radiation dose measured periodically as a precursor of an earthquake;
Based on the detected abnormal value, a scale prediction step for predicting the distance to the epicenter of the earthquake corresponding to the abnormal value and the magnitude of the earthquake that may occur after the abnormal value has occurred,
An earthquake prediction method characterized by comprising:   The earthquake prediction method according to claim 1, further comprising a time prediction step of predicting the occurrence time of the earthquake based on a time when the abnormal value detected in the abnormal value detection step occurs.   The anomaly detection step is a spatial radiation dose when the spatial radiation dose measured by the spatial radiation dose measuring device is lower than the dose per unit time of the normal public dose limit as recommended by the International Commission on Radiological Protection. 3. The earthquake prediction method according to claim 1, wherein an abnormal value is detected based on the earthquake. The data collection step collects data on the amount of space radiation measured by a space radiation dose measuring device installed at a plurality of measurement points,
The abnormal value detection step detects the abnormal value for each of a plurality of measurement points,
The earthquake according to any one of claims 1 to 3, wherein the magnitude prediction step predicts the distance to the epicenter of the earthquake corresponding to the abnormal value and the magnitude of the earthquake for each of a plurality of measurement points. Prediction method.   The earthquake prediction method according to any one of claims 1 to 4, wherein the space radiation dose measuring device transmits data representing the measured space radiation dose through a computer network.   6. The earthquake prediction method according to claim 1, wherein the space radiation dose measuring device is a monitoring post for periodically monitoring the radiation dose at the measurement point.   The abnormal value detecting step detects the abnormal value corresponding to the earthquake based on a ratio of the deviation detected in the abnormal value detecting step to a standard deviation of the space radiation dose measured periodically. The earthquake prediction method according to claim 1, wherein the method is an earthquake prediction method. A determination stage for determining whether or not the radio wave received by a receiving device installed on the ground is affected by an earthquake precursor,
A predetermined region setting step for setting a predetermined region for predicting an earthquake based on the prediction result of the scale prediction step and the determination result of the determination step;
The prediction step of predicting that the probability that an earthquake will occur in the predetermined region is increased when it is determined that there is an influence in both the scale prediction step and the determination step. The earthquake prediction method according to claim 1. Data collection means for collecting data on the spatial radiation dose at the measurement point periodically measured by a spatial radiation dose measurement device installed on the ground of the measurement point;
An abnormal value detecting means for detecting an abnormal value of deviation from the average of the spatial radiation dose measured periodically as a precursor of an earthquake;
Based on the detected abnormal value, a scale prediction step for predicting the distance to the epicenter of the earthquake corresponding to the abnormal value and the magnitude of the earthquake that may occur after the abnormal value has occurred,
An earthquake prediction system characterized by comprising: