CN116009076A - Seismic signal processing method, medium and equipment - Google Patents
Seismic signal processing method, medium and equipment Download PDFInfo
- Publication number
- CN116009076A CN116009076A CN202111236167.4A CN202111236167A CN116009076A CN 116009076 A CN116009076 A CN 116009076A CN 202111236167 A CN202111236167 A CN 202111236167A CN 116009076 A CN116009076 A CN 116009076A
- Authority
- CN
- China
- Prior art keywords
- time window
- seismic data
- sliding time
- mathematical morphology
- scale
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention discloses a seismic signal processing method, a medium and equipment, wherein the method comprises the following steps: decomposing the original seismic data according to a plurality of different sliding time windows; for each sliding time window, representing the original seismic data in the time window as the seismic data under mathematical morphology, removing noise signals of the seismic data under mathematical morphology, and obtaining a seismic data gather under mathematical morphology of the time window; calculating the segment scale coefficient of each sliding time window; and obtaining the reconstructed seismic data based on the segment scale coefficient of each sliding time window and the seismic data gather under the mathematical morphology. The invention utilizes the advantages of mathematical morphology, reconstructs signals based on sliding time windows, achieves the aim of multi-scale signal reconstruction, abandons the fixed coefficient and the specific scale of the inherent denoising method, achieves better denoising effect, fully protects effective signals, and has good effects on noise removal and effective signal protection in seismic monitoring.
Description
Technical Field
The invention belongs to the technical field of seismic data processing, and particularly relates to a seismic signal processing method, a medium and electronic equipment.
Background
In the current domestic geological exploration situation, the utilization of hydraulic fracturing in the exploration and development process is increasingly common, and microseism monitoring is an indispensable method for signal monitoring in the hydraulic fracturing process. Because microseismic signals are very small, they are difficult to monitor, and noise suppression and signal detection become the primary tasks of microseism monitoring. Therefore, improving the signal-to-noise ratio of the microseismic signals is always a great weight of research, and the current common method mainly comprises frequency filtering, but is not applicable under the condition that noise and signal frequency bands are overlapped; yet another approach is to suppress f-x domain noise with integrated empirical mode decomposition and adaptive threshold, and the current integrated empirical mode decomposition approach at the front of comparison is based on mathematical morphology methods that decompose seismic data primarily in mathematical morphology to obtain scale factors for use in the decomposition, and reconstruct from the scale factors and decomposed seismic data, which has the advantage of preserving signal energy and hardly damaging the spectrum.
However, when the signal reconstruction is carried out by the current mathematical morphology method, only a fixed scale factor can be used, the loss of an effective signal part can be caused by using the fixed scale factor, and meanwhile, noise can not be well removed.
Therefore, a method that can sufficiently protect an effective signal and has a good denoising effect is required.
Disclosure of Invention
The invention aims to provide a method capable of fully protecting effective signals and good in denoising effect.
In a first aspect, the present invention provides a seismic signal processing method, comprising: decomposing the original seismic data according to a plurality of different sliding time windows; for each sliding time window, representing the original seismic data in the time window as the seismic data under mathematical morphology, and removing noise signals of the seismic data under mathematical morphology to obtain a seismic data gather under the mathematical morphology of the time window; calculating the segment scale coefficient of each sliding time window; and obtaining the reconstructed seismic data based on the segment scale coefficient of each sliding time window and the seismic data gather under the mathematical morphology.
Optionally, the seismic data trace set under mathematical morphology of the time window is obtained by: the original seismic data in the time window are decomposed according to a plurality of scales, and data represented by the original data of each scale under mathematical morphology are obtained; removing the data represented under mathematical morphology by the original data of the scale where the noise signal is from the data represented under mathematical morphology by the original data of all scales in the time window; and taking the data after noise signals are removed as a seismic data gather under the mathematical morphology of the time window.
Optionally, the seismic data under the mathematical morphology of the time window is expressed as:
wherein f is the original seismic data in the sliding time window, lambda represents the lambda-th scale, s is a structural element, y λ Representing the data of the original data of the lambda-th scale after the action of the structural element of the lambda-th scale, F λ The original data representing the lambda-th scale is data represented under mathematical morphology, and n represents the number of scales.
Optionally, the segment scale factor for each sliding time window is calculated by: for each sliding time window, an initial segmentation scale coefficient expression corresponding to the time window is obtained, L2 norm constraint is recorded in the initial segmentation scale coefficient expression, a segmentation scale coefficient expression added with the norm constraint is obtained, and a segmentation scale coefficient expression added with the norm constraint is calculated to obtain a segmentation scale coefficient.
Optionally, the initial segment scale factor expression is:
wherein ,αi For the segment scale factor of the ith sliding window, R i An operator for the ith sliding time window, f is the ith slidingRaw seismic data for time window, F i Is the seismic data trace set under the mathematical morphology of the ith sliding time window.
Optionally, the segment scale coefficient expression after adding the norm constraint is:
wherein ,αi For the segment scale factor of the ith sliding window, R i An operator of the ith sliding time window, F is the original seismic data of the ith sliding time window, F i Is the seismic data gather under the mathematical morphology of the ith sliding time window, and lambda is the constraint coefficient.
Optionally, the calculated segment scale factor of the sliding time window is:
wherein ,αi For the segment scale factor of the ith sliding window, R i For the operator of the ith sliding time window,
transpose the operator of the ith sliding time window, F is the original seismic data of the ith sliding time window, F i For the seismic data gather under mathematical morphology of the ith sliding time window, +.>
Is the seismic data gather under the mathematical morphology of the ith sliding time window, lambda is a constraint coefficient, and I is an identity matrix.
Optionally, the reconstructed seismic data is obtained using the following formula:
wherein ,
reconstructed seismic data, R i Operator for ith sliding window, +.>
Transpose of operator for ith sliding time window, F i Is the seismic data trace set under the mathematical morphology of the ith sliding time window, alpha i The segmentation scale factor of the ith sliding time window.
In a second aspect, the present invention also provides an electronic device, including: a memory storing executable instructions; and a processor executing the executable instructions in the memory to implement the seismic signal processing method.
In a third aspect, the present invention also provides a computer readable storage medium storing a computer program which when executed by a processor implements the above seismic signal processing method.
The invention has the beneficial effects that: the seismic signal processing party of the invention utilizes the advantages of Mathematical Morphology (MMD), reconstructs signals based on sliding time windows, achieves the aim of multi-scale signal reconstruction, abandons the fixed coefficient and the specific scale of the inherent denoising method, achieves better denoising effect, fully protects effective signals, and has good effects on noise removal and effective signal protection in seismic monitoring.
The invention has other features and advantages which will be apparent from or are set forth in detail in the accompanying drawings and the following detailed description, which are incorporated herein, and which together serve to explain certain principles of the invention.
Drawings
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of exemplary embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the invention.
FIG. 1 illustrates a flow chart of a method of seismic signal processing according to one embodiment of the invention.
FIG. 2 shows a diagram of raw signals and mathematical morphology operators of a seismic signal processing method according to an embodiment of the invention.
FIG. 3 illustrates a multi-scale morphology decomposition example diagram of a seismic signal processing method according to one embodiment of the invention.
Fig. 4 shows a composite signal method application diagram of a seismic signal processing method according to an embodiment of the invention.
Detailed Description
Preferred embodiments of the present invention will be described in more detail below. While the preferred embodiments of the present invention are described below, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein.
The invention provides a seismic signal processing method, which comprises the following steps: decomposing the original seismic data according to a plurality of different sliding time windows; for each sliding time window, representing the original seismic data in the time window as the seismic data under mathematical morphology, removing noise signals of the seismic data under mathematical morphology, and obtaining a seismic data gather under mathematical morphology of the time window; calculating the segment scale coefficient of each sliding time window; and obtaining the reconstructed seismic data based on the segment scale coefficient of each sliding time window and the seismic data gather under the mathematical morphology.
And carrying out scale reconstruction by adopting unfixed coefficients, dividing data of different scales according to sliding time windows, calculating a segmentation scale coefficient aiming at each sliding time window, adding the segmentation scale coefficient into L2 norm constraint to obtain a final segmentation scale coefficient, and carrying out seismic data reconstruction based on the final segmentation scale coefficient and the seismic data in the sliding time window corresponding to the final segmentation scale coefficient to obtain the reconstructed seismic data. Unlike the fixed scale in traditional mathematical morphology methods, the present application (SWMMR) divides the signal into different scales by sliding time windows with overlap, removes scales containing a lot of noise, and calculates the reconstruction coefficients of other scales by the least square method with l 2-norm constraint for each time window. Better denoising and less loss of the effective signal can be achieved by combining the reconstructed parts.
According to an exemplary embodiment, the seismic signal processing party utilizes the advantages of Mathematical Morphology (MMD), reconstructs signals based on sliding time windows, achieves the aim of multi-scale signal reconstruction, abandons the fixed coefficient and the specific scale of the inherent denoising method, achieves better denoising effect, fully protects effective signals, and has good effects on noise removal and effective signal protection in seismic monitoring.
Alternatively, the set of seismic data traces under mathematical morphology of the time window is obtained by: decomposing the original seismic data in the time window according to a plurality of scales to obtain data represented by the original data of each scale under mathematical morphology; removing the data represented under mathematical morphology by the original data of the scale where the noise signal is from the data represented under mathematical morphology by the original data of all scales in the time window; and taking the data after noise signals are removed as a seismic data gather under mathematical morphology of a time window.
In particular, mathematical morphology is an efficient graphical processing tool based on grarens and topology for describing the quantization structure of images. The basic purpose of mathematical morphology is to select the shape of a structural element according to the characteristics of the signal, the basic operations being erosion and expansion, each of which is represented by the following formula:
where f (z) represents the value at z, s (x) is a structural element, and the opening and closing operations are a combination of corrosion and expansion, as shown in the following formulas (3) (4):
the choice of structural elements will also affect the outcome of the calculation, shape, amplitude and length being the three key components of the structural elements.
Representing the original seismic data by a mathematical morphology method, wherein the original seismic data are expressed as the sum of corresponding scales under the action of structural elements; different scales are set by utilizing multi-scale morphological decomposition, and structural elements are divided into multiple scales, so that a multi-scale seismic signal is obtained.
Alternatively, the seismic data under mathematical morphology of the time window is expressed as:
wherein f is the original seismic data in the sliding time window, lambda represents the lambda-th scale, s is a structural element, y λ Representing the data of the original data of the lambda-th scale after the action of the structural element of the lambda-th scale, F λ The original data representing the lambda-th scale is data represented under mathematical morphology, and n represents the number of scales.
Specifically, the above formula is adopted to obtain the data represented by the original data of each scale under mathematical morphology.
Alternatively, the segment scale factor for each sliding time window is calculated by: for each sliding time window, an initial segmentation scale coefficient expression corresponding to the time window is obtained, L2 norm constraint is recorded in the initial segmentation scale coefficient expression, a segmentation scale coefficient expression added with the norm constraint is obtained, and a segmentation scale coefficient expression added with the norm constraint is calculated to obtain a segmentation scale coefficient.
Alternatively, the initial segment scale factor expression is:
wherein ,αi For the segment scale factor of the ith sliding window, R i An operator of the ith sliding time window, F is the original seismic data of the ith sliding time window, F i Is the seismic data trace set under the mathematical morphology of the ith sliding time window.
Specifically, different sliding time windows are set according to different decomposition scales, a sliding time window with a proper length is selected, then the sliding time window slides downwards, the seismic data of mathematical morphology are decomposed into the seismic data in a plurality of sliding time windows, and initial segmentation scale coefficients corresponding to the time windows are calculated and obtained according to the seismic data in the same time window.
Alternatively, the segment scale factor expression after adding the norm constraint is:
wherein ,αi For the segment scale factor of the ith sliding window, R i An operator of the ith sliding time window, F is the original seismic data of the ith sliding time window, F i Is the seismic data gather under the mathematical morphology of the ith sliding time window, and lambda is the constraint coefficient.
Specifically, on the basis of the initial segmentation scale factor, L2-norm is added for constraint.
As an alternative, the calculated segment scale factor of the sliding time window is:
wherein ,αi For the segment scale factor of the ith sliding window, R i For the operator of the ith sliding time window,
transpose the operator of the ith sliding time window, F is the original seismic data of the ith sliding time window, F i For the seismic data gather under mathematical morphology of the ith sliding time window, +.>
Is the seismic data gather under the mathematical morphology of the ith sliding time window, lambda is a constraint coefficient, and I is an identity matrix.
Specifically, the expression of the segmentation scale coefficient added with the L2 norm constraint is calculated and arranged, and the segmentation scale coefficient is obtained.
Alternatively, the reconstructed seismic data is obtained using the following formula:
wherein ,
reconstructed seismic data, R i Operator for ith sliding window, +.>
Transpose of operator for ith sliding time window, F i Is the seismic data trace set under the mathematical morphology of the ith sliding time window, alpha i The segmentation scale factor of the ith sliding time window.
Specifically, after obtaining the segment scale coefficient of each sliding time window and the seismic data trace set under the mathematical morphology, substituting the segment scale coefficient into a reconstruction formula to obtain the reconstructed seismic data.
It should be noted that the length of the sliding window should be equal to or greater than the number of scales, otherwise a case of under-fitting would occur.
In a second aspect, the present invention also provides an electronic device, including: a memory storing executable instructions; and the processor runs the executable instructions in the memory to realize the seismic signal processing method.
In a third aspect, the present invention also provides a computer readable storage medium storing a computer program which when executed by a processor implements the above seismic signal processing method.
Example 1
FIG. 1 illustrates a flow chart of a method of seismic signal processing according to one embodiment of the invention. FIG. 2 shows a diagram of raw signals and mathematical morphology operators of a seismic signal processing method according to an embodiment of the invention. FIG. 3 illustrates a multi-scale morphology decomposition example diagram of a seismic signal processing method according to one embodiment of the invention. Fig. 4 shows a composite signal method application diagram of a seismic signal processing method according to an embodiment of the invention.
As shown in connection with fig. 1, 2, 3 and 4, the seismic signal processing method includes:
step 1: decomposing the original seismic data according to a plurality of different sliding time windows;
step 2: for each sliding time window, representing the original seismic data in the time window as the seismic data under mathematical morphology, removing noise signals of the seismic data under mathematical morphology, and obtaining a seismic data gather under mathematical morphology of the time window;
step 3: calculating the segment scale coefficient of each sliding time window;
step 4: and obtaining the reconstructed seismic data based on the segment scale coefficient of each sliding time window and the seismic data gather under the mathematical morphology.
Wherein, the seismic data gather under mathematical morphology of the time window is obtained by: decomposing the original seismic data in the time window according to a plurality of scales to obtain data represented by the original data of each scale under mathematical morphology; removing the data represented under mathematical morphology by the original data of the scale where the noise signal is from the data represented under mathematical morphology by the original data of all scales in the time window; and taking the data after noise signals are removed as a seismic data gather under mathematical morphology of a time window.
Wherein, the seismic data under mathematical morphology of the time window is expressed as:
wherein f is the original seismic data in the sliding time window, lambda represents the lambda-th scale, s is a structural element, y λ Representing the data of the original data of the lambda-th scale after the action of the structural element of the lambda-th scale, F λ The original data representing the lambda-th scale is data represented under mathematical morphology, and n represents the number of scales.
The segment scale coefficient of each sliding time window is calculated through the following steps: for each sliding time window, an initial segmentation scale coefficient expression corresponding to the time window is obtained, L2 norm constraint is recorded in the initial segmentation scale coefficient expression, a segmentation scale coefficient expression added with the norm constraint is obtained, and a segmentation scale coefficient expression added with the norm constraint is calculated to obtain a segmentation scale coefficient.
The initial segmentation scale factor expression is:
wherein ,αi For the segment scale factor of the ith sliding window, R i An operator of the ith sliding time window, F is the original seismic data of the ith sliding time window, F i Is the seismic data trace set under the mathematical morphology of the ith sliding time window.
The expression of the segmentation scale coefficient after adding the norm constraint is as follows:
wherein ,αi Is the firstSegment scale factor of i sliding time windows, R i An operator of the ith sliding time window, F is the original seismic data of the ith sliding time window, F i Is the seismic data gather under the mathematical morphology of the ith sliding time window, and lambda is the constraint coefficient.
The calculated segmentation scale coefficient of the sliding time window is as follows:
wherein ,αi For the segment scale factor of the ith sliding window, R i For the operator of the ith sliding time window,
transpose the operator of the ith sliding time window, F is the original seismic data of the ith sliding time window, F i For the seismic data gather under mathematical morphology of the ith sliding time window, +.>
Is the seismic data gather under the mathematical morphology of the ith sliding time window, lambda is a constraint coefficient, and I is an identity matrix.
The reconstructed seismic data is obtained by adopting the following formula:
wherein ,
reconstructed seismic data, R i Operator for ith sliding window, +.>
Transpose of operator for ith sliding time window, F i Is the seismic data trace set under the mathematical morphology of the ith sliding time window, alpha i For the ith sliding movementSegment scale coefficients of the window.
As shown in fig. 2, which shows the original signal and four operations in mathematical morphology, the black line shows the signal, the black and blue dash-dot lines show corrosion and expansion, and the red and yellow dash-dot lines show opening and closing. The result of corrosion is that all values are less than the original signal, while the expansion is the opposite. The opening operation will decrease the peak and the closing will fill the trough.
In fig. 3 is an example of a multi-scale decomposition, the first result is a rake wavelet, the second result is random noise, the third result is low frequency noise, the fourth is the received raw data, and the 5 th to 11 th results are the results of the multi-scale decomposition. Scale 1 contains most random noise, scale 2-6 is the decomposed part of the signal, and scale 7 is the extracted low frequency energy.
Fig. 4 is an application of the composite signal. The first trace represents a Rake wavelet with a frequency domain of 100Hz, the second trace represents random noise, the third trace represents noise data, the signal-to-noise ratio is-11.3121 dB, the 4 th to 10 th traces represent components of the multi-scale morphological decomposition, the 11 th and 12 th traces represent the results of the present and conventional methods, respectively, and the 13 th and 14 th traces are the corresponding reconstruction errors. The multi-scale morphological decomposition may extract a large amount of unwanted information, such as small-scale random noise (e.g., 4 th and 5 th traces) and a large amount of low-frequency noise (10 th trace), and then use the 6 th-9 th lines to obtain the final denoising result.
Example two
The present disclosure provides an electronic device including: a memory storing executable instructions; and the processor runs the executable instructions in the memory to realize the seismic signal processing method.
An electronic device according to an embodiment of the present disclosure includes a memory and a processor.
The memory is for storing non-transitory computer readable instructions. In particular, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like.
The processor may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device to perform the desired functions. In one embodiment of the present disclosure, the processor is configured to execute the computer readable instructions stored in the memory.
It should be understood by those skilled in the art that, in order to solve the technical problem of how to obtain the good user experience effect, the present embodiment may also include well-known structures such as a communication bus, an interface, and the like, and these well-known structures are also included in the protection scope of the present disclosure.
The detailed description of the present embodiment may refer to the corresponding description in the foregoing embodiments, and will not be repeated herein.
Example III
The present disclosure provides a computer readable storage medium storing a computer program which when executed by a processor implements the above-described seismic signal processing method.
A computer-readable storage medium according to an embodiment of the present disclosure has stored thereon non-transitory computer-readable instructions. When executed by a processor, perform all or part of the steps of the methods of embodiments of the present disclosure described above.
The computer-readable storage medium described above includes, but is not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or removable hard disk), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).
The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described.
Claims (10)
1. A method of seismic signal processing, comprising:
decomposing the original seismic data according to a plurality of different sliding time windows;
for each sliding time window, representing the original seismic data in the time window as the seismic data under mathematical morphology, and removing noise signals of the seismic data under mathematical morphology to obtain a seismic data gather under the mathematical morphology of the time window;
calculating the segment scale coefficient of each sliding time window;
and obtaining the reconstructed seismic data based on the segment scale coefficient of each sliding time window and the seismic data gather under the mathematical morphology.
2. The method of seismic signal processing according to claim 1, wherein the set of seismic data traces under mathematical morphology of the time window is obtained by:
the original seismic data in the time window are decomposed according to a plurality of scales, and data represented by the original data of each scale under mathematical morphology are obtained;
removing the data represented under mathematical morphology by the original data of the scale where the noise signal is from the data represented under mathematical morphology by the original data of all scales in the time window;
and taking the data after noise signals are removed as a seismic data gather under the mathematical morphology of the time window.
3. The method of seismic signal processing according to claim 2, wherein the seismic data under mathematical morphology of the time window is represented as:
wherein f is the original seismic data in the sliding time window, lambda represents the lambda-th scale, s is a structural element, y λ Representing the data of the original data of the lambda-th scale after the action of the structural element of the lambda-th scale, F λ The original data representing the lambda-th scale is data represented under mathematical morphology, and n represents the number of scales.
4. The seismic signal processing method of claim 1, wherein the segment scale factor for each sliding time window is calculated by:
for each sliding time window, an initial segmentation scale coefficient expression corresponding to the time window is obtained, L2 norm constraint is recorded in the initial segmentation scale coefficient expression, a segmentation scale coefficient expression added with the norm constraint is obtained, and a segmentation scale coefficient expression added with the norm constraint is calculated to obtain a segmentation scale coefficient.
5. The seismic signal processing method of claim 4, wherein the initial segment scale factor expression is:
wherein ,αi For the segment scale factor of the ith sliding window, R i An operator of the ith sliding time window, F is the original seismic data of the ith sliding time window, F i Is the seismic data trace set under the mathematical morphology of the ith sliding time window.
6. The seismic signal processing method of claim 5, wherein the segment scale factor expression after adding the norm constraint is:
wherein ,αi For the segment scale factor of the ith sliding window, R i An operator of the ith sliding time window, F is the original seismic data of the ith sliding time window, F i Is the seismic data gather under the mathematical morphology of the ith sliding time window, and lambda is the constraint coefficient.
7. The method of seismic signal processing according to claim 6, wherein the calculated segment scale coefficients of the sliding time window are:
wherein ,αi For the segment scale factor of the ith sliding window, R i For the operator of the ith sliding time window,
transpose the operator of the ith sliding time window, F is the original seismic data of the ith sliding time window, F i For the seismic data gather under mathematical morphology of the ith sliding time window, +.>
Is the seismic data gather under the mathematical morphology of the ith sliding time window, lambda is a constraint coefficient, and I is an identity matrix.
8. The method of seismic signal processing according to claim 7, wherein the reconstructed seismic data is obtained using the formula:
wherein ,
reconstructed seismic data, R i Operator for ith sliding window, +.>
Transpose of operator for ith sliding time window, F i Is the seismic data trace set under the mathematical morphology of the ith sliding time window, alpha i The segmentation scale factor of the ith sliding time window.
9. An electronic device, the electronic device comprising:
a memory storing executable instructions;
a processor executing the executable instructions in the memory to implement the seismic signal processing method according to any one of claims 1-8.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the seismic signal processing method of any of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111236167.4A CN116009076B (en) | 2021-10-22 | 2021-10-22 | Seismic signal processing method, medium and equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111236167.4A CN116009076B (en) | 2021-10-22 | 2021-10-22 | Seismic signal processing method, medium and equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116009076A true CN116009076A (en) | 2023-04-25 |
| CN116009076B CN116009076B (en) | 2025-08-01 |
Family
ID=86021725
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111236167.4A Active CN116009076B (en) | 2021-10-22 | 2021-10-22 | Seismic signal processing method, medium and equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116009076B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116819627A (en) * | 2023-06-30 | 2023-09-29 | 中海石油(中国)有限公司深圳分公司 | Method, device, equipment and medium for enhancing weak earthquake signal |
| CN117034087A (en) * | 2023-06-06 | 2023-11-10 | 博睿康科技（常州）股份有限公司 | Multi-level detection model of signal time window |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090292475A1 (en) * | 2007-05-25 | 2009-11-26 | Aftab Alam | Time-Space Varying Spectra for Seismic Processing |
| WO2010014118A1 (en) * | 2008-08-01 | 2010-02-04 | Ikelle Luc T | Statistical decoding and imaging of multishot and single-shot seismic data |
| WO2014165512A1 (en) * | 2013-04-02 | 2014-10-09 | Schlumberger Canada Limited | Processing seismic attributes using mathematical morphology |
| CN106291696A (en) * | 2015-06-04 | 2017-01-04 | 中国石油化工股份有限公司 | A kind of low signal-to-noise ratio seismic signal recognition methods and system |
| CN107272066A (en) * | 2017-06-22 | 2017-10-20 | 东华理工大学 | A kind of noisy seismic signal first-arrival traveltime pick-up method and device |
| CN109143369A (en) * | 2017-06-28 | 2019-01-04 | 中国石油化工股份有限公司 | The method for efficiently filtering out seismic data random disturbances based on shape filtering |
| CN109633646A (en) * | 2019-01-21 | 2019-04-16 | 中国人民解放军陆军工程大学 | Bistatic ISAR imaging method based on weighted L1 norm constraint |
| CN112213782A (en) * | 2020-09-29 | 2021-01-12 | 中国石油大学(北京) | Method, device and server for processing split-phase seismic data |
| WO2021108099A1 (en) * | 2019-11-27 | 2021-06-03 | Bp Corporation North America Inc. | Non-linear solution to seismic data conditioning using trained dictionaries |
-
2021
- 2021-10-22 CN CN202111236167.4A patent/CN116009076B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090292475A1 (en) * | 2007-05-25 | 2009-11-26 | Aftab Alam | Time-Space Varying Spectra for Seismic Processing |
| WO2010014118A1 (en) * | 2008-08-01 | 2010-02-04 | Ikelle Luc T | Statistical decoding and imaging of multishot and single-shot seismic data |
| WO2014165512A1 (en) * | 2013-04-02 | 2014-10-09 | Schlumberger Canada Limited | Processing seismic attributes using mathematical morphology |
| CN106291696A (en) * | 2015-06-04 | 2017-01-04 | 中国石油化工股份有限公司 | A kind of low signal-to-noise ratio seismic signal recognition methods and system |
| CN107272066A (en) * | 2017-06-22 | 2017-10-20 | 东华理工大学 | A kind of noisy seismic signal first-arrival traveltime pick-up method and device |
| CN109143369A (en) * | 2017-06-28 | 2019-01-04 | 中国石油化工股份有限公司 | The method for efficiently filtering out seismic data random disturbances based on shape filtering |
| CN109633646A (en) * | 2019-01-21 | 2019-04-16 | 中国人民解放军陆军工程大学 | Bistatic ISAR imaging method based on weighted L1 norm constraint |
| WO2021108099A1 (en) * | 2019-11-27 | 2021-06-03 | Bp Corporation North America Inc. | Non-linear solution to seismic data conditioning using trained dictionaries |
| CN112213782A (en) * | 2020-09-29 | 2021-01-12 | 中国石油大学(北京) | Method, device and server for processing split-phase seismic data |
Non-Patent Citations (1)
| Title |
|---|
| 刘佳宾等: "小波变换在某气田地震剖面滤波中的应用", 中国煤炭地质, no. 10, 31 October 2019 (2019-10-31) * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117034087A (en) * | 2023-06-06 | 2023-11-10 | 博睿康科技（常州）股份有限公司 | Multi-level detection model of signal time window |
| CN116819627A (en) * | 2023-06-30 | 2023-09-29 | 中海石油(中国)有限公司深圳分公司 | Method, device, equipment and medium for enhancing weak earthquake signal |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116009076B (en) | 2025-08-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112183407B (en) | Tunnel seismic wave data denoising method and system based on time-frequency domain spectral subtraction | |
| CN114418886B (en) | Robust denoising method based on depth convolution self-encoder | |
| CN116009076B (en) | Seismic signal processing method, medium and equipment | |
| Ghanati et al. | Filtering and parameter estimation of surface-NMR data using singular spectrum analysis | |
| Oboué et al. | An advanced median filter for improving the signal-to-noise ratio of seismological datasets | |
| Li et al. | Contourlet based seismic reflection data non-local noise suppression | |
| CN108303740A (en) | A kind of aviation electromagnetic data noise drawing method and device | |
| CN118425887A (en) | Synthetic aperture radar interference suppression method, system, storage device and electronic device based on dynamic threshold subspace projection | |
| Li et al. | Seismic noise suppression using weighted nuclear norm minimization method | |
| Berger et al. | Adaptive regularized constrained least squares image restoration | |
| CN112817056B (en) | Magnetotelluric signal denoising method and system | |
| CN113009564A (en) | Seismic data processing method and device | |
| CN115310494B (en) | Self-adaptive convolution denoising network near-field microwave signal random noise suppression method | |
| Geng et al. | An iterative low-rank representation for SAR image despeckling | |
| CN117692074A (en) | A low-frequency aliasing noise suppression method suitable for unsteady underwater acoustic target signals | |
| CN113158797B (en) | Microseismic data denoising method, system, electronic equipment and storage medium | |
| CN112578439B (en) | Seismic inversion method based on space constraint | |
| CN118172279A (en) | Method and device for reducing noise of seismic image of fault-preserving layer, electronic equipment and medium | |
| Prakash et al. | Report on image denoising for Gaussian noise affected images | |
| Beddad et al. | Efficient implementation of an improved median filter on TMS320C6416 digital signal processor | |
| CN115877453B (en) | Three-dimensional seismic signal extraction method, device, terminal and storage medium | |
| CN118210051A (en) | Seismic data processing method, device, equipment and medium based on mathematical morphology | |
| CN120067539B (en) | Signal noise reduction method and device based on short-time Fourier transform and slot transform | |
| CN120233419A (en) | A method, system, electronic device and storage medium for removing noise from earthquake records | |
| CN118013265A (en) | An adaptive enhanced gearbox weak fault feature extraction method, system and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |