WO2012001388A2

WO2012001388A2 - Gravity survey data processing

Info

Publication number: WO2012001388A2
Application number: PCT/GB2011/051156
Authority: WO
Inventors: Neil Dyer
Original assignee: Arkex Ltd
Current assignee: Arkex Ltd
Priority date: 2010-07-02
Filing date: 2011-06-21
Publication date: 2012-01-05
Anticipated expiration: 2013-01-02
Also published as: GB201011149D0; WO2012001388A3; GB2481643A

Abstract

A method of processing measured potential field data from an airborne or marine potential field survey to determine a set of field mapping parameters for mapping a field, the method comprising: inputting said measured potential field data, said measured potential field data comprising data defining a plurality of potential field measurements each with an associated measurement position and measurement time; and determining said set of field mapping parameters using an equivalent source model, wherein said determining comprises fitting said measured potential field data to said model wherein said measured potential field data comprises at least one set of potential field measurements each having associated measurement positions which are sufficiently close that any residual noise component is attenuated by a factor of the square root of the number of measurements in said at least one set.

Description

Gravity Survey Data Processing

FIELD OF THE INVENTION This invention relates to improved techniques for processing potential field

measurement data from airborne surveys such as gravity surveys, the design of the survey pattern, and to methods, apparatus and computer program code for such techniques. BACKGROUND TO THE INVENTION

The use of equivalent source techniques in processing potential field data is known in art, for example, WO2007/012895, WO 2007/085875 and WO2008/093139 (all herein incorporated by reference). WO2008/093139 describes a technique which is an augmentation of the equivalent source technique and introduces a separate model to accommodate correlated time domain noise. By using a separate model, operating simultaneously with the equivalent source model, one can make the source parameter estimation less susceptible to this type of noise in the measurements. After the inversion, the augmented model can separately predict the desired potential field spatial distribution and the long wavelength time domain noise accompanying the

measurements.

As taught in WO2008/093139, a measurement, m taken at location (x, y, z) and time t from a geophysical survey can be broken down into a series of terms, m(x, y, z, t) = S(x, y, z) + I(t) + C(t) + n (1) where S(x ,y, z) represents the signal, I(t) the sources of interference, C(t) correlated noise and n purely random noise. The main goal of processing after a survey is to determine, with the best accuracy, the true signal S(x, y, z). Sources of interference I(t) are correctable by suitable measurements of the underlying disturbances and corresponding error coupling transfer functions. Correlated noise is modelled by an interpolation function with a time period that is similar or shorter than the characteristic time period of the noise.

Figure 1 taken from shows a simple time-domain interpolation function, constructed by stringing together piece- wise linear sections at regular intervals. More particularly, Figure 1 shows exponentially correlated noise with a 500 second characteristic time modelled by a piece-wise linear interpolator connecting nodal points (squares) every 400 seconds. In the case of a linear inversion model, the augmented model can be written as t(x, y, z, t) = Ap(x, y, z) + (t) (2) where f is the model forward calculation vector of measurements, p is the vector of equivalent source model parameters (for example, density of discrete mass source units), A is a matrix that relates the responses of the source elements to the measurement locations, λ is the vector of drift model parameters (for example, nodal values in a piece-wise linear model) and B is the matrix that relates the drift model interpolation to the times of the measurements. In this form, given a set of measurements m(x, y ,z, t), the model parameters p and λ can be determined by any standard optimisation technique that minimises the residual in the fit, minimise[L(f - m)] (3) where L represents a measure of the residual; the L₂ norm for example making the optimisation a least squares fit.

After the full inversion, the equivalent source term is used in isolation to either estimate the original signal underlying the measurements,

Ap = S{x, y, z) (4) or to predict different quantities at possibly different locations. Similarly, the second term in (2) can be used in isolation to estimate the drift in the measurements,

Βλ = C(t) . (5)

The remaining un-modelled part of the measurements (the residual fit) is then largely white noise. The white noise is fundamental and its effects can only be reduced by the law of averages through multiple measurements. As explained in WO2008/093139, an equivalent source inversion method can combine multiple measurements of a potential field distribution into a single model. An inverted equivalent source model can then regenerate the signal in the measurements, and within limits, can be used to re-project the data to different locations. In general, the number of adjustable model parameters is less than the number of independent measurements so, by the law of averages, signals recalculated from the model tend to have signal to noise ratios superior to the original measurements.

There is, however, a need for improved data processing and, in particular, for improved handling of noise. SUMMARY OF THE INVENTION

According to a first aspect of the invention, there is provided a method of processing measured potential field data from an airborne or marine potential field survey to determine a set of field mapping parameters for mapping a field, the method

comprising:

inputting said measured potential field data, said measured potential field data comprising data defining a plurality of potential field measurements each with an associated measurement position and measurement time; and

determining said set of field mapping parameters using an equivalent source model, wherein said determining comprises fitting said measured potential field data to said model wherein said measured potential field data comprises at least one set of potential field measurements each having associated measurement positions which are sufficiently close that any residual noise component is attenuated by a factor of the square root of the number of measurements in said at least one set.

The invention thus uses the stacking theorem detailed below and uses multiple measurements of the same quantity to superpose white noise onto the real values of the measurement. In this way, the signal to noise ratio is increased by a factor of the square root of the number of measurements made.

A plurality of sets of potential field measurements may be used with each set having associated measurement positions which are sufficiently close that any residual noise component is attenuated by a factor of the square root of the number of measurements in said at least one set. The plurality of sets may be generated by flying the same survey pattern multiple times or by defining a survey pattern having a plurality of survey lines such that a circle of diameter equal to half the minimum target wavelength placed at any location in said survey intersects several of said plurality of survey paths

A distance between each sufficiently close associated measurement positions of the or each set may be a function of a minimum spatial wavelength of said measurements and may be equal to half of a minimum target wavelength for the survey.

Said determined set of field mapping parameters may be used to calculate the potential field at any field position and the residual random noise component may be represented as an uncertainty component in the calculated potential field. As explained in more detail below, a key principal of a successfully completed equivalent source method is the condition that if the measurements are faithfully reproduced, the same set of elements may be used to calculate the potential field at any other position. Said measured potential field data may comprise one or more of gravimeter data and gravity gradiometer data. Said equivalent source model may be defined in many ways, including defining a plurality of platelets or mass elements which together form the surface of the survey area. The model may comprise a combination of a spatial part representing a spatial variation of said potential field and a temporal part representing time domain noise, for example as explained in detail in WO2008/093139.

Determining said set of field mapping parameters may comprise separating each measurement m taken at location (x, y, z) and time t from a geophysical survey into a series of terms, m{x, y, _z, t) = (S(x, y, z) + n) + I(t) + C(t) (1) where (S(x ,y, z) +n) represents signal from the field to be mapped mixed with random noise, I(t) the source of interference and C(t) correlated noise. Said interference and correlated noise components may be first attenuated by the method.

According to another aspect of the invention, there is provided a computer-implemented system for processing measured potential field data to determine a set of field mapping parameters for mapping a field, the computer-implemented system comprising:

an input to receive said measured potential field data, said measured potential field data comprising data defining a plurality of potential field measurements each with an associated measurement position and measurement time and

a processor to determine said set of field mapping parameters using a model comprising a combination of a spatial part representing a spatial variation of said potential field and a temporal part representing time domain noise, wherein said determining comprises fitting said measured potential field data to said spatial and temporal parts of said model;

wherein said measured potential field data comprises at least one set of potential field measurements each having associated measurement positions which are sufficiently close that any residual noise component is attenuated by a factor of the square root of the number of measurements in said at least one set. According to another aspect of the invention, there is provided a method of defining a potential field survey of a survey surface, the method comprising:

determining a minimum target wavelength of said survey,

defining a plurality of survey paths such that a circle of diameter equal to half the minimum target wavelength placed at any location in said survey intersects several of said plurality of survey paths.

Said plurality of survey paths may be generally parallel and are grouped in clusters of lines such that the distance between the two outer lines of each cluster is less than half the minimum target wavelength. Alternatively, said plurality of survey paths may be arranged in a grid pattern with a maximum dimension of each grid being less than half the minimum target wavelength.

According to another aspect of the invention, there is provided a computer-implemented system for conducting a potential field survey of a survey surface, the computer- implemented system comprising:

an inertial platform configured to

receive a plurality of survey paths above said survey surface, wherein said plurality of survey paths are such that a circle of diameter equal to half a minimum target wavelength of said survey placed at any location in said survey intersects several of said plurality of survey paths,

measure potential field data at points on said paths, and

transmit the field data to a data collection system. The invention further provides processor control code to implement the above-described methods, in particular on a data carrier such as a disk, CD- or DVD-ROM, programmed memory such as read-only memory (Firmware), or on a data carrier such as an optical or electrical signal carrier. Code (and/or data) to implement embodiments of the invention may comprise source, object or executable code in a conventional

programming language (interpreted or compiled) such as C, or assembly code, code for setting up or controlling an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array), or code for a hardware description language such as Verilog (Trade Mark) or VHDL (Very high speed integrated circuit Hardware

Description Language). As the skilled person will appreciate such code and/or data may be distributed between a plurality of coupled components in communication with one another, for example distributed across a network.

The invention further provides a data processing system configured to implement embodiments of the above-described methods.

Any of the features of above aspects may be combined with each other.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects of the invention will now be further described, by way of example only, with reference to the accompanying figures in which:

Figure 1 shows an example time-domain interpolation function for a joint equivalent source and time domain drift model as described in WO2008/093139;

Figure 2 shows a flow diagram of a procedure for processing measured potential field data to attenuate random noise, and

Figure 3a is a schematic graph plotting variation in position against time for four survey paths; Figure 3b is a schematic graph showing the variation in signal measured along the four survey paths of Figure 3b;

Figure 3c is a schematic graph showing the signals of Figure 3b with simulated coherent, interference and white noise;

Figure 3d is a schematic graph showing the signals of Figure 3d with simulated coherent and interference noise attenuated as described in Figure 2; Figure 3e is a schematic graph comparing stacking the results of Figure 3d both with stacking the signals of Figure 3c and with the perfect signal; Figure 4a is a schematic drawing of a survey having a single fold acquisition design;

Figure 4b is a schematic drawing of a survey having a four fold acquisition design;

Figure 4c is a schematic drawing of a survey having a four fold grid stacking acquisition design, and

Figure 5 shows an aircraft with flight survey data, and an example of a data processing system configured to implement the method of Figure 2. DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Figure 2 shows how the method of WO2008/093139 is improved. As explained in WO2008/093139, the white noise is fundamental and its effects can only be reduced by the law of averages through multiple measurements. Thus, the first step in the method is to input measured potential field data including multiple measurements (Step S200). The nature and number of these multiple measurements is key to the present invention and is determined by data stacking theorem.

Data stacking is a method of attenuation of the random noise component. Stacking theorem states that the best estimate of the signal in multiple, co-located measurements with white noise in the individual measurements is the mean signal :

where n is the number of measurements. The standard deviation may be represented as the RMS noise on the set of measurements, ill

where ** ^x ~ ^xi represents the noise.

The signal to noise ratio of the set of measurements is

Thus where multiple measurements of the same quantity are made using equipment which superposes white noise onto the real values of the measurement, the signal to noise ratio is increased by a factor of the square root of the number of measurements made.

The next step (S202) is to separate each measurement, m taken at location (x, y, z) and time t from a geophysical survey into a series of terms, m{x, y, _z, t) = (S(x, y, z) + n) + I(t) + C(t) (1) where S(x ,y, z) represents the signal, I(t) the sources of interference, C(t) correlated noise and n purely random noise. In other words, the signal is separated into three components, one representing interference, one representing correlated noise and a third which is the signal that we are trying to calculate mixed with random noise. This is an important difference from the teaching of WO 2008/09319 which suggests that random noise can be treated as a fourth term. The present invention has recognised that it is not possible to separate and remove random noise, only to attenuate its effects. We have input multiple measurements at the same or close locations. Thus expanding on equation (1), the difference in the measured property of a potential field between two occupations of a given region of space may be constructed as the sum of the variation in the field itself (S), sources of interference (I), random noise (n) and correlated noise (C). d(x, y,z) =

- + (/(£,) - /¾)) + (C(t;) - C(t_j}) + n_t - t, (2)

If = and the correlated and interference noise components are successfully removed then the equation (2) reduces to one including only the signal and random noise terms.

Accordingly, the next step S204 is to remove or at least attenuate the interference term. This is handled as taught in WO 2008/09319. Thus the underlying disturbances are measured and the interference component is removed using an error coupling transfer function. Thereafter, the correlated noise term is handled as set out in step S206 using the teaching of WO 2008/09319. In summary, an augmented model is written as t(x, y, z, t) = Ap{x, y, z) + {t) (3) where f is the model forward calculation vector of measurements, p is the vector of equivalent source model parameters (for example, density of discrete mass source units), A is a matrix that relates the responses of the source elements to the measurement locations (i.e. a spatial component), λ is the vector of drift model parameters (for example, nodal values in a piece-wise linear model) and B is the matrix that relates the drift model interpolation to the times of the measurements (i.e. a temporal part). In this form, given a set of measurements m(x, y ,z, t), the model parameters p and λ can be determined by any standard optimisation technique that minimises the residual in the fit, minimise[L(f - m)] (4) where L represents a measure of the residual; the L₂ norm for example making the optimisation a least squares fit. Critically, the set of measurements are referenced against time and position when input into the optimisation process. Thus, the inversion into the equivalent source layer takes the position of the observations into account but produces a distribution of equivalent source elements with fixed position and variable control parameter p, namely

Ap = S{x, y, z) (4) This term may be used to forward calculate the signal to reproduce the measurements at the observed positions (step S208). Any difference between the measured signals and the forward calculated signal will be a spurious signal due to the residual random noise. As explained above, the effect is attenuated by the use of multiple measurements and the residual noise is presented as uncertainty in the final output.

A key principal of a successfully completed equivalent source method is the condition that if the measurements are faithfully reproduced, the same set of elements may be used to calculate the potential field at any other position. Accordingly, a final optional step S210 is to replace the repeated occupations of a measurement position with a single occupation at some nominated position.

As set out above, the first step is to input measured potential field data including multiple measurements at the same (or similar) locations. It will be recognised that from a moving platform, the positioning of multiple measurements, and the conditions in which those measurements are made cannot be made identical. Thus it is not possible to make measurements of the same quantity when that quantity is varying with position, furthermore the correlated and interference noise components having significant amplitude compared to the desired signal will also be unique for each occupation of a measurement position. Figure 3a and 3b illustrate practical positional variation and the signal variation that arises from the use of a moving platform to gather data. Figure 3a shows the variation of the position of four paths relative to a nominal position of a survey line marked with dotted line. The positional variation may be horizontal or vertical variation. Figure 3b shows the variation in signal for each of the four paths and the nominal position of Figure 3a. The perfect signal for the nominal position is marked with a dotted line. Figure 3c illustrates these signals of Figure 3b with coherent, interference and white noise superposed. Each signal shows that each path has its own pattern of noise. These components of the measurement must be attenuated prior to the application of stacking if improvement to the signal-to-noise ratio is to be achieved without loss of signal fidelity. Figure 3d shows the results of the method after steps S206 when both the simulated interference and coherent noise have been attenuated. Each signal is still affected by white noise with each path having its own pattern of such residual random noise.

Figure 3e illustrates the consequence of attempting data stacking of moving platform measurements of potential field in the absence of the corrections described above in Figure 2 in comparison with stacked data from a corrected acquisition set. The present invention is a method of removal of the component of spatial variation and attenuation of correlated noise and interference noise from the desired signal in a set of measurements coupled with an implementation of data stacking to attenuate random noise with minimal loss of signal and with quantified effect on the signal to noise ratio. As shown in Figure 3e, the stacked paths which have been corrected as described above are a closer match to the perfect signal.

In order to quantify the increase in signal to noise ratio, the number of occupations of a measurement position is defined as the number of occupations within ½ of the minimum wavelength of interest of the nominated measurement position, hereafter referred to as the stack fold.

The measurements may take the form of multiply occupied survey lines (i.e. flying the same survey lines, several occasions at different times) or by multiple occupation of the same region of space by arbitrarily oriented survey lines. The design of the survey including the number of repeat occupations of the same measurement position may be made using the relationship between signal-to-noise improvement ratio and the number of occupations, the pattern and amplitude of the signal to be recovered. Survey design exploiting the facility to stack may take the form of multiply occupied paths (with inherent positional variability), or paths spatially distributed such that multiple paths exist within a region representing half the minimum wavelength of interest. Figure 4a illustrates a single fold acquisition pattern having a plurality of parallel survey lines which are spaced apart by at least half the minimum target wavelength. As shown, a circle of diameter equal to half the minimum target wavelength intersects one path only. Accordingly, multiple measurements of the same (or similar) location are not collected with this survey pattern unless the same survey is flown a plurality of time.

Figure 4b illustrates a four fold acquisition pattern using profile stacking. The pattern comprises a plurality of generally parallel lines which are clustered in groups of four. Thus four lines are sufficiently closely spaced that the distance between the two outer lines is less than half the minimum target wavelength. Furthermore, the gap between the central line(s) in each cluster is approximately half the minimum target wavelength. Thus, a circle having a diameter equal to half the minimum target wavelength placed at any point intersects at least four lines. It will be appreciated that there may be any number of lines which are sufficiently closely spaced such that the distance between the two outer lines is less than half the minimum target wavelength.

Figure 4c illustrates a four fold pattern using a different survey pattern, namely a grid pattern having two sets of parallel survey lines which are generally at right angles to one another. This is a four fold pattern because a circle drawn at any arbitrary location on the survey intersects four survey lines. Figures 4b and 4c represent schematic acquisition plans. Any arbitrary path orientation may be used to design a survey with the critical criterion between that the distribution of paths is such that the number of paths passing through a circle of diameter ½ the minimum wavelength of interest is equal to the desired stack fold. The measurement points within this circle are sufficient close to meet the requirements of the invention. If the positions are sufficiently close as described above, this result indicates that within the construction of normally distributed white noise the signal to noise ratio of the set of measurements is increased by a factor ¾ where n is the number of measurements taken. In other words, the random noise is attenuated and the attenuation increases with the number of measurements. There is no minimum number of measurements implicit in this construction other than one occupation having no effect.

Referring now to Figure 5, this shows an example of an aircraft 10 for conducting a potential field survey to obtain data for processing in accordance with a method as described above. The aircraft 10 comprises an inertial platform 12 on which is mounted a gravity gradiometer 14 (and/or vector magnetometer) which provides potential field survey data to a data collection system 16. The inertial platform 12 is fitted with an inertial measurement unit (IMU) 18 which also provides data to data collection system 16 typically comprising attitude data (for example, pitch, roll and yaw data), angular rate and angular acceleration data, and aircraft acceleration data. The aircraft is also equipped with a differential GPS system 20 and a LIDAR system 22 or similar to provide data on the height of the aircraft above the underlying terrain. Position and time data are preferably obtained from (D)GPS, optionally in combination with the IMU for accuracy.

The aircraft 10 may also be equipped with other instrumentation 24 such as a magnetometer, a TDEM (Time Domain Electromagnetic System) system and/or a hyperspectral imaging system, again feeding into the data collection system. The data collection system 16 also has an input from general aircraft instrumentation 26 which may comprise, for example, an altimeter, air and/or ground speed data and the like. The data collection system 16 may provide some initial data pre-processing, for example to correct the LIDAR data for aircraft motion and/or to combine data from the IMU 18 and DGPS 20. The data collection system 16 may be provided with a communications link 16a and/or non- volatile storage 16b to enable the collected potential field and position data to be stored for later processing. A network interface (not shown) may also be provided. Data processing to generate map data for the potential field survey is generally (but not necessarily) carried out offline, sometimes in a different country to that where the survey data was collected. As illustrated a data processing system 50 comprises a processor 52 coupled to code and data memory 54, an input/output system 56 (for example comprising interfaces for a network and/or storage media and/or other communications), and to a user interface 58 for example comprising a keyboard and/or mouse. The code and/or data stored in memory 54 may be provided on a removable storage medium 60. In operation the data includes data collected from the potential field survey and the code comprises code to process this data to generate map data, in embodiments in accordance with the procedure shown in Figure 2, described above.

No doubt many other effective alternatives will occur to the skilled person.

Although we have described the technique using the preferred example of an airborne potential field survey, embodiments may also be employed for marine potential field surveys conducted from a boat and, more generally, to potential field surveys conducted from other platforms or vehicles.

Furthermore, where we refer to a field, in particular a gravity field, this is not limited to a vector field but includes scalar and tensor fields, a potential field and any derivatives deriving from the potential field.

Potential field data includes, but is not limited to, gravimeter data, gravity gradiometer data, vector magnetometer data and true magnetic gradiometer data. Elements and representations of a potential field may be derived from a scalar quantity.

For gravity, the relevant potential is the gravity scalar potential, Φ(Γ) , defined as

where r , p{r'), G are respectively, the position of measurement of the gravity field, the mass density at location r' , and the gravitational constant. The gravitational acceleration, which is how a gravitational field is experienced, is the spatial derivative of the scalar potential. Gravity is a vector in that it has directionality. It is represented by three components with respect to any chosen Cartesian coordinate system as:

Each of these three components varies in each of the three directions and the nine quantities so generated form the Gravity gradient tensor:

The fundamental equations and relationships of potential fields follow from analysis of the properties of the scalar potential function, its derivatives, its Fourier transforms and other mathematical quantities. The techniques are not restricted to processing gravity data but may also be employed, for example, in processing magnetic field data. The measured potential field data may thus be obtained by measuring magnetic field and/or a flux density vector and/or its magnitude, for example using a measurement made with a magnetic gradiometer. The equivalent source elements may then have, for example, a surface current density or a pole strength.

Data may be analysed and processed using a range of techniques which work with the data collected from the survey as a starting point but which thereafter alter both the data and/or its format so the values associated with the measured quantities all appear on a regular 2-D grid which is on a horizontal, fixed altitude analysis plane (levelling and gridding).

In a survey one can measure G_zz as a function of position, r_measure, using a gravity gradiometer and work with this without needing to generate the other elements of the gravity gradient tensor. This can be used to generate a representation of the underlying mass distribution as described in earlier applications by the present applicant (e.g. WO2008/093139). The gravitational field outside a body can be modelled as if it came from matter situated entirely in a vanishingly thin layer at the surface of the body and which accurately follows the surface of the body. Such a layer defines a two- dimensional equivalent source - i.e. a source of gravity which produces substantially (theoretically exactly) the same gravity signature as does the body itself.

There are many ways in which equivalent sources models can be defined, for example the surface of the survey area may be broken up into small pieces, typically of order 50m on a side, which may be termed platelets or mass elements. Once an equivalent source has been generated, it is then possible to predict any derivative of the gravity scalar potential on a surface by direct forward calculation. In more detail, given the masses (or densities) of each source element, a straightforward calculation is used to predict what value would be obtained for the measured quantity - say a component of the gravity vector or of the gravity gradient tensor - at every measurement point. In general, this will be a summation of the form shown below. Here we use gg as notation for the measured quantity which, as noted above, is G_zz in some preferred embodiments.

88 calculated ^^"measure ) ^~ ∑i ^mass— element ^ ^^"measure ^^"mass— element )

all— masses

In the above equation F is called a Greens function (Blakely, ibid, at page 185, incorporated by reference) and Γ„«¾_β„ defines the location of the mass element (for example the centre of gravity or some other defined point). It will be understood that the invention is not limited to the described embodiments and encompasses modifications apparent to those skilled in the art lying within the spirit and scope of the claims appended hereto.

Claims

CLAIMS:

1. A method of processing measured potential field data from an airborne or marine potential field survey to determine a set of field mapping parameters for mapping a field, the method comprising:

determining said set of field mapping parameters using an equivalent source model, wherein said determining comprises fitting said measured potential field data to said model

wherein said measured potential field data comprises at least one set of potential field measurements each having associated measurement positions which are sufficiently close that any residual noise component is attenuated by a factor of the square root of the number of measurements in said at least one set.

2. A method according to claim 1 wherein said measured potential field data comprises a plurality of sets of potential field measurements with each set having associated measurement positions which are sufficiently close that any residual noise component is attenuated by a factor of the square root of the number of measurements in said at least one set.

3. A method according to claim 1 or claim 2 wherein a distance between each sufficiently close associated measurement positions of the or each set is a function of a minimum spatial wavelength of said measurements.

4. A method according to any one of the preceding claims, wherein said determined set of field mapping parameters are used to calculate the potential field at any field position and the residual random noise component is represented as an uncertainty component in the calculated potential field.

5. A method as claimed in any preceding claim wherein said measured potential field data comprises one or more of gravimeter data and gravity gradiometer data.

6. A method as claimed in any preceding claim wherein said equivalent source model comprises a combination of a spatial part representing a spatial variation of said potential field and a temporal part representing time domain noise.

7. A method as claimed in any preceding claim comprising determining said set of field mapping parameters by separating each measurement m taken at location (x, y, z) and time t from a geophysical survey into a series of terms, m{x, y, _z, t) = (S(x, y, z) + n) + I(t) + C(t) (1) where (S(x ,y, z) +n) represents signal from the field to be mapped mixed with random noise, I(t) the source of interference and C(t) correlated noise.

8. A method as claimed in claim 7, comprising determining said set of field mapping parameters by first attenuating interference and correlated noise.

9. A non-transitory computer-readable storage medium having stored therein instructions that when executed by a processor cause a computer system to perform the method of any one of the preceding claims.

10. A computer-implemented system for processing measured potential field data to determine a set of field mapping parameters for mapping a field, the computer- implemented system comprising:

11. A method of defining a potential field survey of a survey surface, the method comprising:

determining a minimum target wavelength of said survey,

12. A method according to claim 11, wherein said plurality of survey paths are generally parallel and are grouped in clusters of lines such that the distance between the two outer lines of each cluster is less than half the minimum target wavelength.

13. A method according to claim 11, wherein said plurality of survey paths are arranged in a grid pattern with a maximum dimension of each grid being less than half the minimum target wavelength.

14. A computer-implemented system for conducting a potential field survey of a survey surface, the computer-implemented system comprising:

an inertial platform configured to

measure potential field data at points on said paths, and

transmit the field data to a data collection system.