Electromagnetic inversion data weak information extraction method and device

Abstract

The application discloses a kind of electromagnetic inversion data weak information extraction method and device.The method comprises the following steps: according to the electromagnetic observation data of target area, M-dimensional inversion processing and interpolation processing are carried out, and the M-dimensional grid data of electromagnetic observation data is obtained;Resistivity homogenization processing is carried out on the M-dimensional grid data in the following at least one way: average first and then inversion to electromagnetic observation data, interpolation in depth direction and plane direction, average after M-dimensional grid data of electromagnetic observation data is carried out in plane direction, interpolation is carried out after M-dimensional inversion to the electromagnetic observation data after being extracted;The resistivity of corresponding position in the M-dimensional grid data of electromagnetic observation data and the M-dimensional grid data after resistivity homogenization processing is compared, and the region of resistivity anomaly in target area is determined.The weak and small anomaly of resistivity in electromagnetic inversion data can be effectively extracted, and the identification accuracy of abnormal body such as low amplitude structure and thin layer high resistance reservoir in electromagnetic exploration is improved.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology, and in particular to a method and apparatus for extracting weak information from electromagnetic inversion data. Background Technology

[0002] Currently, as exploration areas have shifted from traditionally searching for large structures to areas such as low-amplitude small structures, lithological traps, and remaining oil prediction, electromagnetic exploration has played an increasingly important role, achieving good results in the identification of lithological oil and gas reservoirs and oil and gas detection when combined with seismic exploration.

[0003] Using known data to calibrate resistivity results from electromagnetic data inversion and directly interpreting geological structures, stratigraphic levels, and lithology is a conventional technique. This method is generally effective in areas with large and thick structural or lithological anomalies, but it presents significant challenges when searching for low-amplitude structures, thin-layered high-resistivity sandstone reservoirs, or igneous reservoirs. Delineating weak geological anomalies from electromagnetic exploration data remains a key technical challenge hindering the further development of electromagnetic methods in oil and gas exploration and development.

[0004] To address this challenge, several approaches have been proposed. One approach is to extract weak resistivity anomalies from 3D MT data using interpolation and segmentation techniques. This method extracts anomalous weak resistivity data from 3D MT data, thereby delineating weak geological anomalies. Another approach is to process electromagnetic data from thin sedimentary rocks. This method tracks and searches resistivity anomaly areas based on electromagnetic data to identify weak geological anomalies in thin sedimentary rock layers. Summary of the Invention

[0005] The inventors of this application have discovered that the aforementioned method for extracting weak resistivity anomalies based on 3D MT data requires selecting different interpolation cut-off radii to achieve the best results, making it cumbersome to operate. The aforementioned method for tracking and searching resistivity anomaly regions based on electromagnetic data, due to its use of upward continuation methods and the need for drilling calibration, requires too much known information. Currently, there is no simple and practical method for identifying and extracting weak electromagnetic information from thin-layered high-resistivity sand bodies or igneous reservoirs to meet the geological interpretation needs of both natural and artificial electromagnetic source exploration.

[0006] In view of the above problems, the present invention is proposed to provide a method and apparatus for weak information extraction from electromagnetic inversion data that overcomes or at least partially solves the above problems.

[0007] This invention provides a method for extracting weak information from electromagnetic inversion data, comprising:

[0008] Based on the electromagnetic observation data of the target area, M-dimensional inversion and interpolation processing are performed to obtain M-dimensional grid data of the electromagnetic observation data, where M is a positive integer greater than 0; the M-dimensional grid data includes depth information and resistivity data corresponding to different depths;

[0009] The M-dimensional grid data after resistivity homogenization can be obtained using at least one of the following methods:

[0010] Based on the electromagnetic observation data, the average observed resistivity is determined, and the depth domain single-point resistivity is obtained by inversion of the average observed resistivity. The depth domain single-point resistivity is interpolated in the depth direction and the plane direction to obtain M-dimensional grid data after resistivity homogenization.

[0011] Based on the M-dimensional grid data of the electromagnetic observation data, the depth domain average resistivity is determined, and the depth domain average resistivity is interpolated in the plane direction to obtain the M-dimensional grid data after resistivity homogenization.

[0012] The electromagnetic observation data is thinned, and the thinned electromagnetic observation data is subjected to M-dimensional inversion processing to obtain depth domain resistivity data. The depth domain resistivity data is then interpolated to obtain M-dimensional grid data after resistivity homogenization.

[0013] By comparing the resistivity at corresponding locations in the M-dimensional grid data of electromagnetic observation data and the M-dimensional grid data after resistivity homogenization, regions with resistivity anomalies in the target area are identified.

[0014] In some optional embodiments, the step of performing M-dimensional inversion and interpolation processing on the electromagnetic observation data of the target area to obtain M-dimensional grid data of the electromagnetic observation data includes:

[0015] The electromagnetic observation data of the target area is subjected to M-dimensional inversion processing to obtain depth domain resistivity data, which includes the resistivity of different resistivity layers at different measurement points in the depth direction.

[0016] Regular interpolation processing is performed on the depth domain resistivity data to obtain M-dimensional grid data of electromagnetic observation data.

[0017] In some optional embodiments, based on the M-dimensional grid data of the electromagnetic observation data, the depth-domain average resistivity is determined, and the depth-domain average resistivity is interpolated in the planar direction to obtain homogenized M-dimensional grid data, including:

[0018] Based on the M-dimensional grid data of the electromagnetic observation data, the average resistivity of each resistivity layer in the depth direction is determined to obtain the depth domain average resistivity.

[0019] Based on the average resistivity of the depth domain, the average resistivity of each resistivity layer is copied at the same point on the plane corresponding to each resistivity layer to obtain M-dimensional grid data after resistivity homogenization.

[0020] In some optional embodiments, based on the electromagnetic observation data, an average observed resistivity is determined, and the average observed resistivity is inverted to obtain the depth-domain single-point resistivity. The depth-domain single-point resistivity is then interpolated in both the depth and planar directions to obtain M-dimensional grid data with resistivity homogenization, including:

[0021] Based on the observed resistivity of each measuring point at each frequency point in the electromagnetic observation data of the target area, determine the average value of the observed resistivity and the corresponding average value of the phase at each frequency point.

[0022] One-dimensional inversion processing is performed on the average resistivity observed at each frequency point to obtain the single-point resistivity in the depth domain.

[0023] Based on the single-point resistivity of the depth domain, linear interpolation is performed in the depth direction, and then data at the same point is copied in the plane direction at the same depth to obtain M-dimensional grid data after resistivity homogenization.

[0024] In some optional embodiments, the electromagnetic observation data is thinned, and the thinned electromagnetic observation data undergoes M-dimensional inversion processing to obtain depth-domain resistivity data. The depth-domain resistivity data is then interpolated to obtain M-dimensional grid data after resistivity homogenization, including:

[0025] The electromagnetic observation data of the target area are thinned according to the set thinning rules;

[0026] The thinned electromagnetic observation data is subjected to M-dimensional inversion processing to obtain the thinned depth domain resistivity data, which includes the resistivity of different resistivity layers at different measurement points in the depth direction.

[0027] The thinned depth domain resistivity data is subjected to M-dimensional interpolation to obtain M-dimensional grid data after resistivity homogenization.

[0028] In some optional embodiments, comparing the resistivity at corresponding locations in the M-dimensional grid data of the electromagnetic observation data and the M-dimensional grid data after resistivity homogenization to determine regions with abnormal resistivity in the target region includes:

[0029] The resistivity difference at corresponding locations in the M-dimensional grid data of electromagnetic observation data and the M-dimensional grid data after resistivity homogenization is calculated.

[0030] Calculate the ratio of the difference to the resistivity at the corresponding position in the M-dimensional grid data after resistivity homogenization, and determine the regions with abnormal resistivity in the target area based on the obtained ratio.

[0031] In some optional embodiments, if there is more than one M-dimensional grid data after resistivity homogenization, the regions with abnormal resistivity in the target region obtained based on each M-dimensional grid data after resistivity homogenization are compared, and the regions with abnormal resistivity in the target region are determined according to the comparison results.

[0032] This invention also provides a device for extracting weak information from electromagnetic inversion data, comprising:

[0033] The observation data processing module is used to perform M-dimensional inversion and interpolation processing on the electromagnetic observation data of the target area to obtain M-dimensional grid data of the electromagnetic observation data, where M is a positive integer greater than 0; the M-dimensional grid data includes depth information and resistivity data corresponding to different depths;

[0034] The data homogenization processing module is used to obtain M-dimensional grid data after resistivity homogenization processing using at least one of the following methods:

[0035] Based on the electromagnetic observation data, the average observed resistivity is determined, and the depth domain single-point resistivity is obtained by inversion of the average observed resistivity. The depth domain single-point resistivity is interpolated in the depth direction and the plane direction to obtain M-dimensional grid data after resistivity homogenization.

[0036] Based on the M-dimensional grid data of the electromagnetic observation data, the depth domain average resistivity is determined, and the depth domain average resistivity is interpolated in the plane direction to obtain the M-dimensional grid data after resistivity homogenization.

[0037] The electromagnetic observation data is thinned, and the thinned electromagnetic observation data is subjected to M-dimensional inversion processing to obtain depth domain resistivity data. The depth domain resistivity data is then interpolated to obtain M-dimensional grid data after resistivity homogenization.

[0038] The abnormal region identification module is used to compare the resistivity at corresponding locations in the M-dimensional grid data of electromagnetic observation data and the M-dimensional grid data after resistivity homogenization processing to identify regions with abnormal resistivity in the target area.

[0039] This invention also provides a computer storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method for extracting weak information from electromagnetic inversion data.

[0040] This invention also provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method for extracting weak information from electromagnetic inversion data.

[0041] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0042] The method described in this embodiment of the invention performs M-dimensional inversion and interpolation processing on electromagnetic observation data of the target area to obtain M-dimensional grid data of the electromagnetic observation data. Based on the electromagnetic observation data or the data after thinning the electromagnetic observation data, the resistivity is processed by averaging followed by inversion and interpolation, or by inversion followed by averaging and then interpolation, to obtain M-dimensional grid data after resistivity homogenization. Then, based on the difference between the M-dimensional grid data of the electromagnetic observation data and the M-dimensional grid data after resistivity homogenization, the resistivity anomaly areas in the target area are determined. This allows for accurate identification of resistivity anomaly areas in the electromagnetic inversion data, and even weak anomalies can be accurately identified. This improves the accuracy of resistivity anomaly area identification, thereby enhancing the accuracy of identifying anomalies such as low-amplitude structures and thin-layer high-resistivity reservoirs in electromagnetic exploration. It can also accurately identify anomalies such as sandstone and igneous rocks in geological structures, making the understanding of strata and geology more accurate during geological exploration.

[0043] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0044] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0045] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0046] Figure 1 This is a flowchart of the method for extracting weak information from electromagnetic inversion data in Embodiment 1 of the present invention;

[0047] Figure 2 This is a schematic diagram of the principle of the weak information extraction method for electromagnetic inversion data in Embodiment 2 of the present invention;

[0048] Figure 3 This is a schematic diagram of the two-dimensional geoelectric model in Embodiment 3 of the present invention;

[0049] Figure 4This is a schematic diagram of the apparent resistivity forward modeling response of the two-dimensional geoelectric model in Embodiment 3 of the present invention;

[0050] Figure 5 This is a schematic diagram of the phase forward modeling response of the two-dimensional geoelectric model in Embodiment 3 of the present invention;

[0051] Figure 6 This is a schematic diagram of the two-dimensional inverted resistivity profile in Embodiment 3 of the present invention;

[0052] Figure 7 This is a schematic diagram of the resistivity profile after one-dimensional inversion interpolation in Embodiment 3 of the present invention;

[0053] Figure 8 This is a schematic cross-sectional view of the average value of the inverted resistivity in Embodiment 3 of the present invention;

[0054] Figure 9 This is a schematic diagram of the two-dimensional inverted resistivity profile after thinning and interpolation in Embodiment 3 of the present invention.

[0055] Figure 10 This is one of the schematic diagrams of weak information profiles of electromagnetic inversion data in Embodiment 3 of the present invention;

[0056] Figure 11 This is the second schematic diagram of the weak information profile of electromagnetic inversion data in Embodiment 3 of the present invention;

[0057] Figure 12 This is the third schematic diagram of the weak information profile of electromagnetic inversion data in Embodiment 3 of the present invention;

[0058] Figure 13 This is a schematic diagram of the structure of the weak information extraction device for electromagnetic inversion data in an embodiment of the present invention. Detailed Implementation

[0059] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0060] To address the problem of inaccurate extraction of weak anomaly information from electromagnetic exploration data in existing technologies, this invention provides a method for extracting weak information from electromagnetic inversion data. This method can improve the accuracy of identifying weak anomaly information in electromagnetic exploration, thereby enhancing the ability to identify weak anomalies in thin geological layers.

[0061] Example 1

[0062] Embodiment 1 of the present invention provides a method for extracting weak information from electromagnetic inversion data, the process of which is as follows: Figure 1 As shown, it includes the following steps:

[0063] Step S101: Perform M-dimensional inversion and interpolation processing on the electromagnetic observation data of the target area to obtain M-dimensional grid data of the electromagnetic observation data. M is a positive integer greater than 0; the M-dimensional grid data includes depth information and resistivity data corresponding to different depths.

[0064] In this step, the electromagnetic observation data of the target area is subjected to M-dimensional inversion processing to obtain depth domain resistivity data, which includes the resistivity of different resistivity layers at different measurement points in the depth direction; the depth domain resistivity data is then subjected to regular interpolation processing to obtain M-dimensional grid data of electromagnetic observation data.

[0065] Step S102: Obtain the M-dimensional grid data after resistivity homogenization using at least one of the following methods: first average the electromagnetic observation data and then perform inversion, and interpolate in the depth and planar directions; average the M-dimensional grid data of the electromagnetic observation data and then interpolate in the planar direction; perform M-dimensional inversion on the thinned electromagnetic observation data and then interpolate.

[0066] In this step, one or more methods can be used to obtain the M-dimensional grid data after resistivity homogenization, so that the M-dimensional grid data after resistivity homogenization can be compared with the M-dimensional grid data of electromagnetic observation data to obtain information on weak resistivity anomalies.

[0067] Method 1: Based on the M-dimensional grid data of electromagnetic observation data, determine the depth-domain average resistivity, and interpolate the depth-domain average resistivity in the planar direction to obtain the M-dimensional grid data after resistivity homogenization.

[0068] This method first averages the electromagnetic observation data, then performs inversion, and interpolates in the depth and planar directions to obtain M-dimensional grid data after resistivity homogenization. The steps include:

[0069] 1) Based on the M-dimensional grid data of electromagnetic observation data, determine the average resistivity of each resistivity layer in the depth direction to obtain the depth domain average resistivity.

[0070] 2) Based on the average resistivity of the depth domain, the average resistivity of each resistivity layer is copied at the same point on the plane corresponding to each resistivity layer to obtain M-dimensional grid data after resistivity homogenization.

[0071] This method involves inverting the average resistivity in electromagnetic observation data and then copying the obtained depth-domain average resistivity onto the same point in the plane. Since the depth-domain average resistivity includes the average resistivity of resistivity layers at different depths, no further interpolation is needed in the depth direction. By copying each average resistivity onto its respective layer, homogenized M-dimensional grid data can be obtained.

[0072] Method 2: Based on electromagnetic observation data, determine the average observed resistivity, invert the average observed resistivity to obtain the depth domain single-point resistivity, and interpolate the depth domain single-point resistivity in the depth direction and the plane direction to obtain M-dimensional grid data after resistivity homogenization.

[0073] This method involves averaging the M-dimensional grid data of electromagnetic observations and then interpolating it in the planar direction to obtain M-dimensional grid data after resistivity homogenization. The steps include:

[0074] 1) Based on the observed resistivity of each measuring point at each frequency in the electromagnetic observation data of the target area, determine the average value of the observed resistivity and the corresponding average value of the phase at each frequency.

[0075] 2) Perform one-dimensional inversion processing on the average resistivity observed at each frequency point to obtain the single-point resistivity in the depth domain;

[0076] 3) Based on the single-point resistivity in the depth domain, after linear interpolation in the depth direction, the data at the same point is copied in the plane direction at the same depth to obtain M-dimensional grid data after resistivity homogenization.

[0077] This method obtains the average observed resistivity, or the average apparent resistivity, based on electromagnetic observation data, and then performs inversion to obtain a single-point resistivity. Therefore, interpolation is required in both the depth and planar directions to obtain M-dimensional grid data after resistivity homogenization.

[0078] Method 3: Thin out the electromagnetic observation data, perform M-dimensional inversion processing on the thinned electromagnetic observation data to obtain depth domain resistivity data, and interpolate the depth domain resistivity data to obtain M-dimensional grid data after resistivity homogenization.

[0079] The thinned electromagnetic observation data is inverted in M ​​dimensions and then interpolated to obtain M-dimensional grid data after resistivity homogenization. The steps include the following:

[0080] 1) Perform thinning processing on the electromagnetic observation data of the target area according to the set thinning rules;

[0081] 2) Perform M-dimensional inversion processing on the thinned electromagnetic observation data to obtain the thinned depth domain resistivity data, which includes the resistivity of different resistivity layers at different measurement points in the depth direction.

[0082] 3) Perform M-dimensional interpolation on the thinned depth domain resistivity data to obtain M-dimensional grid data after resistivity homogenization.

[0083] This method involves thinning the electromagnetic observation data, leaving only a portion of the observation data in an M-dimensional grid, and then using this portion of the observation data as the source data for interpolation to obtain M-dimensional grid data after resistivity homogenization. This processed data is then used for subsequent comparison processing.

[0084] Step S103: Compare the resistivity at corresponding positions in the M-dimensional grid data of the electromagnetic observation data and the M-dimensional grid data after resistivity homogenization to determine the regions with abnormal resistivity in the target area.

[0085] By comparing the M-dimensional grid data obtained after resistivity homogenization using the above different processing methods with the M-dimensional grid data of electromagnetic observation data, weak resistivity anomalies in the electromagnetic observation data can be detected. The specific comparison method can be selected according to actual needs, such as performing difference calculation, ratio calculation, or performing difference calculation followed by ratio calculation, etc.

[0086] An optional comparison method includes: calculating the difference between the resistivity at corresponding locations in the M-dimensional grid data of electromagnetic observation data and the M-dimensional grid data after resistivity homogenization; calculating the ratio of the difference to the resistivity at corresponding locations in the M-dimensional grid data after resistivity homogenization; and determining the regions with resistivity anomalies in the target area based on the obtained ratio.

[0087] Optionally, if there is more than one M-dimensional grid data after resistivity homogenization, the regions with resistivity anomalies in the target region obtained based on each M-dimensional grid data after resistivity homogenization are compared, and the regions with resistivity anomalies in the target region are determined according to the comparison results. Since different methods can be selected to obtain the M-dimensional grid data after resistivity homogenization in step S102, if more than one method is used for calculation, more than one M-dimensional grid data after resistivity homogenization can be obtained. In this case, a comparison result can be obtained for each M-dimensional grid data after resistivity homogenization. If the comparison results are consistent, it means that the resistivity anomaly regions obtained by the different methods are relatively accurate. If they are inconsistent, other methods can be used to further determine which comparison result is more accurate for obtaining the resistivity anomaly region.

[0088] The method described in this embodiment performs M-dimensional inversion and interpolation processing on electromagnetic observation data of the target area to obtain M-dimensional grid data of the electromagnetic observation data. Based on the electromagnetic observation data or the data after thinning the electromagnetic observation data, the resistivity is processed by averaging followed by inversion and interpolation, or by inversion followed by averaging and then interpolation, to obtain M-dimensional grid data after resistivity homogenization. Then, based on the difference between the M-dimensional grid data of the electromagnetic observation data and the M-dimensional grid data after resistivity homogenization, the resistivity anomaly areas in the target area are determined. This allows for accurate identification of resistivity anomaly areas in the electromagnetic inversion data, and even weak anomalies can be accurately identified. This improves the accuracy of resistivity anomaly area identification, thereby enhancing the accuracy of identifying anomalies such as low-amplitude structures and thin-layer high-resistivity reservoirs in electromagnetic exploration. It can also accurately identify anomalies such as sandstone and igneous rocks in geological structures, making the understanding of strata and geology more accurate during geological exploration.

[0089] Example 2

[0090] Embodiment 2 of the present invention provides a specific implementation process for a method for extracting weak information from electromagnetic inversion data. This process is illustrated using three methods to obtain M-dimensional grid data after resistivity homogenization processing as an example. The flow is as follows: Figure 2 As shown, it includes the following steps:

[0091] Step S200: Collect electromagnetic observation data of the target area.

[0092] Step S201: Perform M-dimensional inversion processing on the electromagnetic observation data of the target area to obtain depth domain resistivity data.

[0093] One-dimensional, two-dimensional, or three-dimensional inversion processing is performed on the two-dimensional or three-dimensional electromagnetic observation data collected in the target area to obtain depth domain resistivity data. The electromagnetic observation data includes electromagnetic data from natural and artificial sources; n is the number of electromagnetic data measurement points, and nk is the number of resistivity layers in the depth direction; the two-dimensional inversion can use at least one of the following: Occam inversion, Gauss-Newton inversion, conjugate gradient inversion, nonlinear conjugate gradient inversion, simulated annealing inversion, and swarm intelligence inversion; the three-dimensional inversion can use at least one of the following: Occam inversion, Gauss-Newton inversion, finite-memory quasi-Newton inversion, conjugate gradient inversion, nonlinear conjugate gradient inversion, integral equation inversion, simulated annealing inversion, and swarm intelligence inversion.

[0094] Step S202: Perform regular interpolation on the depth domain resistivity data to obtain M-dimensional grid data of electromagnetic observation data.

[0095] The depth domain resistivity data obtained above Perform regularized interpolation. For two-dimensional... The data is interpolated into a uniform two-dimensional grid using triangular mesh or kriging methods. For three dimensions The data is first interpolated linearly in the depth domain, and then for each point at the same depth, it is interpolated into a uniform 3D grid data using triangular mesh or kriging methods on a 2D plane. Where nx is the number of grid cells in the x-direction, ny is the number of grid cells in the y-direction, and nz is the number of grid cells in the depth direction.

[0096] Step S203: Based on the M-dimensional grid data of the electromagnetic observation data, determine the average resistivity of each resistivity layer in the depth direction to obtain the depth domain average resistivity.

[0097] For the obtained two-dimensional grid data Or 3D mesh data The average value is obtained using the following formula.

[0098]

[0099]

[0100] in, and The depth-domain average resistivity is obtained from two-dimensional and three-dimensional mesh data, respectively.

[0101] Step S204: Based on the average resistivity of the depth domain, the average resistivity of each resistivity layer is copied at the same point on the plane corresponding to each resistivity layer to obtain M-dimensional grid data after resistivity homogenization.

[0102] The depth-domain average resistivity corresponding to the two-dimensional mesh data obtained in step S203 Or the depth-domain average resistivity corresponding to the 3D mesh data According to two-dimensional grid data Or 3D mesh data The planar position is copied to the data of the same point, denoted as . or Obtain homogenized two-dimensional or three-dimensional grid data of resistivity.

[0103] Step S205: Based on the observed resistivity of each measuring point at each frequency in the electromagnetic observation data of the target area, determine the average value of the observed resistivity at each frequency and the corresponding average value of the phase.

[0104] For the two-dimensional or three-dimensional electromagnetic observation data collected in the target area, the average value of all relevant parameters at all measuring points is calculated as follows:

[0105]

[0106]

[0107] Among them, R i,j The parameter representing the i-th measurement point at the j-th frequency can be the apparent resistivity, the real part, the imaginary part, or the amplitude of the electromagnetic field component. P is the average value of the above parameters at frequency j; i,j This represents the phase of the i-th measurement point at the j-th frequency. nfre represents the average phase value of the j-th frequency point; nfre represents the number of frequency points.

[0108] When the relevant parameter is the observed resistivity at each measuring point, what is determined is the average observed resistivity. and the corresponding phase average value Among them, R i,j This represents the observed resistivity at the i-th measuring point at the j-th frequency. Let P be the average observed resistivity at frequency j. i,j This represents the phase of the i-th measurement point at the j-th frequency. This represents the average phase value at frequency j.

[0109] Step S206: Perform one-dimensional inversion processing on the average resistivity observed at each frequency point to obtain the single-point resistivity in the depth domain.

[0110] The average observed resistivity obtained using step S205 and phase average One-dimensional inversion of electromagnetic data is performed to obtain resistivity data in the depth domain at a single point. Where nz1D is the number of depth points after one-dimensional inversion; one-dimensional inversion can be performed using at least one of the following: Occam inversion, Gauss-Newton inversion, nonlinear conjugate gradient inversion, generalized inverse inversion, simulated annealing inversion, and swarm intelligence inversion.

[0111] Step S207: Based on the single-point resistivity in the depth domain, perform linear interpolation in the depth direction, and then copy the data at the same point in the plane direction at the same depth to obtain M-dimensional grid data after resistivity homogenization.

[0112] The resistivity data of the single-point depth domain obtained in step S206 In the depth direction, linear interpolation is used to create a two-dimensional grid data. Or 3D mesh data Points at the same depth are denoted as Then The value is based on two-dimensional grid data Or 3D mesh data The planar position is copied to the data of the same point, denoted as . or Obtain homogenized two-dimensional or three-dimensional grid data of resistivity.

[0113] Step S208: Perform thinning processing on the electromagnetic observation data of the target area according to the set thinning rules.

[0114] Two-dimensional or three-dimensional electromagnetic observation data collected in the target area are thinned to 1 / Nd of the original data, where Nd >= 10. The thinned data is then interpolated onto the collected points to form the interpolated thinned electromagnetic observation data.

[0115] Step S209: Perform M-dimensional inversion processing on the thinned electromagnetic observation data to obtain the thinned depth domain resistivity data. The depth domain resistivity data includes the resistivity of different resistivity layers at different measurement points in the depth direction.

[0116] The thinned electromagnetic observation data is inverted in one, two, or three dimensions to obtain depth domain resistivity data.

[0117] Step S210: Perform M-dimensional interpolation on the thinned depth domain resistivity data to obtain M-dimensional grid data after resistivity homogenization.

[0118] The depth domain resistivity data obtained in step S209 Perform regularized interpolation. For two-dimensional... The data can be interpolated into a uniform two-dimensional grid using triangular mesh or kriging methods. For three dimensions The data is first interpolated linearly in the depth domain, and then for each point at the same depth, it is interpolated into a uniform 3D grid data using triangular mesh or kriging methods on a 2D plane.

[0119] Step S211: Compare the resistivity at corresponding positions in the M-dimensional grid data of the electromagnetic observation data and the M-dimensional grid data after resistivity homogenization to determine the regions with abnormal resistivity in the target area.

[0120] For the two-dimensional grid data of the electromagnetic observation data and the homogenized two-dimensional grid data of resistivity obtained above, the weak information of the electromagnetic inversion data can be obtained by using at least one of the formulas, and the resistivity at corresponding positions in the M-dimensional grid data of the electromagnetic observation data and the M-dimensional grid data after resistivity homogenization can be compared:

[0121]

[0122]

[0123]

[0124] For the three-dimensional grid data of the electromagnetic observation data and the resistivity homogenized three-dimensional grid data obtained above, the weak information of the electromagnetic inversion data can be obtained by using at least one of the formulas, and the resistivity at corresponding positions in the M-dimensional grid data of the electromagnetic observation data and the resistivity homogenized M-dimensional grid data can be compared:

[0125]

[0126]

[0127]

[0128] dR i,k and dR i,j,k This refers to the weak information extracted from the two-dimensional and three-dimensional electromagnetic inversion data. Based on this weak information, regions with resistivity anomalies in the target area can be identified.

[0129] The above methods can effectively extract weak anomalies from electromagnetic inversion resistivity profiles, improving the ability of electromagnetic exploration to identify anomalies such as low-amplitude structures and thin-layered high-resistivity reservoirs, including sand bodies and igneous rocks.

[0130] Example 3

[0131] Embodiment 3 of the present invention provides an example of using the above method to extract weak information from electromagnetic inversion data.

[0132] Taking a two-dimensional geoelectric model as an example, the electromagnetic method used is magnetotellurics from a natural source. The model is 15 km long and 3 km deep. There are six high-resistivity anomalies at different depths between 0.8 and 2.0 km, each measuring 500 x 60 m. Figure 2 As shown. Figure 3 The image shows the apparent resistivity (left) and phase (right) forward modeling response of the magnetotelluric TM mode, equivalent to measured magnetotelluric data. For example... Figure 3 The figure shows an example of a two-dimensional geoelectric model, in which six high-resistivity anomalies at different depths are marked with several locations where R=70.

[0133] like Figure 4 The image shown is an example of apparent resistivity data in the TM mode of a two-dimensional geoelectric model. The area in the middle of the long bar marked with the number 33, where the color gradually darkens, corresponds to the part of the right bar where the value is less than 33. Above the area marked with the number 49, the area where the color gradually darkens corresponds to the part of the right bar where the value is greater than 49. Figure 5The image shows an example of phase forward response data. In the image, the darker area in the middle of the long bar marked 1.25 corresponds to the portion of the right-hand bar where the values ​​are less than 1.25. Below the area marked 1.75 (49), the gradually darkening areas correspond to the portions of the right-hand bar where the values ​​are greater than 1.75. Subsequent figures can be understood similarly to this description.

[0134] ① Figure 4 and Figure 5 The data is subjected to conventional two-dimensional inversion to obtain depth domain resistivity data.

[0135] ② The inversion data obtained in step ① is subjected to regular interpolation and plotted to obtain M-dimensional grid data of electromagnetic observation data. For example... Figure 6 The figure shows a two-dimensional inverted resistivity profile in the standard TM mode. It can be seen that the existence of these six thin-layered high-resistivity anomalies is difficult to detect from this figure; the figure fails to reflect weak resistivity anomalies.

[0136] ③Calculate the average resistivity of each measuring point using formula (1) from the inversion data generated in step ②. The depth-domain average resistivity is obtained.

[0137] ④ Figure 4 and Figure 5 The average resistivity and average phase of all measuring points were calculated using the simulated two-dimensional electromagnetic data according to formulas (3) and (4). and

[0138] ⑤ Use the average resistivity obtained in step ④ and phase average One-dimensional inversion of electromagnetic data is performed to obtain resistivity data in the depth domain at a single point.

[0139] ⑥ Resistivity data of the single-point depth domain obtained in step ⑤ The data in the depth direction is converted into AND values ​​using linear interpolation. Points at the same depth are denoted as Then The value according to The planar position is copied to the data of the same point, denoted as . This yields M-dimensional grid data after resistivity homogenization, which is then interpolated and copied to form a graph, as shown below. Figure 7 The image shows an example of a resistivity profile after one-dimensional inversion interpolation.

[0140] ⑦ The depth-domain average resistivity obtained in step ③ Data according to The planar position is copied to the data of the same point, denoted as . This yields M-dimensional grid data after resistivity homogenization. The copied data is then plotted as follows: Figure 8 The image shows an example of an average resistivity profile obtained through inversion.

[0141] ⑧ Figure 4 and Figure 5 The measured magnetotelluric observation data was reduced to 1 / 20 and then interpolated to the measurement point location. The interpolated data was then subjected to a two-dimensional inversion, denoted as... Regular interpolation is performed on the inversion data to obtain M-dimensional grid data after resistivity homogenization. The interpolated data is then plotted as follows: Figure 9 The figure shows the two-dimensional inverted resistivity profile after thinning interpolation.

[0142] ⑨ Obtain the weak information dR of the electromagnetic inversion data according to any one of formulas (5), (6), and (7). i,k .

[0143] Figure 10 , Figure 11 and Figure 12 The figures show the weak information profiles of the electromagnetic inversion data obtained by formulas (5), (6), and (7), respectively. The figures show the difference calculation of the resistivity at corresponding positions in the M-dimensional grid data of the electromagnetic observation data and the M-dimensional grid data after resistivity homogenization. The ratio of the difference to the resistivity at the corresponding position in the M-dimensional grid data after resistivity homogenization is calculated, and the resulting data is expressed as a ratio or percentage. The comparison results clearly show the areas of resistivity anomalies. The dark squares in the figures represent... Figure 3 Location of high-resistivity anomalies in the model. It can be seen that the centers of the high-value clusters in the comparison results are basically located at... Figure 3 The high-resistivity anomalies are located in the same positions in the model.

[0144] from Figure 10 , Figure 11 and Figure 12 It can be seen that after obtaining the two-dimensional grid data after resistivity homogenization using three different methods, and comparing it with the two-dimensional grid data of electromagnetic observation data, the comparison results can all show the location of resistivity anomalies and accurately identify even small differences, thus accurately identifying anomalous geological targets.

[0145] The above example uses two-dimensional grid data for illustration. The process of comparison using grid data of other dimensions is similar and will not be repeated here.

[0146] Based on the same inventive concept, embodiments of the present invention also provide a device for extracting weak information from electromagnetic inversion data. This device can be installed in a computer device with computing processing capabilities, and its structure is as follows. Figure 13 As shown, it includes:

[0147] The observation data processing module 11 is used to perform M-dimensional inversion processing and interpolation processing on the electromagnetic observation data of the target area to obtain M-dimensional grid data of the electromagnetic observation data, where M is a positive integer greater than 0; the M-dimensional grid data includes depth information and resistivity data corresponding to different depths;

[0148] Data homogenization processing module 12 is used to obtain M-dimensional grid data after resistivity homogenization processing using at least one of the following methods:

[0149] Based on the electromagnetic observation data, the average observed resistivity is determined, and the depth domain single-point resistivity is obtained by inversion of the average observed resistivity. The depth domain single-point resistivity is interpolated in the depth direction and the plane direction to obtain M-dimensional grid data after resistivity homogenization.

[0150] Based on the M-dimensional grid data of the electromagnetic observation data, the depth domain average resistivity is determined, and the depth domain average resistivity is interpolated in the plane direction to obtain the M-dimensional grid data after resistivity homogenization.

[0151] The electromagnetic observation data is thinned, and the thinned electromagnetic observation data is subjected to M-dimensional inversion processing to obtain depth domain resistivity data. The depth domain resistivity data is then interpolated to obtain M-dimensional grid data after resistivity homogenization.

[0152] The abnormal region determination module 13 is used to compare the resistivity at corresponding positions in the M-dimensional grid data of electromagnetic observation data and the M-dimensional grid data after resistivity homogenization processing to determine the regions with abnormal resistivity in the target region.

[0153] This invention also provides a computer storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method for extracting weak information from electromagnetic inversion data.

[0154] This invention also provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method for extracting weak information from electromagnetic inversion data.

[0155] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0156] Unless otherwise specifically stated, terms such as processing, calculation, operation, determination, display, etc., may refer to the actions and / or processes of one or more processing or computing systems or similar devices that represent the manipulation and conversion of data representing physical (e.g., electronic) quantities within the registers or memory of the processing system into other data similarly representing physical quantities within the memory, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0157] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0158] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than are explicitly stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby explicitly incorporated into the detailed description, with each claim representing a separate preferred embodiment of the invention.

[0159] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of this disclosure.

[0160] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.

[0161] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or outside the processor; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.

[0162] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term "comprising" as used in the specification or claims is interpreted in a manner similar to the term "including," as interpreted when used as a conjunction in the claims. Additionally, the use of any term "or" in the specification of the claims is intended to mean "non-exclusive or."

Claims

1. A method for extracting weak information from electromagnetic inversion data, characterized in that, include: Based on the electromagnetic observation data of the target area, M-dimensional inversion and interpolation processing are performed to obtain M-dimensional grid data of the electromagnetic observation data, where M is a positive integer greater than 0; the M-dimensional grid data includes depth information and resistivity data corresponding to different depths; The M-dimensional grid data after resistivity homogenization can be obtained using at least one of the following methods: Based on the electromagnetic observation data, the average observed resistivity is determined, and the depth domain single-point resistivity is obtained by inversion of the average observed resistivity. The depth domain single-point resistivity is interpolated in the depth direction and the plane direction to obtain M-dimensional grid data after resistivity homogenization. Based on the M-dimensional grid data of the electromagnetic observation data, the depth domain average resistivity is determined, and the depth domain average resistivity is interpolated in the plane direction to obtain the M-dimensional grid data after resistivity homogenization. The electromagnetic observation data is thinned, and the thinned electromagnetic observation data is subjected to M-dimensional inversion processing to obtain depth domain resistivity data. The depth domain resistivity data is then interpolated to obtain M-dimensional grid data after resistivity homogenization. The resistivity at corresponding positions in the M-dimensional grid data of electromagnetic observation data and the M-dimensional grid data after resistivity homogenization is compared to determine the regions with abnormal resistivity in the target area. This includes: calculating the difference between the resistivity at corresponding positions in the M-dimensional grid data of electromagnetic observation data and the M-dimensional grid data after resistivity homogenization; calculating the ratio of the difference to the resistivity at the corresponding position in the M-dimensional grid data after resistivity homogenization; and determining the regions with abnormal resistivity in the target area based on the obtained ratio. The step of performing M-dimensional inversion and interpolation processing on the electromagnetic observation data of the target area to obtain M-dimensional grid data of the electromagnetic observation data includes: The electromagnetic observation data of the target area is subjected to M-dimensional inversion processing to obtain depth domain resistivity data, which includes the resistivity of different resistivity layers at different measurement points in the depth direction. Regular interpolation processing is performed on the depth domain resistivity data to obtain M-dimensional grid data of electromagnetic observation data.

2. The method as described in claim 1, characterized in that, Based on the M-dimensional grid data of the electromagnetic observation data, the depth-domain average resistivity is determined, and the depth-domain average resistivity is interpolated in the planar direction to obtain homogenized M-dimensional grid data, including: Based on the M-dimensional grid data of the electromagnetic observation data, the average resistivity of each resistivity layer in the depth direction is determined to obtain the depth domain average resistivity. Based on the average resistivity of the depth domain, the average resistivity of each resistivity layer is copied at the same point on the plane corresponding to each resistivity layer to obtain M-dimensional grid data after resistivity homogenization.

3. The method as described in claim 1, characterized in that, Based on the electromagnetic observation data, the average observed resistivity is determined. The average observed resistivity is then inverted to obtain the depth-domain single-point resistivity. This depth-domain single-point resistivity is interpolated in both the depth and planar directions to obtain M-dimensional grid data after resistivity homogenization, including: Based on the observed resistivity of each measuring point at each frequency point in the electromagnetic observation data of the target area, determine the average value of the observed resistivity and the corresponding average value of the phase at each frequency point. One-dimensional inversion processing is performed on the average resistivity observed at each frequency point to obtain the single-point resistivity in the depth domain. Based on the single-point resistivity of the depth domain, linear interpolation is performed in the depth direction, and then data at the same point is copied in the plane direction at the same depth to obtain M-dimensional grid data after resistivity homogenization.

4. The method as described in claim 1, characterized in that, The electromagnetic observation data is thinned, and then M-dimensional inversion processing is performed on the thinned electromagnetic observation data to obtain depth domain resistivity data. The depth domain resistivity data is then interpolated to obtain M-dimensional grid data after resistivity homogenization, including: The electromagnetic observation data of the target area are thinned according to the set thinning rules; The thinned electromagnetic observation data is subjected to M-dimensional inversion processing to obtain the thinned depth domain resistivity data, which includes the resistivity of different resistivity layers at different measurement points in the depth direction. The thinned depth domain resistivity data is subjected to M-dimensional interpolation to obtain M-dimensional grid data after resistivity homogenization.

5. The method according to any one of claims 1-4, characterized in that, If there is more than one M-dimensional grid data after resistivity homogenization, compare the regions with abnormal resistivity in the target region obtained based on each M-dimensional grid data after resistivity homogenization, and determine the regions with abnormal resistivity in the target region based on the comparison results.

6. A device for extracting weak information from electromagnetic inversion data, characterized in that, include: The observation data processing module is used to perform M-dimensional inversion and interpolation processing on electromagnetic observation data of the target area to obtain M-dimensional grid data of the electromagnetic observation data, where M is a positive integer greater than 0. The M-dimensional grid data includes depth information and resistivity data corresponding to different depths. The process of performing M-dimensional inversion and interpolation processing on electromagnetic observation data of the target area to obtain M-dimensional grid data of the electromagnetic observation data includes: performing M-dimensional inversion processing on the electromagnetic observation data of the target area to obtain depth domain resistivity data, which includes the resistivity of different resistivity layers at different measuring points in the depth direction; and performing regular interpolation processing on the depth domain resistivity data to obtain M-dimensional grid data of the electromagnetic observation data. The data homogenization processing module is used to obtain M-dimensional grid data after resistivity homogenization processing using at least one of the following methods: Based on the electromagnetic observation data, the average observed resistivity is determined, and the depth domain single-point resistivity is obtained by inversion of the average observed resistivity. The depth domain single-point resistivity is interpolated in the depth direction and the plane direction to obtain M-dimensional grid data after resistivity homogenization. Based on the M-dimensional grid data of the electromagnetic observation data, the depth domain average resistivity is determined, and the depth domain average resistivity is interpolated in the plane direction to obtain the M-dimensional grid data after resistivity homogenization. The electromagnetic observation data is thinned, and the thinned electromagnetic observation data is subjected to M-dimensional inversion processing to obtain depth domain resistivity data. The depth domain resistivity data is then interpolated to obtain M-dimensional grid data after resistivity homogenization. The abnormal region determination module is used to compare the resistivity at corresponding positions in the M-dimensional grid data of electromagnetic observation data and the M-dimensional grid data after resistivity homogenization processing, and to determine the regions with abnormal resistivity in the target region. This includes: calculating the difference between the resistivity at corresponding positions in the M-dimensional grid data of electromagnetic observation data and the M-dimensional grid data after resistivity homogenization processing; calculating the ratio of the difference to the resistivity at the corresponding position in the M-dimensional grid data after resistivity homogenization processing; and determining the regions with abnormal resistivity in the target region based on the obtained ratio.

7. A computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, which, when executed by a processor, implement the electromagnetic inversion data weak information extraction method according to any one of claims 1-5.

8. A computer device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the electromagnetic inversion data weak information extraction method according to any one of claims 1-5.

Images