Multi-source coverage electromagnetic observation data inversion methods, media and equipment

Abstract

The present invention relates to the technical field of geophysical electromagnetic exploration, and in particular to a multi-source coverage electromagnetic observation data inversion method, a medium and equipment. The multi-source coverage electromagnetic observation data inversion method comprises: arranging electromagnetic measurement points and signal emission sources in a target detection area, and collecting measured electromagnetic field data; calculating theoretical primary electromagnetic fields of each electromagnetic measurement point in the measurement area under the condition of all signal emission sources, and fitting the measured electromagnetic field data of all electromagnetic measurement points by a bisection method to obtain a background resistivity; constructing an initial inversion resistivity model based on the background resistivity; constructing a resistivity inversion objective function of the target detection area according to a model constraint term and a data fitting term; and performing iteration until RMS < G or q >Q iterations, output the resistivity model. In the present invention, through a multi-source observation system and differencing data between electromagnetic measurement points during inversion, the source effect caused by a resistivity anomaly body outside the target detection area is eliminated, so as to further improve the precision of a resistivity structure.

Description

Technical Field

[0001] This invention relates to the field of geophysical electromagnetic exploration technology, and in particular to a method, medium, and equipment for inverting observation data from a multi-source covering electromagnetic method. Background Technology

[0002] Geophysical forward and inverse modeling is an important technique in geophysical exploration. Controlled-source electromagnetic methods are particularly sensitive to resistivity anomalies (especially low-resistivity bodies). By transmitting signals from a signal source, collecting data from electromagnetic measurement points, and performing forward and inverse modeling, the electrical distribution of underground geological bodies can be determined, which is of great significance in practical engineering projects and scientific research.

[0003] However, the artificial source electromagnetic method still has limitations at present. In particular, there is a certain distance between the signal source and the electromagnetic measuring point. The field source effect generated by the electrical anomaly outside the target area will be superimposed on the measured signal at the electromagnetic measuring point. This makes it difficult to obtain the true electrical distribution of the target area by directly inverting the measurement area data in actual engineering. How to remove the field source effect in the measured signal of the electromagnetic measuring point and quickly obtain the high-precision electrical distribution of the target area is the key to the further development of the artificial source electromagnetic method.

[0004] Therefore, it is necessary to provide a new method, medium, and equipment for inverting multi-source coverage electromagnetic observation data to solve the above-mentioned technical problems. Summary of the Invention

[0005] The main objective of this invention is to provide a method, medium, and equipment for inverting observation data using a multi-source coverage electromagnetic method, aiming to solve the problem of how to remove the field source effect in the measured signal of the electromagnetic measuring point and quickly obtain the high-precision electrical distribution of the target area.

[0006] To achieve the above objectives, this invention proposes a method for inverting observation data from a multi-source covering electromagnetic method, comprising the following steps: S1: Electromagnetic measuring points and signal emission sources are deployed in the target detection area to collect measured electromagnetic field data. ; S2: Calculate the theoretical primary electromagnetic field at each electromagnetic measuring point in the test area under all signal emission source conditions. The background resistivity was obtained by fitting the measured electromagnetic field data of all electromagnetic measuring points using the bisection method. S3: Set the current iteration number An initial inversion resistivity model is constructed based on the background resistivity, denoted as . ; S4: Constructing a data fitting term based on the difference between residuals of theoretical electromagnetic field data and measured electromagnetic field data between different electromagnetic measurement points, constructing a model constraint term based on the current resistivity model, and constructing a resistivity inversion objective function for the target detection area according to the model constraint term and the data fitting term ; wherein: the theoretical electromagnetic field data is the sum of a theoretical primary electromagnetic field and a theoretical secondary electromagnetic field; S5: According to the th inversion iteration's inverted resistivity model calculating the root mean square fitting difference RMS of the inverted resistivity model , if RMS<G or >Q times, output the resistivity model corresponding to the target detection area , otherwise proceed to S6, wherein: G is a set threshold, and Q is a set total number of iterations; S6: Updating the inverted resistivity model based on the resistivity inversion objective function to obtain the inverted resistivity model for the th inversion iteration th inversion iteration's inverted resistivity model , and return to step S4.

[0007] Optionally, said S1 comprises: S1.1, dispersedly arranging electromagnetic measurement points above the target detection area, the electromagnetic measurement points are numbered , ; S1.2, uniformly arranging signal emission sources along the circumferential direction with the center point of the target detection area as the geometric center, the signal emission sources are numbered , ; wherein: the distance from each electromagnetic measurement point to any signal emission source is greater than 5 times the target detection depth h of the designed measurement area; S1.3, for the signal emitted by each signal emission source, all electromagnetic measurement points in the target detection area sequentially collect measured electromagnetic fields at frequency or time points, wherein: the frequency or time points are numbered , wherein: the number of the frequency or time point is , .

[0008] Optionally, said S2 comprises: S2.1, assuming that the underground medium of the target detection area is homogeneous, performing uniform mesh division on the signal emission source, the target detection area and the intermediate area between the two respectively by using three-dimensional meshes, wherein: the mesh size of the signal emission source and the target detection area is the same, and the mesh size of the intermediate area between the two is 3 to 5 times the mesh size of the signal emission source and the target detection area; S2.2, Assume the background resistivity is That is, the resistivity values ​​on the mesh obtained by S2.1 are all unknowns. The total field method based on the finite element method was used to calculate the unknown quantities at each electromagnetic measuring point in the measurement area under all signal emission source conditions. Theoretical primary electromagnetic field ; S2.3, will Under the condition of a single signal transmission source, within the test area Electromagnetic measuring points Measured electromagnetic field at a frequency or time point As a fitting term for the electromagnetic field, the objective function for background resistivity inversion is established using the least squares method. The specific expression is as follows: ; in: Electromagnetic measuring point Signal transmission source for measurement Frequency or time point Background resistivity is The theoretical first electromagnetic field; S2.4. Based on the background resistivity inversion objective function, a bisection method is used to search for the result that... The value corresponding to the minimum The theoretical primary electromagnetic field is then calculated using the background resistivity.

[0009] Optionally, in step S3, a distance is set radially outward from the geometric center of the target detection area. The area within the specified range is used as a buffer zone, with a grid size of [missing information]. The target detection area is divided into three-dimensional meshes, with a mesh size of [missing information]. The buffer region is divided into three-dimensional meshes, and the background resistivity obtained from S2 is... Assigning values ​​to each grid cell yields the initial inverted resistivity model; where: , , Set distance The radius of the target detection area is 1 to 2 times.

[0010] Optionally, S4 includes: S4.1, Utilizing theoretical primary electromagnetic fields and background resistivity By conducting finite element forward modeling based on the secondary field, the theoretical secondary electromagnetic field at the electromagnetic measurement point is obtained. And the sensitivity matrix; S4.2, According to the theory of primary electromagnetic field and theoretical second electromagnetic field Calculate the difference between the residuals of theoretical and measured electromagnetic field data at different electromagnetic measurement points to obtain the data fitting term. ; S4.3 Construct the resistivity inversion objective function for the target detection region based on the current resistivity model and data fitting terms. : ; in: These are model constraint terms, derived from the smoothing constraint matrix. Compared with the current resistivity model The second norm is constructed, , This is the regularization factor for the model constraint terms.

[0011] Optionally, in S4.2, the data fitting term The specific formula is as follows: ; in: For constraints on each difference, , and Electromagnetic measuring points and electromagnetic measuring points Signal transmission source for measurement Frequency or time point Measured electromagnetic field data; Electromagnetic measuring point and electromagnetic measuring points Signal transmission source for measurement Frequency or time point The difference between the residuals of theoretical electromagnetic field data and measured electromagnetic field data. , , and Electromagnetic measuring points and electromagnetic measuring points Signal transmission source for measurement Frequency or time point Theoretical electromagnetic field data.

[0012] Optionally, S6 includes: S6.1 Solving for the gradient of the resistivity inversion objective function with respect to the inversion resistivity model based on the resistivity inversion objective function and the sensitivity matrix; S6.2. Based on the resistivity inversion objective function and gradient calculation, update the step size and search direction, and update the inverted resistivity model using the updated step size and search direction. Get the first Inversion resistivity model of the second inversion iteration ; S6.3, Call the step size adjustment formula to adjust and update the step size, let Return to step S4.

[0013] Optionally, in S6.1, the resistivity inversion objective function For the current model The formula for calculating the partial derivative, i.e., the gradient, is as follows: ; in: The objective function for resistivity inversion For the current model The partial derivatives; For model constraint terms For the current model The partial derivatives, ; For data fitting terms For the current model The partial derivatives of .

[0014] In addition, the present invention provides a readable storage medium storing computer program instructions, which, when executed by a processor, implement the multi-source overlay electromagnetic observation data inversion method as described above.

[0015] The present invention also provides an electronic device, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, wherein the computer program instructions are executed by the processor using the multi-source overlay electromagnetic observation data inversion method as described above.

[0016] This invention removes the field source effect (interference information) caused by resistivity anomalies outside the target area by using a multi-source observation system and subtracting the data between electromagnetic measurement points during inversion. This reduces the inversion calculation area to the vicinity of the measurement area, thereby further improving the accuracy of the resistivity structure below the target area and providing theoretical and methodological support for artificial source electromagnetic exploration. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram showing the distribution of the signal transmitting source and electromagnetic measuring points in an embodiment of the present invention; Figure 2 This is a schematic diagram of the mesh division in S2.1 of this embodiment of the invention; Figure 3 This is a schematic diagram of the mesh partitioning of the initial inversion resistivity model in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the principle of eliminating the influence of the emission source and electromagnetic wave propagation path on the difference of electromagnetic field residuals at different measuring points within the target detection area in this embodiment of the invention.

[0019] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0021] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0022] Furthermore, in this invention, descriptions involving "first," "second," etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0023] In this invention, unless otherwise explicitly specified and limited, the terms "connection," "fixed," etc., should be interpreted broadly. For example, "fixed" can mean a fixed connection, a detachable connection, or an integral part; it can mean a mechanical connection or an electrical connection; it can mean a direct connection or an indirect connection through an intermediate medium; it can mean the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0024] Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are feasible for those skilled in the art. If the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0025] This invention proposes a method, medium, and equipment for inverting observation data using a multi-source coverage electromagnetic method, aiming to solve the problem of how to remove the field source effect in the measured signal of the electromagnetic measuring point and quickly obtain the high-precision electrical distribution of the target area.

[0026] This embodiment provides a method for inverting observation data from a multi-source coverage electromagnetic method, including the following steps: S1: Electromagnetic measuring points and signal emission sources are deployed in the target detection area to collect measured electromagnetic field data. ; See Figure 1 S1 includes: S1.1, Distributed above the target detection area There are [number] electromagnetic measuring points, numbered as follows: , ; S1.2, Distribute uniformly along the circumferential direction with the center point of the target detection area as the geometric center. There are 1 signal transmitter, and the signal transmitter number is... , Among them, the distance between each electromagnetic measuring point and any signal transmitting source is greater than 5 times the target detection depth h of the designed measuring area; S1.3. For each signal source, the signal emitted is sequentially collected by all electromagnetic measuring points within the target detection area. Measured electromagnetic field at a frequency or time point Where: the frequency or time point number is , .

[0027] S2: Calculate the theoretical primary electromagnetic field at each electromagnetic measuring point in the test area under all signal emission source conditions. The background resistivity was obtained by fitting the measured electromagnetic field data of all electromagnetic measuring points using the bisection method. See Figure 2 S2 includes: S2.1 Assuming the underground medium in the target detection area is uniform, a three-dimensional mesh is used to uniformly divide the signal source, the target detection area, and the intermediate area between them. The mesh size of the signal source and the target detection area is the same, and the mesh size of the intermediate area between them is 3 to 5 times that of the mesh size of the signal source and the target detection area. S2.2, Assume the background resistivity is That is, the resistivity values ​​on the mesh obtained by S2.1 are all unknowns. The total field method based on the finite element method was used to calculate the unknown quantities at each electromagnetic measuring point in the measurement area under all signal emission source conditions. Theoretical primary electromagnetic field ; S2.3, will Under the condition of a single signal transmission source, within the test area Electromagnetic measuring points Measured electromagnetic field at a frequency or time point As a fitting term for the electromagnetic field, the objective function for background resistivity inversion is established using the least squares method. The specific expression is as follows: ; in: Electromagnetic measuring point Signal transmission source for measurement Frequency or time point Background resistivity is The theoretical first electromagnetic field; S2.4. Based on the background resistivity inversion objective function, a bisection method is used to search for the result that... The value corresponding to the minimum The theoretical primary electromagnetic field is then calculated using the background resistivity.

[0028] In this embodiment, in order to find the value of the objective function that minimizes the objective function... Utilizing electromagnetic fields Contains only one unknown. And with The magnitudes of these resistivity values ​​exhibit a negative correlation. The minimum background resistivity inversion objective function can be directly obtained using the bisection method: Based on the approximate background range of crustal resistivity (1 Ω·m – 2000 Ω·m), calculations are performed respectively. , and intermediate resistivity Based on the magnitude of the values, select the resistivity corresponding to the two smallest objective function values, 1 Ω·m and 1001 Ω·m (rounding up or down according to the principle of maximizing the resistivity range). Continue the calculation using the bisection method as above. , and We obtain the resistivity corresponding to the two minimum objective function values ​​as 1 Ω·m and 501 Ω·m. We repeat the above steps until the difference between the two resistivity values ​​is less than or equal to 2, at which point the resistivity is 83 Ω·m and 85 Ω·m. Then we calculate the resistivity at this point. , and The minimum resistivity, 84 Ω·m, is the background resistivity. ( (84Ω·m), at which point the background resistivity error is less than or equal to 1Ω·m.

[0029] S3: Set the current iteration number An initial inversion resistivity model is constructed based on the background resistivity, denoted as . ; See Figure 3 In step S3, a distance is set radially outward from the geometric center of the target detection area. The area within the specified range is used as a buffer zone, with a grid size of [missing information]. The target detection area is divided into three-dimensional meshes, with a mesh size of [missing information]. The buffer region is divided into three-dimensional meshes, and the background resistivity obtained from S2 is... Assigning values ​​to each grid cell yields the initial inverted resistivity model; where: , , Set distance The radius of the target detection area is 1 to 2 times.

[0030] S4: Construct a data fitting term based on the difference between the theoretical and measured electromagnetic field data residuals at different electromagnetic measurement points; construct model constraint terms based on the current resistivity model; and construct the resistivity inversion objective function for the target detection area based on the model constraint terms and the data fitting term. Among them: theoretical electromagnetic field data It is the sum of the theoretical first-order electromagnetic field and the theoretical second-order electromagnetic field; See Figure 4 Given a fixed distance between the measuring point and the transmitting source, the electromagnetic wave propagation paths from the same transmitting source to different measuring points exhibit high similarity in areas outside the target detection region. By subtracting the electromagnetic field residuals at different measurement points to eliminate similar electromagnetic propagation paths and field sources outside the target detection area, the influence of interference information (field source effect) from other regions on the inversion results is eliminated, reducing the inversion calculation area to the vicinity of the measurement area and improving the inversion accuracy. S4 includes: S4.1, Utilizing theoretical primary electromagnetic fields and background resistivity By conducting finite element forward modeling based on the secondary field, the theoretical secondary electromagnetic field at the electromagnetic measurement point is obtained. and the sensitivity matrix; the specific formula is as follows: ; in: It is a large sparse coefficient matrix. , The imaginary unit, Angular frequency, For the right-hand term, , The stiffness matrix is ​​only related to the geometry of the mesh. and These are the mass matrices for the primary and secondary fields, respectively, and their values ​​are related to the element geometry, resistivity, and magnetic susceptibility. The measurement point at the emission source was obtained through traditional adjoint forward modeling. and frequency (or time point) Sensitivity matrix under the condition The sensitivity matrix represents the sensitivity of the theoretical electromagnetic field to changes in the resistivity model. Let the theoretical electromagnetic field ( Since the interpolation operator is for a known grid, the specific formula for calculating the sensitivity matrix is: ; in: For the measurement point at the emission source ,frequency (or time point) The theoretical electromagnetic field under the given conditions. Since this calculation formula has the same coefficient matrix as the equation in the forward modeling, only the right-hand side is different, it can be solved concurrently during the forward modeling.

[0031] S4.2, According to the theory of primary electromagnetic field and theoretical second electromagnetic field Calculate the difference between the residuals of theoretical and measured electromagnetic field data at different electromagnetic measurement points to obtain the data fitting term. ; In S4.2, the data fitting term The specific formula is as follows: ; in: For constraints on each difference, , and Electromagnetic measuring points and electromagnetic measuring points Signal transmission source for measurement Frequency or time point Measured electromagnetic field data; Electromagnetic measuring point and electromagnetic measuring points Signal transmission source for measurement Frequency or time point the difference between the residual of theoretical electromagnetic field data and the residual of measured electromagnetic field data, , , and are respectively electromagnetic measurement points and electromagnetic measurement point measured signal emission source at , frequency or time point is theoretical electromagnetic field data.

[0032] S4.3, constructing a resistivity inversion objective function for the target detection area according to the current resistivity model and data fitting terms : ; wherein: is a model constraint term, which is composed of a smoothing constraint matrix and the two-norm of the current resistivity model , , is the regularization factor of the model constraint term.

[0033] In this embodiment, after obtaining the gradient of the objective function, global optimization inversion methods such as L-BFGS and nonlinear conjugate gradient are used to solve the objective function to obtain the resistivity model below the survey area. Finally, according to the resistivity model obtained by inversion, the precise position of the abnormal area in the target detection area can be directly obtained.

[0034] S5: According to the inversion iteration's inverted resistivity model calculate the root mean square fitting difference RMS of the inverted resistivity model of the inverted resistivity model. If RMS<G or >Q times, output the resistivity model corresponding to the target detection area , otherwise enter S6, wherein: G is a set threshold, Q is the set total number of iterations; S6: Updating the inverted resistivity model based on the resistivity inversion objective function to obtain the updating the inverted resistivity model based on the resistivity inversion objective function to obtain the inversion iteration's inverted resistivity model , and return to step S4.

[0035] Said S6 includes: S6.1, solving the gradient of the resistivity inversion objective function with respect to the inverted resistivity model based on the resistivity inversion objective function and the sensitivity matrix; S6.2, calculating the update step size and search direction based on the resistivity inversion objective function and the gradient, and updating the inverted resistivity model by using the update step size and the search direction to obtain the inversion iteration's inverted resistivity model ; S6.3, Call the step size adjustment formula to adjust and update the step size, let Return to step S4.

[0036] In this embodiment, the step size is obtained based on line search, and specifically should satisfy the Wolfe criterion, expressed as follows: ; ; in: Step size, For the search direction, and Given a constant and need to satisfy (In this embodiment, =10 -4 , =0.9), For gradient, , for The corresponding resistivity inversion objective function; , For inverse resistivity model The corresponding resistivity inversion objective function.

[0037] The most common method to find a step size that satisfies the above conditions is the backtracking method. After giving an initial step size, the step size is continuously reduced exponentially to find the first point that satisfies the Wolfe criterion, which is the updated step size.

[0038] This embodiment also provides a readable storage medium storing computer program instructions, which, when executed by a processor, implement the multi-source overlay electromagnetic observation data inversion method as described above.

[0039] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0040] This embodiment also includes an electronic device, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, wherein the computer program instructions are executed by the processor to perform the multi-source overlay electromagnetic observation data inversion method as described above.

[0041] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0042] The electronic device can be a mobile phone, desktop computer, laptop, handheld computer, cloud server, or other computing device. The electronic device may include, but is not limited to, processors and memory. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0043] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0044] The memory can be used to store the computer program and / or modules. The processor implements the computer program by running or executing the computer program and / or modules stored in the memory, and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0045] If the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0046] The above description is only a preferred embodiment of the present invention and does not limit the scope of the present invention. All equivalent structural transformations made under the inventive concept of the present invention using the contents of the present invention specification and drawings, or direct / indirect applications in other related technical fields, are included within the protection scope of the present invention.

Claims

1. A method for inverting observation data using multi-source coverage electromagnetic method, characterized in that, Includes the following steps: S1: Electromagnetic measuring points and signal emission sources are deployed in the target detection area to collect measured electromagnetic field data. ; S2: Calculate the theoretical primary electromagnetic field at each electromagnetic measuring point in the test area under all signal emission source conditions. The background resistivity was obtained by fitting the measured electromagnetic field data of all electromagnetic measuring points using the bisection method. S3: Set the current iteration number An initial inversion resistivity model is constructed based on the background resistivity, denoted as . ; S4: Construct a data fitting term based on the difference between the theoretical and measured electromagnetic field data residuals at different electromagnetic measurement points; construct model constraint terms based on the current resistivity model; and construct the resistivity inversion objective function for the target detection area based on the model constraint terms and the data fitting term. Among them: theoretical electromagnetic field data It is the sum of the theoretical first-order electromagnetic field and the theoretical second-order electromagnetic field; S5: According to the inversion resistivity model of the current inversion iteration calculate the root mean square fitting difference RMS of the inversion resistivity model , if RMS < G or > Q times, output the resistivity model corresponding to the target detection area , otherwise enter S6, wherein: G is a set threshold, and Q is a set total number of iterations; S6: Based on the resistivity inversion objective function, the inversion resistivity model Update to get the first Inversion resistivity model of the second inversion iteration Then return to step S4.

2. The method for inverting observation data using multi-source coverage electromagnetic method according to claim 1, characterized in that, S1 includes: S1.1, Distributed above the target detection area N There are [number] electromagnetic measuring points, and the electromagnetic measuring point numbers are as follows: i , i =1,2,…, N ; S1.2, Distribute uniformly along the circumferential direction with the center point of the target detection area as the geometric center. S There are 1 signal transmitter, and the signal transmitter number is... k , k =1,2,…, S Among them, the distance between each electromagnetic measuring point and any signal transmitting source is greater than 5 times the target detection depth h of the designed measuring area; S1.

3. For each signal source, the signal emitted is sequentially collected by all electromagnetic measuring points within the target detection area. F Measured electromagnetic field at a frequency or time point Where: the frequency or time point number is f , f =1,2,…, F .

3. The method for inverting observation data using multi-source coverage electromagnetic method according to claim 2, characterized in that, S2 includes: S2.1 Assuming the underground medium in the target detection area is uniform, a three-dimensional mesh is used to uniformly divide the signal source, the target detection area, and the intermediate area between them. The mesh size of the signal source and the target detection area is the same, and the mesh size of the intermediate area between them is 3 to 5 times that of the mesh size of the signal source and the target detection area. S2.2, Assume the background resistivity is That is, the resistivity values ​​on the mesh obtained by S2.1 are all unknowns. The total field method based on the finite element method was used to calculate the unknown quantities at each electromagnetic measuring point in the measurement area under all signal emission source conditions. Theoretical primary electromagnetic field ; S2.3, will Under the condition of a single signal transmission source, within the test area N Electromagnetic measuring points F Measured electromagnetic field at a frequency or time point As a fitting term for the electromagnetic field, the objective function for background resistivity inversion is established using the least squares method. The specific expression is as follows: ； in: Electromagnetic measuring point i Signal transmission source for measurement k Frequency or time point f Background resistivity is The theoretical first electromagnetic field; S2.

4. Based on the background resistivity inversion objective function, a bisection method is used to search for the result that... The value corresponding to the minimum The theoretical primary electromagnetic field is then calculated using the background resistivity.

4. The method for inverting observation data using multi-source coverage electromagnetic method according to claim 3, characterized in that, In step S3, with the geometric center of the target detection area as the center, a region within a predetermined radial distance R outside the target detection area is designated as a buffer region. The target detection area is divided into three-dimensional meshes with a mesh size of X, and the buffer region is divided into three-dimensional meshes with a mesh size of Y. The background resistivity obtained in step S2 is then used to... The values ​​are assigned to each grid to obtain the initial inversion resistivity model; where: X∈[1,2), Y∈[2,4], Y>X, and the distance R is set to 1-2 times the radius of the target detection area.

5. The method for inverting multi-source coverage electromagnetic observation data according to claim 4, characterized in that, S4 includes: S4.1, Utilizing theoretical primary electromagnetic fields and background resistivity By conducting finite element forward modeling based on the secondary field, the theoretical secondary electromagnetic field at the electromagnetic measurement point is obtained. And the sensitivity matrix; S4.2, According to the theory of primary electromagnetic field and theoretical second electromagnetic field Calculate the difference between the residuals of theoretical and measured electromagnetic field data at different electromagnetic measurement points to obtain the data fitting term. ; S4.3 Construct the resistivity inversion objective function for the target detection region based on the current resistivity model and data fitting terms. : ； in: These are model constraint terms, derived from the smoothing constraint matrix. Compared with the current resistivity model The second norm is constructed, , This is the regularization factor for the model constraint terms.

6. The method for inverting multi-source coverage electromagnetic observation data according to claim 5, characterized in that, In S4.2, the data fitting term The specific formula is as follows: ； in: For constraints on each difference, , and Electromagnetic measuring points i and electromagnetic measuring points j Signal transmission source for measurement k Frequency or time point f Measured electromagnetic field data; Electromagnetic measuring point i and electromagnetic measuring points j Signal transmission source for measurement k Frequency or time point f The difference between the residuals of theoretical electromagnetic field data and measured electromagnetic field data. i≠j , , and Electromagnetic measuring points i and electromagnetic measuring points j Signal transmission source for measurement k Frequency or time point f Theoretical electromagnetic field data.

7. The method for inverting multi-source coverage electromagnetic observation data according to any one of claims 1-6, characterized in that, S6 includes: S6.1 Solving for the gradient of the resistivity inversion objective function with respect to the inversion resistivity model based on the resistivity inversion objective function and the sensitivity matrix; S6.

2. Based on the resistivity inversion objective function and gradient calculation, update the step size and search direction, and update the inverted resistivity model using the updated step size and search direction. Get the first Inversion resistivity model of the second inversion iteration ; S6.3, Call the step size adjustment formula to adjust and update the step size, let Return to step S4.

8. The method for inverting multi-source coverage electromagnetic observation data according to claim 7, characterized in that, In S6.1, the resistivity inversion objective function For the current model The formula for calculating the partial derivative, i.e., the gradient, is as follows: ； in: The objective function for resistivity inversion For the current model The partial derivatives; Model constraint terms For the current model The partial derivatives, ; For data fitting terms For the current model The partial derivatives of .

9. A readable storage medium, characterized in that, It stores computer program instructions, which, when executed by a processor, implement the multi-source coverage electromagnetic observation data inversion method as described in any one of claims 1 to 8.

10. An electronic device, characterized in that, include: The method for inverting multi-source covered electromagnetic observation data as described in any one of claims 1 to 8 includes at least one processor, at least one memory, and computer program instructions stored in the memory, wherein the computer program instructions are executed by the processor.

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