CN115166842A - Tunnel direct-current resistivity self-adaptive inversion method and system based on variable grids - Google Patents
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Abstract
The invention discloses a tunnel direct current resistivity self-adaptive inversion method and a system based on a variable grid, which comprises the following steps: obtaining theoretical observation data according to the constructed tunnel direct-current resistivity forward and backward model; constructing a self-adaptive inversion target function of the tunnel direct-current resistivity, and obtaining new model parameters by adopting the self-adaptive inversion target function according to a comparison result of theoretical observation data and actual observation data; performing grid division on the inversion region, determining an abnormal body region and an abnormal body boundary region according to the variable quantity of the model parameters and the gradient factor of the model, and performing grid encryption on the abnormal body region and the abnormal body boundary region; and updating the inversion region after grid encryption, updating the tunnel direct-current resistivity forward-inversion model according to the new model parameters and the model parameter conversion matrix until the inversion iteration ending condition is reached, and thus obtaining the tunnel direct-current resistivity distribution image of the updated inversion region. The imaging and positioning precision of the water-containing structure in front of the tunnel is improved.
Description
Technical Field
The invention relates to the technical field of advanced prediction water exploration, in particular to a tunnel direct-current resistivity self-adaptive inversion method and system based on a variable grid.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The tunnel direct-current resistivity method is an effective advanced prediction water exploration method, but due to the volume effect of a potential field and the multiple solution of inversion, the imaging and identification of the water-containing structure by the tunnel direct-current resistivity method are to be further improved.
To the knowledge of the inventor, the following key problems of the tunnel direct current resistivity still exist at present and are not solved:
1. under a fixed inversion grid mode, the dependence of an inversion result on an inversion grid is large, the superposition of a real abnormal body region boundary and a model grid subdivision boundary cannot be guaranteed, the contrast of an inversion model is small, geological interpretation is relatively difficult, and tunnel direct-current resistivity inversion cannot accurately depict the abnormal body boundary.
2. The inversion multi-solution is strong, the potential field has a volume effect, and the effect of describing the boundary of the abnormal body is further improved while the inversion efficiency of the tunnel direct-current resistivity is ensured.
Disclosure of Invention
In order to solve the problems, the invention provides a tunnel direct-current resistivity self-adaptive inversion method and a tunnel direct-current resistivity self-adaptive inversion system based on variable grids, and introduces a self-adaptive inversion idea to perform grid self-adaptive encryption of different degrees in an abnormal body and a boundary region of the abnormal body, so that the dependence of inversion on model regularization constraint is reduced, and the imaging and positioning accuracy of a water-containing structure in front of a tunnel is improved.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a variable grid-based tunnel dc resistivity adaptive inversion method, including:
obtaining theoretical observation data according to the constructed tunnel direct-current resistivity forward and backward model;
constructing a self-adaptive inversion target function of the tunnel direct-current resistivity, and obtaining new model parameters by adopting the self-adaptive inversion target function according to a comparison result of theoretical observation data and actual observation data;
performing grid division on the inversion region, determining an abnormal body region and an abnormal body boundary region according to the variable quantity of the model parameters and the gradient factor of the model, and performing grid encryption on the abnormal body region and the abnormal body boundary region;
and updating the inversion area after the grid is encrypted, updating the tunnel direct-current resistivity forward and inversion model according to the new model parameters and the model parameter conversion matrix until reaching the inversion iteration end condition, and thus obtaining the tunnel direct-current resistivity distribution image of the updated inversion area.
As an alternative embodiment, the adaptive inversion objective function is:
wherein the content of the first and second substances,
is the difference between the actual observed data Δ d and the theoretical observed data;
is a model constraint term; j is a sensitivity matrix; λ is the Lagrangian operator and m is the model parameter.
As an alternative embodiment, the process of determining the abnormal body region includes: determining the model parameter variation of each grid unit in the inversion region, and determining an initial abnormal body region according to a preset threshold; and constructing an abnormal body region optimization standard matrix according to the distance between the central point of each grid unit in the initial abnormal body region and the central point of the initial abnormal body region and the model parameter variation so as to optimize the initial abnormal body region to obtain the abnormal body region.
As an optionIn an alternative embodiment, the anomaly region optimization criteria matrix has the element c i Element c i Expressed as:
wherein, alpha, beta and lambda are weight coefficients respectively; d i The distance between the central point of each grid unit and the central point of the initial abnormal body region, smt i Is the variation of the model parameters.
As an alternative implementation, the grid units of the initial abnormal body region are sorted in a descending order according to the elements of the abnormal body region optimization standard matrix, and the grid units are selected according to a certain proportion to serve as the final abnormal body region.
As an alternative embodiment, the process of determining the anomaly boundary region includes: and estimating the number of units occupied by the boundary of the abnormal body according to the range of the abnormal body area, determining the selection proportion of the boundary units of the abnormal body area, and determining the boundary area of the abnormal body according to the model gradient factor and the selection proportion of the boundary units.
As an alternative embodiment, the model gradient factor is:
wherein m is i And the model parameters after the ith inversion iteration are obtained.
As an alternative implementation mode, according to the model gradient factor of the inversion region, the grid cells of the inversion region are sorted by a descending method, and the boundary region of the abnormal body is determined according to the selection proportion of the boundary cells; and meanwhile, rejecting the abnormal body boundary area and the common grid unit of the abnormal body area.
As an alternative embodiment, the process of performing mesh encryption on the abnormal body area and the abnormal body boundary area includes: sequentially carrying out primary encryption and multiple times of encryption; the primary encryption means that the hexahedron is divided into a plurality of tetrahedrons in a mode of adding nodes; the multi-time encryption is to divide the tetrahedron after the one-time encryption into a plurality of tetrahedrons.
As an alternative embodiment, the inversion iteration end condition includes: the maximum inversion number and the minimum grid cell volume of the inversion region are set.
In a second aspect, the present invention provides a variable grid-based adaptive inversion system for direct current resistivity of a tunnel, including:
the theoretical observation data determining module is configured to obtain theoretical observation data according to the constructed tunnel direct-current resistivity forward-inversion model;
the inversion equation calculation module is configured to construct a self-adaptive inversion target function of the tunnel direct-current resistivity, and a new model parameter is obtained by adopting the self-adaptive inversion target function according to a comparison result of theoretical observation data and actual observation data;
the grid encryption module is configured to perform grid division on the inversion region, determine an abnormal body region and an abnormal body boundary region according to the model parameter variation and the model gradient factor, and perform grid encryption on the abnormal body region and the abnormal body boundary region;
and the inversion module is configured to update the inversion area after grid encryption, update the tunnel direct-current resistivity forward-inversion model according to the new model parameters and the model parameter conversion matrix until an inversion iteration ending condition is reached, and obtain an updated tunnel direct-current resistivity distribution image of the inversion area.
In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method of the first aspect is performed.
In a fourth aspect, the present invention provides a computer readable storage medium for storing computer instructions which, when executed by a processor, perform the method of the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a tunnel direct-current resistivity self-adaptive inversion method and system based on a variable grid, which introduce a self-adaptive inversion idea, accurately define an abnormal body and a boundary region thereof, perform grid self-adaptive encryption of different degrees in the abnormal body and the boundary region thereof, gradually self-adaptively optimize the grid quality of an inversion region, reduce the dependence of inversion on model regularization constraint and accurately identify and image a water-containing structure more pertinently.
The invention provides a tunnel direct-current resistivity self-adaptive inversion method and system based on a variable grid.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a flowchart of a variable-grid-based tunnel dc resistivity adaptive inversion method according to embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of a geoelectric model provided in embodiment 1 of the present invention;
fig. 3 (a) -3 (c) are graphs of the adaptive inversion results of the tunnel dc resistivity based on the variable grid according to embodiment 1 of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example 1
The embodiment provides a tunnel direct-current resistivity adaptive inversion method based on a variable grid, which can be used for identification and imaging of a water-containing structure, as shown in fig. 1, and includes:
obtaining theoretical observation data according to the constructed tunnel direct-current resistivity forward and backward model;
constructing a self-adaptive inversion target function of the tunnel direct-current resistivity, and if the difference between theoretical observation data and actual observation data is larger, solving an inversion equation according to the self-adaptive inversion target function to update model parameters;
performing grid division on the inversion region, determining an abnormal body region and an abnormal body boundary region according to the variable quantity of the model parameters and the gradient factor of the model, and performing grid encryption on the abnormal body region and the abnormal body boundary region;
and updating the inversion area after grid encryption, realizing unbiased transmission of model parameters according to the new model parameters and the constructed model parameter conversion matrix, continuously performing iterative inversion until the inversion iteration ending condition is reached, and thus obtaining the tunnel direct-current resistivity distribution image of the updated inversion area.
In the embodiment, a tunnel direct-current resistivity forward-inverse model is constructed by performing geological survey, searching information such as tunnel size and the like and according to the information such as the tunnel size and the like, so as to obtain theoretical observation data.
As an optional implementation manner, the tunnel direct-current resistivity forward-inverse model adopts a mixed grid mainly comprising a hexahedral grid as an initial model, can better fit a complex geometric model based on an unstructured grid, and has the advantage of being convenient for local encryption; the partial encryption is carried out on the electrode area by adopting the non-structural tetrahedron, so that the forward accuracy of the electrode area can be improved, and the number of inversion area units can be reduced as much as possible.
In this embodiment, a survey line and an electrode with a certain polar distance are arranged in a tunnel to form a tunnel dc resistivity detection space, and a tunnel dc resistivity advanced detection method is used to collect actual observation data.
As an alternative embodiment, the measuring lines and electrodes are arranged according to observation device modes such as a tunnel direct current resistivity two-stage method, a tunnel direct current resistivity three-stage method, a tunnel direct current resistivity focusing depth measurement and the like.
In this embodiment, theoretical observation data is obtained according to the forward-inverse tunnel dc resistivity model, and if a difference between the theoretical observation data and actual observation data exceeds a preset threshold, the model parameters need to be updated.
In this embodiment, the sensitivity matrix J and the model constraint term are calculated
Constructing a self-adaptive inversion target function aiming at the tunnel direct-current resistivity variable grid, and updating a model parameter m through inversion iteration i (ii) a The adaptive inversion objective function is:
wherein the content of the first and second substances,
is the difference between the actual observed data Δ d and the theoretical forward observed data, isA data fitting term;
is a model constraint term; j is a sensitivity matrix; lambda is Lagrange operator, adjusted
And
the weight of the inversion algorithm is used for balancing the influence of the two on the result in the inversion iteration process, and the stability and the reliability of the inversion effect can be improved.
As can be appreciated, the sensitivity matrix J is associated with model constraint terms
The calculation of (2) is performed by a conventional calculation method, and is not limited herein.
In this embodiment, a detection inversion region is subjected to mesh division, an abnormal body region is determined according to a model parameter variation and a model gradient factor, and mesh encryption optimization is performed on the abnormal body region and an abnormal body boundary region.
Firstly, determining an abnormal body area; specifically, the method comprises the following steps:
determining the model parameter variation of each grid unit in the inversion region, and determining an initial abnormal body region according to a preset threshold;
and constructing an abnormal body region optimization standard matrix according to the distance between the central point of each grid unit in the initial abnormal body region and the central point of the initial abnormal body region and the model parameter variation so as to optimize the initial abnormal body region to obtain the abnormal body region.
Furthermore, the net with the model parameter variation larger than the preset threshold value is selected as the initial abnormal body area.
Furthermore, in order to more accurately select the abnormal body region and avoid the unit of non-abnormal body region in each abnormal body region, the initial abnormal body region is optimized, and the element of the abnormal body region optimization standard matrix C is C i (ii) a Element c i Expressed as:
wherein, alpha, beta and lambda are respectively weight coefficients for adjusting the distance between the central point of each grid unit in the initial abnormal body area and the central point in the initial abnormal body area and the variation of the model parameters, and the numerical values of the weight coefficients are related to the volume of the abnormal body, the roughness of the grid subdivision and the inversion iteration times; d i The distance between the central point of each grid unit and the central point of the initial abnormal body region, smt i Is the variation of the model parameters.
Furthermore, the grid unit numbers of the initial abnormal body area are sorted in a descending order according to the elements of the abnormal body area optimization standard matrix C, and grid units are selected according to a certain proportion to serve as the final abnormal body area.
Secondly, determining an abnormal body boundary area; specifically, the method comprises the following steps:
and estimating the number of units occupied by the abnormal body boundary according to the range of each abnormal body region, determining the boundary unit selection proportion of the abnormal body region, and determining the abnormal body boundary region according to the model gradient factor and the boundary unit selection proportion.
Further, the model gradient factor is:
further, calculating a model gradient factor of the inversion region, sequencing grid cells of the inversion region according to a descending method, and determining an abnormal body boundary region according to a boundary cell selection ratio; meanwhile, the public units of the abnormal body boundary area and the abnormal body area are removed, and double encryption in a certain area is avoided.
In this embodiment, the process of performing mesh encryption on the abnormal body area and the abnormal body boundary area includes sequentially performing one-time encryption and multiple-time encryption;
the primary encryption is to divide the original hexahedron into a plurality of tetrahedrons by adding nodes to the hexahedron in the region; the multiple encryption refers to a process of dividing the once encrypted tetrahedral encryption into a plurality of tetrahedrons, namely adding a node in the original tetrahedral unit to divide the original tetrahedral unit into 4 tetrahedral units.
In the embodiment, after grid encryption, the inversion region is updated iteratively, and the tunnel direct-current resistivity forward-inversion model is updated according to the new model parameters and the model parameter conversion matrix, so that unbiased transmission of model parameters after grid encryption optimization of the inversion region is realized; and simultaneously recalculating the sensitivity matrix and constructing a model constraint item, and entering the next iteration.
After the grid encryption optimization of the inversion region is completed, the grid number, the serial number and the types of partial grid units of the inversion region are changed, so that the inversion region needs to be updated iteratively; meanwhile, a model parameter conversion matrix G is constructed, and m can be guaranteed i And m 0 Realizing unbiased transmission of model parameters according to a grid encryption optimization strategy of the inversion region;
wherein the content of the first and second substances,
m 0 ＝Gm i
m i the model parameters after the ith (last) inversion iteration are M-dimensional column vectors; m is 0 The updated model parameter is used as the model parameter m in the current inversion iteration process 0 The number is M + N dimension column vector, and N is the increased unit number after the inversion region is optimized.
As an alternative embodiment, the model parameter transformation matrix expression is:
wherein, w i Constant 1,m, n represents the cell number for grid optimization, G 2 Is an M × N matrix.
In this embodiment, the maximum inversion number and the minimum grid unit volume of the inversion region are set as inversion iteration end conditions; through continuous iterative inversion, when the grid unit volume of the inversion area is smaller than the minimum grid unit volume or the inversion iteration times reach the maximum inversion times, the model parameters which finally meet the requirements can be obtained, and the finally updated tunnel direct-current resistivity forward-inversion model is obtained, so that the resistivity distribution image of the detection area is obtained.
As shown in the geoelectricity model diagram of FIG. 2, the tunnel face profile is a circle with a diameter of 10m, a low-resistance body with a resistivity rho of 14m × 14m × 4m is arranged in front of the tunnel 2 =10 Ω m, background resistivity ρ of tunnel surrounding rock 1 =1000 Ω m, tunnel cavity resistivity ρ 3 ＝1×10 8 Omega m. The distance between the front interface of the low-resistance body and the tunnel face is 6m; a tunnel resistivity method observation mode of a multi-isotropic source array is adopted, 48 measuring electrodes are arranged on a palm surface, the measuring electrodes are evenly distributed on 3 measuring lines, the distance between each measuring electrode and the palm surface is 1m, and 8 moving power supply profiles are arranged at positions which are 0m, 2.5m, 4.5m, 6.5m, 11.5m, 14.5m, 22.5m and 28m away from the palm surface respectively. The obtained inversion results are shown in fig. 3 (a) -3 (c), and the results show that the water-containing structure identification and imaging method based on the adaptive inversion of the variable grids can accurately depict the shape and the location of the water-containing structure target body.
Example 2
The embodiment provides a tunnel direct current resistivity self-adaptive inversion system based on a variable grid, which comprises:
the theoretical observation data determining module is configured to obtain theoretical observation data according to the constructed tunnel direct-current resistivity forward-inversion model;
the inversion equation calculation module is configured to construct a self-adaptive inversion target function of the tunnel direct-current resistivity, and a new model parameter is obtained by adopting the self-adaptive inversion target function according to a comparison result of theoretical observation data and actual observation data;
the grid encryption module is configured to perform grid division on the inversion region, determine an abnormal body region and an abnormal body boundary region according to the model parameter variation and the model gradient factor, and perform grid encryption on the abnormal body region and the abnormal body boundary region;
and the inversion module is configured to update the inversion area after grid encryption, update the tunnel direct-current resistivity forward-inversion model according to the new model parameters and the model parameter conversion matrix until an inversion iteration ending condition is reached, and obtain an updated tunnel direct-current resistivity distribution image of the inversion area.
It should be noted that the modules correspond to the steps described in embodiment 1, and the modules are the same as the corresponding steps in the implementation examples and application scenarios, but are not limited to the disclosure in embodiment 1. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
In further embodiments, there is also provided:
an electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of embodiment 1. For brevity, further description is omitted herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processor, a digital signal processor DSP, an application specific integrated circuit ASIC, an off-the-shelf programmable gate array FPGA or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method described in embodiment 1.
The method in embodiment 1 may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. The tunnel direct-current resistivity self-adaptive inversion method based on the variable grid is characterized by comprising the following steps:
obtaining theoretical observation data according to the constructed tunnel direct-current resistivity forward and backward model;
constructing a self-adaptive inversion target function of the tunnel direct-current resistivity, and obtaining new model parameters by adopting the self-adaptive inversion target function according to a comparison result of theoretical observation data and actual observation data;
performing grid division on the inversion region, determining an abnormal body region and an abnormal body boundary region according to the variable quantity of the model parameters and the gradient factor of the model, and performing grid encryption on the abnormal body region and the abnormal body boundary region;
and updating the inversion region after grid encryption, updating the tunnel direct-current resistivity forward-inversion model according to the new model parameters and the model parameter conversion matrix until the inversion iteration ending condition is reached, and thus obtaining the tunnel direct-current resistivity distribution image of the updated inversion region.
2. The variable-grid-based tunnel direct-current resistivity adaptive inversion method of claim 1, wherein an adaptive inversion objective function is:
wherein the content of the first and second substances,
is the difference between the actual observed data Δ d and the theoretical observed data;
is a model constraint term; j is a sensitivity matrix; λ is the Lagrangian operator and m is the model parameter.
3. The variable-grid-based adaptive inversion method of direct-current resistivity of tunnels according to claim 1, wherein the process of determining the anomaly region comprises: determining the model parameter variation of each grid unit in the inversion region, and determining an initial abnormal body region according to a preset threshold; and constructing an abnormal body region optimization standard matrix according to the distance between the central point of each grid unit in the initial abnormal body region and the central point of the initial abnormal body region and the model parameter variation so as to optimize the initial abnormal body region to obtain the abnormal body region.
4. The variable-grid-based adaptive inversion method for direct-current resistivity of tunnels as claimed in claim 3, wherein the element of the anomaly region optimization criterion matrix is c i Element c i Expressed as:
wherein, alpha, beta and lambda are weight coefficients respectively; d i The distance between the central point of each grid unit and the central point of the initial abnormal body region, smt i Is the variation of the model parameter;
and sorting the grid units of the initial abnormal body area in a descending order according to the elements of the abnormal body area optimization standard matrix, and selecting the grid units according to a certain proportion to serve as a final abnormal body area.
5. The variable-grid-based adaptive inversion method of direct current resistivity of tunnels according to claim 1, wherein the process of determining the boundary region of the anomaly comprises: and estimating the number of units occupied by the boundary of the abnormal body according to the range of the abnormal body area, determining the selection proportion of the boundary units of the abnormal body area, and determining the boundary area of the abnormal body according to the model gradient factor and the selection proportion of the boundary units.
6. The variable-grid-based adaptive inversion method of direct-current resistivity of tunnels according to claim 5, wherein the model gradient factor is:
wherein m is i Model parameters after the ith inversion iteration are obtained;
according to the model gradient factor of the inversion region, sorting the grid cells of the inversion region according to a descending method, and determining an abnormal body boundary region according to the boundary cell selection proportion; and meanwhile, rejecting the abnormal body boundary area and the common grid unit of the abnormal body area.
7. The variable-grid-based tunnel direct-current resistivity adaptive inversion method of claim 1, wherein the process of grid encryption of the anomaly region and the anomaly boundary region comprises the following steps: sequentially carrying out primary encryption and multiple times of encryption; the primary encryption means that the hexahedron is divided into a plurality of tetrahedrons in a mode of adding nodes; the multi-time encryption means that the tetrahedron subjected to the one-time encryption is divided into a plurality of tetrahedrons;
or, the inversion iteration end condition comprises: the maximum inversion number and the minimum grid cell volume of the inversion region are set.
8. Variable grid-based tunnel direct current resistivity self-adaptive inversion system is characterized by comprising the following steps:
the theoretical observation data determining module is configured to obtain theoretical observation data according to the constructed tunnel direct-current resistivity forward-inversion model;
the inversion equation calculation module is configured to construct a self-adaptive inversion target function of the tunnel direct-current resistivity, and a new model parameter is obtained by adopting the self-adaptive inversion target function according to a comparison result of theoretical observation data and actual observation data;
the grid encryption module is configured to perform grid division on the inversion region, determine an abnormal body region and an abnormal body boundary region according to the model parameter variation and the model gradient factor, and perform grid encryption on the abnormal body region and the abnormal body boundary region;
and the inversion module is configured to update the inversion area after grid encryption, update the tunnel direct-current resistivity forward-inversion model according to the new model parameters and the model parameter conversion matrix until an inversion iteration ending condition is reached, and obtain an updated tunnel direct-current resistivity distribution image of the inversion area.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of any of claims 1-7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 7.
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