Time-frequency electromagnetic resistivity inversion method based on resistivity and layer thickness threshold model constraint

Abstract

The application discloses a time-frequency electromagnetic resistivity inversion method based on resistivity and layer thickness threshold model constraints, comprising the following steps: determining an inversion depth, collecting geological information of a target area, constructing a geological model, and obtaining a two-dimensional geological model information graph of an inversion profile; determining inversion stratum information according to physical properties, and determining the electrical property layer thickness and resistivity value of the target area to constitute a solution space of the geological model; performing forward modeling on a group of random models in the solution space of the geological model to obtain a forward modeling response function; constructing a target function by using the forward modeling response function and the geological model; determining a fitting difference function of the target function and a preset iteration number, and obtaining a constraint term of the geological model; obtaining a correction amount of the geological model according to the constraint term, and combining the fitting difference to perform correction or performing correction according to the preset iteration number. In conclusion, the application has the advantages of simple logic, strong correlation, reliable inversion and the like.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geophysical electromagnetic exploration inversion, and particularly to a time-frequency electromagnetic resistivity inversion method based on resistivity and layer thickness threshold model constraints. BACKGROUND

[0002] In the field of oil and gas, it is usually necessary to use geophysical methods to explore target areas where oil and gas may exist. Among them, time-frequency electromagnetic, as a geophysical method applied to the exploration of underground medium, mainly obtains resistivity results by studying the vertical magnetic field and horizontal electric field signals in the long wire source electromagnetic method, and calculating the signals by using an inversion algorithm to achieve the purpose of identifying the distribution of underground strata and finding favorable areas for oil and gas.

[0003] At present, the inversion method for time-frequency electromagnetic in the prior art is mainly nonlinear optimization inversion, and the inversion effect and identification accuracy need to be further improved. Meanwhile, the existing methods mainly use single-parameter constraints for constraint inversion, which makes the inversion effect and accuracy not greatly improved.

[0004] For example, the Chinese invention patent with the patent publication number CN104280782A and the name of one-dimensional joint inversion method for time-frequency electromagnetic and magnetotelluric data, the transmission frequency of the joint inversion time-frequency electromagnetic is 0.025-100Hz, the frequency of the magnetotelluric is 0.0005-320Hz, the distance between each time-frequency electromagnetic measuring point and all magnetotelluric measuring points is calculated to obtain the nearest magnetotelluric measuring point, the data of the electromagnetic measuring point is extracted, the Jacobian partial derivative matrix of the joint inversion of time-frequency electromagnetic and magnetotelluric is calculated, the objective function is calculated according to the regularization inversion principle, the conjugate gradient iteration algorithm is used to minimize the objective function, and the joint inversion is completed when the fitting error of the objective function reaches the set error standard or the iteration number exceeds the set maximum iteration number.

[0005] The above-mentioned technology has the following technical defects:

[0006] First, the free inversion result has poor interpretability and large difference from the actual geological condition;

[0007] Second, the inversion result has strong multiple solutions, the inversion result has obvious body effect, and the actual production cannot meet the requirement of high precision;

[0008] Third, the current time-frequency electromagnetic mainly uses one-dimensional inversion method, and it is difficult to obtain good results for imaging of two-dimensional complex geological bodies;

[0009] Fourth, the constraint inversion mainly uses single-parameter constraints, and it is difficult to set a reasonable model combined with the actual geological condition;

[0010] Fifth, the 1D (one-dimensional) model setting makes it difficult to consider the correlation between adjacent measuring points in the same stratum, resulting in a poorer geological regularity of the model.

[0011] In addition, existing technologies also employ three-dimensional inversion techniques. For example, the Chinese invention patent "Patent Publication No.: CN104375195A, Title: Multi-source Multi-component Three-dimensional Joint Inversion Method for Time-frequency Electromagnetic Sources" determines the initial resistivity model for time-frequency electromagnetic three-dimensional inversion based on known resistivity logging data and seismic exploration data. It calculates the maximum and minimum coordinates in the horizontal x and y directions, determines the inversion range in the horizontal direction, selects the vertical grid size for multi-source multi-component time-frequency electromagnetic three-dimensional inversion, calculates the primary field of the time-frequency electromagnetic emission source in the initial model, calculates the Green's tensor between each hexahedron underground, calculates the derivative of each time-frequency electromagnetic field source, and uses an iterative algorithm of conjugate gradients to minimize the objective function. After several iterations, the multi-source multi-component time-frequency electromagnetic three-dimensional inversion is completed. However, this joint inversion technique uses too many parameters, has a complex processing flow, and is slower in computation.

[0012] Therefore, there is an urgent need to propose a time-frequency electromagnetic resistivity inversion method based on resistivity and layer thickness threshold model constraints that is logically simple, highly correlated, and reliable. Summary of the Invention

[0013] To address the aforementioned problems, the present invention aims to provide a time-frequency electromagnetic resistivity inversion method based on resistivity and layer thickness threshold model constraints. The technical solution adopted by the present invention is as follows:

[0014] The time-frequency electromagnetic resistivity inversion method based on resistivity and layer thickness threshold model constraints includes the following steps:

[0015] Determine the inversion depth, collect geological information of the target area, construct a geological model, and obtain a two-dimensional geological model information map of the inversion profile;

[0016] Based on physical properties, inverted stratigraphic information is determined, and the thickness and resistivity of the electrical layer in the target area are determined, thus forming the solution space of the geological model;

[0017] A random set of models is performed in the solution space of the geological model to obtain the forward modeling response function;

[0018] The objective function is constructed using the forward response function and the geological model;

[0019] Determine the fitting difference function of the objective function and the preset number of iterations, and obtain the constraint terms of the geological model;

[0020] The correction amount of the geological model is obtained based on the constraint terms, and then corrected by combining the fitting difference or by the preset number of iterations.

[0021] Further, the geological information includes stratum distribution, stratum thickness range, regional geological structure pattern, two-dimensional seismic profile, electrical well logging data and stratum rock physical property information in the target region.

[0022] Further, the stratum information is determined according to the physical property, and the electrical layer thickness and resistivity value of the target region are determined to form a solution space of the geological model, including the following steps:

[0023] The one-dimensional resistivity threshold parameter and layer thickness threshold parameter of the known point are determined by using the electrical well logging and rock sample physical information at the known point of the geological information of the target region;

[0024] The resistivity threshold parameter is set as the sum of the right and left inflection points of the normal distribution curve N(R) of the statistical result and the forward correction term, and the expression is:

[0025]

[0026] Wherein, R ri represents the right inflection point of the normal distribution curve; R li represents the left inflection point of the normal distribution curve, c(R ri ) represents the forward correction term of the right inflection point of the normal distribution curve; c(R li ) represents the forward correction term of the left inflection point of the normal distribution curve; F(R ri ) represents the forward response function of the right inflection point of the normal distribution curve; F(R li ) represents the forward response function of the left inflection point of the normal distribution curve; p represents the actual observation data.

[0027] The remaining points of the same layer in the geological model are interpolated by using the least square method, and the expression is:

[0028] Fit=(X T X) -1 X T G

[0029] Wherein, Fit represents the fitting term; X represents the offset distance of the measuring line; X T represents the transpose matrix of the X matrix; G represents the model threshold parameter.

[0030] Further, the objective function of the geological model is constructed, including the following steps:

[0031] The parameters of the geological model are preset The expression is:

[0032]

[0033] Wherein, represents the resistivity parameter of the geological model in the initial state. a geological model layer thickness parameter representing an initial state; ran n a random number representing 0-1; a minimum value of a geological model resistivity; a maximum value of a geological model resistivity; a minimum value of a geological model layer thickness; a maximum value of a geological model layer thickness;

[0034] forward a set of models within a solution space of a geological model by using a random function to obtain a forward response function F(G);

[0035] combine the collected time-frequency electromagnetic electric field signals and magnetic field signals to construct a target function T(G), and an expression of the target function T(G) is:

[0036]

[0037] wherein G represents a model threshold parameter; and grd(G) represents a gradient of a geological model parameter; -1 represents a Lagrange operator; W represents a weight of the time-frequency electromagnetic electric field signals and magnetic field signals; B represents a balance factor; and Δ represents an expected fitting difference.

[0038] Further, an expression of the balance factor B is:

[0039]

[0040] wherein m represents a median function; represents a weight of the electric field signals; represents a weight of the magnetic field signals.

[0041] Further, an expression of the fitting difference delta is:

[0042]

[0043] wherein E x represents a horizontal electric field signal in the observation data; H z represents a vertical magnetic field component in the observation data; and N represents a quantity of the observation data.

[0044] Further, a constraint term of the geological model is obtained an expression of which is:

[0045]

[0046] wherein W min represents a minimum model weight; G pri represents a prior model; and rou(G) represents a model roughness.

[0047] Further, the correction amount C of the geological model is obtained according to the constraint term G , and the expression is:

[0048]

[0049] Wherein, J represents the Jacobian matrix; represents the model after the a-th iteration.

[0050] Further, the expression of the geological model after the correction is:

[0051]

[0052] Wherein, represents the model after the a+1-th iteration; S a represents the step length of the a-th iteration.

[0053] Compared with the prior art, the present application has the following beneficial effects:

[0054] (1) The present application ingeniously collects the known geological information related to the target area, including the stratum distribution in the area, the stratum thickness range, the regional geological structure pattern, the 2d (two-dimensional) seismic profile, the electrical well logging data, the stratum rock physical property information (including but not limited to lithology, resistivity information), and uses the same to construct a geological model. In addition, according to the physical property, the inversion stratum information is determined, the regional electrical layer thickness and resistivity value are determined, the stratum structure pattern is referenced to the regional geological structure pattern suggestion, the two-dimensional geological model information graph of the inversion profile is obtained. On the basis of the traditional method, the present application improves the inversion method by introducing the geological model double-parameter threshold constraint, improves the inversion effect by providing the resistivity and stratum thickness two information constraints, improves the oil and gas geophysical exploration precision, and provides a new idea and method for obtaining the high-resolution underground stratum distribution and structure pattern and other requirements.

[0055] (2) The present application combines the 2d seismic profile and the 1d threshold parameter of the known point collected in the geological information of the target area, performs interpolation on other points of the same layer by the least square method to obtain the threshold parameter of the unknown point, and the continuity between different measuring points of the same layer of the 2d model is better, and is more in line with the geological law.

[0056] (3) The present application determines the iteration stopping condition according to the actual production needs, that is, the iteration number and the fitting difference, and the iteration can be stopped when one of the two is satisfied; in addition, the target function of the present application comprehensively considers the gradient of the geological model parameter, the Lagrange operator, the weight of the observation data, that is, the electric field Ex and the magnetic field Hz signal, the balance factor and the expected fitting difference, and the advantage is that the iteration termination condition is considered by using multiple parameters, and the overfitting situation caused by purely pursuing the minimum fitting difference is effectively avoided.

[0057] (4) The present application obtains a model correction quantity according to a geological model constraint term, and the model is modified through the model correction quantity, and each iteration cycle makes the model more approximate to the true result until the data is gradually fitted or the iteration number is satisfied. The advantage is that the inversion result is more accurate than the traditional free inversion or single parameter constraint inversion result.

[0058] (5) The present application introduces a geological model double parameter threshold constraint inversion method to improve it, and the multiple solution of inversion is reduced through the double constraint of resistivity and layer thickness.

[0059] In summary, the present application has the advantages of simple logic, strong correlation, reliable inversion, etc., and has high practical value and popularization value in the field of geophysical electromagnetic exploration inversion technology. BRIEF DESCRIPTION OF DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments, and it should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope of protection, and for those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0061] Figure 1 The present application is a logical flowchart.

[0062] Figure 2 The present application is a geological model information diagram.

[0063] Figure 3 The present application is an inversion result diagram.

[0064] Figure 4 The present application is a result diagram using traditional nonlinear free inversion. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application will be further described below in combination with the drawings and embodiments, and the embodiments of the present application include but are not limited to the following embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0066] In this embodiment, the term "and / or" is only a description of the association relationship of the associated objects, which means that there can be three kinds of relationships, for example, A and / or B, which can represent three cases of A alone, A and B together, and B alone.

[0067] The terms "first" and "second" and the like in the description and claims of the present embodiments are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. For example, a first target object and a second target object are used for distinguishing between two structurally similar objects but not for describing one object before or after another.

[0068] In the present embodiments, the words "exemplary" and "for example" are used to mean serving as an example, instance, or illustration, at 99 100 least with respect to the specific embodiments described and not necessarily as preferable or better with respect to other embodiments. The words "exemplary" and "for example" are used herein to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" is not necessarily to be construed as preferred or advantageous over other embodiments or designs.

[0069] In the description of the present embodiments, the meaning of "a plurality of" is two or more unless otherwise specified. For example, a plurality of processing units means two or more processing units; a plurality of systems means two or more systems.

[0070] As shown in Figures 1 to 4 The present embodiments provide a time-frequency electromagnetic resistivity inversion method based on resistivity and layer thickness threshold model constraints, which mainly includes the following steps:

[0071] Step one:

[0072] (1) Determine the inversion depth according to the observation data quality and the actual production needs.

[0073] (2) Collect relevant known geological information of the target area, including the stratum distribution in the region, the stratum thickness range, the regional geological structure style, the 2d seismic profile, the electrical logging data, the stratum rock physical property information (including but not limited to lithology, resistivity information) to construct the geological model of step one (3).

[0074] (3) Determine the inversion stratum information according to the physical property, determine the regional electrical layer thickness and resistivity value, and refer to the regional geological structure style suggestion to obtain the two-dimensional geological model information graph of the inversion profile.

[0075] (4) Through the collected information, determine the 1d resistivity and layer thickness threshold parameters of the point using the known information such as electrical logging and rock sample physical property at the known point, and constitute the solution space of the geological model; The model solution space is and For the layer thickness solution space The thickness variation range is generally set to 0.01%-50%; when the layer thickness information is very accurate, it can be set to 0.01%-10%; when there is no information or the regional geological condition is very complex and controversial, the thickness variation range can be set to 20%-200%. In this embodiment, the threshold parameters of different strata should be reasonably set according to the mastered information.

[0076] (5) Similarly, for the known point 1d resistivity solution space Should be mainly determined by the collected rock physical information and the statistical results of electrical logging data, And Set to the sum of the left and right inflection points of the normal distribution curve N(R) and the forward correction term, and the calculation formula is as follows:

[0077]

[0078]

[0079] Where, R ri represents the right inflection point of the normal distribution curve; R li represents the left inflection point of the normal distribution curve, c(R ri ) represents the forward correction term of the right inflection point of the normal distribution curve; c(R li ) represents the forward correction term of the left inflection point of the normal distribution curve; F(R ri ) represents the forward response function of the right inflection point of the normal distribution curve; F(R li ) represents the forward response function of the left inflection point of the normal distribution curve; p represents the actual observation data.

[0080] (6) Combined with the collected 2d seismic profile and the 1d threshold parameter of the known point, the least squares method is used to interpolate other points in the same layer to obtain the threshold parameter of the unknown point. The continuity between different measuring points of the same layer of the 2d model is better, and it is more in line with the geological law. The least squares method formula is as follows:

[0081] Fit=(X T X) -1 X T G

[0082] Where, Fit is the fitting term, X is the measuring line offset distance, and G is the model threshold parameter.

[0083] Step two:

[0084] (1) The iteration stopping condition is determined according to the actual production needs, that is, the iteration number and the fitting difference. One of them can stop iteration.

[0085] (2) In order to ensure the accuracy and effectiveness of the inversion results and meet the needs of constructing the objective function, the geological model parameters G should be set first, which is expressed as:

[0086]

[0087] wherein, represents the initial state of the geological model resistivity parameters; represents the initial state of the geological model layer thickness parameters; ran n represents a random number between 0 and 1; represents the minimum value of the geological model resistivity; represents the maximum value of the geological model resistivity; represents the minimum value of the geological model layer thickness; represents the maximum value of the geological model layer thickness.

[0088] (3) A set of models in the geological model solution space is randomly generated using a random function, and the forward response function F(G) is obtained.

[0089] (4) After obtaining the model parameters and the forward results, the objective function can be constructed by combining the collected time-frequency electromagnetic electric field signals Ex and magnetic field signals Hz. The formula of the objective function is as follows:

[0090]

[0091] wherein, G represents the model threshold parameter; grd(G) represents the gradient of the geological model parameters; ξ -1 represents the Lagrange operator; W represents the weight of the time-frequency electromagnetic electric field signals and magnetic field signals; B represents the balance factor; Δ represents the expected fitting difference.

[0092] wherein, the formula of the balance factor B is:

[0093]

[0094] wherein, m is the median function.

[0095] Step three:

[0096] (1) Determine whether the results meet the iteration termination conditions, such as the number of iterations, the fitting difference, etc. The fitting difference delta is calculated using the following formula:

[0097]

[0098] wherein, E x represents the horizontal electric field signal in the observed data; H z represents the vertical magnetic field component in the observed data, and N is the number of observed data.

[0099] If satisfied, the result can be output, if not satisfied, repeat step three (2), (3) and step two iteration until the iteration termination condition is met.

[0100] (2) After the objective function is constructed, the model constraint term Using the following formula:

[0101]

[0102] In the formula, W min is the minimum model weight, G pri is the prior model, and rou(G) is the model roughness.

[0103] (3) Then the model correction amount C G can be obtained:

[0104]

[0105] In the formula, is the model after the a-th iteration, and J is the Jacobian matrix.

[0106] The model can be modified through the model correction amount, and each iteration cycle makes the model more approximate to the true result until the data is gradually fitted or the iteration number is met.

[0107] The model correction uses the following formula:

[0108]

[0109] In the formula, is the model after the a+1-th iteration, S a is the step size of the a-th iteration.

[0110] The following lists an actual case for illustration:

[0111] I. According to step one (1), obtain the data D1 of a certain measuring line in K area, take this data as an example, according to the production demand, confirm the inversion depth as 12km, according to step one (2), synthesize the electrical logging and existing data of the area, and construct the geological model information graph as shown in Figure 2 .

[0112] According to step one (3), the regional background information is sufficient, there are more known electrical logging and rock logging, the resistivity and thickness solution interval of each layer in the model is determined in these points, the thickness change range is generally set to 0.01%-50%; The resistivity solution space is mainly determined by the collected rock physical information and the statistical results of electrical logging data, and the resistivity information is mainly based on the logging deep resistivity curve data, and The sum of the left and right inflection points of the normal distribution curve N(R) of the statistical result and the forward correction term is set.

[0113] In combination with the 2D seismic profile collected and the 1D threshold parameter of the known point, the threshold parameter of the unknown point is obtained by interpolation of other points in the same layer through the least square method.

[0114] II. According to step II (1), the number of iterations is set to 20 times, and the fitting difference is set to 0.05. One of them meets the iteration termination condition, and the result is output.

[0115] The result is judged whether it meets the iteration termination condition, i.e. the number of iterations and the fitting difference.

[0116] The iteration termination condition is not met. Steps III (2) and (3) and step II are repeated to iterate until the iteration termination condition is met.

[0117] The model constraint term is obtained

[0118] The model correction amount C is obtained G ;

[0119] The model is modified by the model correction amount. Each iteration cycle makes the model more approximate to the true result until the data is gradually fitted or the number of iterations is met.

[0120] III. The inversion fitting error is reduced from 0.12 to 0.04 after 20 iterations, which meets the iteration termination condition. The inversion result is gridded and plotted as shown in Figure 3 .

[0121] As shown in Figure 4 , in the embodiment, compared with the traditional nonlinear free inversion result, the resolution of the present application is significantly improved, i.e. the inversion result graph obtained by the present application is more clear and accurate in layering, and the layered medium characteristics of the Mesozoic and below are more obvious. Therefore, the present application is more conducive to subsequent geological interpretation work and meets the precision requirements of actual production. Compared with the prior art, the present application has outstanding substantial features and significant progress.

[0122] The above embodiment is only a preferred embodiment of the present application, and is not a limitation on the protection scope of the present application. Any design principle of the present application and changes made on the basis of non-creative labor shall be within the protection scope of the present application.

Claims

1. A time-frequency electromagnetic resistivity inversion method based on resistivity and layer thickness threshold model constraints, characterized in that, Includes the following steps: Determine the inversion depth, collect geological information of the target area, construct a geological model, and obtain a two-dimensional geological model information map of the inversion profile; Based on physical properties, inverted stratigraphic information is determined, and the thickness and resistivity of the electrical layer in the target area are determined, thus forming the solution space of the geological model; A random set of models is performed in the solution space of the geological model to obtain the forward modeling response function; The objective function is constructed using the forward response function and the geological model; Determine the fitting difference function of the objective function and the preset number of iterations, and obtain the constraint terms of the geological model; The correction amount of the geological model is obtained based on the constraint terms, and then corrected by combining the fitting difference or by the preset number of iterations.

2. The time-frequency electromagnetic resistivity inversion method based on resistivity and layer thickness threshold model constraints according to claim 1, characterized in that, The geological information includes the stratigraphic distribution, stratigraphic thickness range, regional geological structural style, two-dimensional seismic profile, electrical logging data, and stratigraphic rock physical property information within the target area.

3. The time-frequency electromagnetic resistivity inversion method based on resistivity and layer thickness threshold model constraints according to claim 2, characterized in that, Based on physical properties, inverted stratigraphic information is determined, and the thickness and resistivity of the electrical layer in the target area are determined to construct the solution space of the geological model. This includes the following steps: At known points in the target area, the one-dimensional resistivity threshold parameter and layer thickness threshold parameter are determined using electrical logging and physical information from rock samples. The resistivity threshold parameter is set as the sum of the left and right inflection points of the normal distribution curve N(R) of the statistical results and the forward correction term. Its expression is: Among them, R ri R represents the right inflection point of the normal distribution curve. li c(R) represents the left inflection point of the normal distribution curve. ri ) represents the forward correction term at the right inflection point of the normal distribution curve; c(R) li F(R) represents the forward correction term for the left inflection point of the normal distribution curve; ri F(R) represents the forward response function at the right inflection point of the normal distribution curve; li ) represents the forward response function at the left inflection point of the normal distribution curve; p represents the actual observed data; This represents the minimum resistivity of the geological model; This represents the maximum resistivity of the geological model; The least squares method is used to interpolate the remaining points at the same stratum within the geological model. The expression is as follows: Fit=(X T X) -1 X T G Where Fit represents the fitting term; X represents the measurement line offset; X T Let X be the transpose of the X matrix; G represents the model threshold parameter.

4. The time-frequency electromagnetic resistivity inversion method based on resistivity and layer thickness threshold model constraints according to claim 1, 2, or 3, characterized in that, The objective function for constructing a geological model includes the following steps: Preset parameters of the geological model Its expression is: in, Represents the resistivity parameters of the geological model in its initial state; The geological model layer thickness parameter representing the initial state; ran n Represents a random number between 0 and 1; This represents the minimum resistivity of the geological model; This represents the maximum resistivity of the geological model; This represents the minimum thickness of the geological model layer. This represents the maximum thickness of the geological model layer; By using a random function to randomly select a set of models in the solution space of the geological model for forward modeling, the forward modeling response function F(G) is obtained. The objective function T(G) is constructed by combining the collected time-frequency electromagnetic electric field and magnetic field signals, and its expression is as follows: Where G represents the model threshold parameter; grd(G) represents the gradient of the geological model parameter; ξ -1 denoted by Lagrange operator; W represents the weights of the time-frequency electromagnetic electric field signal and the magnetic field signal; B represents the balance factor; Δ represents the expected fit difference; F(G) represents the forward response function; The weights of the electric field signal; This represents the weight of the magnetic field signal.

5. The time-frequency electromagnetic resistivity inversion method based on resistivity and layer thickness threshold model constraints according to claim 4, characterized in that, The expression for the balance factor B is: Where m represents the median function.

6. The time-frequency electromagnetic resistivity inversion method based on resistivity and layer thickness threshold model constraints according to claim 1, characterized in that, The expression for the fitting difference delta is: Among them, E x H represents the horizontal electric field signal in the observed data. z denoted by , representing the vertical magnetic field component in the observation data; N represents the number of observation data; F(G) represents the forward response function.

7. The time-frequency electromagnetic resistivity inversion method based on resistivity and layer thickness threshold model constraints according to claim 5, characterized in that, Obtain the constraint terms of the geological model Its expression is: Among them, w min G represents the minimum model weights; pri represents the prior model; rou(G) represents the model roughness.

8. The time-frequency electromagnetic resistivity inversion method based on resistivity and layer thickness threshold model constraints according to claim 7, characterized in that, The correction amount C of the geological model is obtained based on the constraints. G Its expression is: Where J represents the Jacobian matrix; E represents the model revised after the a-th iteration; x H represents the horizontal electric field signal in the observed data. z This represents the vertical magnetic field component in the observation data.

9. The time-frequency electromagnetic resistivity inversion method based on resistivity and layer thickness threshold model constraints according to claim 8, characterized in that, The corrected expression for the geological model is: in, S represents the model corrected after the (a+1)th iteration; a This represents the step size of the a-th iteration.

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