Frequency electromagnetic sounding inversion method and device based on smooth resistivity model

Abstract

The application discloses a frequency electromagnetic sounding inversion method based on a smooth resistivity model, and comprises the following steps: constructing a resistivity initial model; constructing a frequency electromagnetic sounding inversion initial objective function f(X) 0 based on the resistivity initial model; 0 processing the initial objective function f(X) 0 to obtain a final objective function f(X); constructing a final iteration formula of a resistivity vector variable X according to the final objective function f(X); and performing iteration inversion calculation based on the final iteration formula X k+1 ; judging whether the final iteration formula X k+1 satisfies a predetermined condition of terminating iteration inversion; if the predetermined condition is satisfied, the inversion is ended, and the resistivity vector variable X is output. The application uses a smooth resistivity model between different geological units as a geophysical electrical initial model, constructs a composite inversion objective function with model roughness, and uses a quasi-Newton inversion BFGS algorithm to obtain a Hessian matrix, so that the purpose of quickly obtaining the electrical layer electrical distribution characteristics by using a simple method is achieved.

Description

Technical Field

[0001] This invention relates to the field of frequency (domain) electromagnetic sounding data processing, inversion and interpretation technology, and in particular to a frequency electromagnetic sounding inversion method and apparatus based on a smooth resistivity model. Background Technology

[0002] Frequency electromagnetic sounding is a frequency-domain artificial source electromagnetic sounding method. It utilizes a grounded horizontal current source or a vertical magnetic dipole source formed by an ungrounded loop as the field source. Within a certain range, it measures the electric, magnetic, or electromagnetic field components, and analyzes the resistivity distribution characteristics of the strata through the observed electric and magnetic field components to infer and interpret the occurrence morphology of underground geological bodies. As a mature geophysical exploration method, frequency electromagnetic sounding is environmentally friendly, non-destructive, and has high resolution. It is widely used in structural exploration, exploration of metallic and non-metallic mineral deposits, groundwater exploration, and underground space exploration. It is particularly widely applied in the fields of oil and gas, coal-bearing resources, shale gas, metallic mineral exploration, and hydrogeological and environmental geological exploration.

[0003] Observing electric and magnetic field data and analyzing the formation resistivity distribution using appropriate methods is a crucial step in geophysical interpretation. Currently, commonly used analytical methods include apparent resistivity calculation, approximate inversion, and physical electrical parameter model inversion. Apparent resistivity calculation is an inversion method based on the equality of resistivity in half-space, and is simpler than one-dimensional inversion. Physical electrical parameter model inversion, on the other hand, is a complex model inversion, ranging from simple to complex in one-dimensional, two-dimensional, and three-dimensional inversions. Apparent resistivity calculation and approximate inversion methods (such as Bostick inversion in magnetotelluric sounding) form the basis of frequency electromagnetic sounding. The calculation process is unaffected by human factors, and rich interpretation experience helps improve the accuracy of the interpretation. The physical electrical parameter model inversion problem is essentially an optimization problem.

[0004] Because the forward model in frequency electromagnetic sounding is complex, there is no linear relationship between the observed data and the model parameters to be solved; that is, the frequency electromagnetic sounding inversion problem is nonlinear. Generally, the nonlinear forward model is approximated as a linearized, quadratic function around the previous model parameters, and the physical electrical parameters are solved through iterative inversion. Common iterative inversion algorithms, such as damped least squares, steepest descent, conjugate gradient, Newton's method, quasi-Newton methods, and OCCAM inversion, have all been applied in the field of frequency electromagnetic sounding inversion and have achieved certain results. However, each also has its own problems, affecting the speed and accuracy of frequency electromagnetic sounding inversion. Therefore, how to improve the speed and accuracy of frequency electromagnetic sounding inversion is an urgent problem to be solved. Summary of the Invention

[0005] The present invention aims to solve at least one of the above-mentioned technical problems.

[0006] To this end, this invention proposes a frequency electromagnetic sounding inversion method and apparatus based on a smooth resistivity model, which improves the speed and accuracy of frequency electromagnetic sounding inversion by combining the quasi-Newton BFGS algorithm and the OCCAM inversion method.

[0007] According to a first aspect of this application, a frequency electromagnetic sounding inversion method based on a smooth resistivity model is provided, comprising the following steps:

[0008] S100, Construct the initial resistivity model;

[0009] S110. Construct the initial objective function for frequency electromagnetic sounding inversion based on the aforementioned resistivity initial model. The initial objective function for:

[0010]

[0011] In the formula: Represents the model smoothing information item; R Represents the resistivity-roughness matrix; X Represents the resistivity vector variable; It is the resistivity continuity multiplier; This represents the field measurement data item; ; W The signal-to-noise ratio of the observed data; d This represents the preprocessed observation data vectors for each frequency. F(X) Forward model data vector;

[0012] For the initial objective function Process to obtain the final objective function ;

[0013] S120, According to the final objective function Constructing resistivity vector variables X The final iterative And based on the final iterative formula Perform iterative inversion calculations; the final iterative formula for:

[0014]

[0015] In the formula: express The Hessian matrix; express The gradient vector; Indicates the step size factor; m Represents the resistivity continuity multiplier; RRepresents the resistivity-roughness matrix; Indicates the number of iterations;

[0016] S130, Determine the final iterative formula Does the predetermined condition for terminating the iterative inversion meet? If the predetermined condition is met, the inversion ends, and the resistivity vector variable is output. X .

[0017] In the above method, step S110 further includes: processing the field measured data items. exist X=X k Perform a second-order Taylor series expansion at the point, and the expanded result is... for:

[0018]

[0019] In the formula: express gradient vector ; express Hessian matrix ;

[0020]

[0021] After unfolding Substitute into the initial objective function And obtain the final objective function. for:

[0022] .

[0023] In the above method, step S120 further includes: based on the final objective function Obtain the resistivity vector variable X initial iteration for:

[0024]

[0025] Based on the initial iteration Introducing step size factor To obtain the final iterative formula .

[0026] In the above method, step S120 further includes: based on the final iterative formula Perform iterative inversion calculations; initially set k=0 for the initial iteration; corresponding initial... m 0 =2～４ During the iterative inversion process m k+1 =m k / c , c Choose a fixed value between 1.2 and 2; l k Select from (0, 1).

[0027] In the above method, the predetermined condition is the iterative accuracy preset in the initial resistivity model. and maximum number of iterations; if The inversion ends when the maximum number of iterations is reached.

[0028] The above method also includes: if the final iterative formula If the predetermined conditions are not met, then obtain Hessian matrix Iterative for:

[0029]

[0030] In the formula: , ;

[0031] make k=k+1 According to the iterative formula In the Hessian matrix While iterating, repeat step S120.

[0032] According to a second aspect of this application, a frequency electromagnetic sounding inversion device based on a smooth resistivity model is provided, comprising:

[0033] The resistivity initial model building module is used to build the resistivity initial model;

[0034] The final objective function acquisition module is used to construct an initial objective function for frequency electromagnetic sounding inversion based on the resistivity initial model, wherein the initial objective function includes a model smoothing information term and a field measured data term; and to process the initial objective function to obtain the final objective function.

[0035] The final iterative construction module is used to construct the final iterative formula of the resistivity vector variable according to the final objective function, and to perform iterative inversion calculation based on the final iterative formula;

[0036] The inversion end judgment module is used to determine whether the final iterative formula meets the predetermined conditions for terminating the iterative inversion; if the predetermined conditions are met, the inversion ends and the resistivity vector variable is output.

[0037] The aforementioned device also includes:

[0038] The Hessian matrix iteration module is used to obtain the iterative formula of the Hessian matrix of the field measured data item if the final iterative formula does not meet the predetermined conditions; and to iterate the Hessian matrix according to the iterative formula of the Hessian matrix of the field measured data item; and to input the result of each Hessian matrix iteration into the final iterative formula for iterative inversion calculation.

[0039] According to a third aspect of this application, a terminal is provided, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the frequency electromagnetic depth sounding inversion method based on a smooth resistivity model as described above when running the computer program.

[0040] According to a fourth aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored computer program, wherein, when the computer program is run by a processor, it controls the terminal where the storage medium is located to execute the frequency electromagnetic depth sounding inversion method based on a smooth resistivity model described above.

[0041] According to the technical solution provided in this application, it has at least the following beneficial effects: the inversion method combines the advantages of the OCCAM inversion method and the quasi-Newton BFGS method. By optimizing the resistivity and roughness multiplier of the OCCAM inversion method and the step size factor algorithm of the quasi-Newton method, the speed and accuracy of frequency domain electromagnetic sounding inversion can be greatly improved.

[0042] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0043] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0044] Figure 1 This is a flowchart of the inversion method according to an embodiment of the present invention;

[0045] Figure 2 This is a structural block diagram of the inversion device according to an embodiment of the present invention.

[0046] Figure label:

[0047] Module 10 for initial resistivity model construction, Module 11 for obtaining the final objective function, Module 12 for final iterative construction, Module 13 for determining the end of inversion, and Module 14 for iterating the Hessian matrix. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0049] It should be noted that although functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects, not to describe a specific order or sequence. "Several" means one or more; "multiple" means two or more; "greater than," "less than," and "exceeding" are understood to exclude the stated number; "above," "below," and "within" are understood to include the stated number.

[0050] This application aims to provide a frequency electromagnetic sounding inversion method based on a smooth resistivity model, specifically an improved quasi-Newton resistivity model inversion method based on minimizing the resistivity variation of adjacent geological bodies. This inversion method integrates the OCCAM inversion algorithm and the quasi-Newton BFGS algorithm. Starting from the initial objective function of the OCCAM inversion method, it introduces a Newton iteration algorithm to perform a second-order Taylor series expansion (continuous quadratic function approximation) on the relevant data terms in the initial objective function. The quasi-Newton BFGS algorithm is then used to approximate the second derivative of the relevant data terms in the final objective function. This inversion method combines the advantages of the smooth model-constrained OCCAM inversion method and the quasi-Newton BFGS iterative inversion method. The quasi-Newton BFGS algorithm avoids the large amount of computation and storage required for solving the second derivative of the final objective function, and is the most successful method for solving unconstrained nonlinear inversion problems, widely used in big data analysis, deep learning, and geophysical inversion. The OCCAM inversion method is essentially a conjugate gradient method under simple smooth constraints on the model resistivity parameters, obtaining inversion results of relatively smooth resistivity variations of adjacent geological bodies under less demanding conditions.

[0051] like Figure 1 As shown, a first aspect of this application provides a frequency electromagnetic sounding inversion method based on a smooth resistivity model, the inversion method comprising the following steps:

[0052] Step S100: Construct an initial resistivity model.

[0053] In this step, the raw frequency electromagnetic sounding data collected in the field undergoes preprocessing such as filtering, cropping, and static displacement correction. The apparent resistivity at each frequency is calculated, and an initial resistivity model is estimated and established to estimate and establish the resistivity of the spatially defined geological bodies. The resistivity of the initial model is represented by a vector. X 0 represents.

[0054] Step S110: Construct the initial objective function for frequency electromagnetic sounding inversion based on the resistivity initial model. ; and, for the initial objective function Process to obtain the final objective function .

[0055] In this step, an initial objective function for frequency electromagnetic sounding inversion is established, which includes continuous resistivity information of the model (minimum change in adjacent resistivity). Initial objective function The formula is as follows:

[0056]

[0057] (1)

[0058] In the formula: This represents the model smoothing information (i.e., the continuous resistivity information of the model). R Represents the resistivity-roughness matrix; X Represents the resistivity vector variable; It is the resistivity continuity multiplier; This represents the field measurement data item; ; W The signal-to-noise ratio of the observed data; d This represents the preprocessed observation data vectors for each frequency. F(X) This is the forward model data vector.

[0059] In this step, the field measured data items are... exist X = X k Perform a second-order Taylor series expansion at the point, and the expanded result is... for:

[0060] (2)

[0061] In the formula: for The gradient vector is denoted as... ; for The Hessian matrix, denoted as ; X k Indicate The resistivity vector obtained from the next iteration;

[0062] (3)

[0063] (4)

[0064] Expanding the above second-order Taylor series... Substitute into the constructed initial objective function Substituting formulas (2), (3), and (4) into formula (1), we can obtain the final objective function. for:

[0065] (5)

[0066] S120. According to the final objective function Constructing resistivity vector variables X The final iterative formula, and based on the final iterative formula Perform iterative inversion calculations.

[0067] In this step, the final objective function for inversion is constructed. The purpose is to obtain the model resistivity vector variable X , so that the final objective function The value is zero, or a very small value close to zero; then the final objective function is... Find the gradient and set it to zero, i.e., ▽ f(X)=0 ,get

[0068] (6)

[0069] make The above equation is rearranged to obtain

[0070] (7)

[0071] Thus, the resistivity vector variable can be obtained. X initial iteration for:

[0072] (8)

[0073] Compared with Newton's iterative method The comparison is equivalent to the Hessian matrix in Newton's iterative formula. H use Replace. Due to f 1 (X) The second-order Taylor expansion function is only an approximation of the original objective function, and the above iterative formula (8) may cause... To ensure stable iteration, a step size factor is introduced in this step. To obtain the resistivity vector variable X The final iterative for:

[0074] (9)

[0075] Unlike the OCCAM inversion method, m The optimization iteration is not used again to obtain the result. Analysis of previous OCCAM inversion processes... m k The value changes according to the pattern: it is relatively large in the early stages of iteration, gradually decreases in the later stages, and finally approaches zero. Therefore, in this step, based on the final iterative formula... Perform iterative inversion calculations, with the initial iteration set as follows: k=0， Corresponding initial m 0 =2～４ During the iterative inversion process m k+1 =m k / c , c Choosing a fixed value between 1.2 and 2 speeds up the iteration while ensuring the stability of the inversion results. This improves the step size factor of the quasi-Newton iteration method. l k The solution method, considering the case where no step size factor is introduced. l k The value is 1; therefore, when calculating the resistivity vector variable... X During the iteration process, the step size factor l k The selection is made between (0,1), with the principle that... f 1 (X k+1 ) To minimize computation time.

[0076] S130, Determine the final iterative formula Check if the predetermined conditions for terminating the iterative inversion are met; if the predetermined conditions are met, the inversion ends and the resistivity vector variable is output. X .

[0077] In this step, the predetermined condition is the iterative accuracy set in advance during the construction of the initial resistivity model. and the maximum number of iterations; if the final iterative formula During the iteration process, satisfying The inversion ends when the maximum number of iterations is reached. express gradient vector Iteration k+1 The accuracy of subsequent iterations. If the final iterative formula If the predetermined conditions are not met throughout the iteration process, then it is necessary to obtain... Hessian matrix Iterative ,make k=k+1 According to the iterative formula In the Hessian matrix While iterating, repeat step S120. That is, for each Hessian matrix iteration... The result of the iteration is input into the final iterative formula. Perform iterative inversion calculations.

[0078] It should be noted that in the final iterative... The function needs to be calculated during the iteration process. f 1 (X) The second-order partial derivatives are quite complex to calculate. And sometimes... f 1 (X) Hessian matrix H The inability to protect positive definiteness leads to iteration failure. To overcome this problem, similar to the BFGS algorithm in quasi-Newton iterations, an initial Hessian matrix needs to be obtained. H 0 =I For the identity matrix, and obtain f 1 (X) Hessian matrix H Iterative , f 1 (X) Hessian matrix H Through iterative With resistivity vector variable X The Hessian matrix is ​​calculated iteratively. H Iterative for:

[0079] (10)

[0080] In the formula, , .

[0081] like Figure 2 As shown, a second aspect of this application provides a frequency electromagnetic sounding inversion device based on a smooth resistivity model, comprising:

[0082] Resistivity initial model construction module 10 is used to construct the resistivity initial model;

[0083] The final objective function acquisition module 11 is used to construct the initial objective function for frequency electromagnetic sounding inversion based on the resistivity initial model. Initial objective function Includes model smoothing information items and field measured data items ; and, used for the initial objective function Process to obtain the final objective function ;

[0084] Finally, iterative building block 12 is used to build upon the final objective function. Constructing resistivity vector variables X The final iterative And based on the final iterative Perform iterative inversion calculations;

[0085] Inversion End Judgment Module 13 is used to determine the final iterative formula. Check if the predetermined conditions for terminating the iterative inversion are met; if the predetermined conditions are met, the inversion ends and the resistivity vector variable is output. X .

[0086] In some specific embodiments of this application, the initial objective function for:

[0087]

[0088] In the formula: Represents the model smoothing information item; R Represents the resistivity-roughness matrix; X Represents the resistivity vector variable; It is the resistivity continuity multiplier; This represents the field measurement data item; ; W The signal-to-noise ratio of the observed data; d This represents the preprocessed observation data vectors for each frequency. F(X) This is the forward model data vector.

[0089] In some specific embodiments of this application, resistivity vector variable X The final iterative for:

[0090]

[0091] In the formula: express The Hessian matrix; express The gradient vector; Indicates the step size factor; m Represents the resistivity continuity multiplier; R Represents the resistivity-roughness matrix; Indicates the number of iterations.

[0092] In some specific embodiments of this application, the final objective function acquisition module 11 is also used for field measured data items. exist X=X k Perform a second-order Taylor series expansion at the point, and the expanded result is... for:

[0093]

[0094] In the formula: express gradient vector ; express Hessian matrix ;

[0095]

[0096] Furthermore, the final objective function acquisition module 11 is also used to expand the... Substitute into the initial objective function To obtain the final objective function The final objective function for:

[0097] .

[0098] In some specific embodiments of this application, the final iterative construction module 12 is further configured to be based on the final objective function. Obtaining the resistivity vector variable X initial iteration for:

[0099] ;

[0100] Furthermore, the final iterative building block 12 is also used to build upon the initial iterative... Introducing step size factor To obtain the final iterative ;

[0101] Furthermore, the final iterative building block 12 is also used for building upon the final iterative... Perform iterative inversion calculations; initially set k=0 for the initial iteration; corresponding initial... m 0 =2～４ During the iterative inversion process m k+1 =m k / c , c Choose a fixed value between 1.2 and 2; l k Select from (0, 1).

[0102] In some specific embodiments of this application, the predetermined condition is the iterative accuracy preset in the initial resistivity model. and maximum number of iterations; if The inversion ends when the maximum number of iterations is reached.

[0103] In some specific embodiments of this application, a Hessian matrix iteration module 14 is also included, used to determine if the final iterative expression... If the predetermined conditions are not met, then the field measured data items will be obtained. Hessian matrix Iterative ; and, for use based on field measured data items Hessian matrix Iterative Hessian matrix Perform iterations; and, for each Hessian matrix The result of the iteration is input into the final iterative formula. Perform iterative inversion calculations.

[0104] In some specific embodiments of this application, Hessian matrix Iterative for:

[0105]

[0106] In the formula: , .

[0107] A third aspect of this application also provides a terminal, including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it executes the above-described frequency electromagnetic depth sounding inversion method based on a smooth resistivity model.

[0108] Specifically, the processor can be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0109] Specifically, the processor connects to the memory via a bus, which may include pathways for transmitting information. The bus can be a PCI bus or an EISA bus, etc. Buses can be categorized as address buses, data buses, control buses, etc.

[0110] The memory may be ROM or other types of static storage devices that can store static information and instructions, RAM or other types of dynamic storage devices that can store information and instructions, or EEPROM, CD-ROM or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto.

[0111] Optionally, the memory stores the code of the computer program that executes the scheme of this application, and the execution is controlled by the processor. The processor executes the application code stored in the memory to realize the operation of the frequency electromagnetic sounding inversion device based on the smooth resistivity model described above.

[0112] A fourth aspect of this application also provides a computer-readable storage medium, which includes a stored computer program, wherein the computer program, when run by a processor, controls the terminal where the storage medium is located to execute the above-described frequency electromagnetic depth sounding inversion method based on a smooth resistivity model.

[0113] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0114] The above is a detailed description of the preferred embodiments of this application. However, this application is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A frequency electromagnetic sounding inversion method based on a smooth resistivity model, characterized in that, Includes the following steps: S100, Construct the initial resistivity model; S110. Construct the initial objective function for frequency electromagnetic sounding inversion based on the aforementioned resistivity initial model. The initial objective function for: In the formula: Represents the model smoothing information item; R Represents the resistivity-roughness matrix; X Represents the resistivity vector variable; It is the resistivity continuity multiplier; This represents the field measurement data item; ; W The signal-to-noise ratio of the observed data; d This represents the preprocessed observation data vectors for each frequency. F(X) Forward model data vector; For the initial objective function Process to obtain the final objective function ; S120, According to the final objective function Constructing resistivity vector variables X The final iterative And based on the final iterative formula Perform iterative inversion calculations; the final iterative formula for: In the formula: express The Hessian matrix; express The gradient vector; Indicates the step size factor; μ Represents the resistivity continuity multiplier; R Represents the resistivity-roughness matrix; Indicates the number of iterations; S130, Determine the final iterative formula Does the predetermined condition for terminating the iterative inversion meet? If the predetermined condition is met, the inversion ends, and the resistivity vector variable is output. X ; If the final iterative formula If the predetermined conditions are not met, then obtain Hessian matrix Iterative for: In the formula: , ; make k=k+1 According to the iterative formula In the Hessian matrix While iterating, repeat step S120.

2. The inversion method according to claim 1, characterized in that, Step S110 also includes: Field measured data items exist X=X k Perform a second-order Taylor series expansion at the point, and the expanded result is... for: In the formula: express gradient vector ; express Hessian matrix ; After unfolding Substitute into the initial objective function and obtain the final objective function. for: 。 3. The inversion method according to claim 2, characterized in that, Step S120 also includes: Based on the final objective function Obtain the resistivity vector variable X initial iterative formula for: Based on the initial iteration Introducing step size factor To obtain the final iterative formula .

4. The inversion method according to claim 3, characterized in that, Step S120 also includes: Based on the final iterative formula Perform iterative inversion calculations; initially set k=0 for the initial iteration; corresponding initial... μ 0 =2～４ During the iterative inversion process μ k+1 =μ k / c , c Choose a fixed value between 1.2 and 2; λ k Select from (0, 1).

5. The inversion method according to claim 1, characterized in that, The predetermined condition is the iterative accuracy pre-set in the initial resistivity model. and maximum number of iterations; if The inversion ends when the maximum number of iterations is reached.

6. A frequency electromagnetic sounding inversion device based on a smooth resistivity model, characterized in that, include: The resistivity initial model building module is used to build the resistivity initial model; The final objective function acquisition module is used to construct the initial objective function for frequency electromagnetic sounding inversion based on the resistivity initial model. The initial objective function for: In the formula: Represents the model smoothing information item; R Represents the resistivity-roughness matrix; X Represents the resistivity vector variable; It is the resistivity continuity multiplier; This represents the field measurement data item; ; W The signal-to-noise ratio of the observed data; d This represents the preprocessed observation data vectors for each frequency. F(X) Forward model data vector; For the initial objective function Process to obtain the final objective function ; The final iterative building block is used to build upon the final objective function. Constructing resistivity vector variables X The final iterative And based on the final iterative formula Perform iterative inversion calculations; the final iterative formula for: In the formula: express The Hessian matrix; express The gradient vector; Indicates the step size factor; μ Represents the resistivity continuity multiplier; R Represents the resistivity-roughness matrix; Indicates the number of iterations; The inversion end judgment module is used to determine the final iterative formula. Does the predetermined condition for terminating the iterative inversion meet? If the predetermined condition is met, the inversion ends, and the resistivity vector variable is output. X ; The Hessian matrix iteration module is used to obtain the iterative formula of the Hessian matrix of the field measured data item if the final iterative formula does not meet the predetermined conditions; and to iterate the Hessian matrix according to the iterative formula of the Hessian matrix of the field measured data item; and to input the result of each Hessian matrix iteration into the final iterative formula for iterative inversion calculation.

7. A terminal comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor runs the computer program, it executes the frequency electromagnetic depth sounding inversion method based on a smooth resistivity model as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is run by a processor, it controls the terminal where the storage medium is located to execute the frequency electromagnetic depth sounding inversion method based on a smooth resistivity model as described in any one of claims 1 to 5.

Images