Single-point inversion method, device, equipment and medium for transverse constraint of adjacent observation points

Abstract

The application provides a single-point inversion method, device, equipment and medium for processing adjacent measuring point lateral constraints of multiple measuring points, and the method comprises the following steps: establishing adjacent measuring point lateral constraint inversion single measuring point objective function, setting initial values for resistivity model vectors of J measuring points, performing multiple iteration solutions on the adjacent measuring point lateral constraint inversion single measuring point objective function, and obtaining the inversion resistivity model vectors of the J measuring points. Each iteration in the multiple iterations comprises: taking the latest iteration resistivity model vectors of adjacent measuring points as lateral constraints, and correcting the resistivity model vectors of the J measuring points in sequence. The application uses the latest iteration results of adjacent measuring points for lateral constraints, and only corrects the resistivity model vectors of a single measuring point each time, so that the sudden change problem of the resistivity model vectors of adjacent measuring points is effectively overcome, the method is not limited by the size of the measuring network, the solving speed is fast, and the accuracy is higher.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration, and in particular to a single-point inversion method, apparatus, equipment, and medium for handling the lateral constraints of adjacent measuring points among multiple measuring points. Background Technology

[0002] Transient electromagnetic methods (TEM) are an important electrical exploration method, widely used in engineering, environmental, hydrological, and energy exploration fields due to their high data accuracy and resolution. Lateral constraints along the survey line or spatial constraints along the survey line or perpendicular to it are currently a crucial transient electromagnetic inversion method, overcoming the unreasonable phenomena often observed in single-point inversions, such as abrupt changes in adjacent point models and deep stripe anomalies.

[0003] However, using lateral or spatial constraints for inversion requires integrated inversion of all survey lines (two-dimensional survey lines) or all survey points in the work area (three-dimensional survey network). This results in a large number of equations to be solved, high requirements for computing equipment, and inconvenience for on-site data processing. This problem is particularly prominent when the work area is large and there are many survey points, and it may even be difficult to perform lateral constraint inversion solutions for the entire area. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention provide a single-point inversion method, apparatus, device and medium for processing the lateral constraints of adjacent measuring points of multiple measuring points, so as to overcome the above problems or at least partially solve the above problems.

[0005] A first aspect of this invention provides a single-point inversion method for processing lateral constraints between adjacent measurement points of multiple measurement points, comprising: Establish the objective function of a single measuring point by inverting the lateral constraints of adjacent measuring points for J measuring points; Set initial values ​​for the resistivity model vectors of the J measurement points respectively; The objective function of a single measuring point is solved iteratively multiple times by inverting the lateral constraint of adjacent measuring points of J measuring points to obtain the inverted resistivity model vector of J measuring points; Each iteration in the multiple iterations includes: using the resistivity model vector of the most recent iteration of the adjacent measuring points as the lateral constraint, and sequentially correcting the resistivity model vectors of the J measuring points.

[0006] Optionally, establish the objective function for single-point inversion based on the lateral constraints of adjacent measuring points of J measuring points, including: In the objective function of the single measurement point inversion by the lateral constraint of the adjacent measurement points, the model difference term of multiple adjacent measurement points of the single measurement point is added to impose lateral constraints on the single measurement point.

[0007] Optionally, during the i-th iteration, the resistivity model vectors of the J measuring points are sequentially modified using the most recent iteration's resistivity model vectors of adjacent measuring points as lateral constraints, including: Using the resistivity model vector of the most recent iteration of the adjacent measuring points as the lateral constraint, calculate the correction amount of the resistivity model vector of the j-th measuring point; Based on the correction amount corresponding to the j-th measurement point, the resistivity model vector of the j-th measurement point is corrected, and the fitting error of the j-th measurement point is calculated. Based on the change in the fitting error of the j-th measuring point, determine the resistivity model vector of the j-th measuring point in the (i+1)-th iteration process; The resistivity model vector at the (j+1)th measurement point is corrected.

[0008] Optionally, based on the change in the fitting error of the j-th measuring point, the resistivity model vector of the j-th measuring point in the (i+1)-th iteration is determined, including: When the fitting error at the j-th measurement point decreases, the corrected resistivity model vector at the j-th measurement point is used as the resistivity model vector at the j-th measurement point in the (i+1)-th iteration process. If the fitting error at the j-th measurement point does not decrease, the resistivity model vector of the j-th measurement point in the (i-1)-th iteration process is used as the resistivity model vector of the j-th measurement point in the (i+1)-th iteration process.

[0009] Optionally, after completing the i-th iteration, perform the following steps: Determine whether the iteration termination condition is met; When the iteration termination condition is met, the resistivity model vector of the J measurement points after the i-th iteration is taken as the inverted resistivity model vector of the J measurement points.

[0010] Optionally, after completing the i-th iteration, determine whether the iteration termination condition is met, including: The average fitting error is obtained by averaging the fitting errors of the J measurement points during the i-th iteration. If the average fitting error is less than the preset error, or if i+1 > the total number of iterations n, then the iteration termination condition is determined to be met. If the average fitting error is greater than the preset error, or if i+1 < the total number of iterations n, it is determined that the iteration termination condition is not met, and the (i+1)th iteration is performed.

[0011] A second aspect of the present invention also provides a single-point inversion device for processing lateral constraints of adjacent measuring points of multiple measuring points, comprising: The objective function creation module is used to establish the objective function for the inversion of a single measuring point from the lateral constraints of adjacent measuring points of J measuring points; The initial value module is used to set initial values ​​for the resistivity model vectors of the J measurement points respectively; The iterative solution module is used to perform multiple iterations to solve the objective function of the single measuring point inversion with lateral constraints on adjacent measuring points of J measuring points, so as to obtain the inverted resistivity model vector of J measuring points; each iteration in the multiple iterations includes: using the resistivity model vector of the most recent iteration of the adjacent measuring points as the lateral constraint, and sequentially correcting the resistivity model vector of the J measuring points.

[0012] A third aspect of this invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the single-point inversion method for processing the lateral constraints of adjacent measuring points of multiple measuring points as described in the first aspect of this invention.

[0013] A fourth aspect of this invention also provides a computer-readable storage medium storing a computer program / instructions thereon, characterized in that, when the computer program / instructions are executed by a processor, they implement the single-point inversion method for processing the lateral constraints of adjacent measuring points of multiple measuring points as described in the first aspect of this invention.

[0014] The embodiments of the present invention have the following advantages: In this embodiment of the invention, an objective function for inverting a single measuring point by lateral constraints between adjacent measuring points is established, and this objective function is iteratively solved multiple times to obtain the resistivity model vector after inversion for each measuring point. During each iteration, the most recent iteration result of the adjacent measuring points is used for lateral constraints. One iteration is performed for each measuring point sequentially, and the same method is used for the next iteration. The process terminates when the iteration result reaches the set fitting error or the required number of iterations is reached. Because this embodiment uses the most recent iteration result of the adjacent measuring points for lateral constraints and only corrects the resistivity model vector of a single measuring point each time, its matrix size is small. This effectively overcomes the problem of abrupt changes in the resistivity model vector between adjacent measuring points and is not limited by the size of the measuring network. It significantly reduces the computational requirements for lateral constraint inversion, enabling effective lateral constraint inversion in field data processing with fast solution speed and higher accuracy. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a single-point inversion method for handling lateral constraints between adjacent measuring points of multiple measuring points, provided by an embodiment of the present invention. Figure 2 This is an inversion flowchart of a single-point inversion method for handling lateral constraints between adjacent measuring points of multiple measuring points, provided by an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of a single-point inversion device for processing the lateral constraints of adjacent measuring points of multiple measuring points, provided in an embodiment of the present invention. Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0017] Exemplary embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0018] This invention provides a single-point inversion method for handling lateral constraints between adjacent measuring points of multiple measuring points, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the steps of a single-point inversion method for handling lateral constraints between adjacent measuring points in multiple measuring points, provided by an embodiment of the present invention. Figure 1 As shown, the method includes: Step S101: Establish the objective function of single measurement point by inverting the lateral constraint of adjacent measurement points of J measurement points.

[0019] Before inverting each measurement point, all J measurement points to be inverted need to be randomly numbered and their positions scanned so that subsequent measurement points can be inverted in the order of their numbers. At the same time, the position coordinates of each measurement point are obtained by position scanning. Then, multiple measurement points that are close to each measurement point's position are selected as adjacent measurement points. The number of adjacent measurement points is determined according to actual needs, and the order of measurement point numbers is not directly related to the position distance (for example, the three adjacent measurement points of the second measurement point may be the fourth, sixth, and tenth measurement points).

[0020] In step S101, the objective function for the lateral constraint inversion of adjacent measurement points of J measurement points is established, including: adding model difference terms of multiple adjacent measurement points of a single measurement point to the objective function for the lateral constraint inversion of adjacent measurement points of a single measurement point to perform lateral constraints on the single measurement point.

[0021] In this embodiment, based on the principle of lateral constraints, adjacent measuring points of each measuring point are used as lateral constraints to establish a single-measuring-point objective function for the lateral constraint inversion of adjacent measuring points of J measuring points. The establishment of the single-measuring-point objective function for the lateral constraint inversion of adjacent measuring points includes the following steps: A1: Obtain the observation data of the measuring point under k delay times, and represent it as d.

[0022] Collect k observation data points at k delay times: , ,..., And represent the data as a vector, that is

[0023] in, This is the observation data under the first delay time. This is the observation data under the second delay time. This represents the observation data at the k-th delay time.

[0024] A2: Establish the preset resistivity model vector m for the measuring point.

[0025] The earth is assumed to consist of n layers of dielectric material, and the resistivity of each layer is: , ,..., By taking the logarithm of the resistivity of each dielectric layer and representing it as a vector, a preset resistivity model vector is obtained:

[0026] in, The resistivity of the first dielectric layer. The resistivity of the second dielectric layer. Let be the resistivity of the nth layer of dielectric.

[0027] A3: Based on the principle of least squares and using the interlayer resistivity difference of the model as a regularization term, the objective function for the inversion of a single measuring point with lateral constraints between adjacent measuring points is obtained:

[0028] Where F is the single-point forward modeling operator, C is the data error covariance matrix used for error weighting of the observation data, and u is the regularization factor. , , , This represents the resistivity model vector for adjacent measuring points a, b, and c. As a horizontal constraint factor, , , This represents the distance between adjacent measurement points a, b, and c and the current inversion measurement point. , , This is the inversely weighted lateral constraint factor based on the distance between adjacent measuring points.

[0029] In the aforementioned objective function for single-point inversion with lateral constraints between adjacent measuring points, the terms starting from the third term are lateral constraint terms for adjacent measuring points, i.e., model difference terms for adjacent measuring points. The number of model difference terms for adjacent measuring points depends on the number of adjacent measuring points. Therefore, the single-point objective function for lateral constraint inversion with lateral constraints between adjacent measuring points in this embodiment has a smaller function size compared to the general objective function for lateral inversion. Since each measuring point uses adjacent measuring points for lateral constraints, it overcomes the problem of abrupt changes in the resistivity model vector during conventional single-point inversion calculations.

[0030] Step S102: Set initial values ​​for the resistivity model vectors of the J measurement points respectively.

[0031] Due to the lateral constraint between adjacent measuring points of J measuring points, the inversion objective function for a single measuring point is derived. It is a nonlinear function, and it is difficult to directly calculate the result. The minimum resistivity model vector m is found, so an iterative approach is used to solve for it. During iteration, an initial value m0 needs to be assigned to the resistivity model vector beforehand. The initial resistivity model vector at each point can be the average resistivity model vector of that region.

[0032] Step S103: Iterate and solve the objective function of the single measuring point by the lateral constraint inversion of the adjacent measuring points of the J measuring points multiple times to obtain the inverted resistivity model vector of the J measuring points; Each iteration in the multiple iterations includes: using the resistivity model vector of the most recent iteration of the adjacent measuring points as the lateral constraint, and successively correcting the resistivity model vectors of the J measuring points.

[0033] When performing inversion of a single-point inversion objective function based on lateral constraints of adjacent measuring points, it is necessary to add multiple model difference terms of adjacent measuring points for lateral constraints. However, the resistivity model vectors of adjacent measuring points also need to be inverted, so each measuring point cannot be inverted independently. In this embodiment, a single correction and multiple iterations are used to synchronously invert multiple measuring points. That is, for a certain iteration of inversion of a certain measuring point, the most recent iteration result of the adjacent measuring points is used for lateral constraints. Each measuring point is iterated once in sequence, and then the same method is used for the next iteration of each measuring point until the set fitting error or the number of iterations is reached, and then the process exits. Thus, the single-point inversion method with lateral constraints of adjacent measuring points is realized.

[0034] Furthermore, since the sequential numbering of the J measurement points to be inverted is random, and the iterations are performed sequentially according to the numbering order of the J measurement points, when performing the i-th iteration on a measurement point, the most recent iteration result of the adjacent measurement point used for the lateral constraint may be the i-th iteration result of the adjacent measurement point, or it may be the (i-1)-th iteration result of the adjacent measurement point. For example, when performing the i-th iteration on the 3rd measurement point, if the two adjacent measurement points of the 3rd measurement point are the 1st measurement point and the 6th measurement point, when performing the i-th iteration on the 3rd measurement point, the lateral constraint is the i-th iteration result of the 1st measurement point, and the lateral constraint is the (i-1)-th iteration result of the 6th measurement point.

[0035] In step S103, the resistivity model vector of each measuring point is corrected once for each measuring point. The method for correcting the resistivity model vector of each measuring point specifically includes the following steps: B1: Calculate the vector correction for the resistivity model. .

[0036] The single-point forward operand F is applied to the resistivity model vector. Linearization processing, and let The iterative resistivity model vector correction is obtained. Solve the equation:

[0037] Where A is the single-point forward modeling operator F in the resistivity model vector The Jacobian matrix at the measurement point. Based on the equations for solving the resistivity model vector, the correction amount of the resistivity model vector at the measurement point is calculated. The value of .

[0038] B2: Correct the resistivity model vector according to the resistivity model vector correction amount.

[0039] Initialize the resistivity model vector Add resistivity model vector correction Then, the corrected resistivity model vector m is obtained, i.e. .

[0040] During the iteration process, m is used as a new initial model for further iteration until the lateral constraint inversion of adjacent measurement points yields the single-measurement-point inversion objective function. When the value is minimized, the inverse resistivity model vector for that measurement point is obtained.

[0041] In this embodiment, the equation for solving the correction amount includes resistivity model vectors of multiple adjacent measuring points ( , , Since the resistivity model vectors of adjacent measuring points also need to be inverted, a multi-point synchronous inversion method is adopted, which involves sequentially correcting the resistivity model vectors of each measuring point and then iterating multiple times. That is, for a certain iteration of the inversion of a measuring point, the most recent iteration result of the adjacent measuring points is used for lateral constraint. This process is repeated for each measuring point, and then iterated again until the set fitting error or the number of iterations is reached. This achieves a single-point inversion method with lateral constraints on adjacent measuring points. In this embodiment, the synchronous inversion of multiple measuring points using a single correction and multiple iterations achieves lateral constraint inversion under arbitrary scale network conditions. This means the method is not limited by the size of the measuring area, the number of measuring points, or whether the positions of the measuring points are regular. Furthermore, only the resistivity model vector of a single measuring point is corrected each time, rather than a comprehensive correction of all measuring point resistivity model vectors. This results in a small matrix size, allowing for direct solution methods with high accuracy and stability, such as SVD decomposition, leading to fast solution speed and high accuracy.

[0042] The single correction and multiple iterations of the multi-measurement point synchronous inversion process in this embodiment are combined with Figure 2 To provide further explanation, Figure 2 This is a flowchart of the single-point inversion method for handling lateral constraints between adjacent measuring points in multiple measuring points, as described in this embodiment. The flowchart specifically includes: First, initial values ​​are assigned to the resistivity model vector of the J measurement points obtained from the inversion. , Let J be the initial value for the j-th measurement point, and set a total of N iterations.

[0043] Start the i-th iteration, with the initial i equal to 1.

[0044] Optionally, during the i-th iteration, the resistivity model vectors of the J measuring points are sequentially modified using the most recent iteration's resistivity model vectors of adjacent measuring points as lateral constraints, including: Using the resistivity model vector of the most recent iteration of the adjacent measuring points as the lateral constraint, calculate the correction amount of the resistivity model vector of the j-th measuring point; Based on the correction amount corresponding to the j-th measurement point, the resistivity model vector of the j-th measurement point is corrected, and the fitting error of the j-th measurement point is calculated. Based on the change in the fitting error of the j-th measuring point, determine the resistivity model vector of the j-th measuring point in the (i+1)-th iteration process; The resistivity model vector at the (j+1)th measurement point is corrected.

[0045] In this embodiment, the j-th measurement point is subjected to the i-th iteration, and the resistivity model vector of the most recent iteration of the adjacent measurement point is used as the lateral constraint. The result of the most recent iteration may be the result of the (i-1)-th iteration or the result of the i-th iteration.

[0046] In this embodiment, the correction amount of the resistivity model vector at the j-th measurement point is calculated according to the correction method for the resistivity model vector described in step S103. The resistivity model vector at the j-th measuring point is then corrected, and the corrected resistivity model vector is... .

[0047] In this embodiment, the resistivity model vector of the (i+1)th iteration is determined based on the change in the fitting error. The fitting error is calculated as the error between the forward-calculated value and the actual measured value. The change in error refers to the fitting error of the current i-th iteration being compared with the fitting error of the (i-1)th iteration. Based on the change in error, it is determined whether to update the initial model.

[0048] In this embodiment, after the j-th measuring point completes the i-th resistivity model vector correction, the j+1-th measuring point is corrected for the i-th resistivity model vector in the same way, until all J measuring points have completed the i-th resistivity model vector correction.

[0049] Optionally, based on the change in the fitting error of the j-th measuring point, the resistivity model vector of the j-th measuring point in the (i+1)-th iteration is determined, including: When the fitting error at the j-th measurement point decreases, the corrected resistivity model vector at the j-th measurement point is used as the resistivity model vector at the j-th measurement point in the (i+1)-th iteration process. If the fitting error at the j-th measurement point does not decrease, the resistivity model vector of the j-th measurement point in the (i-1)-th iteration process is used as the resistivity model vector of the j-th measurement point in the (i+1)-th iteration process.

[0050] In this embodiment, when the fitting error of the j-th measurement point in the i-th iteration is compared with the fitting error in the (i-1)-th iteration, if the fitting error decreases, then the initial resistivity model vector of the j-th measurement point is updated, i.e. The model corrected in the i-th iteration is used as the new initial resistivity model vector for the (i+1)-th iteration; if the fitting error does not decrease, the initial resistivity model vector remains unchanged.

[0051] Optionally, after completing the i-th iteration, perform the following steps: Determine whether the iteration termination condition is met; When the iteration termination condition is met, the resistivity model vector of the J measurement points after the i-th iteration is taken as the inverted resistivity model vector of the J measurement points.

[0052] In this embodiment, after all J measuring points have completed the i-th iteration, it is determined whether the result of the i-th iteration satisfies the iteration termination condition. If the iteration termination condition is satisfied, the initial resistivity model vector of the J measuring points after the i-th iteration is output as the final resistivity model vector of the J measuring points.

[0053] Optionally, after completing the i-th iteration, determine whether the iteration termination condition is met, including: The average fitting error is obtained by averaging the fitting errors of the J measurement points during the i-th iteration. If the average fitting error is less than the preset error, or if i+1 > the total number of iterations n, then the iteration termination condition is determined to be met. If the average fitting error is greater than the preset error, or if i+1 < the total number of iterations n, it is determined that the iteration termination condition is not met, and the (i+1)th iteration is performed.

[0054] In this embodiment, after all J test points have completed the i-th iteration, the average fitting error of the J test points is calculated. ,Right now:

[0055] And based on the average fitting error With respect to preset error The iteration ends when the magnitude of ...

[0056] This invention also provides a device for single-point inversion of lateral constraints between adjacent measuring points of multiple measuring points, referring to... Figure 3 , Figure 3 This is a schematic diagram of a single-point inversion device for processing lateral constraints between adjacent measuring points of multiple measuring points, provided in an embodiment of the present invention. Figure 3 As shown, the device includes: The objective function creation module is used to establish the objective function for the inversion of a single measuring point from the lateral constraints of adjacent measuring points of J measuring points; The initial value module is used to set initial values ​​for the resistivity model vectors of the J measurement points respectively; The iterative solution module is used to perform multiple iterations to solve the objective function of the single measuring point inversion with lateral constraints on adjacent measuring points of J measuring points, so as to obtain the inverted resistivity model vector of J measuring points; each iteration in the multiple iterations includes: using the resistivity model vector of the most recent iteration of the adjacent measuring points as the lateral constraint, and sequentially correcting the resistivity model vector of the J measuring points.

[0057] In one embodiment, the iterative solution module includes: The correction calculation module is used to calculate the correction amount of the resistivity model vector of the j-th measuring point, with the resistivity model vector of the most recent iteration of the adjacent measuring points as the lateral constraint. The error calculation module is used to correct the resistivity model vector of the j-th measuring point according to the correction amount corresponding to the j-th measuring point, and to calculate the fitting error of the j-th measuring point. The error judgment module is used to determine the resistivity model vector of the j-th measuring point in the (i+1)-th iteration process based on the change of the fitting error of the j-th measuring point.

[0058] In one embodiment, the error judgment module includes: The model update module is used to, when the fitting error of the j-th measurement point decreases, use the corrected resistivity model vector of the j-th measurement point as the resistivity model vector of the j-th measurement point in the (i+1)-th iteration process; and when the fitting error of the j-th measurement point does not decrease, use the resistivity model vector of the j-th measurement point in the (i-1)-th iteration process as the resistivity model vector of the j-th measurement point in the (i+1)-th iteration process.

[0059] In one embodiment, the iterative solution module further includes: The iteration end judgment module is used to determine whether the iteration end condition is met after the i-th iteration is completed. When the iteration end condition is met, the resistivity model vector of the J measurement points after the i-th iteration is used as the inverted resistivity model vector of the J measurement points.

[0060] This invention also provides an electronic device, with reference to... Figure 4 , Figure 4 This is a schematic diagram of the electronic device proposed in an embodiment of this application. Figure 4As shown, the electronic device 100 includes a memory 110 and a processor 120. The memory 110 and the processor 120 are connected via a bus for communication. The memory 110 stores a computer program that can run on the processor 120, thereby realizing a single-point inversion method for processing the lateral constraints of adjacent measurement points of multiple measurement points disclosed in the embodiments of this application.

[0061] This application also provides a computer-readable storage medium storing a computer program / instruction thereon, which, when executed by a processor, implements a single-point inversion method for processing lateral constraints of adjacent measuring points of multiple measuring points as disclosed in this application.

[0062] This invention provides a method, apparatus, device, and medium for single-point inversion of resistivity models with lateral constraints between adjacent measuring points in a multi-measuring-point system. The method includes: establishing a single-point objective function for lateral constraint inversion of adjacent measuring points for each measuring point; iterating the objective function multiple times to obtain the inverted resistivity model vector for each measuring point; using the most recent iteration result of adjacent measuring points for lateral constraints in each iteration; and repeating the iteration for each measuring point using the same method. The process terminates when the iteration result reaches a set fitting error or the required number of iterations is reached. This invention uses the most recent iteration result of adjacent measuring points for lateral constraints and corrects only the resistivity model vector of a single measuring point each time, rather than correcting the resistivity model vectors of all measuring points as a whole. This results in a smaller matrix size, effectively overcoming the problem of abrupt changes in resistivity model vectors between adjacent measuring points, and is not limited by the size of the measuring network. It significantly reduces the computational requirements for lateral constraint inversion, enabling effective lateral constraint inversion in field data processing with fast solution speed and higher accuracy.

[0063] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0064] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, electronic devices, and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0065] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0067] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0068] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0069] The present invention provides a detailed description of a single-point inversion method, apparatus, device, and medium for processing lateral constraints of adjacent measuring points of multiple measuring points. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, those skilled in the art will recognize that there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A single-point inversion method for handling lateral constraints between adjacent measuring points of multiple measuring points, characterized in that, include: Establish the objective function of a single measuring point by inverting the lateral constraints of adjacent measuring points for J measuring points; Set initial values ​​for the resistivity model vectors of the J measurement points respectively; The objective function of a single measuring point is solved iteratively multiple times by inverting the lateral constraint of adjacent measuring points of J measuring points to obtain the inverted resistivity model vector of J measuring points; Each iteration in the multiple iterations includes: using the resistivity model vector of the most recent iteration of the adjacent measuring points as the lateral constraint, and sequentially correcting the resistivity model vector of the J measuring points; In the i-th iteration, the resistivity model vectors of the J measuring points are sequentially corrected using the most recent iteration's resistivity model vectors of adjacent measuring points as lateral constraints, including: Using the resistivity model vector of the most recent iteration of the adjacent measuring points as the lateral constraint, calculate the correction amount of the resistivity model vector of the j-th measuring point; Based on the correction amount corresponding to the j-th measurement point, the resistivity model vector of the j-th measurement point is corrected, and the fitting error of the j-th measurement point is calculated. Based on the change in the fitting error of the j-th measuring point, determine the resistivity model vector of the j-th measuring point in the (i+1)-th iteration process; The resistivity model vector at the (j+1)th measurement point is corrected; Specifically, based on the change in the fitting error of the j-th measuring point, the resistivity model vector of the j-th measuring point in the (i+1)-th iteration is determined, including: When the fitting error at the j-th measurement point decreases, the corrected resistivity model vector at the j-th measurement point is used as the resistivity model vector at the j-th measurement point in the (i+1)-th iteration process. If the fitting error at the j-th measurement point does not decrease, the resistivity model vector of the j-th measurement point in the (i-1)-th iteration process is used as the resistivity model vector of the j-th measurement point in the (i+1)-th iteration process. After completing the i-th iteration, perform the following steps: Determine whether the iteration termination condition is met; When the iteration termination condition is met, the resistivity model vector of the J measurement points after the end of the i-th iteration is taken as the inverted resistivity model vector of the J measurement points. After completing the i-th iteration, determining whether the iteration termination condition is met includes: The average fitting error is obtained by averaging the fitting errors of the J measurement points during the i-th iteration. If the average fitting error is less than the preset error, or if i+1 > the total number of iterations n, then the iteration termination condition is determined to be met. If the average fitting error is greater than the preset error, or if i+1 < the total number of iterations n, it is determined that the iteration termination condition is not met, and the (i+1)th iteration is performed.

2. The method according to claim 1, characterized in that, Establish the objective function for a single measuring point by inverting the lateral constraints of adjacent measuring points for J measuring points, including: In the objective function of the single measurement point inversion by the lateral constraint of the adjacent measurement points, the model difference term of multiple adjacent measurement points of the single measurement point is added to impose lateral constraints on the single measurement point.

3. A single-point inversion device for processing lateral constraints between adjacent measuring points of multiple measuring points, characterized in that, The single-point inversion method for performing the lateral constraint of adjacent measuring points for processing multiple measuring points as described in claim 1 or 2 includes: The objective function creation module is used to establish the objective function for the inversion of a single measuring point from the lateral constraints of adjacent measuring points of J measuring points; The initial value module is used to set initial values ​​for the resistivity model vectors of the J measurement points respectively; The iterative solution module is used to perform multiple iterations to solve the objective function of the single measuring point inversion with lateral constraints on adjacent measuring points of J measuring points, so as to obtain the inverted resistivity model vector of J measuring points; each iteration in the multiple iterations includes: using the resistivity model vector of the most recent iteration of the adjacent measuring points as the lateral constraint, and sequentially correcting the resistivity model vector of the J measuring points.

4. The apparatus according to claim 3, characterized in that, The iterative solution module includes: The correction calculation module is used to calculate the correction amount of the resistivity model vector of the j-th measuring point, with the resistivity model vector of the most recent iteration of the adjacent measuring points as the lateral constraint. The error calculation module is used to calculate the fitting error of the j-th measurement point; The error judgment module is used to determine the resistivity model vector of the j-th measuring point in the (i+1)-th iteration process based on the change of the fitting error of the j-th measuring point.

5. An electronic device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the single-point inversion method for processing the lateral constraints of adjacent measuring points of multiple measuring points as described in claim 1 or 2.

6. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instruction is executed by the processor, it implements the single-point inversion method for processing the lateral constraints of adjacent measuring points of multiple measuring points as described in claim 1 or 2.

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