CN117991348A

CN117991348A - Space self-adaptive unstructured element resistivity tomography method and system

Info

Publication number: CN117991348A
Application number: CN202410126376.0A
Authority: CN
Inventors: 尚耀军
Original assignee: Guangdong Heli Engineering Investigation Institute
Current assignee: Guangdong Heli Engineering Investigation Institute
Priority date: 2024-01-30
Filing date: 2024-01-30
Publication date: 2024-05-07

Abstract

A space self-adaptive non-structural element resistivity tomography method and a system thereof relate to the technical field of geological exploration. The method comprises the following steps: acquiring resistivity and acquisition depth of a geological condition of a complex structure in a plurality of acquisition layers, and generating a first non-structural element corresponding to each acquisition layer according to each resistivity and each acquisition depth; performing inversion operation on each first unstructured element to obtain a first resistivity of each first unstructured element, and adjusting the first unstructured elements according to the first resistivity to obtain a second unstructured element; performing inversion operation on each second unstructured element to obtain a second resistivity of each second unstructured element; and analogically, finally generating the apparent resistivity layer image of the geological condition of the complex structure until the iteration is finished. The effect of improving the accuracy of resistivity tomography is achieved.

Description

Space self-adaptive unstructured element resistivity tomography method and system

Technical Field

The application relates to the technical field of geological exploration, in particular to a space self-adaptive non-structural element resistivity tomography method and system.

Background

As the demand for more accurate, higher resolution subsurface models continues to rise in the areas of subsurface resource exploration and environmental monitoring, complex structural geology exploration has become a currently important and challenging problem. In complex geological environments, media properties appear anisotropic, and we understand the environment, collect data, and process information through various tools and methods in order to make such an environment more stable, cognizable, and describable. Resistivity Tomography (ERT) is a technique of electrical exploration that is sensitive to earth volume responses with different resistivity differences. Because of this feature, ERT technology has been widely used in hydrogeology, geotechnical engineering, mineral resources, environmental engineering, and archaeological exploration. However, given the complexity and variability of unstructured environments, there is a need to further refine and optimize current ERT techniques to better accommodate the need for complex structural geologic condition detection

At present, the existing resistivity tomography method generally needs to assume the underground resistivity distribution condition, inversion calculation is carried out according to the underground resistivity distribution condition, finally, inversion results are converted into images, the prior art mostly adopts regularized non-structural element unit imaging, but in practical application, the underground resistivity distribution may have abrupt change or discontinuous conditions at different depths, the regularized non-structural elements are difficult to describe on abrupt interfaces or curved surface forms, and therefore, the resistivity distribution results obtained in the prior art have large differences with the geologic body structural forms, and the resistivity layer images are inaccurate.

Disclosure of Invention

The application provides a space self-adaptive unstructured element resistivity tomography method and a space self-adaptive unstructured element resistivity tomography system, which have the effect of improving resistivity tomography accuracy.

In a first aspect, the present application provides a spatially adaptive unstructured element resistivity tomography method comprising:

Acquiring resistivity and acquisition depth of a geological condition of a complex structure in a plurality of acquisition layers, and generating a first non-structural element corresponding to each acquisition layer according to each resistivity and each acquisition depth;

Performing inversion operation on each first unstructured element to obtain a first resistivity of each first unstructured element, and adjusting the first unstructured elements according to the first resistivity to obtain a second unstructured element;

Performing inversion operation on each second unstructured element to obtain a second resistivity of each second unstructured element;

And repeating the inversion operation until the inversion operation reaches a preset iteration termination condition, and generating a apparent resistivity layer image of the geological condition of the complex structure according to the second resistivity and the acquisition depth of each second non-structural element.

By adopting the technical scheme, the resistivity data is acquired at a plurality of acquisition layers of the heterogeneous stratum, and the initial unstructured element is established by combining the acquisition depth information, so that the simulation of the distribution of the resistivity body in the complex geological environment can be realized. And then, by adopting an iterative inversion calculation and automatic optimization mode of the unstructured elements, the accuracy of resistivity calculation can be continuously improved, and the result can more accurately reflect the resistivity distribution state in the stratum. And the resistivity calculation result is visualized to generate a resistivity tomography image, and the resistivity tomography image is combined with actual geological information to form visual and targeted analytic expression, so that the application capability of the resistivity technology in a complex environment is greatly improved, and the accuracy of the resistivity tomography image is improved.

Optionally, matching a depth resolution and a horizontal resolution of each of the acquisition layers according to each of the first resistivities and each of the acquisition depths; determining a grid shape according to the distribution position of each first resistivity in each acquisition layer; and generating the first unstructured element corresponding to each acquisition layer according to the depth resolution, the horizontal resolution and the grid shape.

By adopting the technical scheme, the depth resolution and the horizontal resolution are matched, so that the grid node setting accords with the actual measurement condition, and the calculation accuracy is improved. Considering the resistivity distribution locations, each resistivity volume may be covered, avoiding important information from being ignored. And grid shape optimization is carried out to realize reasonable discretization of the complex stratum structure. According to the scheme, various factors such as measurement characteristics, stratum distribution, calculation requirements and the like are comprehensively considered, so that the quality of the first non-structural element is obviously improved, and a foundation is laid for the follow-up accurate inversion calculation. The refined and intelligent grid generation technology promotes innovation of fusion of resistivity measurement and explanation, and enhances adaptability of the method.

Optionally, acquiring an initial acquisition point position and an initial depth position in the horizontal direction in each acquisition layer; determining a first grid coordinate of each resistivity according to the initial acquisition point positions and the horizontal positions of the resistivity in the distribution positions; determining second grid coordinates of each of the resistivities according to the initial depth positions and the depth positions of the resistivities in the distribution positions; and matching each resistivity to the corresponding first unstructured element according to each first grid coordinate and each second grid coordinate.

By adopting the technical scheme, when the mapping of the resistivity data to the first unstructured element is realized, not only the coordinate information of the initial acquisition point is considered, but also the specific distribution position of the resistivity in the acquisition layer is comprehensively utilized, and the accurate correspondence of the resistivity value and the unstructured element unit is realized by matching the space coordinate and the depth coordinate. The matching mode fully considers the space distribution state of resistivity, and avoids data errors possibly caused by simple interpolation or integral mapping. The resistivity value can be clearly projected to the non-structural element unit reflecting the actual distribution position, thereby ensuring the accuracy of the data. According to the scheme, the degree of fit between the resistivity information and the calculation unstructured element is improved from the data layer, so that the resistivity distribution characteristics in the complex environment can be mapped into an unstructured element model without distortion, and a reliable data base is provided for the follow-up accurate inversion calculation.

Optionally, determining a target resistivity of each of the first unstructured elements according to each of the resistivity of each of the first unstructured elements and a spatial range of the first unstructured elements; determining a surface potential measurement value according to the target resistivity and the acquisition depth; and if the surface potential measured value does not exceed the standard potential value range, taking the target resistivity as the first resistivity.

By adopting the technical scheme, the accuracy and rationality of the inversion result of the first time are fully considered when the first resistivity is determined. On one hand, setting target resistivity as constraint condition to prevent inversion from deviating from actual condition; on the other hand, the optimal resistivity distribution condition on the premise of meeting the actual measurement requirement is found out through calculation and inspection of the earth surface potential. The calculation mode of adding the constraint and the inspection can obviously improve the quality of the inversion of the first stage, find out the first resistivity distribution which is closer to reality, and provide reliable input for the inversion of the second stage. It avoids the error accumulation caused by the simple use of the first inversion result.

Optionally, determining a target difference from the first resistivity and the resistivity; if the target difference value is larger than a preset difference value, the first non-structural element is used as the non-structural element to be adjusted; and determining a correction coefficient according to the target difference value, and determining the second unstructured element according to the correction coefficient and the unstructured element to be adjusted.

By adopting the technical scheme, an active feedback mechanism between the resistivity inversion result and the non-structural element adjustment is established. And the accuracy of the inversion result in the first stage is judged by setting a target difference value, and a correction coefficient is introduced to perform quantitative adjustment, so that the automatic optimization of the second non-structural element is realized. The inversion result actively feeds back the adjustment mode of the unstructured element, and the accuracy and the automation degree of the resistivity simulation calculation can be remarkably improved. The method does not depend on one-time inversion only, but adds the concept of continuous iterative optimization, so that the description of the resistivity body under the complex stratum condition is more accurate. The scheme fully fuses resistivity inversion and non-structural element modeling technologies, promotes resistivity measurement interpretation to develop to an intelligent and refined direction, and greatly improves the capability of the resistivity technology for adapting to complex geology.

Optionally, determining a spatial range correction value according to the target difference value; and generating the second unstructured element according to the spatial range correction value and the first unstructured element.

By adopting the technical scheme, an active correction model between the resistivity inversion result and the optimization of the non-structural element space range is established. And determining a quantitative correction value of the space range by analyzing the inversion error of the first stage, and adjusting the coverage range of the second non-structural element according to the correction value to realize automatic optimization of the non-structural element model. The space range correction based on inversion feedback can effectively cover each resistivity body in the complex stratum by the second structural element, and the accuracy of subsequent simulation calculation is improved. The method avoids subjective influence of manual judgment, and enables adjustment of the range of the structural element to be more intelligent. The scheme belongs to an innovative design of depth fusion of resistivity inversion and structural element modeling, promotes the development of resistivity tomography technology to the direction of refinement and intellectualization, and can remarkably improve the application effect of resistivity in complex environments.

Optionally, obtaining geological information of each acquisition layer; generating a resistivity distribution sub-image of each acquisition layer according to the second resistivity and the acquisition depth of each second non-structural element; and generating a apparent resistivity layer image of the geological condition of the complex structure according to each resistivity distribution sub-image and each geological information.

By adopting the technical scheme, geological information of each acquisition layer is further acquired on the basis of resistivity calculation, and the three-dimensional resistivity result obtained by calculation is expressed as a visual resistivity tomography image combined with the geological information. The visual expression form ensures that the resistivity calculation result in a complex environment is more visual and vivid. The spatial distribution of the resistivity body is combined with geological information of each layer, and the image has fine characteristics of resistivity and reflects actual physical properties, so that the interpretation efficiency and accuracy are improved.

In a second aspect of the application, a spatially adaptive non-structural element resistivity tomography system is provided.

The data acquisition module is used for acquiring the resistivity and the acquisition depth of the geological condition of the complex structure in a plurality of acquisition layers, and generating a first non-structural element corresponding to each acquisition layer according to each resistivity and each acquisition depth;

The first inversion module is used for carrying out inversion operation on each first unstructured element to obtain a first resistivity of each first unstructured element, and adjusting the first unstructured element according to the first resistivity to obtain a second unstructured element;

the second inversion module is used for carrying out inversion operation on each second unstructured element to obtain a second resistivity of each second unstructured element;

And the image generation module is used for repeating the inversion operation until the inversion operation reaches a preset iteration termination condition, and generating a apparent resistivity layer image of the geological condition of the complex structure according to the second resistivity and the acquisition depth of each second non-structural element.

A spatially adaptive non-structural element resistivity tomography system includes a memory, a processor, and a program stored on the memory and executable on the processor, the program being capable of implementing a spatially adaptive non-structural element resistivity tomography method when loaded and executed by the processor.

In a third aspect of the application, a computer readable storage medium is provided.

A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to implement a spatially adaptive unstructured element resistivity tomography method.

In summary, one or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:

1. According to the application, the resistivity data is acquired from a plurality of acquisition layers of the heterogeneous stratum, and the initial unstructured element is established by combining the acquisition depth information, so that the simulation of the resistivity body distribution in the complex geological environment can be realized. And then, by adopting an iterative inversion calculation and automatic optimization mode of the unstructured elements, the accuracy of resistivity calculation can be continuously improved, and the result can more accurately reflect the resistivity distribution state in the stratum. And the resistivity calculation result is visualized to generate a resistivity tomography image, and the resistivity tomography image is combined with actual geological information to form visual and targeted analytic expression, so that the application capability of the resistivity technology in a complex environment is greatly improved, and the accuracy of the resistivity tomography image is improved.

2. According to the application, when the mapping of the resistivity data to the first unstructured element is realized, not only the coordinate information of the initial acquisition point is considered, but also the specific distribution position of the resistivity in the acquisition layer is comprehensively utilized, and the accurate correspondence of the resistivity value to the unstructured element unit is realized by matching the space coordinate and the depth coordinate. The matching mode fully considers the space distribution state of resistivity, and avoids data errors possibly caused by simple interpolation or integral mapping. The resistivity value can be clearly projected to the non-structural element unit reflecting the actual distribution position, thereby ensuring the accuracy of the data. According to the scheme, the degree of fit between the resistivity information and the calculation unstructured element is improved from the data layer, so that the resistivity distribution characteristics in the complex environment can be mapped into an unstructured element model without distortion, and a reliable data base is provided for the follow-up accurate inversion calculation.

3. The application establishes an active correction model between the resistivity inversion result and the optimization of the non-structural element space range. And determining a quantitative correction value of the space range by analyzing the inversion error of the first stage, and adjusting the coverage range of the second non-structural element according to the correction value to realize automatic optimization of the non-structural element model. The space range correction based on inversion feedback can effectively cover each resistivity body in the complex stratum by the second structural element, and the accuracy of subsequent simulation calculation is improved. The method avoids subjective influence of manual judgment, and enables adjustment of the range of the unstructured element to be more intelligent. The scheme belongs to an innovative design of depth fusion of resistivity inversion and structural element modeling, promotes the development of resistivity tomography technology to the direction of refinement and intellectualization, and can remarkably improve the application effect of resistivity in complex environments.

Drawings

FIG. 1 is a schematic flow chart of a spatially adaptive unstructured element resistivity tomography method provided by an embodiment of the present application;

FIG. 2 is a schematic diagram of a spatially adaptive unstructured element resistivity tomography system according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of an electronic device according to the disclosure.

Reference numerals illustrate: 300. an electronic device; 301. a processor; 302. a communication bus; 303. a user interface; 304. a network interface; 305. a memory.

Detailed Description

In order that those skilled in the art will better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments.

In describing embodiments of the present application, words such as "for example" or "for example" are used to mean serving as examples, illustrations, or descriptions. Any embodiment or design described herein as "such as" or "for example" in embodiments of the application should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "or" for example "is intended to present related concepts in a concrete fashion.

In the description of embodiments of the application, the term "plurality" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating an indicated technical feature. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.

In order to facilitate understanding of the method and system provided by the embodiments of the present application, a description of the background of the embodiments of the present application is provided before the description of the embodiments of the present application.

The embodiment of the application discloses a space self-adaptive unstructured element resistivity tomography method, which comprises the steps of obtaining first resistivity and acquisition depth of each acquisition layer in an unstructured geological environment, performing inversion operation, adjusting each first unstructured element according to an inversion result of the inversion operation, and performing the inversion operation, so that a resistivity layer image of the unstructured geological environment is generated according to the inversion result of the inversion operation. The method is mainly used for solving the problem that the resistivity imaging is inaccurate due to the fact that the difference exists in the resistivity distribution results obtained through simple single inversion calculation under the condition that the underground resistivity distribution possibly has abrupt changes or discontinuities at different depths.

Those skilled in the art will appreciate that the problems associated with the prior art are solved by the present application, and a detailed description of a technical solution according to an embodiment of the present application is provided below, wherein the detailed description is given with reference to the accompanying drawings.

Referring to fig. 1, a spatially adaptive non-structural element resistivity tomography method includes S10 to S40, specifically including the steps of:

S10: and acquiring the resistivity and the acquisition depth of the geological condition of the complex structure in a plurality of acquisition layers, and generating a first non-structural element corresponding to each acquisition layer according to each resistivity and each acquisition depth.

Wherein, the geological condition of the complex structure refers to the environment in which irregularly changing stratum structures and geological conditions exist in the geologic body. The main characteristics of the method include: the stratum structure is complex, and irregular geologic structures such as buckling, fracture and the like exist. Lithology changes are complex, such as lithology interbedded or discontinuous distribution of sandstone, mudstone and the like. The underground water has complex conditions and various types of underground water such as pore water, crevice water and the like. Mineral products, energy sources and other resources are unevenly distributed, and rich areas and lean areas alternate. The physical parameters of the geologic body have large differences, such as the parameters of resistivity, density, sonic velocity and the like, and the parameters have severe changes. There is anisotropy in the formation and the geophysical parameters vary with direction. The geological scale span is large, and multi-scale geological body characteristics exist. The time effect is remarkable, and the geological condition changes along with the time evolution.

Specifically, electrodes are distributed on a plurality of acquisition layers at certain intervals through an electrical prospecting device, currents with different frequencies are transmitted, potential differences among the layers are measured, and a first resistivity is calculated by combining current intensity parameters. The depth coordinates of each layer of electrode are recorded as the acquisition depth. Then, according to the distribution range and the position information of the measured first resistivity value of each layer and the corresponding acquisition depth, the resistivity change condition and the depth distribution characteristic of each acquisition layer can be determined. From these first resistivity and acquisition depth data, an adaptive first unstructured element corresponding to each layer can be generated. The non-structural element generation principle is that the sensitivity matrix calculated according to the conventional grid section method corrects and subdivides an imaging region, the correction method adopts a non-structural finite element method, the position grid with the severe change of the resistivity gradient is subdivided, and the position grid with the gentle change is thicker. In conjunction with the acquisition depth, appropriate subdivision is also made in the vertical direction. The result generated in this way is called a first unstructured element, can be adaptively adjusted according to the resistivity distribution condition of each acquisition layer, and lays a foundation for the follow-up accurate inversion imaging.

On the basis of the above embodiment, the specific step of generating the first unstructured element further includes S11 to S13:

s11: and matching the depth resolution and the horizontal resolution of each acquisition layer according to each resistivity and each acquisition depth.

The depth resolution and the horizontal resolution are two key parameters when the resistivity unstructured element is constructed, and represent the subdivision degree of the unstructured element in the vertical direction and the horizontal direction.

Depth resolution refers to the distance between adjacent layers of non-structural elements in the vertical direction or the number of layers of non-structural elements. The higher the depth resolution, the denser the vertical non-structural elements, and finer vertical structural changes can be identified.

Horizontal resolution refers to the distance or cell side length of non-structural elements adjacent to non-structural element nodes in the horizontal direction. The higher the horizontal resolution, the denser the horizontal non-structural elements, and finer horizontal structural changes can be identified.

Illustratively, the gradient of the change in the horizontal and vertical directions thereof is analyzed from the obtained first resistivity data of each layer. When the horizontal variation gradient is large, it is necessary to increase the horizontal resolution of the layer, i.e. to design denser non-structural elements in the horizontal direction to capture the details of the variation in resistivity. When the vertical variation gradient is large, the vertical resolution needs to be improved, and more sub-divided non-structural elements are designed in the vertical direction. And determining the corresponding optimized depth resolution and horizontal resolution according to the resistivity distribution characteristics of each layer. After matching, finer non-structural element designs can be realized by increasing resolution at locations where resistivity changes dramatically. This can reduce inversion errors due to the excessively rough non-structural elements, and improve imaging accuracy. And in the position with more gentle change, lower resolution can be adopted, so that the total number of non-structural elements is optimized, and the calculated amount is reduced. Matching depth with horizontal resolution, generating self-adaptive non-structural elements customized for geological conditions of each acquisition layer is an important link for realizing accurate resistivity tomography, and can remarkably improve the accuracy of dividing the non-structural elements in a complex environment.

S12: and determining the grid shape according to the distribution position of each resistivity in each acquisition layer.

Illustratively, from the distribution range and position coordinates of the resistivities of the respective layers that have been obtained, the profile morphology of the resistivity distribution can be analyzed. For example, there is a circular distribution, a lamellar distribution, or an irregular distribution of resistivity. According to the distribution forms, non-structural elements with different shapes can be defined for matching. For a circular distribution, a locally dense unstructured element of circular or elliptical shape can be generated; for a layered distribution, rod-like or ribbon-like non-structural elements can be generated. And comprehensively considering the resistivity distribution forms of all the acquisition layers, and designing a grid shape with the outline conforming to the distribution form. The irregular grid conforming to the resistivity distribution form generated in this way can cover the variation range of the resistivity to the maximum extent, and the dense approximation is carried out at the position where the variation is severe. The method can better reflect the real form of resistivity distribution than regular unstructured elements, improves inversion accuracy and can reduce calculation amount.

S13: and generating a first unstructured element corresponding to each acquisition layer according to the depth resolution, the horizontal resolution and the grid shape.

Illustratively, to comprehensively consider the personalized features of resistivity distribution of each acquisition layer in the unstructured environment, an adaptive unstructured element customized for geological conditions of each layer is designed. Only the non-structural elements generated in this way can effectively improve the collection efficiency and inversion accuracy of resistivity data in a complex environment. The specific process is that the optimized depth resolution, horizontal resolution and grid shape matching resistivity distribution of each acquisition layer are obtained according to the analysis of the previous steps. In the first acquisition layer, generating a two-dimensional non-structural element with a contour shape matched with the resistivity distribution according to the depth resolution and horizontal resolution requirements; in the second acquisition layer, repeating the process to generate a two-dimensional non-structural element which accords with the resolution requirement and the distribution form of the two-dimensional non-structural element; with this push, the layer-by-layer design. And stacking the two-dimensional unstructured elements of each layer according to the boundary relation among the layers in the vertical direction to construct a three-dimensional first unstructured element. The non-structural elements generated in this way can be customized according to the change rule of the geological conditions specific to each acquisition layer, so that the inversion model achieves high-precision expression of the complex environment. The application of the self-adaptive unstructured element can greatly improve the effect of resistivity tomography in an unstructured environment.

In an alternative embodiment of the present application, there is also a process of adding the first resistivity to the first unstructured element, the specific steps including S14 to S16:

S14: acquiring an initial acquisition point position and an initial depth position in the horizontal direction in each acquisition layer; and determining the first grid coordinate of each resistivity according to the initial acquisition point positions and the horizontal positions of the resistivity in each distribution position.

Illustratively, in making resistivity measurements, it is necessary to accurately record the plane and depth coordinates of the initial acquisition points of each layer to determine the spatial location of each resistivity data point. After the non-structural element is generated, judging which unit of the non-structural element is in according to the horizontal coordinates of the resistivity point; and determining the layer of the unstructured element with resistivity according to the depth coordinates. Each resistivity point is assigned a determined first grid coordinate. Therefore, each resistivity data can be correctly mapped into the corresponding unit of the unstructured element, the resistivity distribution information can be ensured to be completely filled into the unstructured element, and a foundation is laid for the follow-up accurate inversion modeling. This process may eliminate data registration errors.

S15: and determining the second grid coordinates of each resistivity according to the initial depth positions and the depth positions of the resistivity in each distribution position.

Illustratively, an initial depth value of resistivity at each acquisition layer is obtained from the measurements. Then, the actual depth position of each resistivity is detected, and a certain deviation from the initial depth is possible. And combining the depth difference value with the depth resolution of the unstructured element, and judging that the specific depth position of the resistivity is equivalent to the depth value of the first layer of the unstructured element, namely the second grid coordinate. Depth information of resistivity can be accurately registered into corresponding horizons of the non-structural elements by the second coordinates. The mapping deviation caused by the initial depth is avoided, the matching precision of the resistivity depth distribution to the unstructured elements is improved, and the method is important for ensuring the follow-up accurate inversion. The scheme overcomes the uncertainty of resistivity depth change in a complex environment, realizes accurate three-dimensional mapping, greatly improves the registration effect of resistivity data to unstructured elements, and is a key link for obtaining high-precision results by resistivity tomography.

S16: and matching each resistivity to a corresponding first unstructured element according to each first grid coordinate and each second grid coordinate.

Illustratively, according to the first grid coordinates of each measured resistivity point, judging the corresponding position of each non-structural element; then, the vertical horizon of the non-structural elements is determined according to the second grid coordinates. By integrating the two coordinate information, the three-dimensional units of the unstructured elements corresponding to each first resistivity can be directly matched. Automatic batch processing can be performed, and matching of large-scale resistivity to unstructured elements can be rapidly realized. In this way, the first resistivity data can be inserted into the corresponding cell of the unstructured element without deviation. The method avoids the error of manual mapping in a complex environment, greatly improves the accuracy of the representation of the resistivity information in the unstructured element, and lays a foundation for the subsequent fine inversion modeling.

S20: and carrying out inversion operation on each first unstructured element to obtain a first resistivity of each first unstructured element, and adjusting the first unstructured element according to the first resistivity to obtain a second unstructured element.

The inversion operation refers to a process of solving resistivity distribution by performing a first numerical inversion calculation based on the constructed initial unstructured element in the resistivity tomography method.

Specifically, the designed first unstructured element is input into a computer, and the first resistivity of each unit is solved in a numerical calculation mode according to the first resistivity value of each unit in the unstructured element. The process needs to be applied to numerical methods such as a finite element method, a finite difference method and the like to carry out complex non-structural element calculation. After the first resistivity is obtained, whether the first non-structural element needs to be further optimized or not is judged according to the distribution condition of the first resistivity. If the unstructured elements are not dense enough in the region with severe resistivity changes, local adjustment of the unstructured elements is needed to obtain a second unstructured element. This adjustment is critical to achieving the second inversion accuracy. Through the first inversion and the adjustment of the unstructured elements according to the result, the initial solution of resistivity distribution can be realized, the unstructured elements are further optimized and calculated, an optimized model is built for the second inversion, and the final imaging effect is improved.

On the basis of the above embodiment, the specific step of determining the first resistivity further includes S21 to S22:

S21: and determining the target resistivity of each first unstructured element according to each resistivity in each first unstructured element and the spatial range of the first unstructured element.

Illustratively, the overall analysis is performed based on the initial resistivity values of the cells in the first unstructured element in combination with the spatial extent of the unstructured element. The average value of the resistivity of each cell is calculated as the target resistivity in units of unstructured elements. The resistivity target range may also be determined based on known geological conditions of the field. After setting these target resistivities, the convergence result of the inversion operation is constrained by the target values at the time of performing the second inversion calculation. This avoids the expansion of error accumulation from the first inversion, allowing the second inversion result to more accurately approximate the actual resistivity distribution. By setting the target resistivity, the first inversion deviation can be corrected to a certain extent, the second inversion is guided to the expected result, and the accuracy and the reliability of the resistivity tomography are remarkably improved.

S22: determining a surface potential measured value according to the target resistivity and the acquisition depth; and if the surface potential measured value does not exceed the standard potential value range, taking the target resistivity as the first resistivity.

Illustratively, the target resistivity distribution is put into a pre-established unstructured meta-model, and the theoretical surface potential distribution is calculated according to the acquisition depth situation through the operation mode. Then, it is compared with the surface potential value actually measured at the corresponding line position. If the calculation result accords with the error range of the standard potential value, the target resistivity is calculated correctly and can be used as a second resistivity result. If the comparison finds that the measured value exceeds the error range, the target resistivity needs to be readjusted, and the operation is repeated until the measured value passes the verification. Through earth surface potential verification, the accuracy of the second resistivity result can be effectively verified, inversion error accumulation expansion is avoided, and the reliability of the result is greatly improved. This is an important guarantee for high accuracy of resistivity tomography methods.

On the basis of the above embodiment, the specific step of determining the second unstructured element further includes S23 to S24:

S23: determining a target difference value according to the first resistivity and the resistivity; if the target difference is greater than the preset difference, the first non-structural element is used as the non-structural element to be adjusted.

Illustratively, the second resistivity data is compared point by point with the first resistivity data, and an absolute or relative error, i.e., a target difference, between the two is calculated. If the difference value is generally larger than the preset allowable error range, indicating that the system deviation exists, and adjusting the unstructured meta-model to improve the calculation accuracy. When the target difference exceeds the threshold, the first non-structural element is marked as a non-structural element to be adjusted. Next, it is possible to check whether the regions of the unstructured elements, in which the resistivity changes drastically, are not sufficiently resolved, thereby purposefully increasing the horizontal or vertical resolution of these local regions, and to add subdivided unstructured element elements for local adjustment to eliminate previous deviations. Through the automatic error checking and non-structural element optimizing mechanism, accumulation of inversion errors can be effectively eliminated, and accuracy and reliability of resistivity tomography are guaranteed.

S24: and determining a correction coefficient according to the target difference value, and determining a second non-structural element according to the correction coefficient and the non-structural element to be adjusted.

Illustratively, the correction coefficient, that is, the magnitude of the current non-structural element adjustment, is determined according to the magnitude of the target difference. On the basis of the first unstructured element to be adjusted, determining how to adjust the unstructured element parameters according to the correction coefficient, for example, increasing node density in the horizontal and vertical directions in a local area with severe resistivity change, and carrying out refinement and partitioning or expanding the unstructured element range in a certain direction. After such targeted adjustment, a second unstructured element is obtained. The newly generated second unstructured element may better approximate the resistivity profile of the formation. The accuracy of the calculation result can be greatly improved by carrying out the second inversion on the basis of the optimized non-structural elements. The automatic correction mechanism enables resistivity tomography to be optimized iteratively continuously, so that high-precision inversion and imaging are realized.

On the basis of the above embodiment, the specific step of determining the correction coefficient further includes S241 to S243:

S241: and determining a spatial range correction value according to the target difference value.

Illustratively, if the target difference is large, it is explained that there is a case where the non-structural element range is insufficient to cover the resistivity abnormal change region. A spatial range correction value can then be determined on the basis of the difference, for example by extending 10 meters in a certain direction. The spatial extent of the second unstructured element is then redetermined based on the expansion of the correction value based on the extent of the first unstructured element. If the target difference is smaller, the range of the unstructured element can be properly narrowed, and the calculated amount is reduced. Through the process of adjusting the range of the unstructured element according to the errors, the space boundary errors of inversion can be reduced, and the accuracy of the resistivity distribution result can be improved.

S242: and generating a second unstructured element according to the spatial range correction value and the first unstructured element.

For example, after the spatial range correction value is determined, the range is expanded or contracted in accordance with the direction and the size of the correction value based on the first unstructured element. For example, if it is determined that 10 meters are being extended in the x-axis forward direction, the first unstructured element is extended in that direction, adding an unstructured element unit in the range of 10 meters. After such targeted adjustment, a second unstructured element with optimized range can be generated. In this way, the newly generated second unstructured element can more accurately match the spatial distribution range of the resistivity volume. Resistivity inversion is carried out on the optimized non-structural elements in the range, so that truncation errors of inversion boundaries can be greatly reduced, and imaging effect is improved. By automatically adjusting the range of the unstructured element, the self-adaptive optimization of the model can be realized, so that the imaging of the complex resistivity abnormal body is more accurate.

S30: and carrying out inversion operation on each second unstructured element to obtain the second resistivity of each second unstructured element.

Specifically, the second unstructured element subjected to adjustment optimization is used as a calculation model. And carrying out complex three-dimensional resistivity inversion operation by adopting numerical calculation methods such as a finite element method or a finite difference method. And (3) according to the set target resistivity constraint condition, obtaining a third resistivity value of each unstructured element unit through iterative calculation. As the quality of the unstructured element is improved, the calculation result can truly reflect the three-dimensional distribution condition of the resistivity body in the stratum, and high precision and resolution are achieved. And (3) through post-treatment, a clear resistivity tomography result can be generated.

S40: and repeating the inversion operation until the inversion operation reaches a preset iteration termination condition, and generating a apparent resistivity layer image of the geological condition of the complex structure according to the second resistivity and the acquisition depth of each second non-structural element.

The apparent resistivity layer image of the geological condition of the complex structure refers to dividing the image into layers with different resistivity intervals according to the three-dimensional resistivity distribution obtained by calculation in a complex and irregular geological environment, and generating a resistivity profile through visualization so as to visually represent the spatial distribution form of each resistivity body. The resistivity layer image of the unstructured geological environment reflects the approximate state of resistivity distribution under complex geological conditions, can rapidly identify resistivity abnormal bodies, and is a key result expression form of resistivity tomography.

Specifically, the purpose of this step is to convert the three-dimensional resistivity distribution result obtained by calculation into a clear resistivity profile, and visually represent the spatial distribution of different resistivity bodies under complex geological conditions.

Repeating inversion operation, detecting the second resistivity after each inversion operation, and judging that the preset iteration termination condition is reached when the error rate of the second resistivity and the standard resistivity is within a preset threshold value. After the inversion operation is finished, third resistivity data of each second unstructured element unit are automatically read, and units with similar resistivity are divided into corresponding resistivity intervals according to the acquisition depth. And rendering the units with different resistivity intervals onto a two-dimensional section plane according to the space coordinates of the non-structural element units to form a resistivity section graph expressed by the color gradation. Thus, the distribution effect of the complex three-dimensional resistivity heterosome on the two-dimensional section can be intuitively expressed. The different colors correspond to the values and ranges of different resistivities, exhibiting the morphology of the resistivity volume under unstructured geological conditions. The imaging result is more visual, and the subsequent geological analysis is facilitated.

On the basis of the above embodiment, the specific step of generating a resistivity layer image of the unstructured geological environment further includes S41 to S42:

S41: obtaining geological information of each acquisition layer; and generating a resistivity distribution sub-image of each acquisition layer according to the second resistivity and the acquisition depth of each second unstructured element.

The geological information refers to information on the aspects of structure, property, composition and the like of a certain geographical area or geological body, and mainly comprises the following categories: formation information such as the name, serial number, lithology, formation relationship, etc. of the formation. Structural information such as geologic structures including folds, breaks, and intergranular fissures.

For example, geological information such as lithology and physical properties of each acquisition layer in the investigation region is acquired from a borehole section or the like. And layering third resistivity data in the second unstructured element according to the acquisition depth by the computer, and selecting a color identifier of a resistivity value corresponding to each layer of lithology characteristic. A distributed sub-image of resistivity for each acquisition layer is generated. Therefore, the resistivity distribution conditions of different acquisition layers can be clearly seen on the resistivity tomography image, and the geological properties behind the images of each layer can be correspondingly improved, so that the resolvability of the image is greatly improved. Such as which colors represent coal seams, which are sandstone, etc. By combining the expression means of known geological information and resistivity, the analysis of the resistivity tomography on the complex environment can be more targeted, and more practical information is provided for the development and utilization of subsequent geological resources.

S42: and generating a apparent resistivity layer image of the geological condition of the complex structure according to each resistivity distribution sub-image and each geological information.

For example, lithology or physical information of the corresponding formation may be noted in the resistivity distribution sub-image of each acquisition layer. The computer then integrates the sub-images into a unified three-dimensional coordinate and reconstructs a comprehensive resistivity profile from their spatial positional relationship. Thus, both quantitative resistivity results and qualitative geologic information are contained on one resistivity tomography image. This combination allows the image to have both high resolution fine features of resistivity inversion and direct readability of the geologic information. The corresponding relation between different stratum and resistivity body is intuitively displayed, and the imaging accuracy is greatly improved.

Referring to fig. 2, a spatially adaptive unstructured element resistivity tomography system according to an embodiment of the present application is provided, the system comprising: the system comprises a data acquisition module, a first inversion module, a second inversion module and an image generation module, wherein:

On the basis of the above embodiment, the data acquisition module is further configured to match a depth resolution and a horizontal resolution of each acquisition layer according to each first resistivity and each acquisition depth; determining the grid shape according to the distribution position of each first resistivity in each acquisition layer; and generating a first unstructured element corresponding to each acquisition layer according to the depth resolution, the horizontal resolution and the grid shape.

On the basis of the embodiment, the data acquisition module further comprises an initial acquisition point position and an initial depth position in the horizontal direction in each acquisition layer; determining first grid coordinates of each resistivity according to the initial acquisition point positions and the horizontal positions of the resistivity in each distribution position; determining second grid coordinates of each resistivity according to the initial depth positions and the depth positions of the resistivity in each distribution position; and matching each resistivity to a corresponding first unstructured element according to each first grid coordinate and each second grid coordinate.

On the basis of the above embodiment, the first inversion module is further configured to determine a target resistivity of each first unstructured element according to each resistivity in each first unstructured element and a spatial range of the first unstructured element; determining a surface potential measured value according to the target resistivity and the acquisition depth; and if the surface potential measured value does not exceed the standard potential value range, taking the target resistivity as the first resistivity.

On the basis of the above embodiment, the first inversion module further includes determining a target difference value according to the first resistivity and the resistivity; if the target difference value is larger than the preset difference value, the first non-structural element is used as the non-structural element to be adjusted; and determining a correction coefficient according to the target difference value, and determining a second non-structural element according to the correction coefficient and the non-structural element to be adjusted.

On the basis of the above embodiment, the first inversion module further includes determining a spatial range correction value according to the target difference value; and generating a second unstructured element according to the spatial range correction value and the first unstructured element.

On the basis of the embodiment, the image generation module is further used for acquiring geological information of each acquisition layer; generating a resistivity distribution sub-image of each acquisition layer according to the second resistivity and the acquisition depth of each second unstructured element; and generating a apparent resistivity layer image of the geological condition of the complex structure according to each resistivity distribution sub-image and each geological information.

It should be noted that: in the device provided in the above embodiment, when implementing the functions thereof, only the division of the above functional modules is used as an example, in practical application, the above functional allocation may be implemented by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to implement all or part of the functions described above. In addition, the embodiments of the apparatus and the method provided in the foregoing embodiments belong to the same concept, and specific implementation processes of the embodiments of the method are detailed in the method embodiments, which are not repeated herein.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.

In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, such as a division of units, merely a division of logic functions, and there may be additional divisions in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some service interface, device or unit indirect coupling or communication connection, electrical or otherwise.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in whole or in part in the form of a software product stored in a memory, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method of the various embodiments of the present application. And the aforementioned memory includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a magnetic disk or an optical disk.

The above are merely exemplary embodiments of the present disclosure and are not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure.

This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a scope and spirit of the disclosure being indicated by the claims.

Claims

1. A spatially adaptive unstructured element resistivity tomography method, comprising:

2. The spatially adaptive non-structural element resistivity tomography method of claim 1, wherein the generating a first non-structural element for each acquisition layer based on each resistivity and each acquisition depth comprises:

Matching the depth resolution and the horizontal resolution of each acquisition layer according to each first resistivity and each acquisition depth;

Determining a grid shape according to the distribution position of each first resistivity in each acquisition layer;

And generating the first unstructured element corresponding to each acquisition layer according to the depth resolution, the horizontal resolution and the grid shape.

3. The spatially adaptive unstructured element resistivity tomography method of claim 2, further comprising, after generating the first unstructured element for each acquisition layer based on the depth resolution, the horizontal resolution, and the grid shape:

acquiring an initial acquisition point position and an initial depth position in the horizontal direction in each acquisition layer;

determining a first grid coordinate of each resistivity according to the initial acquisition point positions and the horizontal positions of the resistivity in the distribution positions;

determining second grid coordinates of each of the resistivities according to the initial depth positions and the depth positions of the resistivities in the distribution positions;

And matching each resistivity to the corresponding first unstructured element according to each first grid coordinate and each second grid coordinate.

4. The method of claim 1, wherein inverting each of the first unstructured elements to obtain a first resistivity of each of the first unstructured elements comprises:

determining a target resistivity of each of the first unstructured elements according to each of the resistivity of each of the first unstructured elements and the spatial extent of the first unstructured elements;

determining a surface potential measurement value according to the target resistivity and the acquisition depth;

and if the surface potential measured value does not exceed the standard potential value range, taking the target resistivity as the first resistivity.

5. The spatially adaptive unstructured element resistivity tomography method of claim 1, wherein the adjusting the first unstructured element based on the first resistivity results in a second unstructured element comprising:

determining a target difference value according to the first resistivity and the resistivity;

if the target difference value is larger than a preset difference value, the first non-structural element is used as a non-structural element to be adjusted;

and determining a correction coefficient according to the target difference value, and determining the second unstructured element according to the correction coefficient and the unstructured element to be adjusted.

6. The spatially adaptive unstructured element resistivity tomography method of claim 5, wherein the determining a correction factor based on the target difference and determining the second unstructured element based on the correction factor and the unstructured element to be adjusted comprises:

determining a spatial range correction value according to the target difference value;

And generating the second unstructured element according to the spatial range correction value and the first unstructured element.

7. The spatially adaptive non-structural element resistivity tomography method of claim 1 wherein the generating a apparent resistivity layer image of the complex structural geological condition from the second resistivity and the acquisition depth of each of the second non-structural elements includes:

Obtaining geological information of each acquisition layer;

Generating a resistivity distribution sub-image of each acquisition layer according to the second resistivity and the acquisition depth of each second non-structural element;

and generating a apparent resistivity layer image of the geological condition of the complex structure according to each resistivity distribution sub-image and each geological information.

8. A spatially adaptive unstructured-element resistivity tomography system, the system comprising:

9. A computer readable storage medium storing instructions which, when executed, perform the spatially adaptive non-structural element resistivity tomography method steps of any of claims 1-7.