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

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

Spatial adaptive non-structured element resistivity tomography method and system

Technical Field

The present application relates to the field of geological exploration technology, and in particular to a spatially adaptive non-structured element resistivity tomography method and system.

Background technique

As the demand for more accurate and higher-resolution underground models continues to increase in the fields of underground resource exploration and environmental monitoring, complex structural geological detection has become an important and challenging issue today. In complex geological environments, the properties of the medium are anisotropic. We use various tools and methods to understand the environment, collect data, and process information in order to make this environment more stable, recognizable, and describable. Electrical resistivity tomography (ERT) is a technique for electrical exploration that is sensitive to geoelectric bodies with different resistivity differences. Due to this characteristic, ERT technology has been widely used in the detection of hydrogeology, geotechnical engineering, mineral resources, environmental engineering, and archaeology. However, considering the complexity and variability of unstructured environments, we need to further improve and optimize the current ERT technology to better meet the needs of detecting complex structural geological conditions.

At present, the existing resistivity tomography methods usually need to assume the underground resistivity distribution, and perform inversion calculations based on the underground resistivity distribution, and finally convert the inversion results into images. The existing technologies mostly use regularized non-structured element unit imaging, but in actual applications, the underground resistivity distribution may be abrupt or discontinuous at different depths. Regularized non-structured elements are difficult to characterize abrupt interfaces or curved surface morphology. Therefore, the resistivity distribution results obtained by the existing technology are greatly different from the structural morphology of the geological body, resulting in inaccurate resistivity layer images.

Summary of the invention

The present application provides a spatially adaptive non-structured element resistivity tomography method and system, which has the effect of improving the accuracy of resistivity tomography.

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

Obtaining resistivity and acquisition depths of multiple acquisition layers under complex structural geological conditions, and generating first non-structural elements corresponding to each acquisition layer according to each resistivity and each acquisition depth;

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

Performing an inversion operation on each of the second non-structural elements to obtain a second resistivity of each of the second non-structural elements;

The inversion operation is repeated until the inversion operation reaches a preset iteration termination condition, and an apparent resistivity layer image of the complex structural geological condition is generated according to the second resistivity of each of the second non-structural elements and the acquisition depth.

By adopting the above technical solution, resistivity data is obtained in multiple acquisition layers of heterogeneous strata, and the initial non-structural element is established in combination with the acquisition depth information, so that the distribution of resistivity volume in complex geological environments can be simulated. Then, by using iterative inversion calculation and automatic optimization of non-structural elements, the accuracy of resistivity calculation can be continuously improved, so that the results can more accurately reflect the resistivity distribution state in the stratum. The resistivity calculation results are visualized to generate resistivity tomography images, which are combined with actual geological information to form an intuitive and targeted analytical expression, greatly improving the application ability of resistivity technology in complex environments and improving the accuracy of resistivity tomography.

Optionally, the depth resolution and horizontal resolution of each acquisition layer are matched according to each first resistivity and each acquisition depth; the grid shape is determined according to the distribution position of each first resistivity in each acquisition layer; and the first non-structural element corresponding to each acquisition layer is generated according to the depth resolution, the horizontal resolution and the grid shape.

By adopting the above technical solution and matching the depth resolution and horizontal resolution, the grid node settings can be made to meet the actual measurement conditions, which is conducive to improving the calculation accuracy. Considering the resistivity distribution position, each resistivity body can be covered to avoid important information being ignored. And the grid shape is optimized to achieve reasonable discretization of complex stratigraphic structures. This solution comprehensively considers multiple factors such as measurement characteristics, stratigraphic distribution and calculation needs, which significantly improves the quality of the first non-structural element and lays the foundation for subsequent precise inversion calculations. This refined and intelligent grid generation technology has promoted the innovation of the fusion of resistivity measurement and interpretation and enhanced the adaptability of the method.

Optionally, the initial acquisition point position and the initial depth position in the horizontal direction in each acquisition layer are obtained; the first grid coordinates of each resistivity are determined according to the initial acquisition point position and the horizontal position of the resistivity in each distribution position; the second grid coordinates of each resistivity are determined according to the initial depth position and the depth position of the resistivity in each distribution position; and each resistivity is matched to the corresponding first non-structural element according to each first grid coordinate and each second grid coordinate.

By adopting the above technical solution, when realizing the mapping of resistivity data to the first non-structural element, 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 resistivity value and the non-structural element unit are accurately corresponded by matching the spatial coordinates and the depth coordinates. This matching method fully considers the spatial distribution state of the resistivity and avoids the data errors that may be caused by simple interpolation or overall mapping. The resistivity value can be clearly projected onto the non-structural element unit that reflects its actual distribution position, ensuring the accuracy of the data. This solution improves the fit between the resistivity information and the calculated non-structural element from the data level, so that the resistivity distribution characteristics in complex environments can be mapped to the non-structural element model without distortion, providing a reliable data basis for subsequent precise inversion calculations.

Optionally, the target resistivity of each of the first non-structure elements is determined according to the resistivity in each of the first non-structure elements and the spatial range of the first non-structure element; the surface potential measurement value is determined according to the target resistivity and the acquisition depth; if the surface potential measurement value does not exceed the standard potential value range, the target resistivity is used as the first resistivity.

By adopting the above technical solution, the accuracy and rationality of the first inversion result are fully considered when determining the first resistivity. On the one hand, the target resistivity is set as a constraint to prevent the inversion from deviating from the actual situation; on the other hand, the surface potential is calculated and tested to find the optimal resistivity distribution under the premise of meeting the actual measurement requirements. This calculation method that adds constraints and tests can significantly improve the quality of the first stage inversion, find a first resistivity distribution that is closer to the actual situation, and provide reliable input for the second stage inversion. It avoids the error accumulation caused by simply using the first inversion result.

Optionally, a target difference is determined based on the first resistivity and the resistivity; if the target difference is greater than a preset difference, the first non-structural element is used as the non-structural element to be adjusted; a correction coefficient is determined based on the target difference, and the second non-structural element is determined based on the correction coefficient and the non-structural element to be adjusted.

By adopting the above technical scheme, an active feedback mechanism between the resistivity inversion results and the adjustment of non-structured elements is established. By setting the target difference to judge the accuracy of the first-stage inversion results, the correction coefficient is introduced for quantitative adjustment, and the automatic optimization of the second non-structured element is realized. This inversion result actively feeds back the adjustment of non-structured elements, which can significantly improve the accuracy and automation of resistivity simulation calculations. It no longer relies solely on a single inversion, but incorporates the concept of continuous iterative optimization to make the characterization of resistivity bodies under complex formation conditions more accurate. This scheme fully integrates resistivity inversion and non-structured element modeling technology, promotes the development of resistivity measurement interpretation in the direction of intelligence and refinement, and greatly improves the ability of resistivity technology to adapt to complex geology.

Optionally, a spatial range correction value is determined according to the target difference; and the second non-structure element is generated according to the spatial range correction value and the first non-structure element.

By adopting the above technical solution, an active correction model between the resistivity inversion results and the optimization of the spatial range of non-structural elements was established. By analyzing the inversion error of the first stage, the quantitative correction value of the spatial range is determined, and the coverage range of the second non-structural element is adjusted according to the correction value to achieve automatic optimization of the non-structural element model. This spatial range correction based on inversion feedback can effectively cover each resistivity body in the complex formation with the second structural element, thereby improving the accuracy of subsequent simulation calculations. It avoids the subjective influence of manual judgment and makes the adjustment of the structural element range more intelligent. This solution is an innovative design that deeply integrates resistivity inversion and structural element modeling, which promotes the development of resistivity tomography technology in the direction of refinement and intelligence, and can significantly improve the effect of resistivity application in complex environments.

Optionally, geological information of each of the acquisition layers is obtained; a resistivity distribution sub-image of each of the acquisition layers is generated based on the second resistivity of each of the second non-structural elements and the acquisition depth; and an apparent resistivity layer image of the complex structural geological conditions is generated based on each of the resistivity distribution sub-images and the geological information.

By adopting the above technical solution, on the basis of resistivity calculation, the geological information of each acquisition layer is further obtained, and the calculated three-dimensional resistivity results are expressed as a visual resistivity tomography image combined with geological information. This visual expression makes the resistivity calculation results in complex environments more intuitive and vivid. The spatial distribution of the resistivity body is combined with the geological information of each layer. The image not only has the fine characteristics of the resistivity, but also reflects the actual physical properties, which improves the efficiency and accuracy of the interpretation.

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

A data acquisition module, used to acquire the resistivity and acquisition depth of multiple acquisition layers under complex structural geological conditions, and generate a first non-structural element corresponding to each acquisition layer according to each resistivity and each acquisition depth;

A first inversion module is used to perform an inversion operation on each of the first non-structure elements to obtain a first resistivity of each of the first non-structure elements, and adjust the first non-structure element according to the first resistivity to obtain a second non-structure element;

A second inversion module is used to perform an inversion operation on each of the second non-structure elements to obtain a second resistivity of each of the second non-structure elements;

An image generation module is used to repeat the inversion operation until the inversion operation reaches a preset iteration termination condition, and generate an apparent resistivity layer image of the complex structural geological condition according to the second resistivity of each second non-structural element and the acquisition depth.

A spatially adaptive non-structured element resistivity tomography system comprises a memory, a processor and a program stored in the memory and executable on the processor. The program can realize a spatially adaptive non-structured element resistivity tomography method when loaded and executed by the processor.

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

A computer-readable storage medium stores a computer program, which, when executed by a processor, enables the processor to implement a spatially adaptive non-structured element resistivity tomography method.

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

1. This application obtains resistivity data from multiple acquisition layers in heterogeneous strata, and establishes initial non-structural elements in combination with acquisition depth information, so as to simulate the distribution of resistivity volumes in complex geological environments. Then, by using iterative inversion calculations and automatic optimization of non-structural elements, the accuracy of resistivity calculations can be continuously improved, so that the results can more accurately reflect the resistivity distribution state in the strata. The resistivity calculation results are visualized to generate resistivity tomography images, which are combined with actual geological information to form an intuitive and targeted analytical expression, greatly improving the application ability of resistivity technology in complex environments and improving the accuracy of resistivity tomography.

2. This application not only considers the coordinate information of the initial acquisition point when realizing the mapping of resistivity data to the first non-structure element, but also comprehensively utilizes the specific distribution position of the resistivity in the acquisition layer, and realizes the accurate correspondence between the resistivity value and the non-structure element unit by matching the spatial coordinates and the depth coordinates. This matching method fully considers the spatial distribution state of the resistivity and avoids the data errors that may be caused by simple interpolation or overall mapping. The resistivity value can be clearly projected onto the non-structure element unit that reflects its actual distribution position, ensuring the accuracy of the data. This scheme improves the fit between the resistivity information and the calculated non-structure element from the data level, so that the resistivity distribution characteristics in complex environments can be mapped to the non-structure element model without distortion, providing a reliable data foundation for subsequent precise inversion calculations.

3. This application establishes an active correction model between the resistivity inversion results and the optimization of the spatial range of non-structural elements. By analyzing the inversion error of the first stage, the quantitative correction value of the spatial range is determined, and the coverage range of the second non-structural element is adjusted according to the correction value to achieve automatic optimization of the non-structural element model. This spatial range correction based on inversion feedback can effectively cover each resistivity body in the complex formation with the second structural element, thereby improving the accuracy of subsequent simulation calculations. It avoids the subjective influence of manual judgment and makes the adjustment of the non-structural element range more intelligent. This scheme is an innovative design that deeply integrates resistivity inversion and structural element modeling, which promotes the development of resistivity tomography technology in the direction of refinement and intelligence, and can significantly improve the effect of resistivity application in complex environments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic flow chart of a spatially adaptive non-structured element resistivity tomography method provided in an embodiment of the present application;

FIG2 is a schematic diagram of the structure of a spatially adaptive non-structured element resistivity tomography system disclosed in an embodiment of the present application;

FIG. 3 is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present application.

Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory.

Detailed ways

In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

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

At present, the existing resistivity tomography methods usually need to assume the underground resistivity distribution, and perform inversion calculations based on the underground resistivity distribution, and finally convert the inversion results into images. The existing technologies mostly use regularized non-structured element unit imaging, but in actual applications, the underground resistivity distribution may be abrupt or discontinuous at different depths. Regularized non-structured elements are difficult to characterize abrupt interfaces or curved surface morphology. Therefore, the resistivity distribution results obtained by the existing technology are greatly different from the structural morphology of the geological body, resulting in inaccurate resistivity layer images.

The embodiment of the present application discloses a spatially adaptive non-structured element resistivity tomography method, which obtains the first resistivity and acquisition depth of each acquisition layer in an unstructured geological environment, performs an inversion operation, and then adjusts each first non-structured element according to the inversion result of the inversion operation, and performs an inversion operation, thereby generating a resistivity layer image of the unstructured geological environment according to the inversion result of the inversion operation. It is mainly used to solve the problem that the underground resistivity distribution may be abrupt or discontinuous at different depths. Often, through a simple single inversion calculation, the resistivity distribution results obtained are different, resulting in inaccurate resistivity imaging.

After the above background content is introduced, those skilled in the art can understand the problems existing in the prior art. The technical solutions in the embodiments of the present application will be described in detail below in conjunction with the drawings in the embodiments of the present application. The described embodiments are only part of the embodiments of the present application, not all of the embodiments.

Referring to FIG. 1 , a spatially adaptive non-structured element resistivity tomography method is provided, the method comprising S10 to S40, specifically comprising the following steps:

S10: Obtain resistivity and acquisition depths of multiple acquisition layers under complex structural geological conditions, and generate first non-structural elements corresponding to each acquisition layer according to each resistivity and each acquisition depth.

Among them, complex structural geological conditions refer to the environment in which there are irregular changes in the stratigraphic structure and geological conditions in the geological body. Its main characteristics include: complex stratigraphic structure, irregular folds, fractures and other geological structures. Complex lithology changes, such as interlayers or discontinuous distribution of sandstone, mudstone and other lithologies. Complex groundwater conditions, with multiple types of groundwater such as pore water and fissure water. Mineral, energy and other resources are unevenly distributed, with alternations of rich and poor areas. The physical parameters of geological bodies vary greatly, such as resistivity, density, acoustic wave velocity and other parameters that change dramatically. There is anisotropy in the strata, and geophysical parameters change with direction. The geological scale span is large, and there are multi-scale geological body characteristics. The time effect is significant, and the geological conditions evolve and change with time.

Specifically, through electrical exploration equipment, electrodes are arranged at a certain interval in multiple acquisition layers, currents of different frequencies are transmitted, the potential difference between the layers is measured, and the first resistivity is calculated in combination with the current intensity parameter. The depth coordinates of the electrodes in each layer are recorded as the acquisition depth. Subsequently, according to the distribution range and position information of the measured first resistivity values of each layer, as well as the corresponding acquisition depth, the resistivity change and depth distribution characteristics of each acquisition layer can be determined. Based on these first resistivity and acquisition depth data, the adaptive first non-structural element corresponding to each layer can be generated. The principle of non-structural element generation is to correct and divide the imaging area according to the sensitivity matrix calculated by the conventional grid profile method. The correction method adopts the non-structural finite element method. The grid is subdivided at the location where the resistivity gradient changes violently, and the grid is coarser at the location where the change is gentle. Combined with the acquisition depth, appropriate subdivision is also performed in the vertical direction. The result generated in this way is called the first non-structural element, which can be adaptively adjusted according to the resistivity distribution of each acquisition layer, laying the foundation for subsequent accurate inversion imaging.

Based on the above embodiment, the specific steps of generating the first non-structural element further include S11 to S13:

S11: Matching the depth resolution and horizontal resolution of each acquisition layer according to each resistivity and each acquisition depth.

Among them, depth resolution and horizontal resolution are two key parameters when constructing resistivity non-structural elements, representing the degree of subdivision of non-structural elements in the vertical and horizontal directions.

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

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

Exemplarily, according to the first resistivity data of each layer that has been obtained, the horizontal and vertical gradients are analyzed. When the horizontal gradient is large, it is necessary to improve the horizontal resolution of the layer, that is, to design denser non-structural elements in the horizontal direction to capture the details of the resistivity change. When the vertical gradient is large, it is necessary to improve the vertical resolution and design more subdivided non-structural elements in the vertical direction. According to the resistivity distribution characteristics of each layer, the corresponding optimized depth resolution and horizontal resolution are determined. After matching, at locations where the resistivity changes dramatically, a more refined non-structural element design can be achieved by improving the resolution. This can reduce the inversion error caused by the non-structural elements being too rough and improve the accuracy of imaging. In locations where the changes are relatively gentle, a lower resolution can be used to optimize the total number of non-structural elements and reduce the amount of calculation. Matching depth and horizontal resolution to generate adaptive non-structural elements customized for the geological conditions of each acquisition layer is an important link in achieving accurate resistivity tomography, which can significantly improve the accuracy of the division of non-structural elements in complex environments.

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

For example, according to the distribution range and position coordinates of the resistivity of each layer that have been obtained, the contour morphology of the resistivity distribution can be analyzed. For example, the resistivity has an annular distribution, a layered distribution or an irregular distribution. According to these distribution forms, non-structural elements of different shapes can be defined for matching. For annular distribution, circular or elliptical local dense non-structural elements can be generated; for layered distribution, rod-shaped or strip-shaped non-structural elements can be generated. Taking into account the resistivity distribution morphology of all acquisition layers, a grid shape whose contour conforms to its distribution morphology is designed. The irregular grid generated in this way that conforms to the resistivity distribution morphology can cover the range of resistivity changes to the maximum extent and perform dense approximation at the location where it changes drastically. This can better reflect the true form of the resistivity distribution than regular non-structural elements, improve the accuracy of inversion, and reduce the amount of calculation.

S13: Generate a first non-structural element corresponding to each acquisition layer according to the depth resolution, the horizontal resolution and the grid shape.

For example, in order to comprehensively consider the personalized characteristics of the resistivity distribution of each acquisition layer in an unstructured environment, an adaptive unstructured element customized for the geological conditions of each layer is designed. Only the unstructured element generated in this way can effectively improve the acquisition efficiency and inversion accuracy of resistivity data in a complex environment. The specific process is to optimize the depth resolution, horizontal resolution and grid shape matching the resistivity distribution of each acquisition layer obtained by the analysis of the previous steps. In the first acquisition layer, according to its depth resolution and horizontal resolution requirements, a two-dimensional unstructured element with a contour shape matching its resistivity distribution is generated; in the second acquisition layer, the process is repeated to generate a two-dimensional unstructured element that meets its resolution requirements and distribution morphology; and so on, layer by layer. In the vertical direction, according to the boundary relationship between the layers, the two-dimensional unstructured elements of each layer are stacked to construct a three-dimensional first unstructured element. The unstructured element generated in this way can be customized according to the change law of the geological conditions unique to each acquisition layer, so that the inversion model can achieve high-precision expression of the complex environment. The application of this adaptive unstructured element will greatly improve the effect of resistivity tomography in an unstructured environment.

In an optional embodiment of the present application, there is also a process of adding the first resistivity to the first non-structural element, and the specific steps include S14 to S16:

S14: Acquire the initial acquisition point position and the initial depth position in the horizontal direction in each acquisition layer; determine the first grid coordinates of each resistivity according to the initial acquisition point position and the horizontal position of the resistivity in each distribution position.

For example, when performing resistivity measurement, it is necessary to accurately record the plane coordinates and depth coordinates of the initial acquisition points of each layer to determine the spatial position of each resistivity data point. After the non-structure element is generated, the horizontal coordinates of the resistivity point are used to determine which unit of the non-structure element it is in; and the layer of the non-structure element in which the resistivity is located is determined based on its depth coordinates. Each resistivity point is assigned a certain first grid coordinate. In this way, each resistivity data will be correctly mapped to the corresponding unit of the non-structure element, ensuring that the resistivity distribution information can be fully injected into the non-structure element, laying the foundation for subsequent accurate inversion modeling. This processing can eliminate data registration errors.

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

Exemplarily, the initial depth value of the resistivity in each acquisition layer is obtained based on the measurement. Then, the actual depth position of each resistivity is detected, which may deviate from the initial depth. This depth difference is combined with the depth resolution of the non-structural element to determine which layer of the non-structural element the specific depth position of the resistivity is equivalent to, that is, the depth value of the second grid coordinate. The depth information of the resistivity can be accurately aligned to the corresponding layer of the non-structural element through the second coordinate. This avoids the mapping deviation caused by relying solely on the initial depth, improves the matching accuracy of the resistivity depth distribution to the non-structural element, and is crucial to ensure subsequent accurate inversion. This scheme overcomes the uncertainty of resistivity depth changes in complex environments, realizes accurate three-dimensional mapping, greatly improves the registration effect of resistivity data to non-structural elements, and is a key link in obtaining high-precision results from resistivity tomography.

S16: Match each resistivity to a corresponding first non-structural element according to each first grid coordinate and each second grid coordinate.

For example, according to the first grid coordinates of each measured resistivity point, its corresponding position in the non-structure element is determined; then according to the second grid coordinates, its vertical layer in the non-structure element is determined. Combining these two coordinate information, the three-dimensional unit of each first resistivity corresponding to the non-structure element can be directly matched. Automatic batch processing can be performed to quickly achieve large-scale resistivity to non-structure element matching. In this way, the first resistivity data can be placed in the corresponding unit of the non-structure element without deviation. This avoids the error of manual mapping in complex environments, greatly improves the representation accuracy of resistivity information in non-structure elements, and lays the foundation for subsequent fine inversion modeling.

S20: performing an inversion operation on each first non-structure element to obtain a first resistivity of each first non-structure element, and adjusting the first non-structure element according to the first resistivity to obtain a second non-structure element.

The inversion operation refers to the process of performing the first numerical inversion calculation based on the constructed initial non-structured element in the resistivity tomography method to solve the resistivity distribution.

Specifically, the designed first non-structure element is input into the computer, and the first resistivity of each unit in the non-structure element is solved by numerical calculation according to the first resistivity value of each unit in the non-structure element. This process requires the use of numerical methods such as the finite element method and the finite difference method to perform complex non-structure element calculations. After obtaining the first resistivity, it is necessary to determine whether the first non-structure element needs to be further optimized based on its distribution. If the non-structure elements in the area where the resistivity changes dramatically are not dense enough, it is necessary to locally adjust the non-structure elements to obtain the second non-structure element. This adjustment is the key to achieving the accuracy of the second inversion. Through the first inversion and adjusting the non-structure elements according to its results, the preliminary solution of the resistivity distribution can be achieved, and the non-structure elements can be further optimized and calculated to establish an optimization model for the second inversion and improve the final imaging effect.

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

S21: Determine the target resistivity of each first non-structure element according to each resistivity in each first non-structure element and the spatial range of the first non-structure element.

Exemplarily, an overall analysis is performed based on the initial resistivity values of each cell in the first non-structural element and the spatial range of the non-structural element. Taking the non-structural element as a unit, the resistivity average value of each unit is calculated as the target resistivity. The resistivity target range can also be determined based on the known geological conditions of the site. After setting these target resistivities, when performing the second inversion calculation, the convergence result of the inversion operation is constrained by the target value. This can avoid the accumulation and expansion of errors caused by the first inversion, and make the second inversion result more accurately close to the actual resistivity distribution. By setting the target resistivity, the deviation of the first inversion can be corrected to a certain extent, and the second inversion can be directed to the expected result, which significantly improves the accuracy and reliability of resistivity tomography.

S22: Determine the surface potential measurement value according to the target resistivity and the acquisition depth; if the surface potential measurement value does not exceed the standard potential value range, use the target resistivity as the first resistivity.

Exemplarily, the target resistivity distribution is brought into the pre-established unstructured element model, and the theoretical surface potential distribution is calculated through the aforementioned operation mode according to the acquisition depth. Then, it is compared with the actual surface potential value measured at the corresponding survey line position. If the calculated result meets the error range of the standard potential value, it is considered that the target resistivity calculation is correct and can be used as the second resistivity result. If the comparison finds that it exceeds the error range, it is necessary to readjust the target resistivity and repeat the operation until the measurement value is verified. Through surface potential verification, the accuracy of the second resistivity result can be effectively tested, the accumulation and expansion of inversion errors can be avoided, and the credibility of the results can be greatly improved. This is an important guarantee for the resistivity tomography method to achieve high precision.

Based on the above embodiment, the specific step of determining the second non-structural element further includes S23 to S24:

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

Exemplarily, the second resistivity data is compared point by point with the first resistivity data, and the absolute error or relative error between the two, that is, the target difference, is calculated. If the difference is generally greater than the preset allowable error range, it means that there is a systematic deviation, and the non-structural element model is adjusted to improve the calculation accuracy. When the target difference exceeds the threshold, the first non-structural element will be marked as a non-structural element to be adjusted. Next, it can be checked whether the area where the resistivity changes drastically in the non-structural element has insufficient resolution, so as to improve the horizontal or vertical resolution of these local areas in a targeted manner, add subdivided non-structural element units, and make local adjustments to eliminate previous deviations. Through this automated error detection and non-structural element optimization mechanism, the accumulation of inversion errors can be effectively eliminated, ensuring the accuracy and reliability of resistivity tomography.

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

Exemplarily, the correction coefficient, that is, the amplitude of the non-structural element adjustment this time, is determined according to the size of the target difference. On the basis of the first non-structural element to be adjusted, the correction coefficient is used to determine how to adjust the non-structural element parameters, such as increasing the node density in the horizontal and vertical directions in the local area where the resistivity changes dramatically, performing refined partitioning, or expanding the range of non-structural elements in a certain direction. After this targeted adjustment, the second non-structural element can be obtained. The newly generated second non-structural element can better approximate the resistivity distribution of the formation. Performing a second inversion based on the optimized non-structural element can greatly improve the accuracy of the calculation results. This automatic correction mechanism enables resistivity tomography to be continuously iteratively optimized, thereby achieving high-precision inversion and imaging.

Based on the above embodiment, the specific steps of determining the correction coefficient further include S241 to S243:

S241: Determine the spatial range correction value according to the target difference.

For example, if the target difference is large, it means that the range of the non-structure element is not enough to cover the area of abnormal resistivity change. At this time, a spatial range correction value can be determined according to the difference size, for example, extending 10 meters in a certain direction. Then, based on the range of the first non-structure element, this correction value is expanded to redefine the spatial range of the second non-structure element. If the target difference is small, the range of the non-structure element can be appropriately narrowed to reduce the amount of calculation. Through this process of adjusting the range of the non-structure element according to the error, the spatial boundary error of the inversion can be reduced, which helps to improve the accuracy of the resistivity distribution results.

S242: Generate a second non-structure element according to the spatial range correction value and the first non-structure element.

Exemplarily, after determining the spatial range correction value, the range is expanded or reduced based on the first non-structural element according to the direction and size of the correction value. For example, if it is determined that the expansion is to be 10 meters along the positive direction of the x-axis, the first non-structural element is expanded in this direction, and a non-structural element unit with a range of 10 meters is added. After this targeted adjustment, a second non-structural element with an optimized range can be generated. In this way, the newly generated second non-structural element can more accurately match the spatial distribution range of the resistivity body. Performing resistivity inversion on the non-structural element optimized in this range can greatly reduce the truncation error of the inversion boundary and improve the imaging effect. By automatically adjusting the range of the non-structural element, the adaptive optimization of the model can be achieved, making the imaging of complex resistivity anomalies more accurate.

S30: performing an inversion operation on each second non-structure element to obtain a second resistivity of each second non-structure element.

Specifically, the adjusted and optimized second non-structural element is used as the calculation model. Numerical calculation methods such as the finite element method or the finite difference method are used to perform complex three-dimensional resistivity inversion operations. According to the set target resistivity constraints, the third resistivity value of each non-structural element unit can be obtained through iterative calculation. Since the quality of non-structural elements is improved, the calculation results can truly reflect the three-dimensional distribution of resistivity bodies in the formation, achieving high accuracy and resolution. Through post-processing, clear resistivity tomography profile imaging results can be generated.

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

Among them, the apparent resistivity layer image of complex structural geological conditions refers to the division of the complex and irregular geological environment into layers of different resistivity intervals according to the calculated three-dimensional resistivity distribution, and the generation of resistivity profiles through visualization to intuitively represent the spatial distribution of each resistivity body. The resistivity layer image of the unstructured geological environment reflects the general situation of resistivity distribution under complex geological conditions, can quickly identify resistivity anomalies, and is a key result expression form of resistivity tomography.

Specifically, the purpose of this step is to convert the calculated three-dimensional resistivity distribution results into a clear resistivity profile diagram, which intuitively represents the spatial distribution of different resistivity bodies under complex geological conditions.

Repeat the inversion operation, and detect the second resistivity after each inversion operation. When the error rate between the second resistivity and the standard resistivity is within the preset threshold, it is judged that the preset iteration termination condition is reached. After the inversion operation is completed, the third resistivity data of each second non-structural element unit is automatically read, and the units with similar resistivity are divided into corresponding resistivity intervals according to the acquisition depth. According to the spatial coordinates of the non-structural element unit, the units with different resistivity intervals are rendered on the two-dimensional section plane to form a resistivity profile expressed in color scale. In this way, the distribution effect of complex three-dimensional resistivity heterogeneity on the two-dimensional section can be intuitively expressed. Different colors correspond to different resistivity values and ranges, showing the morphology of the resistivity body under unstructured geological conditions. This imaging result is more intuitive and convenient for subsequent geological analysis.

Based on the above embodiment, the specific steps of generating the resistivity layer image of the unstructured geological environment also include S41 to S42:

S41: Acquire geological information of each acquisition layer; generate a resistivity distribution sub-image of each acquisition layer according to the second resistivity and acquisition depth of each second non-structural element.

Among them, geological information refers to information on the structure, properties, composition, etc. of a certain geographical area or geological body, mainly including the following categories: Stratigraphic information, such as the name, sequence number, lithology, stratigraphic relationship, etc. Structural information, such as folds, faults, intercrystalline cracks and other geological structures.

Exemplarily, geological information such as lithology and physical properties of each acquisition layer in the study area is obtained from the borehole profile. The computer stratifies the third resistivity data in the second non-structural element according to the acquisition depth, and selects the color identification corresponding to the resistivity value in combination with the lithological characteristics of each layer. Generate a resistivity distribution sub-image for each acquisition layer. In this way, the resistivity distribution of different acquisition layers can be clearly seen on the resistivity tomography image, and the geological attributes behind each layer of the image can also be matched, which greatly improves the image's parseability. For example, which colors represent coal seams and which are sandstones. Through this expression method that combines known geological information with resistivity, the resistivity tomography can be made more targeted in analyzing complex environments, providing more practical information for the subsequent development and utilization of geological resources.

S42: Generate an apparent resistivity layer image of complex structural geological conditions based on each resistivity distribution sub-image and each geological information.

For example, the lithology or physical property information of the corresponding strata can be marked in the resistivity distribution sub-images of each acquisition layer. Then, the computer integrates these sub-images into a unified three-dimensional coordinate system and reconstructs a comprehensive resistivity profile according to their spatial position relationship. In this way, both quantitative resistivity results and qualitative geological information are included in a resistivity tomography image. This combination enables the image to have both the high-resolution and fine features of resistivity inversion and the direct readability of geological information. The corresponding relationship between different strata and resistivity bodies is intuitively displayed, greatly improving the accuracy of imaging.

2 , a spatially adaptive non-structured element resistivity tomography system is provided in an embodiment of the present application. The system includes: a data acquisition module, a first inversion module, a second inversion module, and an image generation module, wherein:

A data acquisition module, used to acquire the resistivity and acquisition depth of multiple acquisition layers under complex structural geological conditions, and generate a first non-structural element corresponding to each acquisition layer according to each resistivity and each acquisition depth;

A first inversion module is used to perform an inversion operation on each first non-structure element to obtain a first resistivity of each first non-structure element, and adjust the first non-structure element according to the first resistivity to obtain a second non-structure element;

A second inversion module is used to perform an inversion operation on each second non-structure element to obtain a second resistivity of each second non-structure element;

The image generation module is used to repeat the inversion operation until the inversion operation reaches a preset iteration termination condition, and generate an apparent resistivity layer image of complex structural geological conditions according to the second resistivity and acquisition depth of each second non-structural element.

On the basis of the above embodiment, the data acquisition module is also used to match the depth resolution and horizontal resolution of each acquisition layer according to each first resistivity and each acquisition depth; determine the grid shape according to the distribution position of each first resistivity in each acquisition layer; generate the first non-structural element corresponding to each acquisition layer according to the depth resolution, horizontal resolution and grid shape.

On the basis of the above embodiment, the data acquisition module also includes acquiring the initial acquisition point position and the initial depth position in the horizontal direction in each acquisition layer; determining the first grid coordinates of each resistivity according to each initial acquisition point position and the horizontal position of the resistivity in each distribution position; determining the second grid coordinates of each resistivity according to each initial depth position and the depth position of the resistivity in each distribution position; and matching each resistivity to the corresponding first non-structural element according to each first grid coordinate and each second grid coordinate.

Based on the above embodiments, the first inversion module is also used to determine the target resistivity of each first non-structure element according to the resistivity in each first non-structure element and the spatial range of the first non-structure element; determine the surface potential measurement value according to the target resistivity and the acquisition depth; if the surface potential measurement value does not exceed the standard potential value range, the target resistivity is used as the first resistivity.

Based on the above embodiment, the first inversion module also includes determining a target difference based on 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; according to the target difference, a correction coefficient is determined, and according to the correction coefficient and the non-structural element to be adjusted, a second non-structural element is determined.

Based on the above embodiment, the first inversion module further includes determining a spatial range correction value according to the target difference; and generating a second non-structure element according to the spatial range correction value and the first non-structure element.

Based on the above embodiments, the image generation module is also used to obtain geological information of each acquisition layer; generate a resistivity distribution sub-image of each acquisition layer according to the second resistivity and acquisition depth of each second non-structural element; and generate an apparent resistivity layer image of complex structural geological conditions according to each resistivity distribution sub-image and various geological information.

It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

In the several implementation modes provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple 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 each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims (9)

1. A spatially adaptive non-structured element resistivity tomography method, comprising: Obtaining resistivity and acquisition depths of multiple acquisition layers under complex structural geological conditions, and generating first non-structural elements corresponding to each acquisition layer according to each resistivity and each acquisition depth; Performing an inversion operation on each of the first non-structure elements to obtain a first resistivity of each of the first non-structure elements, and adjusting the first non-structure element according to the first resistivity to obtain a second non-structure element; Performing an inversion operation on each of the second non-structural elements to obtain a second resistivity of each of the second non-structural elements; The inversion operation is repeated until the inversion operation reaches a preset iteration termination condition, and an apparent resistivity layer image of the complex structural geological condition is generated according to the second resistivity of each of the second non-structural elements and the acquisition depth. 2. The spatially adaptive non-structured element resistivity tomography method according to claim 1, characterized in that the step of generating the first non-structured element corresponding to each acquisition layer according to each resistivity and each acquisition depth comprises: According to each of the first resistivities and each of the acquisition depths, matching the depth resolution and the horizontal resolution of each of the acquisition layers; Determining a grid shape according to the distribution position of each of the first resistivities in each of the acquisition layers; The first non-structural elements corresponding to each of the acquisition layers are generated according to the depth resolution, the horizontal resolution and the grid shape. 3. The spatially adaptive non-structured element resistivity tomography method according to claim 2, characterized in that after generating the first non-structured element corresponding to each acquisition layer according to the depth resolution, the horizontal resolution and the grid shape, the method further comprises: Acquire the initial acquisition point position and initial depth position in the horizontal direction of each acquisition layer; Determining first grid coordinates of each of the resistivities according to the positions of each of the initial acquisition points and the horizontal positions of the resistivities in each of the distribution positions; Determining second grid coordinates of each of the resistivities according to each of the initial depth positions and the depth position of the resistivity in each of the distribution positions; According to each of the first grid coordinates and each of the second grid coordinates, each of the resistivities is matched to the corresponding first non-structural element. 4. The spatially adaptive non-structured element resistivity tomography method according to claim 1, wherein the performing an inversion operation on each of the first non-structured elements to obtain the first resistivity of each of the first non-structured elements comprises: determining a target resistivity of each of the first non-structure elements according to each of the resistivities in each of the first non-structure elements and a spatial range of the first non-structure elements; Determining a surface potential measurement value according to the target resistivity and the acquisition depth; If the measured value of the ground surface potential does not exceed the standard potential value range, the target resistivity is used as the first resistivity. 5. The spatially adaptive non-structured element resistivity tomography method according to claim 1, wherein the adjusting the first non-structured element according to the first resistivity to obtain the second non-structured element comprises: determining a target difference 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; A correction coefficient is determined according to the target difference, and the second non-structural element is determined according to the correction coefficient and the non-structural element to be adjusted. 6. The spatially adaptive non-structure element resistivity tomography method according to claim 5, characterized in that the step of determining a correction coefficient according to the target difference, and determining the second non-structure element according to the correction coefficient and the non-structure element to be adjusted comprises: Determining a spatial range correction value according to the target difference; The second non-structure element is generated according to the spatial range correction value and the first non-structure element. 7. The spatially adaptive non-structured element resistivity tomography method according to claim 1, characterized in that the step of generating the apparent resistivity layer image of the complex structural geological condition according to the second resistivity of each second non-structured element and the acquisition depth comprises: Acquiring geological information of each of the acquisition layers; generating a resistivity distribution sub-image of each acquisition layer according to the second resistivity of each second non-structural element and the acquisition depth; An apparent resistivity layer image of the complex structural geological condition is generated according to each of the resistivity distribution sub-images and each of the geological information. 8. A spatially adaptive non-structured element resistivity tomography system, characterized in that the system comprises: A data acquisition module, used to acquire the resistivity and acquisition depth of multiple acquisition layers under complex structural geological conditions, and generate a first non-structural element corresponding to each acquisition layer according to each resistivity and each acquisition depth; A first inversion module is used to perform an inversion operation on each of the first non-structure elements to obtain a first resistivity of each of the first non-structure elements, and adjust the first non-structure element according to the first resistivity to obtain a second non-structure element; A second inversion module is used to perform an inversion operation on each of the second non-structure elements to obtain a second resistivity of each of the second non-structure elements; An image generation module is used to repeat the inversion operation until the inversion operation reaches a preset iteration termination condition, and generate an apparent resistivity layer image of the complex structural geological condition according to the second resistivity of each second non-structural element and the acquisition depth. 9. A computer-readable storage medium, characterized in that the computer-readable storage medium stores instructions, and when the instructions are executed, the steps of the spatially adaptive non-structured element resistivity tomography method according to any one of claims 1 to 7 are executed.