Karst space exploration system and method based on multi-source data mutual feedback inversion

Abstract

The invention relates to the technical field of karst space exploration, in particular to a karst space exploration method based on multi-source data mutual feedback inversion. The system comprises a data acquisition module used for acquiring multi-source geological data of a research area; the data preprocessing module is used for preprocessing the collected multi-source geological data; the multi-source data mutual feedback inversion module is used for constructing a geologic model of a karst space, and comprises a background value determination unit used for measuring a bedrock resistivity background value; the interpretation method construction unit is used for performing mutual feedback inversion on the multi-source data, generating a karst structure network in combination with geological information input and an artificial intelligence algorithm, searching an optimal path through hydrogeological simulation, and predicting a karst space; the karst space modeling module is used for constructing a three-dimensional geologic model of a karst space; and the visual output module is used for visually displaying the three-dimensional geologic model of the karst space. According to the technical scheme, the precision, reliability and efficiency of karst space exploration can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of karst space exploration, and in particular to a karst space exploration system and method based on multi-source data mutual feedback inversion. Background Art

[0002] Karst landforms, due to their complex geological structure and unique geomorphic features, have always been a hot topic and a difficult subject in geological research. The exploration of karst space is of great significance for understanding the development mechanism of karst systems, assessing geological disaster risks, and rationally developing groundwater resources. However, traditional karst space exploration methods often rely on single geophysical exploration or geological drilling data. The limitations of these data sources limit the accuracy and reliability of the exploration results.

[0003] In recent years, with the rapid development of geological data acquisition technology, the collection and analysis of multi-source geological data has gradually become a new trend in karst space exploration. Multi-source geological data covers geophysical data (such as resistivity, electromagnetic induction, etc.), geochemical data (such as water quality analysis, soil element content, etc.), drilling data (such as core description, drilling log, etc.) and topographic data (such as elevation, slope, flow direction, etc.), which reflect the geological characteristics and environmental conditions of karst space from different angles.

[0004] Traditional technologies often rely on a single type of data collection method. For example, although geophysical exploration methods alone can obtain certain physical property information underground, they cannot fully reflect the geological picture of karst areas. Drilling data alone can obtain direct information about rock samples, but the sampling points are limited, making it difficult to cover the entire study area, and the cost is high and the efficiency is low. The limitations of this single data source make the geological information obtained incomplete and it is easy to miss important geological features, which will cause large errors in subsequent analysis and decision-making.

[0005] In addition, the traditional geophysical interpretation method has a prominent problem of multi-solution. Due to the lack of effective fusion and mutual feedback inversion of multi-source data, it is difficult to accurately determine the structure and distribution law of the karst space. For example, when determining the background value of bedrock resistivity, the traditional manual steel ruler measurement method has problems such as large electrode distance error and low measurement efficiency, and the rock mass may be damaged during the measurement process, affecting the accuracy of the measurement results. In addition, previous modeling methods are difficult to accurately reflect the channel characteristics and flow paths of the karst space, and cannot provide a reliable basis for further research and utilization of the karst space. Summary of the invention

[0006] The purpose of the present invention is to propose a karst space exploration method based on multi-source data mutual feedback inversion, and the technical solution can improve the accuracy, reliability and efficiency of karst space exploration.

[0007] To achieve the above object, in a first aspect, the present invention provides a karst space exploration system based on multi-source data mutual feedback inversion, including: A data acquisition module for acquiring multi-source geological data of the study area. The data acquisition module includes a geophysical data acquisition unit, a geochemical data acquisition unit, a drilling data acquisition unit, and a topographic and geomorphic data acquisition unit; A data preprocessing module for preprocessing the acquired multi-source geological data; A multi-source data mutual feedback inversion module for realizing the mutual feedback inversion of multi-source data and constructing a geological model of the karst space, including: A background value determination unit for measuring the background value of the bedrock resistivity by using the adaptive small quadrupole method; An interpretation method construction unit takes the background value of the formation resistivity in the study area as a reference value, performs mutual feedback inversion on multi-source data, combines geological information input and artificial intelligence algorithms, generates a karst structure network, searches for the optimal path through hydrogeological simulation, and predicts the karst space; A karst space modeling module for constructing a three-dimensional geological model of the karst space according to the result of the multi-source data mutual feedback inversion module; A visualization output module for visually displaying the three-dimensional geological model of the karst space.

[0008] Advantages of the basic solution: The data acquisition module uses multi-source data acquisition units to cover the acquisition of multi-source geological data such as geophysics, geochemistry, drilling, and topographic and geomorphic data. Geological information of the study area is comprehensively obtained from different angles, avoiding the limitations of a single data source and providing a richer and more accurate data basis for subsequent analysis. By acquiring multi-source geological data, preprocessing it, and performing mutual feedback inversion, the accuracy and reliability of karst space exploration are improved.

[0009] The data preprocessing module ensures the quality and reliability of the data, avoiding interference from incorrect data to subsequent analysis. Data format conversion enables data from different sources to be in a unified format, facilitating integration and analysis. Data standardization eliminates the influence of dimensions and units, enabling different types of data to be compared and comprehensively analyzed under the same standard, improving the accuracy and effectiveness of data analysis.

[0010] Multi-source data mutual feedback inversion module: The adaptive small four-pole method is used to measure the background value of bedrock resistivity. This method has many advantages. For example, it does not require artificial measurement of electrode positions on the bedrock, reducing the electrode distance error of traditional manual steel tape measurement; it avoids the construction process of electric drill boring, improving the measurement work efficiency; at the same time, it ensures the integrity of the rock mass, eliminates the influence of human factors on the measurement results, and can also measure multiple points one by one on the same rock surface, improving the measurement accuracy of the bedrock resistivity background value and providing more accurate basic data for subsequent multi-source data mutual feedback inversion. Interpretation method construction unit: Taking the background value of formation resistivity in the study area as a reference value, mutual feedback inversion of multi-source data is carried out. Using the intelligent processing ability of geological information and artificial intelligence algorithms, in-depth mining and intelligent analysis of multi-source data are carried out to generate a karst structure network, which can more accurately reflect the main characteristics and distribution laws of the karst space. In addition, by searching for the optimal path through hydrogeological simulation, intelligent prediction of the karst space is realized, providing scientific guidance for karst space exploration, and helping to understand the development trend and potential risks of the karst space in advance.

[0011] The karst space modeling module constructs a three-dimensional geological model, which can intuitively display the structure and characteristics of the karst space, including geological structure, lithology distribution, channel characteristics and flow paths, etc., providing a reliable basis for subsequent karst space exploration work, enabling researchers to more clearly understand the internal situation of the karst space and facilitating the formulation of targeted exploration plans.

[0012] The visualization output module can realize dynamic three-dimensional display and interaction, which is more intuitive, improves the exploration efficiency and accuracy, enables researchers to more deeply understand the characteristics of the karst space, and at the same time facilitates communication and cooperation with other relevant personnel, promoting the development of karst space exploration work.

[0013] As an implementable preferred solution, the geophysical data acquisition unit uses seismic exploration and electromagnetic exploration methods to obtain geophysical data of the study area, including seismic wave velocity and electromagnetic wave velocity; The geochemical data acquisition unit uses water sample analysis and soil analysis methods to obtain geochemical data of the study area, including water quality and soil composition; The drilling data acquisition unit uses drilling sampling and core description methods to obtain core samples and drilling data of the study area; The topographic and geomorphic data acquisition unit uses remote sensing images and digital elevation models to obtain topographic and geomorphic data of the study area.

[0014] As an implementable preferred solution, the data preprocessing module includes: The data cleaning unit is used to remove outliers and duplicate values in multi-source geological data; The data format conversion unit is used to convert data from different sources into a unified format; The data standardization unit is used to convert the data into a dimensionless form.

[0015] As an implementable preferred solution, the karst space modeling module adopts digital mapping technology to fuse multi-source data to form a fine geological model and a channel model; The visualization output module adopts virtual reality and / or augmented reality to realize the dynamic display and interactive operation of the three-dimensional geological model.

[0016] As an implementable preferred solution, the data acquisition module further includes a drone inspection unit, which is used to control the drone carrying a high-definition camera and a multi-spectral sensor to inspect the study area, and obtain high-resolution topographic and geomorphic images and vegetation coverage information.

[0017] As an implementable preferred solution, the drone inspection unit is also used to further include a unit for planning flight routes and adjusting the control parameters of the drone according to the geological characteristics and exploration requirements of the study area. The control parameters include flight speed, altitude, and heading angle.

[0018] As an implementable preferred solution, the data preprocessing module also preprocesses and corrects the topographic and geomorphic images and vegetation coverage information collected by the drone inspection, including image denoising, color correction, geometric correction, and performs stitching and fusion.

[0019] As an implementable preferred solution, the multi-source data mutual feedback inversion module is also used to fuse and mutually feedback invert the topographic and geomorphic images and vegetation coverage information obtained by the drone inspection with the original multi-source geological data.

[0020] As an implementable preferred solution, the data acquisition module further includes a method optimization unit, which is used to construct a detailed database of geophysical and geochemical exploration methods; for different scenarios, select geophysical and geochemical exploration methods and their combined applications.

[0021] In a second aspect, the present invention also provides a karst space exploration method based on multi-source data mutual feedback inversion, which utilizes the karst space exploration system of multi-source data mutual feedback inversion as described above. Description of the Drawings

[0022] Figure 1 It is a schematic structural diagram of a karst space exploration system based on multi-source data mutual feedback inversion.

[0023] Figure 2 It is a schematic structural diagram of an electronic device according to an embodiment of the present invention. Detailed Embodiments

[0024] To make the technical solutions and their advantages of this application clearer, the technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings. It can be understood that the specific embodiments described herein are only partial embodiments of the present invention, which are only used to explain this application rather than limit this application. It should be noted that the technical features or combinations of technical features described in the following embodiments should not be considered isolated, and they can be combined with each other to achieve better technical effects. The same reference numerals appearing in the accompanying drawings of the following embodiments represent the same features or components, which can be applied to different embodiments.

[0025] In addition, unless otherwise defined, the technical terms or scientific terms used in the description of the present invention should have the ordinary meanings understood by those of ordinary skill in the technical field to which the present invention belongs.

[0026] Reference numerals: The electronic device 500 includes a processor 501, a communication interface 502, a memory 503, and a bus 504.

[0027] The following further describes the present invention in detail with reference to the accompanying drawings: Embodiment 1 Refer to Figure 1 , a karst space exploration system based on multi-source data mutual feedback inversion, includes: a data acquisition module, a data preprocessing module, a multi-source data mutual feedback inversion module, a karst space modeling module, and a visualization output module.

[0028] The data acquisition module is used to acquire multi-source geological data of the study area, including geophysical data, geochemical data, drilling data, topographic and geomorphic data, etc. Geophysical data can be obtained by methods such as seismic exploration and electromagnetic exploration; geochemical data can be obtained by methods such as water sample analysis and soil analysis; drilling data can be obtained by methods such as drilling sampling and core description; topographic and geomorphic data can be obtained by methods such as remote sensing images and digital elevation models.

[0029] The data acquisition module is used to acquire multi-source geological data of the study area. The data acquisition module includes a geophysical data acquisition unit, a geochemical data acquisition unit, a drilling data acquisition unit, and a topographic and geomorphic data acquisition unit.

[0030] The geophysical data acquisition unit uses methods such as seismic exploration and electromagnetic exploration to obtain geophysical data such as seismic wave velocity and electromagnetic wave velocity in the study area. Seismic exploration is a method of detecting underground structures by exciting seismic waves and using the characteristics of seismic waves propagating in the strata. Electromagnetic exploration is a method of detecting underground structures by using the characteristics of electromagnetic waves propagating in the strata.

[0031] The geochemical data acquisition unit uses methods such as water sample analysis and soil analysis to obtain geochemical data such as water quality and soil composition in the study area. Water sample analysis can understand the chemical composition and pollution status of groundwater; soil analysis can understand the composition and properties of soil.

[0032] The drilling data acquisition unit uses methods such as drilling sampling and core description to obtain core samples and drilling data in the study area. Drilling sampling is to obtain underground core samples through drilling for analyzing the composition and structure of underground rocks; core description is to describe and record the core samples in detail to understand the characteristics and variation laws of underground rocks.

[0033] The topographic and geomorphic data acquisition unit uses methods such as remote sensing images and digital elevation models to obtain topographic and geomorphic data in the study area. Remote sensing images are image data of the Earth's surface obtained through remote sensing platforms such as satellites or airplanes; digital elevation models are digital models generated from elevation data, which can reflect the terrain undulation and geomorphic characteristics of the study area.

[0034] The data preprocessing module is used to preprocess the multi-source geological data collected. The data preprocessing module includes a data cleaning unit, a data format conversion unit, and a data standardization unit.

[0035] The data cleaning unit cleans the multi-source geological data collected, removing outliers and duplicate values in the data. Outliers refer to data points that differ significantly from normal data, which may be caused by instrument failures, operation errors, etc.; duplicate values refer to exactly the same data points, which may be caused by repeated data collection or data redundancy. The data cleaning unit removes these outliers and duplicate values by setting reasonable thresholds and filtering conditions to ensure the accuracy of subsequent analysis.

[0036] The data format conversion unit converts data from different sources into a unified format. Since data from different sources may use different data formats and storage methods, format conversion is required for subsequent analysis. The data format conversion unit can select appropriate data format conversion methods according to needs, such as data import and export tools, data format conversion software, etc.

[0037] The data standardization unit converts the data into a dimensionless form for subsequent analysis. Since data from different sources may use different dimensions and units, data standardization is required to eliminate the influence of dimensions and units. The data standardization unit can use common data standardization methods, such as min-max standardization, Z-score standardization, etc.

[0038] The multi-source data mutual feedback inversion module is used to realize the mutual feedback inversion of multi-source data and construct a geological model of the karst space. It includes a background value determination unit, a detection data optimization unit, and an interpretation method construction unit.

[0039] The background value determination unit is used to determine the background value of the bedrock resistivity of the main strata in the study area. In this embodiment, the background value determination unit measures the background value of the bedrock resistivity by using the adaptive small four-electrode method. The adaptive small four-electrode method includes structures such as a scale, an electrode assembly, and an unlockable positioning assembly. When in use, the electrode assembly is pre-fixed on the scale, and there is no need to manually measure the electrode position on the bedrock, which can effectively reduce the electrode distance error in the measurement of bedrock physical parameters by traditional manual steel tape measurement. During the measurement process, the electric drill drilling construction process can be avoided, improving the work efficiency of measuring the background value of the bedrock resistivity. At the same time, since the rock surface does not need to be damaged, the integrity of the rock mass is guaranteed, and the influence of human factors on the measurement results is eliminated. Multiple point-by-point measurements can be carried out on the same rock surface to improve the measurement accuracy of the background value of the bedrock resistivity.

[0040] The interpretation method construction unit is used to construct a mutual feedback inversion interpretation method based on multi-source data to achieve fine characterization and modeling of the karst space. In this embodiment, the interpretation method construction unit mainly includes the following steps: The interpretation method construction unit takes the background value of the formation resistivity in the study area as a reference value, and performs mutual feedback inversion on geophysical data (such as seismic wave velocity, electromagnetic wave velocity, etc.), geochemical data (such as water quality, soil composition, etc.), drilling data (such as core samples, drilling records, etc.), and topographic and geomorphic data. According to the characteristics and correlations of different data, cross-validation and mutual feedback analysis of the data are carried out to identify the true and false, eliminate the false and approach the true, and reduce the multi-solution nature of geophysical exploration interpretation.

[0041] The interpretation method construction unit will also combine geological information input, such as formation distribution, structural characteristics, lithological changes, etc., and artificial intelligence algorithms to comprehensively analyze multi-source data. Utilizing the intelligent processing capabilities of geological information and artificial intelligence algorithms, deep mining and intelligent analysis of multi-source data are carried out to generate a karst structure network. The karst structure network can reflect the main characteristics and distribution laws of the karst space, providing an important basis for subsequent modeling.

[0042] Specifically, using artificial intelligence algorithms, features are extracted from multi-source data, combined with geological information and the extracted features, an initial model of the karst structure is constructed, and the initial model is optimized and adjusted using artificial intelligence algorithms. Through iterative training and learning, the accuracy and generalization ability of the model are improved. Based on the results of intelligent analysis, a karst structure network is generated. The karst structure network is represented as a set of nodes (such as karst caves, karst pipelines, etc.) and edges (representing the connection relationships between nodes), revealing the main characteristics and distribution laws of the karst space.

[0043] The interpretation method construction unit searches for the optimal path through hydrogeological simulation to achieve intelligent prediction of the karst space. Using hydrogeological simulation technology, according to the karst structure network and the characteristics of the groundwater flow field, it searches for the optimal path and potential channels in the karst space. Through simulation and analysis, the system can predict the development trend and potential risks of the karst space, providing scientific guidance for karst space exploration.

[0044] The karst space modeling module is used to construct a three-dimensional geological model of the karst space based on the results of the multi-source data mutual feedback inversion module. In this embodiment, the karst space modeling module adopts digital mapping technology to fuse multi-source data to form a fine geological model and a channel model. The fine geological model can reflect the geological structure and lithology distribution of the karst space; the channel model can reflect the channel characteristics and flow paths of the karst space. By constructing the three-dimensional geological model, the structure and characteristics of the karst space can be intuitively displayed, providing a reliable basis for subsequent exploration.

[0045] The visualization output module is used to visually display the three-dimensional geological model of the karst space, facilitating users to intuitively understand the structure and characteristics of the karst space. In this embodiment, the visualization output module adopts technical means such as virtual reality (VR) and augmented reality (AR) to realize the dynamic display and interactive operation of the three-dimensional geological model. Users can use virtual reality devices or augmented reality devices to observe and analyze the structure and characteristics of the karst space immersive, improving the exploration efficiency and accuracy.

[0046] Embodiment 2 The distinguishing technical feature of this embodiment from the above embodiment is that the data acquisition module further includes a drone inspection unit. Using a drone equipped with a high-definition camera and a multispectral sensor, it quickly inspects the study area to obtain high-resolution topographic and geomorphic images and vegetation cover information, providing more detailed ground feature data for karst space exploration, thereby further improving the exploration efficiency and accuracy.

[0047] In this embodiment, a multi-rotor drone is selected as the inspection platform. It has a compact structure, simple operation, easy to carry, and can maintain a stable flight state under complex terrain and bad weather conditions. The flight control system of the drone must have functions such as autonomous navigation, obstacle avoidance flight, and fixed-point hovering to ensure the smooth progress of the inspection task.

[0048] The imaging acquisition device on the drone uses a high-definition camera and a multispectral sensor. The high-definition camera is used to obtain high-resolution topographic images of the study area; the multispectral sensor is used to obtain vegetation cover information. Through spectral data in different bands, information such as the health status, species distribution, and growth environment of vegetation can be analyzed, and then the hydrogeological conditions of the karst space can be inferred. The high-definition camera and the multispectral sensor need to be fixed under the drone through a special bracket to ensure stable data collection during flight.

[0049] The drone inspection module is also used to plan flight routes according to the geological characteristics and exploration requirements of the study area, avoid repeated collection and omission, and adjust parameters such as the flight speed, altitude, and heading angle of the drone.

[0050] The data preprocessing module preprocesses and corrects the topographic images and vegetation cover information collected during the drone inspection, including steps such as image denoising, color correction, and geometric correction, to improve the accuracy and reliability of the data. At the same time, the collected data also needs to be stitched and fused to form a complete topographic map and vegetation cover map of the study area, which serves as the input for subsequent multi-source data mutual feedback inversion and karst space modeling.

[0051] The multi-source data mutual feedback inversion module fuses and mutually feeds back the topographic images and vegetation cover information obtained from the drone inspection with the original geophysical data, geochemical data, drilling data, etc. By comparing and analyzing the characteristics and correlations of data from different sources, the geological structure and distribution law of the karst space can be further revealed. Specifically, the topographic images can reflect the morphological characteristics and distribution range of karst landforms; the vegetation cover information can infer the hydrogeological conditions and ecological environment of the karst space; the geophysical data can reveal the physical properties and structural forms of the karst space; the geochemical data can reflect the chemical composition and pollution status of the karst space. The fusion and mutual feedback inversion of the above data will provide more comprehensive and accurate information support for karst space modeling.

[0052] Example Three The distinguishing technical feature of this embodiment from the above embodiments is that the data acquisition module further includes a method optimization unit, which is used to construct a detailed database of geophysical and geochemical exploration methods, including information such as the application scope, data processing process, advantages and limitations of each method. For different scenarios (such as different strata lithology and geological structures, different hydrogeological conditions, different topographies and landforms, and different pollution source types, etc.), geophysical and geochemical exploration methods and their combined applications are optimized to obtain reliable detection data.

[0053] Based on the analysis of the geological conditions and exploration requirements of the study area, combined with the data in the geophysical and geochemical exploration method database, several geophysical and geochemical exploration methods most suitable for the current detection task are automatically selected.

[0054] Taking into account the advantages and limitations of various geophysical and geochemical exploration methods, the geophysical and geochemical exploration methods are reasonably combined through an improved particle swarm optimization algorithm to improve the reliability and effectiveness of detection data. The specific method is as follows: Initialize the particle swarm. The particle swarm consists of multiple particles, and each particle represents a possible combination scheme of geophysical and geochemical exploration methods. The dimension of the particle is equal to the number of geophysical and geochemical exploration methods preliminarily screened out.

[0055] Randomly generate the initial position (i.e., the weight or selection situation of each method) and velocity (i.e., the adjustment direction of the weight or selection situation) of each particle.

[0056] The position vector is expressed as:

[0057] The velocity vector is expressed as:

[0058] Among them, is the number of methods, is the particle index.

[0059] Calculate the initial fitness value of each particle. The formula is as follows:

[0060] Among them, represents the combination scheme of geophysical and geochemical exploration methods, , , , are the weights of data acquisition efficiency, detection depth, resolution, and cost-benefit respectively, which are determined according to specific detection tasks and geological conditions; represents the data acquisition efficiency, which is measured by the amount of effective data obtained per unit time, is normalized to [0, , is the maximum value of data acquisition efficiency among all schemes; represents the detection depth, which is measured by the amount of effective data obtained per unit time, is normalized to [0, , is the maximum value of detection depth among all schemes; represents the resolution, is normalized to [0, , is the maximum value of resolution among all schemes (the higher the resolution, the larger the normalized value); is normalized to [0, , is the maximum value of data acquisition efficiency among all schemes; Indicates the cost - benefit, is normalized to , +∞], which is the cost of the scheme with the best cost - benefit (i.e., the lowest cost or the highest resource volume) among all schemes.

[0061] The normalization process can ensure that each evaluation index has comparability and the same dimension in the fitness function.

[0062] For each particle, compare its current fitness value with its individual historical best fitness value. If the current value is better, update the individual best position. .

[0063] Among all particles, find the particle with the best current fitness value and update the global best position. .

[0064] According to the individual best position and the global best position, update the velocity and position of each particle; The velocity update formula is as follows:

[0065] The position update formula is as follows:

[0066] where, is the inertia weight, and are learning factors, and are random numbers, is the number of iterations.

[0067] As the number of iterations increases, gradually decrease the inertia weight to enhance the local search ability of the algorithm. The decreasing strategy is as follows:

[0068] where, is the maximum number of iterations.

[0069] According to the gap between the fitness value of the particle and the global best fitness value, dynamically adjust the learning factors to keep the particle in balance between exploration and exploitation. When the particle is close to the global optimum, decrease to increase the learning of the global optimum. When the particle is far from the global optimum, increase to encourage exploration.

[0070] When the number of iterations reaches the preset maximum value or the termination condition, stop the iteration.

[0071] According to the final global best position, determine the optimal combination scheme of geophysical and geochemical exploration methods.

[0072] The embodiments of the present disclosure also provide a karst space exploration method based on multi-source data mutual feedback inversion. This method utilizes a karst space exploration system based on multi-source data mutual feedback inversion. The method includes: Collect multi-source geological data of the study area through a data acquisition module; Preprocess the collected multi-source geological data through a data preprocessing module; Implement mutual feedback inversion of multi-source data through a multi-source data mutual feedback inversion module to construct a geological model of the karst space; Construct a three-dimensional geological model of the karst space through a karst space modeling module according to the results of the multi-source data mutual feedback inversion module; Visually display the three-dimensional geological model of the karst space through a visualization output module.

[0073] The embodiments of the present disclosure also provide a storage medium. A computer program is stored in the storage medium. When the computer program is executed by a processor, all steps of the above-mentioned karst space exploration method based on multi-source data mutual feedback inversion can be implemented.

[0074] Those of ordinary skill in the art can understand that implementing all or part of the process in the karst space exploration method based on multi-source data mutual feedback inversion can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of various embodiments of the karst space exploration method based on multi-source data mutual feedback inversion. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0075] An embodiment of the present application further provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the above-mentioned karst space exploration method based on multi-source data mutual feedback inversion are implemented. In the embodiment of the present application, the processor is the control center of the computer system, which can be the processor of a physical machine or the processor of a virtual machine.

[0076] Referring to Figure 2 , the electronic device 500 includes: at least one processor 501, at least one communication interface 502, at least one memory 503, and at least one bus 504. Among them, the bus 504 is used to realize the connection and communication between these components, the communication interface 502 is used to communicate with other node devices for signaling or data, and the memory 503 stores machine-readable instructions executable by the processor 501. When the electronic device 500 runs, the processor 501 communicates with the memory 503 through the bus 504. When the machine-readable instructions are called by the processor 501, the steps of the above-mentioned karst space exploration method based on multi-source data mutual feedback inversion are executed.

[0077] The above content is only an embodiment of the present invention. Common knowledge such as specific structures and characteristics known in the art are not described in detail here. Those of ordinary skill in the art know all the common technical knowledge in the technical field to which the invention belongs before the application date or the priority date, can know all the prior arts in this field, and have the ability to apply conventional experimental means before this date. Those of ordinary skill in the art can, under the inspiration given by the present application and combined with their own abilities, improve and implement this solution. Some typical well-known structures or well-known methods should not become an obstacle for those of ordinary skill in the art to implement the present application. It should be noted that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can still be made, which should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicality of the patent. The protection scope required by the present application should be based on the content of its claims, and the specific implementation manners and the like recorded in the specification can be used to interpret the content of the claims.

Claims

1. A karst spatial exploration system based on multi-source data mutual feedback inversion, characterized in that: include: A data acquisition module is used to collect multi-source geological data of the study area, and the data acquisition module includes a geophysical data acquisition unit, a geochemical data acquisition unit, a drilling data acquisition unit and a topographic data acquisition unit; Data preprocessing module, used to preprocess the collected multi-source geological data; The multi-source data mutual feedback inversion module is used to realize the mutual feedback inversion of multi-source data and construct the geological model of karst space, including: The background value determination unit is used to measure the bedrock resistivity background value by using an adaptive small quadrupole method; The interpretation method construction unit uses the background value of formation resistivity in the study area as a reference value, performs mutual feedback inversion on multi-source data, combines geological information input and artificial intelligence algorithms, generates a karst structure network, and searches for the optimal path through hydrogeological simulation to predict karst space; The karst space modeling module is used to construct a three-dimensional geological model of the karst space based on the results of the multi-source data mutual feedback inversion module; The visualization output module is used to visualize the three-dimensional geological model of the karst space.

2. The karst space exploration system based on multi-source data mutual feedback inversion according to claim 1 is characterized in that: The geophysical data acquisition unit uses seismic exploration and electromagnetic exploration methods to obtain geophysical data of the study area, including seismic wave velocity and electromagnetic wave velocity; The geochemical data acquisition unit uses water sample analysis and soil analysis methods to obtain geochemical data of the study area, including water quality and soil composition; The drilling data acquisition unit uses drilling sampling and core description methods to obtain core samples and drilling data in the study area; The topographic data acquisition unit uses remote sensing images and digital elevation model methods to obtain topographic data of the study area.

3. The karst space exploration system based on multi-source data mutual feedback inversion according to claim 1 is characterized in that: The data preprocessing module comprises: The data cleaning unit is used to remove outliers and duplicate values ​​from multi-source geological data; The data format conversion unit is used to convert data from different sources into a unified format; Data normalization units are used to convert data into dimensionless form.

4. The karst space exploration system based on multi-source data mutual feedback inversion according to claim 1 is characterized in that: The karst spatial modeling module adopts digital mapping technology to fuse multi-source data to form a fine geological model and channel model; The visualization output module uses virtual reality and / or augmented reality to achieve dynamic display and interactive operation of the three-dimensional geological model.

5. The karst space exploration system based on multi-source data mutual feedback inversion according to claim 1 is characterized in that: The data acquisition module also includes a drone inspection unit, which is used to control a drone equipped with a high-definition camera and a multi-spectral sensor to inspect the study area and obtain high-resolution topographic images and vegetation coverage information.

6. The karst space exploration system based on multi-source data mutual feedback inversion according to claim 5 is characterized in that: The UAV inspection unit is also used to plan the route and adjust the control parameters of the UAV according to the geological characteristics and exploration requirements of the study area. The control parameters include flight speed, altitude, and heading angle.

7. The karst space exploration system based on multi-source data mutual feedback inversion according to claim 6 is characterized in that: The data preprocessing module also preprocesses and corrects the topographic images and vegetation coverage information collected by the UAV inspection, including image denoising, color correction, geometric correction, and splicing and fusion.

8. The karst space exploration system based on multi-source data mutual feedback inversion according to claim 7 is characterized in that: The multi-source data mutual feedback inversion module is also used to fuse and mutual feedback invert the topographic images and vegetation coverage information obtained by the drone inspection with the original multi-source geological data.

9. The karst space exploration system based on multi-source data mutual feedback inversion according to claim 1 is characterized in that: The data acquisition module also includes a method optimization unit, which is used to build a detailed database of geophysical and geochemical exploration methods; and select geophysical and geochemical exploration methods and their combined applications for different scenarios.

10. A karst spatial exploration method based on multi-source data mutual feedback inversion, characterized by: A karst space exploration system based on multi-source data mutual feedback inversion as described in any one of claims 1 to 9 is used.

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