A landslide geologic body identification imaging method and device

Abstract

This application discloses a method and apparatus for identifying and imaging landslide geological bodies, comprising the following steps: constructing an initial model of the landslide geological body based on landslide disaster data of the target area; performing geophysical simulation based on the initial model to extract field data acquisition parameters of the target area; conducting natural and / or artificial electric field detection based on the field data acquisition parameters to obtain electric field observation data; inverting the electric field observation data to extract imaging data; and performing inversion imaging based on the imaging data to obtain a two-dimensional profile and a three-dimensional slice of the target area. The technical solution of this application can perform detailed identification and imaging of landslide geological disaster structures, providing crucial data information for subsequent landslide geological disaster early warning and engineering management. Its advantages of low cost, speed, high precision, and non-destructive green exploration are significant.

Description

Technical Field

[0001] This application relates to the field of geological data processing technology, and in particular to a method and apparatus for identifying and imaging landslide geological bodies. Background Technology

[0002] Landslides are one of the major types of geological hazards, posing a significant threat to the lives and property of people in their affected areas. Therefore, it is urgent to conduct surveys, designs, and engineering remediation to eliminate these hazards. Taking Guizhou Province as an example, landslides in Guizhou are mainly surface and shallow sliding, with most sliding surfaces less than 15 meters deep. The sliding surfaces often occur at the rock-soil interface.

[0003] Currently, the methods used in landslide geological hazard investigation include drilling, trenching, pitting, hydrological surveys, and geophysical exploration. However, the application of integrated investigation methods is relatively limited. In particular, there is a lack of research and technological breakthroughs in the effectiveness and refined identification of geophysical methods. These methods cannot meet the needs of areas with high requirements for green investigation and harsh conditions for drilling equipment transportation and construction sites. The application of new methods and technologies is urgently needed for the refined detection of landslide bodies and their scale evaluation. Summary of the Invention

[0004] This application provides a method and apparatus for identifying and imaging landslide geological bodies, which can accurately identify the disaster structure of landslide geological bodies and perform imaging.

[0005] Firstly, this application provides a method for identifying and imaging landslide geological bodies, employing the following technical solution: A method for identifying and imaging landslide geological bodies includes the following steps: Based on the landslide geological disaster data of the target area, an initial model of the landslide geological body is constructed; Geophysical simulation was performed based on the initial model of the landslide geological body to extract field data acquisition parameters for the target area. Based on the field data acquisition parameters, natural and / or artificial electric fields are detected to obtain electric field observation data; The electric field observation data is inverted to extract imaging data; Inversion imaging is performed based on the imaging data to obtain a two-dimensional cross-sectional view and a three-dimensional slice view of the target area.

[0006] Furthermore, the initial model of the landslide geological body mentioned above includes a landslide structure and lithological geological model, a landslide geological hazard system conceptual model, a geophysical and geological attribute correlation model, and a landslide structure characteristics and prediction indicator model.

[0007] Furthermore, the aforementioned landslide structure and lithological geological model includes a stratigraphic framework, a rock mass framework, and a three-dimensional distribution of lithology; the landslide geological hazard system conceptual model includes landslide dynamics sources, landslide transport driving forces, and intrinsic factors of landslide transport; the geophysical and geological attribute correlation model includes a rock property database; and the landslide structure characteristics and prediction indicator model includes planar indicators, profile indicators, and comprehensive indicators.

[0008] Furthermore, the above geophysical simulation includes the following steps: The geological structural units in the initial model of the landslide geological body are assigned initial geophysical parameter values ​​based on rock type and characteristics of the landslide geological hazard body. According to the planned exploration methods, excitation field sources and observation systems are set in the initial model of the landslide geological body; Based on the excitation field source and the observation system, the geophysical field response is numerically calculated using the initial model of the landslide geological body to obtain the field data acquisition parameters.

[0009] Furthermore, the above-mentioned inversion of the electric field observation data includes the following steps: Geophysical inversion is performed based on the electric field observation data and the initial model of the landslide geological body to obtain an inversion image with geophysical medium properties. The quantitative features of the inverted image are extracted, and the quantitative features are correlated with geological significance to obtain classification data; A three-dimensional geological model is constructed based on the classification data. The three-dimensional geological model is then subjected to positive verification and external evidence constraints to obtain a dynamic geological model with uncertainty assessment. The electric field observation data is input into the dynamic geological model to obtain imaging data.

[0010] Furthermore, the inversion imaging steps for the above-mentioned two-dimensional cross-sectional image include: The area below each measuring point in the target region is set as a horizontal layered medium, and the optimal resistivity-depth model of each measuring point is solved by a smooth model inversion algorithm. Iteratively adjust the resistivity and thickness of each layer of the optimal resistivity-depth model, and output a one-dimensional resistivity-depth curve for each measurement point; The one-dimensional resistivity-depth curves of each measurement point are arranged along the profile to obtain the two-dimensional profile.

[0011] Furthermore, the aforementioned three-dimensional slice images are obtained through resistivity tomography or direct imaging based on a current-sensing model.

[0012] Furthermore, before performing inversion imaging on the imaging data, the following steps are also included: The imaging data is format-normalized and defective pixels are removed to obtain standardized data; The standardized data is subjected to system noise suppression and human noise suppression to obtain denoised data; The denoised data is normalized to convert the transient response values ​​of each measurement point in the denoised data into apparent resistivity. Inversion imaging is then performed based on the apparent resistivity to obtain a two-dimensional profile and a three-dimensional slice of the target region.

[0013] Furthermore, the above method also includes: Extract the spatial geometric and statistical characteristics of similar geophysical parameters from the field data acquisition parameters and the electric field observation data; Anomalies are identified from the spatial geometric features and statistical features. The geometric, physical, and morphological features of each anomaly are calculated, and the compactness of the anomaly is quantified by a shape index. Based on the deviation between the field data acquisition parameters and the compactness of the electric field observation data, the initial model of the landslide geological body is adjusted to obtain the modified landslide geological body model. Key disaster-causing factors of landslide geological structure are selected. Based on the key disaster-causing factors and the modified landslide geological model, the weight of each key disaster-causing factor to known geological disaster-causing factors is determined by the evidence weight method or logistic regression method. The landslide geological structure fine identification and disaster-causing factor discrimination advantage index of each three-dimensional grid cell is calculated to obtain the landslide geological structure identification advantage index model. Landslide geological hazard prediction is performed based on the landslide geological structure identification favorable index model, and the prediction results are output.

[0014] Secondly, this application provides a landslide geological body identification imaging device, which adopts the following technical solution: A landslide geological body identification imaging device, employing the landslide geological body identification imaging method described above, includes: The model building module is used to construct an initial model of the landslide geological body based on the landslide geological body disaster data of the target area; The geophysical simulation module is used to perform geophysical simulation based on the initial model of the landslide geological body and extract field data acquisition parameters of the target area. The data acquisition module is used to detect natural and / or artificial electric fields based on the field data acquisition parameters to obtain electric field observation data; The data extraction module is used to invert the electric field observation data and extract imaging data; An imaging module is used to perform inversion imaging based on the imaging data to obtain a two-dimensional cross-sectional view and a three-dimensional slice view of the target area.

[0015] In summary, this application includes at least one of the following beneficial technical effects: This application provides a method and apparatus for identifying and imaging landslide geological bodies. Based on landslide geological body disaster data of the target area, an initial model of the landslide geological body is constructed based on the structure, geological evolution, and theory of landslide geological hazards. Geophysical simulation is carried out to extract geophysical indicators of landslide geological hazard structural characteristics and to select field data acquisition parameters that are suitable for the target area. Then, natural and / or artificial electric field detection is performed based on the field data acquisition parameters to collect geophysical information across the entire area and obtain electric field observation data. Finally, under the constraints of prior conditions such as landslide geological hazard theory and specific case field investigation evidence, the collected geophysical data is processed by forward and inverse modeling to extract imaging data and obtain two-dimensional profile maps and three-dimensional slice maps of the target area. This method offers significant advantages in terms of low cost, speed, high precision, and non-destructive green exploration. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the landslide geological body identification and imaging method in the embodiments of this application.

[0017] Figure 2 This is a schematic diagram of the landslide structure and lithological geological model in the embodiments of this application.

[0018] Figure 3 This is a schematic diagram of the conceptual model of the landslide geological hazard system in the embodiments of this application.

[0019] Figure 4 This is a schematic diagram of the landslide geophysical and geological property correlation model in the embodiments of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0021] In one embodiment of this application, a landslide geological body identification imaging method and device are provided, which are applied to the fine identification of landslide geological hazard structures, detect and search for landslide geological hazard bodies and their morphology in hidden areas, and can serve geological hazard prevention and control work to protect people's lives and property.

[0022] Taking the Maling landslide geological hazard in Xingyi, Guizhou Province as an example, this region has highly dissected terrain, a complex geological environment, and frequent geological disasters. Landslides are one of the main types of geological hazards, posing a significant threat to the lives and property of people in the affected area. Therefore, it is urgent to carry out surveying, design, and engineering remediation to eliminate the disaster. Landslides in Guizhou are mainly surface and shallow sliding, with most sliding surfaces less than 15m deep. The sliding surfaces often use the rock-soil interface as the sliding surface. The landslide geological body identification and imaging method and device provided in this embodiment can accurately identify and image the geological characteristics of landslides in this region, which is beneficial for evaluating the scale of landslide geological hazards and provides key data information for subsequent landslide geological hazard early warning and engineering remediation. Its advantages of low cost, speed, high precision, and non-destructive green exploration are significant, making it worthy of widespread application in this field.

[0023] To address the above problems, this application discloses a landslide geological body identification imaging method, which adopts the following technical solution: Reference Figure 1 A method for identifying and imaging landslide geological bodies, comprising the following steps: S101: Construct an initial model of the landslide geological body based on the landslide geological body disaster data of the target area; In this implementation, based on the theory of landslide geological disaster formation and hazard, historical data on geology, geophysics, geochemistry, drilling engineering, etc., in the target area can be collected. With the constraint of extracting geological conditions highly adapted to the target area through on-site geological surveys, and on the basis of fully identifying and absorbing objective geological evidence and on-site geological survey results from historical data, a refined landslide geological structure and hazard-causing geological evolution model is constructed. Based on comprehensive big data such as geological surveys and previous basic results, the constructed landslide geological structure and hazard-causing geological evolution model is corrected and improved, so that the model is highly adapted to the objective geological reality of the application area, providing an initial model of the landslide geological body for geophysical simulation.

[0024] In one embodiment of this application, the initial model of the landslide geological body includes a landslide structure and lithological geological model, a landslide geological hazard system conceptual model, a geophysical and geological attribute correlation model, and a landslide structure characteristics and prediction indicator model.

[0025] In this embodiment, the landslide structure and lithological geological model specifically comprises: Stratigraphic framework: clearly defining the stratigraphic sequence, lithological assemblage series, and contact relationships (conformity, unconformity, fault contact) of the application area. For example, Permian limestone strata, Triassic sandstone strata, etc. Fault framework: the spatial distribution (strike, dip, dip angle), properties (normal faults, reverse faults, strike-slip faults), stages, and assemblage relationships of major faults, folds, joints, and other structural units (e.g., fault intersections, anticline detachment areas). Rock mass framework: the lithological structure of the landslide body, the morphology, scale, occurrence, and contact zones with the surrounding rocks of the sliding surface and the sliding bed. Three-dimensional lithological distribution: showcasing the spatial distribution of different lithological units (e.g., limestone, sandstone, claystone, clastic rocks, etc.) in the form of cross-sectional views, plan views, and preliminary three-dimensional block models.

[0026] The conceptual model of a landslide geological hazard system includes the following: Landslide dynamics: Possible sources of landslide dynamics (such as active fault forces, gravitational forces, atmospheric precipitation, and natural or anthropogenic destructive forces on geological structures). Landslide transport driving forces: Atmospheric precipitation or groundwater flowing along transport channels (such as main faults, fracture zones, unconformities, porous rock layers, and joints) leads to a decrease in friction on the landslide surface, and transport is carried out under the influence of driving forces (tectonic pressure, thermal convection, gravitational forces, etc.). Internal causes of landslide transport: The presence of soft rock between hard rocks is caused by two factors: first, the softening of soft rock under the influence of surface water and groundwater; and second, the disruption of the originally stable geological structure.

[0027] The geophysical and geological property correlation model includes a rock property database. By collecting data from previous well logging and core sample measurements, it establishes the typical physical property parameter ranges for key rock types (density, magnetism, electrical properties, elastic wave velocity, etc.).

[0028] Landslide structural characteristics and predictive indicator models include: Planar indicators: distribution of landslide back edge fissure zone, overlay pattern of comprehensive geological information such as remote sensing, and spatial relationship between known landslide fissures and linear structures (faults, fold axes).

[0029] Profile features: typical occurrence morphology of landslide structures (layered, vein-like, network-like, sac-like), spatial combination (e.g., "hard rock-soft rock-hard rock"), and vertical zonation (density of fractures and fissures, lithological structure).

[0030] Comprehensive indicators: Establish a multi-dimensional prediction criterion that integrates "structure, lithology, landslide influence zone, and geophysical anomalies".

[0031] S102: Perform geophysical simulation based on the initial model of the landslide geological body, and extract field data acquisition parameters for the target area; In this embodiment, based on the initial model of the landslide geological body and the geoelectric field theory, geophysical simulation of the landslide geological hazard body can be carried out. By simulating with different observation devices, the characteristics of the geophysical field are extracted to obtain the geological structure characteristics of the target area. The patterns are summarized to extract characteristic markers, and field data acquisition parameters that are suitable for the work area are selected.

[0032] In one embodiment of this application, the geophysical simulation is based on the numerical calculation of the geophysical field response of the initial model of the landslide geological body using the finite difference method, finite element method, or integral method. It can also be implemented using professional gravity, magnetoelectric, seismic, and forward modeling software (such as ResIPy, GM-SYS, RES2DMOD, Oasis Montaj, etc.).

[0033] Geological structural units are assigned corresponding initial geophysical parameter values ​​based on their rock type and landslide geological hazard characteristics, for example: Density (for gravity simulation): Hard rock 1 (limestone) is set to 2.3~3.0 g / cm³. 3 The g / cm³ content for hard rock and dolomite was set at 2.4 ~ 2.9 g / cm³. 3 For soft rocks such as sandstone and claystone, the g / cm³ value was set at 1.8–2.8 g / cm³. 3 .

[0034] Resistivity / polarizability (for electromagnetic simulation): landslide body <1000 Ω·m, soft rock layer of landslide surface <300 Ω·m and polarizability >5%, hard rock bedrock of landslide body set to >5000 Ω·m.

[0035] Natural potential (used for simulation by the natural electric field method): There will be obvious potential differences in landslide fissures, landslide surfaces, and contact surfaces between soft and hard surfaces. The specific values ​​need to be set based on actual measurement data.

[0036] Then, based on the exploration technology to be used in the plan, the corresponding excitation field source (such as gravity field, artificial electromagnetic field source, seismic source, natural electric field source) and observation system (survey line position, measuring point spacing, flight altitude, frequency / track spacing, etc.) are set in the simulation model to make it consistent with the subsequent electric field observation data acquisition scheme.

[0037] Geophysical simulation can calculate and generate theoretical geophysical field response data (such as resistivity profiles, polarizability profiles, spontaneous potential profiles, gravity anomaly maps, magnetic anomaly maps, seismic profiles, etc.). The output of geophysical simulation is a forward modeling response dataset of multiple geophysical fields and corresponding theoretical anomaly maps, which are used for comparative analysis with the electric field observation data processing and imaging results obtained in subsequent steps.

[0038] S103: Perform natural and / or artificial electric field detection based on the field data acquisition parameters to obtain electric field observation data; In this embodiment, to improve the accuracy of detection and interpretation, the density of measuring points is determined based on the specifications of the target geological body. The field data acquisition parameters that are suitable for the work area are optimized by combining numerical simulation. Natural and artificial electric field detection is carried out on the ground to collect multi-scale global geophysical source data and to accurately measure multi-parameter information such as electric field, current, and natural potential. This provides reliable raw geophysical data for the next stage of data processing and anomaly information extraction.

[0039] In one embodiment of this application, it is necessary to interpret the electric field observation data. Specifically, the electric field observation data is input into the initial model of the landslide geological body, and geophysical inversion is performed to obtain the geophysical medium properties. After interpretation, the geological interpretation results of the study area are obtained.

[0040] Direct visual interpretation from inverted images is subjective and inefficient. It requires first quantifying anomalies in the images into calculable and comparable feature parameters. Then, by establishing a prior knowledge base, the quantified features can be correlated with geological significance. Finally, by integrating other relevant information with the categorized discrete anomalies, a continuous and consistent three-dimensional geological model can be constructed. Finally, the three-dimensional geological model is positively validated and constrained by external evidence, resulting in a dynamic geological model with uncertainty assessment, indicating which parts are reliable and which are inferences.

[0041] S104: Invert the electric field observation data, extract the imaging data, and perform inversion imaging based on the imaging data to obtain a two-dimensional profile and a three-dimensional slice of the target area.

[0042] In this embodiment, the electric field observation data first needs to be preprocessed, which can be done through methods such as overlay and filtering. Secondly, processing based on geological prior information constraints and geophysical forward and inverse modeling can be implemented to extract multi-parameter information, providing data for rapid imaging. The entire imaging area is primarily based on inverse-extracted data such as potential, current, resistivity, and polarizability to rapidly generate images reflecting the underground electrical structure, providing fundamental data for subsequent interpretation and identification.

[0043] In one embodiment of this application, preprocessing the electric field observation data requires data format standardization and bad pixel removal. The raw binary data collected by different instruments is uniformly converted into a standard format (such as *.xyz or *.gps format), including flight time, GPS coordinates (X, Y, Z), flight attitude (roll, pitch, yaw), and transient response values ​​for each time gate. Then, the data for each time gate is statistically analyzed, and its mean is calculated. and standard deviation Measurement points that meet the following conditions will be marked as "bad points" and removed: in, It is the voltage value of the i-th measurement point at time gate t, and k is the threshold factor (usually taken as 3~5).

[0044] Then, system noise and human noise suppression are performed, including background field subtraction, power frequency filtering, and motion noise compensation. Finally, data normalization is performed. The transient response value V(t) of each measuring point is normalized to the apparent resistivity. For time-domain airborne transient electromagnetic (ATEM) data, a late asymptotic formula is commonly used for fast approximation transformation, and the formula is as follows: in, The permeability of free space, The emitted magnetic moment is denoted by t, and the decay time is t. This step converts the physical response from a voltage value into an electrical parameter directly related to the subsurface medium, which is fundamental to imaging.

[0045] In one embodiment of this application, a two-dimensional profile of the target area can be rapidly inverted using a one-dimensional layered model. Assuming a horizontal layered medium lies beneath each measuring point, smooth model inversion algorithms such as Occam or Marquardt inversion are used to solve for the optimal resistivity-depth model at each measuring point. The inversion iteratively adjusts the model parameter vector m (resistivity and thickness of each layer) to minimize the following objective function Φ: in, It is an observation data vector. Forward response vector For the data weight matrix, The roughness matrix of the model constrains the smoothness of the model. This is a regularization parameter that balances the data fitting term and the model smoothing term.

[0046] Finally, a one-dimensional resistivity-depth curve is output for each measuring point. Arranging the curves of all measuring points in a cross-section creates a "pseudo-section map," resulting in a two-dimensional cross-sectional map of the target area.

[0047] In one embodiment of this application, to obtain more intuitive structural information, resistivity tomography or a direct imaging method based on a current-induced model can be used. For example, "attenuation time constant (Tau) imaging" or the "S-inversion" method can be used. The latter discretizes the subsurface half-space into a large number of cubic units and directly calculates the conductivity contrast (S-value) of each unit through an approximately linear relationship. The induced electromotive force V(t) can be approximately expressed as: in, It is the S value of the j-th unit. It is a kernel function related to the geometric location of the unit and the transmit / receive system. By solving the large linear system of equations d=GS (usually using the least squares or conjugate gradient method), the three-dimensional S-value distribution can be obtained quickly, with the high-value region corresponding to a good conductor.

[0048] The final output is a processed two-dimensional profile and a three-dimensional slice that clearly reflect the underground electrical structure (especially low-resistivity structures). A similar processing procedure is used to process data acquired by high-density electrical resistivity tomography (EDT) and natural electric field methods, outputting two-dimensional profiles and three-dimensional slices.

[0049] S105: Based on electric field observation data, the initial model of the landslide geological body is corrected to obtain the corrected model. The landslide geological hazard is predicted using the corrected model, and the prediction results are output.

[0050] In this embodiment, based on two-dimensional cross-sectional views and three-dimensional slice views, geophysical field characteristic information is extracted by combining the geological model of the landslide geological body with geophysical simulation. This includes extracting the relationship between the information and the geological body in each part of the corresponding model, correcting and improving the constructed geological structure and disaster-causing geological evolution model, and obtaining the landslide geological body structure and disaster-causing geological evolution model that is finally adapted to the application area. This achieves precise identification of the landslide geological hazard structure and its scale evaluation, realizes precise identification of landslide geological hazard structure driven by geophysical parameters, detects and searches for landslide geological hazard bodies in hidden areas, evaluates their morphology, scale and safety, serves the prevention and control of geological disasters, and protects the safety of people's lives and property.

[0051] In one embodiment of this application, the geophysical simulation results in step S102 and the electric field observation data in step S103 are extracted. The spatial geometric and statistical characteristics of the same type of geophysical parameters (such as resistivity, spontaneous potential, etc.) in the geophysical simulation results and electric field observation data are extracted. For each identified anomaly (or a pre-defined landslide geological body in the model), the following quantitative indicators are calculated: geometric features, physical features (such as average resistivity, resistivity contrast, anomaly gradient, etc.), and morphological features. The shape index (SI) is used to quantify the compactness of the anomaly. Where Area and Perimeter represent the area and perimeter of the anomaly on the slice. The closer SI is to 1, the closer the shape is to a circle (possibly a stock or karst sac); the smaller SI is, the more elongated the shape (possibly a fractured zone, slip surface, or fault zone caused by a landslide).

[0052] Establish a knowledge base for mapping "geophysical response-geological body". Associate the pre-set geological body labels in the theoretical model (such as "low-resistivity landslide surface", "low-resistivity fracture zone", "low-resistivity landslide leading and trailing fracture zone") with the generated quantitative geophysical characteristics to form a response characteristic table.

[0053] The initial model of the landslide geological body was revised based on comparative analysis of response feature tables. Specifically, firstly, inconsistency detection and model updating were performed, comparing the classification results with the initial model. If there were discrepancies between the measured landslide surface location, the extent of the leading and trailing edges of the landslide, and the landslide structure and the model, the spatial location and morphology of relevant geological interfaces were manually or semi-automatically adjusted in the 3D modeling software, using the measured data as constraints, to update the model. Secondly, multiple-solution constraints and verification were implemented, using joint inversion of non-seismic geophysical data to reduce the non-uniqueness of the solution. For example, gravity or magnetic data were introduced to establish cross-gradient constraints between density / magnetic susceptibility and resistivity, spontaneous potential, requiring that the changes in different physical property parameters of the model remain spatially consistent during the inversion. A cross-gradient term was added to the objective function. : in, and These are resistivity and density models, respectively. This is the model vector differential operator. Minimize This forces the resistivity abrupt change interface and the density abrupt change interface to coincide spatially, thereby more reliably fixing the geological boundary.

[0054] Based on the revised final model, a quantitative evaluation of landslide geological hazard prediction is conducted. First, a "Fairness Index (FPI) for Fine Identification of Landslide Geological Structure" model is constructed: key hazard-causing factors closely related to the landslide geological structure are selected, and each factor is assigned a value in three-dimensional space based on the revised model. Then, the weight of each factor to known hazard-causing factors is determined using the weight of evidence method or logistic regression, and the Fairness Index (FPI) for Fine Identification of Landslide Geological Structure and Disaster-Causing Factor Discrimination is calculated for each three-dimensional grid cell. Secondly, a three-dimensional characterization of geological hazard causative factors and prediction and estimation of landslide prevention are conducted: A threshold is set for the FPI three-dimensional model (e.g., FPI > 2.0), classifying the geological hazard causative factors into three categories: A, B, and C, based on their risk levels. For the hazard assessment area, the potential geological hazard body can be predicted and estimated based on the anomaly volume (V) obtained from geophysical inversion and the empirical relationship between the physical properties and degree of failure of each unit of the known landslide geological hazard body structure. For example, for low-resistivity landslide surfaces and fractured fracture zones, the structure and scale of the geological hazard body can be estimated, and the prediction of potential geological hazard bodies can be assessed.

[0055] Thus, the final output is not only geological knowledge, but also a three-dimensional target area probability map and geological hazard prediction and assessment that can be directly used for engineering deployment, demonstrating the practical value of the method.

[0056] In practical applications, a geological and geophysical model is usually constructed based on the most objective and representative geological results of the application area. On this basis, simulation calculations are carried out to obtain electric field parameters, extract geophysical field characteristic information and laws, understand the landslide geological structure and the geophysical response characteristics of each part of the disaster-causing geological evolution model of the application area, summarize the laws and extract characteristic markers, and provide calibration criteria for subsequent actual data processing, anomaly extraction and interpretation.

[0057] This application also discloses a landslide geological body identification imaging device, which adopts the following technical solution: A landslide geological body identification imaging device, employing the landslide geological body identification imaging method described above, includes: The model building module is used to construct an initial model of the landslide geological body based on the landslide geological body disaster data of the target area; The geophysical simulation module is used to perform geophysical simulation based on the initial model of the landslide geological body and extract field data acquisition parameters of the target area. The data acquisition module is used to detect natural and / or artificial electric fields based on the field data acquisition parameters to obtain electric field observation data; The data extraction module is used to invert the electric field observation data and extract imaging data; An imaging module is used to perform inversion imaging based on the imaging data to obtain a two-dimensional cross-sectional view and a three-dimensional slice view of the target area.

[0058] The landslide geological body identification imaging device of this application embodiment can realize any of the above-mentioned methods for landslide geological body identification imaging, and the specific working process of each module in the landslide geological body identification imaging device can refer to the corresponding process in the above-mentioned method embodiment.

[0059] In the several embodiments provided in this application, it should be understood that the provided methods and apparatus can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for example, the division of a component is merely a logical functional division, and in actual implementation there may be other division methods, such as multiple components can be combined or integrated into another system, or some features can be ignored or not executed.

[0060] This application also discloses a computer device.

[0061] A computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the landslide geological body identification imaging method described above.

[0062] This application also discloses a computer-readable storage medium.

[0063] A computer-readable storage medium storing a computer program that can be loaded by a processor and executed as any of the landslide geological body identification imaging methods described above.

[0064] The computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device; the program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0065] In summary, the landslide geological body identification imaging method and device provided in this application can comprehensively collect as much geological, geophysical, geochemical, and drilling engineering data as possible from previous research on the geological structure characteristics of the target area. It uses on-site geological surveys to extract geological conditions highly suitable for the application area as a constraint. Based on the thorough identification and absorption of objective geological evidence and on-site geological survey results from previous research, it refines and constructs an initial model of the landslide geological body. This initial model is designed to closely match the objective geological reality of the application area, providing an initial geological model for geophysical simulation. Based on the initial landslide geological body model and grounded in geoelectric field theory, geophysical simulations are conducted using different observation devices and different geological structural properties to extract geophysical field characteristic information. The process involves identifying patterns and geological structural characteristics of the application area, summarizing patterns, extracting characteristic markers, and optimizing field data acquisition parameters to suit the requirements of the work area. To improve the accuracy of detection and interpretation, the density of measuring points is determined based on the specifications of the target geological body. Simulation is used to optimize field data acquisition parameters to suit the requirements of the work area. Natural and artificial electric field detection is conducted on the ground to collect multi-scale, full-domain geophysical source data. Precise measurements of multiple geophysical parameters, such as electric field, current, and spontaneous potential, are performed to provide reliable raw geophysical data for the next stage of data processing and anomaly extraction. Based on high-precision electric field observation data, noise reduction is performed through data preprocessing, including techniques such as overlay and filtering. Furthermore, processing based on geological prior information constraints and geophysical forward and inverse modeling can be implemented to extract multiple parameter information, providing data for rapid imaging. The entire imaging area is rapidly imaged based on inversion data such as potential, current, resistivity, and polarizability, providing fundamental data for subsequent interpretation and identification. Geophysical simulation is combined to extract geophysical field characteristic information, such as the relationship between extracted information and geological features of corresponding parts of the model, refining and improving the initial landslide geological body model, and obtaining the final landslide geological body initial model adapted to the application area. In practical applications, a geological geophysical model is typically constructed based on the most objective and representative geological structure of the application area. Simulation calculations are then performed to obtain electric field parameters, extract geophysical field characteristic information, understand the geological structure characteristics of the application area, summarize patterns, and extract characteristic markers. This provides calibration criteria for subsequent data processing, anomaly extraction, and interpretation, achieving geophysical parameter-driven precise identification of landslide geological hazard structures and assessment of their scale and hazard. The results provide crucial data information for subsequent landslide geological hazard early warning and engineering management. Its advantages of low cost, speed, high precision, and non-destructive green exploration are significant, making it worthy of widespread application in this field.

[0066] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0067] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A method for identifying and imaging landslide geological bodies, characterized in that, Includes the following steps: Based on the landslide geological disaster data of the target area, an initial model of the landslide geological body is constructed; Geophysical simulation was performed based on the initial model of the landslide geological body to extract field data acquisition parameters for the target area. Based on the field data acquisition parameters, natural and / or artificial electric fields are detected to obtain electric field observation data; The electric field observation data is inverted to extract imaging data; Inversion imaging is performed based on the imaging data to obtain a two-dimensional cross-sectional view and a three-dimensional slice view of the target area.

2. The landslide geological body identification imaging method according to claim 1, characterized in that, The initial model of the landslide geological body includes a landslide structure and lithological geological model, a landslide geological hazard system conceptual model, a geophysical and geological attribute correlation model, and a landslide structure characteristics and prediction indicator model.

3. The landslide geological body identification imaging method according to claim 2, characterized in that, The landslide structure and lithological geological model includes a stratigraphic framework, a rock mass framework, and a three-dimensional distribution of lithology; the landslide geological hazard system conceptual model includes landslide dynamics sources, landslide transport driving forces, and intrinsic factors of landslide transport; the geophysical and geological attribute correlation model includes a rock property database; and the landslide structure characteristics and prediction indicator model includes planar indicators, profile indicators, and comprehensive indicators.

4. The landslide geological body identification imaging method according to claim 1, characterized in that, The geophysical simulation includes the following steps: The geological structural units in the initial model of the landslide geological body are assigned initial geophysical parameter values ​​based on rock type and characteristics of the landslide geological hazard body; According to the planned exploration methods, excitation field sources and observation systems are set in the initial model of the landslide geological body; Based on the excitation field source and the observation system, the geophysical field response is numerically calculated using the initial model of the landslide geological body to obtain the field data acquisition parameters.

5. The landslide geological body identification imaging method according to claim 1, characterized in that, The inversion of the electric field observation data includes the following steps: Geophysical inversion is performed based on the electric field observation data and the initial model of the landslide geological body to obtain an inversion image with geophysical medium properties. The quantitative features of the inverted image are extracted, and the quantitative features are correlated with geological significance to obtain classification data; A three-dimensional geological model is constructed based on the classification data. The three-dimensional geological model is then subjected to positive verification and external evidence constraints to obtain a dynamic geological model with uncertainty assessment. The electric field observation data is input into the dynamic geological model to obtain imaging data.

6. The landslide geological body identification imaging method according to claim 1, characterized in that, The inversion imaging steps of the two-dimensional profile include: The area below each measuring point in the target region is set as a horizontal layered medium, and the optimal resistivity-depth model of each measuring point is solved by a smooth model inversion algorithm. Iteratively adjust the resistivity and thickness of each layer of the optimal resistivity-depth model, and output a one-dimensional resistivity-depth curve for each measurement point; The one-dimensional resistivity-depth curves of each measurement point are arranged along the profile to obtain the two-dimensional profile.

7. The landslide geological body identification imaging method according to claim 1, characterized in that, The three-dimensional slice images are obtained by resistivity tomography or direct imaging based on a current-sensing model.

8. The landslide geological body identification imaging method according to claim 1, characterized in that, Before performing inversion imaging on the imaging data, the following steps are also included: The imaging data is format-normalized and defective pixels are removed to obtain standardized data; The standardized data is subjected to system noise suppression and human noise suppression to obtain denoised data; The denoised data is normalized to convert the transient response values ​​of each measurement point in the denoised data into apparent resistivity. Inversion imaging is then performed based on the apparent resistivity to obtain a two-dimensional profile and a three-dimensional slice of the target region.

9. The landslide geological body identification imaging method according to any one of claims 1-8, characterized in that, The method further includes: Extract the spatial geometric and statistical characteristics of similar geophysical parameters from the field data acquisition parameters and the electric field observation data; Anomalies are identified from the spatial geometric features and statistical features. The geometric, physical, and morphological features of each anomaly are calculated, and the compactness of the anomaly is quantified by a shape index. Based on the deviation between the field data acquisition parameters and the compactness of the electric field observation data, the initial model of the landslide geological body is adjusted to obtain the modified landslide geological body model. Key disaster-causing factors of landslide geological structure are selected. Based on the key disaster-causing factors and the modified landslide geological model, the weight of each key disaster-causing factor to known geological disaster-causing factors is determined by the evidence weight method or logistic regression method. The landslide geological structure fine identification and disaster-causing factor discrimination advantage index of each three-dimensional grid cell is calculated to obtain the landslide geological structure identification advantage index model. Landslide geological hazard prediction is performed based on the landslide geological structure identification favorable index model, and the prediction results are output.

10. A landslide geological body identification imaging device, employing the landslide geological body identification imaging method as described in any one of claims 1-9, characterized in that, include: The model building module is used to construct an initial model of the landslide geological body based on the landslide geological body disaster data of the target area; The geophysical simulation module is used to perform geophysical simulation based on the initial model of the landslide geological body and extract field data acquisition parameters of the target area. The data acquisition module is used to detect natural and / or artificial electric fields based on the field data acquisition parameters to obtain electric field observation data; The data extraction module is used to invert the electric field observation data and extract imaging data; An imaging module is used to perform inversion imaging based on the imaging data to obtain a two-dimensional cross-sectional view and a three-dimensional slice view of the target area.

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