Disaster state prediction method and system based on three-dimensional geologic body modeling

Abstract

The invention discloses a disaster state prediction method and system based on three-dimensional geologic body modeling, and relates to the technical field of geological disaster prediction, and the method comprises the steps: collecting and fusing multi-source data, collecting underground geologic body chromatography data through seismic tomography and geological radar, and obtaining surface form data through unmanned aerial vehicle aerial photogrammetry and ground survey; seamless joint and unified expression of underground geophysical prospecting data and surface form data are achieved through the multi-source data acquisition and fusion technology, the precision and comprehensiveness of geological disaster prediction are remarkably improved, and particularly, the seismic tomography and geological radar technology is adopted, unmanned aerial vehicle aerial photogrammetry and ground survey are combined, and the geological disaster prediction precision and comprehensiveness are improved. A three-dimensional geologic structure database containing underground geologic structures and surface form information is constructed, data sources of disaster prediction are enriched, and the accuracy and timeliness of data are ensured through a space-time consistency correction formula.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster prediction, and in particular to a disaster status prediction method and system based on three-dimensional geological body modeling. Background Art

[0002] With the frequent occurrence of geological disasters, accurate prediction and prevention of geological disasters have become an important issue to protect people's lives and property and maintain social stability. Geological disasters, such as landslides, mudslides, and ground collapses, are often sudden and destructive, posing a serious threat to human society. Traditional geological disaster prediction methods mostly rely on empirical judgment, single data source analysis, or simple physical models, which are difficult to fully and accurately reflect the complex mechanical behavior and spatiotemporal evolution characteristics of geological bodies. In recent years, with the rapid development of computer technology, remote sensing technology, and geophysical detection technology, multi-source data fusion and three-dimensional geological body modeling technology have gradually become research hotspots in the field of geological disaster prediction, providing possibilities for improving prediction accuracy.

[0003] Traditional geological disaster prediction technology has many limitations. First, the data source is single and mostly relies on surface observations or limited underground detection data. It is difficult to fully obtain the three-dimensional structure and mechanical properties of the geological body, resulting in great uncertainty in the prediction results. Second, traditional models often simplify the mechanical behavior of the geological body, ignore the time effect and the time-dependent degradation characteristics of the geological body, and cannot accurately simulate the dynamic evolution process of geological disasters. In addition, traditional methods lack an effective closed-loop correction mechanism, making it difficult to dynamically adjust and optimize the prediction model based on real-time monitoring data, further limiting the improvement of prediction accuracy. Therefore, there is an urgent need for a new geological disaster prediction technology that can integrate multi-source data, accurately simulate the dynamic behavior of geological bodies, and have closed-loop correction capabilities.

[0004] Therefore, the development of disaster status prediction methods and systems based on three-dimensional geological body modeling not only improves the accuracy and timeliness of geological disaster prediction, but also provides strong technical support for the prevention and response to geological disasters. Summary of the Invention

[0005] The purpose of the present invention is to make up for the shortcomings of the existing technology and provide a disaster status prediction method and system based on three-dimensional geological body modeling. Through multi-source data acquisition and fusion technology, a three-dimensional geological structure database is constructed, and based on this database, a three-dimensional geological body time-dependent degradation tensor model and a discrete element-time-dependent coupling model are constructed. It can accurately simulate the mechanical behavior evolution and spatiotemporal degradation characteristics of the geological body, realize the dynamic prediction and deduction of geological disasters, and at the same time, introduce a closed-loop correction and iterative update mechanism to ensure the continuous optimization and accuracy improvement of the prediction results.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: On the one hand, a disaster status prediction method based on three-dimensional geological body modeling, the specific steps of the prediction method include:

[0007] S100, multi-source data acquisition and fusion: Seismic tomography and geological radar are used to collect underground geological body tomography data, and UAV aerial photogrammetry and ground surveys are used to obtain surface morphology data. Simultaneously pre-processing is performed, and a spatiotemporal consistency correction formula is used to unify the underground geophysical and surface morphology data to construct a 3D geological structure database.

[0008] S200, 3D geological model construction: Relying on a 3D geological structure database, geological modeling software is used to define the model space range and perform grid division, assign physical properties and lithology identifiers to the geological body, extract and define the initial state parameters of the geological body, and construct a 3D geological body time-dependent degradation tensor model;

[0009] S300, Discrete Element-Time-Dependent Model Coupling: Import the 3D geological structure database into the discrete element software for discretization. Utilize the 3D geological body time-dependent degradation tensor model to quantify the geological body time-dependent characteristics and convert them into discrete element model time-dependent parameters to establish the discrete element-time-dependent coupled model.

[0010] S400, dynamic disaster prediction and deduction: Using the discrete element-time coupled model as the initial input, setting simulation boundary conditions and time parameters, simulating the evolution of geological body mechanical behavior, calculating the probability of geological disasters at different spatial locations and times, and generating a three-dimensional disaster prediction matrix including spatial location, failure mode, and time probability;

[0011] S500, closed-loop correction and iterative update: Deploy monitoring equipment to collect real-time data, compare the monitoring data with the simulation data to calculate the deviation, drive the adaptive correction of model parameters through closed-loop correction adaptive control equations, re-enter the corrected parameters into the model for iterative simulation, and update the three-dimensional disaster prediction matrix.

[0012] Furthermore, in said S100, in the multi-source data acquisition and fusion, underground geological body tomography data acquired by seismic tomography and geological radar is:

[0013] Seismic tomography: Using a distributed fiber-optic seismic monitoring system, at least three survey lines are deployed in the target area based on the geological structure trend. Each survey line is equipped with no fewer than 20 fiber-optic detectors. Seismic waves are excited by controlled vibroseis or artificial blasting, and P-wave and S-wave data are collected using a cross-observation system to form a three-dimensional seismic travel-time data set.

[0014] In terms of geological radar: a multi-channel geological radar system is used to perform grid scanning of the target area with a 50MHz-1GHz shielded antenna. Data is collected according to the standard that the distance between measuring points does not exceed 0.5m and the distance between measuring lines does not exceed 2m. The electromagnetic wave reflection signal is converted into depth information through time-depth conversion. After filtering and gain compensation processing, an image data set of faults and cavity structures within 0-50m underground is obtained.

[0015] Furthermore, in the multi-source data acquisition and fusion step S100, the subsurface geophysical and surface morphological data are uniformly expressed using a time-space consistency correction formula, and the mathematical expression is: Among them, Φ(x, y, z, t) is the fused four-dimensional spatiotemporal evolution field function of the geological body, which comprehensively reflects the characteristics of the geological body in the spatial x, y, z and time t dimensions. x, y, z represent the coordinate variables of the spatial dimension, which are used to determine the position of the geological body or monitoring point in three-dimensional space. t represents time and is used to describe the evolution of geological body characteristics and disaster-related physical quantities over time. Φ g (x, y, z) is the underground geophysical field function, which integrates the seismic wave velocity and resistivity information of the underground geological body obtained through seismic tomography and geological radar technology, Φ s (x, y, z, t) is the function of the terrain variation field, which contains information on terrain variability and rock and soil migration vectors obtained by UAV mapping and ground survey. is the variable Laplace operator, which represents the second-order rate of change of geological structure over time and is used to characterize the dynamic evolution of geological structure. α, β, and γ are weight coefficients used to adjust the contribution of different data sources in the fusion process.

[0016] Furthermore, in the step S200, the three-dimensional geological body time degradation tensor model is constructed in the three-dimensional geological model construction, the formula is: Where D(t) is the mechanical parameter tensor at time t, D0 is the initial mechanical parameter tensor of the geological body, which is the mechanical parameter composition of the geological body in the initial state obtained through indoor rock mechanics tests and geotechnical tests, Λ(r) is the aging degradation rate tensor, which reflects the difference in the degradation rate of mechanical parameters of the geological body due to weathering and consolidation in different directions, O(r) is the stress state tensor at time r, and the stress distribution of the geological body at different times is obtained by real-time calculation through discrete element simulation, t represents time, and r represents the time integral variable.

[0017] Furthermore, the discrete element-time-effect coupling model in the discrete element-time-effect coupling in S300 is constructed as follows: in is the rate of change of shear stress between particles, G is the shear modulus, which is determined by indoor tests or reference to empirical data of geological bodies. is the interparticle shear strain rate, which is calculated from the relative displacement of particles in discrete element simulation, t c is a characteristic time parameter, which represents the viscoelastic relaxation time constant of the geological body and is determined by fitting the experimental data. ij is the interparticle shear stress, τ max (t) is the maximum shear strength between particles at time t, and ω is the shape parameter.

[0018] Furthermore, in the above S400, the dynamic disaster prediction and deduction includes the construction of a three-dimensional disaster prediction matrix including spatial location, damage mode, and time probability:

[0019] (1) Extract the initial state and mechanical property parameters of the discrete element-time-dependent coupling model, set the boundary and time conditions of the discrete element-time-dependent coupling model, and then start the simulation;

[0020] (2) Real-time monitoring of key mechanical parameters of the geological body during the simulation process, recording data at different time points, and analyzing the progressive failure process;

[0021] (3) Determine the three-dimensional spatial coordinate range of the disaster occurrence based on the particle displacement and damage state in the simulation and the critical conditions;

[0022] (4) According to the mechanical response and time-dependent characteristics between particles, the probability of different disaster modes is calculated by the general formula for calculating the probability of failure mode, and the dominant failure mode type is determined;

[0023] (5) Considering the spatiotemporal influence of geological bodies and parameter uncertainty, the probability distribution of disaster occurrence at different time nodes is calculated by combining the probability field equation of disaster evolution;

[0024] (6) The spatial range, damage mode and time probability are integrated into a three-dimensional tensor matrix, and the dynamic evolution trend of the disaster is presented through visualization.

[0025] Furthermore, in the above S400, the probability of different disaster modes is calculated by using the general formula for calculating the probability of destruction mode in the dynamic prediction and deduction of disasters. The formula is: Where: P m (s, t) is the probability of mode m occurring at position x at time t, F int,m (s, t) is the internal trigger stress field of mode m, F ext,m (s,t)||| is the external load field of mode m, F res,m (s, t) is the resistance field of mode m, where m represents the type identifier of the disaster mode, s represents the spatial position, and t represents the time.

[0026] Furthermore, in the above S400, the probability distribution of disaster occurrence at different time nodes is calculated by combining the probability field equation of disaster evolution in the dynamic prediction and deduction of disasters. The calculation formula is: where P(x,y,z,t) is the probability of a geological disaster occurring at the spatial position (x,y,z) and time t, K(x,y,z,t|x′,y',z',t') is the spatiotemporal kernel function, which characterizes the propagation influence domain of the disaster in space and time, (x,y,z,t) is the target spatiotemporal point, (x',y',z',t') is the reference spatiotemporal point, which represents the state of other positions and other times in the geological body, Ω(x',y',z',t'), the disaster triggering intensity function, is used to measure the possibility of a disaster occurring in the geological body at the spatial position (x',y',z') and time t', and F ext is the external load, including the additional load caused by earthquake force and rainfall, F int (t) is the internal stress of the geological body at time t, which is calculated by the discrete element-time-dependent coupling model, and F res (t) is the resistance of the geological body after aging degradation at time t, which is calculated based on the mechanical parameters and structural characteristics of the geological body. t represents time and V represents the integration area.

[0027] Furthermore, in the closed-loop correction and iterative update in S500, the closed-loop correction adaptive control equation is used to drive the adaptive correction of the model parameters, and the formula is: Where T is a positive definite symmetric correction matrix, which is determined by the approximate inverse of the Hessian matrix and is used to adjust the direction and step size of parameter correction. is the gradient of the deviation energy functional J(p) with respect to the model parameter vector p, indicating the direction of parameter adjustment. J(p) is the prediction-measurement deviation energy functional, and the formula is: ρ(s,t) is a weight function used to adjust the importance of the difference between the predicted and measured data at different spatial locations and times. It is set according to the reliability of the monitoring data and the importance of the geological body. pred (s,t) is the predicted displacement field at spatial position (s) and time t, calculated by the discrete element-time coupled model, u obs (s, t) is the measured displacement field at spatial position (s) and time t, measured by displacement monitoring equipment deployed in the target area, is the rate of change of the model parameter vector p over time, V represents the integration area, s represents the spatial position, and t represents the time.

[0028] On the other hand, the disaster status prediction system based on 3D geological body modeling includes: multi-source data acquisition and fusion module, 3D geological model construction module, discrete element-time effect model coupling module, disaster dynamic prediction and deduction module, and closed-loop correction and iterative update module;

[0029] The multi-source data acquisition and fusion module collects underground geological body tomography data through seismic tomography and geological radar; simultaneously obtains surface morphology data through drone aerial photogrammetry and ground survey, simultaneously pre-processes the collected data, and uses a time-space consistency correction formula to unify the expression and construct a three-dimensional geological structure database;

[0030] The three-dimensional geological model construction module: relying on the three-dimensional geological structure database, using geological modeling software to delineate the model space range and perform grid division, assign physical properties and lithology identifiers to the geological body, extract and define initial state parameters, and thus construct a three-dimensional geological body time-dependent degradation tensor model;

[0031] The discrete element-time-effect model coupling module imports the 3D geological structure database into the discrete element software for discretization, quantifies the geological body's time-effect characteristics with the help of the 3D geological body time-effect degradation tensor model, and converts them into time-related parameters of the discrete element model, thereby establishing the discrete element-time-effect coupling model;

[0032] The disaster dynamic prediction and deduction module uses the discrete element-time coupling model as initial input, sets simulation boundary conditions and time parameters, simulates the evolution of geological body mechanical behavior, monitors key mechanical parameters in real time and analyzes the progressive failure process, determines the three-dimensional spatial range of disaster occurrence based on particle displacement and failure state, calculates the probabilities of different disaster modes to determine the dominant failure mode, calculates the probability distribution of disaster occurrence at each time node considering the spatiotemporal influence and parameter uncertainty, and integrates and generates a three-dimensional disaster prediction matrix;

[0033] The closed-loop correction and iterative update module deploys monitoring equipment to collect real-time data, compares it with simulation data to calculate deviations, drives the adaptive correction of model parameters through closed-loop correction adaptive control equations, and re-inputs the corrected parameters into the model for iterative simulation to update the three-dimensional disaster prediction matrix.

[0034] Beneficial effects:

[0035] Through multi-source data acquisition and fusion technology, the seamless connection and unified expression of underground geophysical data and surface morphological data have been achieved, significantly improving the accuracy and comprehensiveness of geological disaster prediction. Specifically, by using seismic tomography and geological radar technology, combined with drone aerial photogrammetry and ground surveys, a three-dimensional geological structure database containing underground geological structure and surface morphological information has been constructed. This not only enriches the data source for disaster prediction, but also ensures the accuracy and timeliness of the data through spatiotemporal consistency correction formulas. In addition, the three-dimensional geological body time-dependent degradation tensor model constructed based on this database can dynamically reflect the changes in the mechanical properties of the geological body at different time points, providing a more scientific and accurate basis for disaster prediction and effectively reducing disaster risks.

[0036] By importing the three-dimensional geological structure database into discrete element software for discretization processing, and combining it with the three-dimensional geological body time-dependent degradation tensor model, a discrete element-time-dependent coupling model was successfully established. This model can accurately simulate the evolution process of the mechanical behavior of the geological body and calculate the probability of geological disasters at different spatial locations and times. More importantly, by deploying monitoring equipment to collect real-time data and comparing it with the simulated data to calculate the deviation, the adaptive correction and iterative update of the model parameters are realized. This closed-loop correction mechanism ensures the continuous optimization and accuracy improvement of the prediction results, which not only improves the intelligence level of disaster prediction, but also provides strong technical support for the early warning and prevention of geological disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort.

[0038] Figure 1 This is a framework diagram of the disaster status prediction method based on three-dimensional geological modeling;

[0039] Figure 2 This is a flow chart of the disaster status prediction system based on three-dimensional geological modeling. DETAILED DESCRIPTION

[0040] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0041] Example 1:

[0042] Implementation of disaster status prediction method based on three-dimensional geological body modeling.

[0043] S100, Multi-Source Data Acquisition and Fusion: In a potential landslide hazard area in a certain mountainous area, multi-dimensional data acquisition was first carried out. To obtain information on the underground geological structure, a distributed fiber-optic seismic monitoring system was used. Three survey lines were precisely laid out along the main trend of the geological structure in the area. Each survey line was evenly equipped with more than 20 fiber-optic detectors. Seismic waves were excited by controlled vibrators, and a cross-observation system was used to comprehensively collect P-wave and S-wave data, thus forming a three-dimensional seismic travel-time dataset covering the target area. At the same time, a multi-channel geological radar system is used to perform a grid scan of the entire area with a shielded antenna of a specific frequency. Data is collected strictly in accordance with the prescribed measurement point spacing and measurement line spacing. The electromagnetic wave reflection signal is converted into intuitive depth information through time-depth conversion technology. After a series of data processing such as filtering and gain compensation, a clear image data set of the key structures of underground faults and cavities is obtained. In addition, UAV aerial photogrammetry technology is combined with ground field surveys to comprehensively obtain the surface morphological data of the area, including details of terrain undulations and rock and soil distribution. Subsequently, the collected underground geological body tomography data and surface morphological data are preprocessed to remove noise and invalid information. Then, the spatiotemporal consistency correction formula is used to unify data from different sources and dimensions into the same spatiotemporal coordinate system for expression. The mathematical expression of the spatiotemporal consistency correction formula is as follows: Among them, Φ(x, y, z, t) is the fused four-dimensional spatiotemporal evolution field function of the geological body, which comprehensively reflects the characteristics of the geological body in the spatial x, y, z and time t dimensions. x, y, z represent the coordinate variables of the spatial dimension, which are used to determine the position of the geological body or monitoring point in three-dimensional space. t represents time and is used to describe the evolution of geological body characteristics and disaster-related physical quantities over time. Φ g (x, y, z) is the underground geophysical field function, which integrates the seismic wave velocity and resistivity information of the underground geological body obtained through seismic tomography and geological radar technology, Φ s (x, y, z, t) is the function of the terrain variation field, which contains information on terrain variability and rock and soil migration vectors obtained by UAV mapping and ground survey. is the variable Laplace operator, which represents the second-order change rate of the geological structure over time and is used to characterize the dynamic evolution of the geological structure. α, β, and γ are weight coefficients, which are used to adjust the contribution of different data sources in the fusion process, and finally build a comprehensive and accurate three-dimensional geological structure database. Figure 1 This provides a solid data foundation for subsequent analysis.

[0044] S200, 3D geological model construction: Relying on the established 3D geological structure database and using professional geological modeling software, the spatial scope of the model is first scientifically delineated according to the regional geological characteristics and the scope of potential disaster impact, and the space is finely gridded to ensure that the model can accurately reflect the spatial distribution of the geological body. Then, the corresponding geological body physical properties (such as density and elastic modulus) and lithology identifiers are assigned to each grid cell to make the model closer to the actual geological conditions. Then, the key parameters of the geological body in its initial state are extracted from the database, and the performance changes of the geological body in the natural environment due to weathering and consolidation are comprehensively considered to define the initial state parameters of the geological body. Finally, a 3D geological body aging degradation tensor model is constructed. The calculation formula is: Where D(t) is the mechanical parameter tensor at time t, D0 is the initial mechanical parameter tensor of the geological body, which is the mechanical parameter composition of the geological body in the initial state obtained through indoor rock mechanics tests and geotechnical tests, Λ(r) is the time-dependent degradation rate tensor, which reflects the difference in the degradation rate of the mechanical parameters of the geological body due to weathering and consolidation in different directions, O(r) is the stress state tensor at time r, and the stress distribution of the geological body at different times is obtained through real-time calculation of discrete element simulation. t represents time, and r represents the time-integrated variable. This model can dynamically reflect the degradation law of the mechanical parameters of the geological body over time, providing a key basis for the time-dependent characteristics of subsequent simulations.

[0045] S300, discrete element-time-dependent model coupling: The 3D geological structure database is imported into the discrete element software, and the geological body is discretized and decomposed into a large number of interacting particle units to simulate the micromechanical behavior of the geological body. In this process, the 3D geological body time-dependent degradation tensor model constructed in the early stage is used to quantitatively analyze the geological body's time-dependent characteristics. These time-dependent characteristic parameters are converted into time-dependent parameters in the discrete element model, such as the strength attenuation coefficient between particles. In this way, the discrete element-time-dependent coupling model is established. The calculation formula is: in is the rate of change of shear stress between particles, G is the shear modulus, which is determined by indoor tests or reference to empirical data of geological bodies. is the interparticle shear strain rate, which is calculated from the relative displacement of particles in discrete element simulation, t c is a characteristic time parameter, which represents the viscoelastic relaxation time constant of the geological body and is determined by fitting the experimental data. ij is the interparticle shear stress, τ max (t) is the maximum shear strength between particles at time t, and ω is the shape parameter. This model can simultaneously consider the discrete characteristics and time-dependent degradation characteristics of the geological body, and more realistically simulate the mechanical response of the geological body in the actual environment.

[0046] S400, dynamic disaster prediction and deduction: Using the discrete element-time coupled model as the initial input, reasonable simulation boundary conditions (such as fixed boundaries and load conditions) and time parameters are set according to the actual geological conditions and environmental factors of the mountainous area, and the simulation process of the mechanical behavior evolution of the geological body is started. During the simulation, the system monitors the key mechanical parameters of the geological body in real time, such as stress distribution and strain changes, and records the data at different time nodes in detail. By analyzing these data, the progressive destruction process of the geological body is deeply studied. When abnormal particle displacement or signs of destruction appear in the simulation, the three-dimensional spatial coordinate range where the landslide disaster may occur is accurately determined based on the preset critical conditions. At the same time, based on the mechanical response characteristics and time-dependent degradation characteristics between particles, the probability calculation formula of the destruction mode is used to calculate the probability of occurrence of different possible destruction modes of the landslide (such as sliding and collapse). The formula is: Where: P m (s, t) is the probability of mode m occurring at position x at time t, F int,m (s, t) is the internal trigger stress field of mode m, F ext,m (s,t)|| is the external load field of mode m, F res,m (s, t) is the resistance field of mode m, m represents the type identifier of the disaster mode, s represents the spatial position, and t represents the time, so as to determine the type of dominant damage mode. In addition, the mutual influence of geological bodies in the time and space dimensions and the uncertainty of parameters are fully considered. Combined with the probability field equation of disaster evolution, the spatial distribution of the probability of disaster occurrence at different time nodes is calculated. The calculation formula is: where P(x,y,z,t) is the probability of a geological disaster occurring at the spatial position (x,y,z) and time t, K(x,y,z,t|x′,y′,z′,t′) is the spatiotemporal kernel function, which characterizes the propagation influence domain of the disaster in space and time, (x,y,z,t) is the target spatiotemporal point, (x',y',z',t′) is the reference spatiotemporal point, which represents the state of other positions and other times in the geological body, Ω(x′,y',z',t'), the disaster triggering intensity function, is used to measure the possibility of a disaster occurring in the geological body at the spatial position (x′,y',z') and time t', and F ext is the external load, including the additional load caused by earthquake force and rainfall, F int (t) is the internal stress of the geological body at time t, which is calculated by the discrete element-time-dependent coupling model, and F res(t) is the resistance of the geological body after time degradation at time t, which is calculated based on the mechanical parameters and structural characteristics of the geological body. t represents time, and V represents the integration area. Finally, the determined spatial range, the determined failure mode, and the calculated time probability are integrated into a three-dimensional tensor matrix and processed through visualization technology to present the evolution trend of landslide hazards in the mountainous area in an intuitive and dynamic manner, providing a scientific basis for disaster warning and prevention.

[0047] S500, closed-loop correction and iterative update. To improve the accuracy of the prediction, various monitoring devices, such as displacement monitors and stress sensors, are deployed in the target mountain area to collect the actual deformation data and mechanical parameters of the geological body in real time. The collected monitoring data is compared with the model simulation data, and the deviation between the two is calculated. Then, the adaptive control equation is corrected through the closed loop, and the model parameters are driven to perform adaptive correction based on the deviation results. The formula is: Where T is a positive definite symmetric correction matrix, which is determined by the approximate inverse of the Hessian matrix and is used to adjust the direction and step size of parameter correction. is the gradient of the deviation energy functional J(p) with respect to the model parameter vector p, indicating the direction of parameter adjustment. J(p) is the prediction-measurement deviation energy functional, and the formula is: ρ(s,t) is a weight function used to adjust the importance of the difference between the predicted and measured data at different spatial locations and times. It is set according to the reliability of the monitoring data and the importance of the geological body. pred (s,t) is the predicted displacement field at spatial position (s) and time t, calculated by the discrete element-time coupled model, u obs (s, t) is the measured displacement field at spatial position (s) and time t, measured by displacement monitoring equipment deployed in the target area, is the rate of change of the model parameter vector p over time, V represents the integration area, s represents the spatial position, and t represents time. The parameters in the model that do not conform to the actual situation are adjusted, and the corrected parameters are re-entered into the model for iterative simulation. The disaster prediction results are generated again and compared with the new monitoring data. The above correction process is repeated until the deviation between the model prediction results and the actual monitoring data meets the accuracy requirements. Finally, the three-dimensional disaster prediction matrix is ​​updated so that the prediction results can more accurately reflect the actual situation of landslide disasters in mountainous areas.

[0048] In summary, in the landslide disaster prediction scenario in mountainous areas, this disaster status prediction method based on three-dimensional geological body modeling integrates underground geological structure and surface morphology information through multi-source data collection and fusion, and constructs an accurate three-dimensional geological structure database; with the help of three-dimensional geological model construction and discrete element-time-effect model coupling, dynamic simulation of geological body time-effect degradation characteristics and mechanical behavior is realized; through disaster dynamic prediction and deduction, comprehensive consideration of spatiotemporal factors and probability models is made to form a three-dimensional disaster prediction matrix including spatial position, failure mode and time probability; finally, through closed-loop correction and iterative update, the model parameters are continuously optimized using real-time monitoring data, thereby improving the accuracy of the prediction. This method systematically combines data collection, model construction, simulation and deduction, and real-time correction, providing a complete technical solution for the scientific prediction and early warning of landslide disasters in mountainous areas, which can effectively assist disaster prevention and control decision-making and reduce disaster risks.

[0049] Example 2:

[0050] An embodiment of a disaster status prediction system based on three-dimensional geological body modeling.

[0051] Multi-source data acquisition and fusion module: When disaster prediction work is carried out in a mine goaf, the data acquisition module is started first. Through seismic tomography equipment, according to the geological structure trend of the goaf and its surroundings, three survey lines are scientifically laid out in the target area. Each survey line is evenly installed with no less than 20 fiber optic detectors. The controllable source is used to excite seismic waves, and P-wave and S-wave data are accurately collected to form a detailed three-dimensional seismic travel time data set. This data set can reflect the wave velocity distribution of the underground rock formation. At the same time, a multi-channel geological radar system is used to conduct a comprehensive grid scan of the target area with a shielded antenna of a specified frequency, strictly adhering to the principle that the distance between measuring points does not exceed 0.5m and the distance between measuring lines does not exceed 2. m standard acquisition data, the radar reflection signal is converted into depth information through time-depth conversion technology, and after filtering and gain compensation processing, a clear image data set of faults and cavity structures within 0-50m underground is obtained, providing intuitive information for identifying potential hidden dangers in goafs. In addition, UAV aerial photogrammetry technology is combined with ground field surveys to obtain surface morphological data, including information on the settlement trend and crack distribution of the goaf surface. After the acquisition is completed, the underground geological body tomography data and surface morphological data are preprocessed to remove interference information, and the underground geophysical and surface morphological data are unified using the time-space consistency correction formula. The mathematical expression of the time-space consistency correction formula is: A three-dimensional geological structure database containing geological structure and surface morphology information of the goaf is constructed to lay a data foundation for the work of subsequent modules.

[0052] 3D geological model construction module: After receiving the 3D geological structure database from the multi-source data acquisition and fusion module, it begins to build a 3D geological model. Using professional geological modeling software, the spatial range of the model is reasonably delineated according to the actual scope and geological characteristics of the goaf, and the space is finely gridded so that the grid can accurately match the structural characteristics of the geological body. Subsequently, each grid cell is assigned corresponding geological body physical properties (such as density, Poisson's ratio) and lithology identifiers to ensure that the model can truly reflect the material properties of the goaf and surrounding geological bodies. Next, various parameters of the geological body in the initial state, such as initial stress and initial displacement, are extracted from the database. The impact of mining activities and natural factors on the geological body are comprehensively considered, and the initial state parameters of the geological body are defined. Finally, a 3D geological body aging degradation tensor model is constructed. The mathematical formula of the 3D geological body aging degradation tensor model is as follows: This model can quantitatively describe the degradation process of mechanical parameters of geological bodies over time due to stress release and rock weathering factors after the formation of goaf, providing key model support for subsequent simulation of the time-dependent evolution of geological bodies.

[0053] Discrete element-time-dependent model coupling module: The 3D geological structure database is imported into the discrete element software, and the goaf and surrounding geological bodies are discretized and abstracted into a large number of interacting particle units to simulate the deformation and destruction process of the geological body under the influence of the goaf. During the discretization process, the 3D geological body time-dependent degradation tensor model constructed in the early stage is used to quantitatively analyze the time-dependent characteristics of the geological body caused by the passage of mining time (such as strength attenuation and reduction in deformation modulus). These time-dependent characteristics are then converted into time-related parameters in the discrete element model, such as the rate of change of the bond strength between particles over time. In this way, the discrete element-time-dependent coupling model is established. The mathematical formula of the discrete element-time-dependent coupling model is as follows: This model can simultaneously consider the discrete particle characteristics and time-varying characteristics of the goaf geological body, and more accurately simulate the evolution process of goaf collapse disasters.

[0054] Disaster dynamic prediction and simulation module: Based on the discrete element-time coupled model, combined with the actual mining situation and geological conditions of the goaf, reasonable simulation boundary conditions (such as displacement constraints at the goaf boundary and the deadweight load of the overlying rock strata) and time parameters are set to initiate the simulation of the mechanical behavior of the goaf geological body. During the simulation, key mechanical parameters of the geological body, such as the stress concentration of the goaf roof and the displacement of the rock strata, are monitored in real time. Detailed data is recorded at different mining time points. By analyzing this data, the progressive failure process of the rock strata in the goaf is studied, such as the evolution of the roof from bending to cracking to collapse. When particle displacement exceeds a critical value or signs of rock strata failure appear in the simulation, the module determines the three-dimensional spatial coordinate range where the goaf collapse disaster may occur based on the preset critical conditions, clarifying the location and range of the potential collapse area. At the same time, based on the mechanical response characteristics and time-dependent degradation characteristics of the particles, the failure mode probability calculation formula is used to calculate the probability of different possible failure modes in the goaf (such as roof collapse and floor heaving). The mathematical formula for the failure mode probability calculation formula is: In this way, the dominant failure mode type is determined. In addition, the mutual influence of geological bodies in the goaf in time and space, as well as the uncertainty of mining parameters and geological parameters are fully considered. Combined with the disaster evolution probability field equation, the spatial distribution of the probability of collapse disaster at different mining time nodes is calculated. The calculation formula of the disaster evolution probability field equation is: Finally, the determined spatial range, determined failure mode, and calculated time probability are integrated into a three-dimensional tensor matrix, and the evolution trend of goaf collapse disasters is presented in the form of three-dimensional dynamic graphics, providing an intuitive decision-making basis for mine safety production and disaster prevention.

[0055] Closed-loop correction and iterative update module: Various monitoring devices, such as total stations, inclinometers, and strain gauges, are deployed in the target area of ​​the mine goaf to collect real-time monitoring data on the displacement data of the goaf surface and the stress data inside the rock formation. The collected monitoring data is compared and analyzed with the simulation data of the disaster dynamic prediction and deduction module, and the deviation value between the two is calculated to evaluate the accuracy of the model prediction. Then, through the closed-loop correction of the adaptive control equation, parameter correction instructions are generated according to the deviation results. The calculation formula of the closed-loop correction adaptive control equation is as follows: The relevant parameters in the driving model (such as the elastic modulus and friction coefficient of the rock formation) are adaptively corrected to make the model more consistent with the actual geological conditions of the goaf. The corrected parameters are re-entered into the model for iterative simulation, and the prediction results of goaf collapse disasters are generated again. They are compared and verified with the new monitoring data. The above correction process is repeated until the deviation between the model prediction results and the actual monitoring data is within the allowable range. Finally, the three-dimensional disaster prediction matrix is ​​updated to make the prediction results of mine goaf collapse disasters more accurate and reliable, providing strong technical support for the safe operation of mines.

[0056] In summary, the mine goaf collapse disaster prediction system realizes the full process automation processing from data acquisition to prediction result correction through the coordinated operation of data acquisition module, model construction module, model coupling module, prediction and deduction module and correction and update module. The system constructs a comprehensive three-dimensional geological structure database through multi-source data acquisition and spatiotemporal consistency correction; uses the three-dimensional geological body time-dependent degradation tensor model and discrete element-time-dependent coupling model to accurately simulate the mechanical evolution process of the goaf geological body under the action of time; with the help of the failure mode probability calculation formula and the disaster evolution probability field equation, the spatiotemporal probability of collapse disasters is quantified; through the closed-loop correction of the adaptive control equation, the dynamic optimization of the model parameters is realized. The system forms a complete goaf collapse disaster prediction system, which can predict the disaster evolution trend in real time and accurately, providing strong technical support for mine safety production, and has significant engineering application value and scientific significance.

Claims

1. A disaster state prediction method based on three-dimensional geological body modeling, characterized in that the steps include: S100, multi-source data acquisition and fusion: Seismic tomography and geological radar are used to collect underground geological body tomography data, and UAV aerial photogrammetry and ground surveys are used to obtain surface morphology data. Simultaneously pre-processing is performed, and a spatiotemporal consistency correction formula is used to unify the underground geophysical and surface morphology data to construct a 3D geological structure database. S200, 3D geological model construction: Relying on a 3D geological structure database, geological modeling software is used to define the model space range and perform grid division, assign physical properties and lithology identifiers to the geological body, extract and define the initial state parameters of the geological body, and construct a 3D geological body time-dependent degradation tensor model; S300, Discrete Element-Time-Dependent Model Coupling: Import the 3D geological structure database into the discrete element software for discretization. Utilize the 3D geological body time-dependent degradation tensor model to quantify the geological body time-dependent characteristics and convert them into discrete element model time-dependent parameters to establish the discrete element-time-dependent coupled model. S400, dynamic disaster prediction and deduction: Using the discrete element-time coupled model as the initial input, setting simulation boundary conditions and time parameters, simulating the evolution of geological body mechanical behavior, calculating the probability of geological disasters at different spatial locations and times, and generating a three-dimensional disaster prediction matrix including spatial location, failure mode, and time probability; S500, closed-loop correction and iterative update: Deploy monitoring equipment to collect real-time data, compare the monitoring data with the simulation data to calculate the deviation, drive the adaptive correction of model parameters through closed-loop correction adaptive control equations, re-enter the corrected parameters into the model for iterative simulation, and update the three-dimensional disaster prediction matrix.

2. The disaster state prediction method based on three-dimensional geological body modeling according to claim 1 is characterized in that: In the above S100, the underground geological body tomography data collected by seismic tomography and geological radar in multi-source data collection and fusion is: Seismic tomography: Using a distributed fiber-optic seismic monitoring system, at least three survey lines are deployed in the target area based on the geological structure trend. Each survey line is equipped with no fewer than 20 fiber-optic detectors. Seismic waves are excited by controlled vibroseis or artificial blasting, and P-wave and S-wave data are collected using a cross-observation system to form a three-dimensional seismic travel-time data set. In terms of geological radar: a multi-channel geological radar system is used to perform grid scanning of the target area with a 50MHz-1GHz shielded antenna. Data is collected according to the standard that the distance between measuring points does not exceed 0.5m and the distance between measuring lines does not exceed 2m. The electromagnetic wave reflection signal is converted into depth information through time-depth conversion. After filtering and gain compensation processing, an image data set of faults and cavity structures within 0-50m underground is obtained.

3. The disaster state prediction method based on three-dimensional geological body modeling according to claim 1, characterized in that: In the above S100, the subsurface geophysical data and the surface morphological data are uniformly expressed using the spatiotemporal consistency correction formula in the multi-source data acquisition and fusion. The mathematical expression is: Among them, Φ(x, y, z, t) is the fused four-dimensional spatiotemporal evolution field function of the geological body, which comprehensively reflects the characteristics of the geological body in the spatial x, y, z and time t dimensions. x, y, z represent the coordinate variables of the spatial dimension, which are used to determine the position of the geological body or monitoring point in three-dimensional space. t represents time and is used to describe the evolution of geological body characteristics and disaster-related physical quantities over time. Φ g (x, y, z) is the underground geophysical field function, which integrates the seismic wave velocity and resistivity information of the underground geological body obtained through seismic tomography and geological radar technology, Φ s (x, y, z, t) is the function of the terrain variation field, which contains information on terrain variability and rock and soil migration vectors obtained by UAV mapping and ground survey. is the variable Laplace operator, which represents the second-order rate of change of geological structure over time and is used to characterize the dynamic evolution of geological structure. α, β, and γ are weight coefficients used to adjust the contribution of different data sources in the fusion process.

4. The disaster status prediction method based on three-dimensional geological body modeling according to claim 1 is characterized in that: The above S200 is the construction of a three-dimensional geological body time-dependent degradation tensor model in the construction of a three-dimensional geological model. The formula is: Where D(t) is the mechanical parameter tensor at time t, D0 is the initial mechanical parameter tensor of the geological body, which is the mechanical parameter composition of the geological body in the initial state obtained through indoor rock mechanics tests and geotechnical tests, Λ(r) is the aging degradation rate tensor, which reflects the difference in the degradation rate of mechanical parameters of the geological body due to weathering and consolidation in different directions, O(r) is the stress state tensor at time r, and the stress distribution of the geological body at different times is obtained by real-time calculation through discrete element simulation, t represents time, and r represents the time integral variable.

5. The disaster status prediction method based on three-dimensional geological body modeling according to claim 1 is characterized in that: The construction of the discrete element-time-effect coupling model in the S300, discrete element-time-effect coupling model, is as follows: in is the rate of change of shear stress between particles, G is the shear modulus, which is determined by indoor tests or reference to empirical data of geological bodies. is the interparticle shear strain rate, which is calculated from the relative displacement of particles in discrete element simulation, t c is a characteristic time parameter, which represents the viscoelastic relaxation time constant of the geological body and is determined by fitting the experimental data. ij is the interparticle shear stress, τ max (t) is the maximum shear strength between particles at time t, and ω is the shape parameter.

6. The disaster status prediction method based on three-dimensional geological body modeling according to claim 1, characterized in that: S400, in the dynamic disaster prediction and deduction, a three-dimensional disaster prediction matrix including spatial location, damage mode, and time probability is constructed: (1) Extract the initial state and mechanical property parameters of the discrete element-time-dependent coupling model, set the boundary and time conditions of the discrete element-time-dependent coupling model, and then start the simulation; (2) Real-time monitoring of key mechanical parameters of the geological body during the simulation process, recording data at different time points, and analyzing the progressive failure process; (3) Determine the three-dimensional spatial coordinate range of the disaster occurrence based on the particle displacement and damage state in the simulation and the critical conditions; (4) According to the mechanical response and time-dependent characteristics between particles, the probability of different disaster modes is calculated by the general formula for calculating the probability of failure mode, and the dominant failure mode type is determined; (5) Considering the spatiotemporal influence of geological bodies and parameter uncertainty, the probability distribution of disaster occurrence at different time nodes is calculated by combining the probability field equation of disaster evolution; (6) The spatial range, damage mode and time probability are integrated into a three-dimensional tensor matrix, and the dynamic evolution trend of the disaster is presented through visualization.

7. The disaster status prediction method based on three-dimensional geological body modeling according to claim 6, characterized in that: In the above S400, the probability of different disaster modes is calculated by using the general formula for calculating the probability of destruction mode in the dynamic prediction and deduction of disasters. The formula is: Where: P m (s, t) is the probability of mode m occurring at position x at time t, F int,m (s, t) is the internal trigger stress field of mode m, F ext,m (s,t)|| is the external load field of mode m, F res,m (s, t) is the resistance field of mode m, where m represents the type identifier of the disaster mode, s represents the spatial position, and t represents the time.

8. The disaster status prediction method based on three-dimensional geological body modeling according to claim 6, characterized in that: In the above S400, the probability distribution of disaster occurrence at different time nodes is calculated by combining the probability field equation of disaster evolution in the dynamic prediction and deduction of disasters. The calculation formula is: where P(x,y,z,t) is the probability of a geological disaster occurring at the spatial position (x,y,z) and time t, K(x,y,z,t|x',y',z',t') is the spatiotemporal kernel function, which characterizes the propagation influence domain of the disaster in space and time, (x,y,z,t) is the target spatiotemporal point, (x',y',z',t') is the reference spatiotemporal point, which represents the state of other positions and other times in the geological body, Ω(x',y',z',t'), the disaster triggering intensity function, is used to measure the possibility of a disaster occurring in the geological body at the spatial position (x',y',z') and time t', and F ext is the external load, including the additional load caused by earthquake force and rainfall, F int (t) is the internal stress of the geological body at time t, which is calculated by the discrete element-time-dependent coupling model, and F res (t) is the resistance of the geological body after aging degradation at time t, which is calculated based on the mechanical parameters and structural characteristics of the geological body. t represents time and V represents the integration area.

9. The disaster status prediction method based on three-dimensional geological body modeling according to claim 1, characterized in that: In the closed-loop correction and iterative update in S500, the closed-loop correction adaptive control equation is used to drive the adaptive correction of the model parameters. The formula is: Where T is a positive definite symmetric correction matrix, which is determined by the approximate inverse of the Hessian matrix and is used to adjust the direction and step size of parameter correction. is the gradient of the deviation energy functional J(p) with respect to the model parameter vector p, indicating the direction of parameter adjustment. J(p) is the prediction-measurement deviation energy functional, and the formula is: ρ(s,t) is a weight function used to adjust the importance of the difference between the predicted and measured data at different spatial locations and times. It is set according to the reliability of the monitoring data and the importance of the geological body. pred (s,t) is the predicted displacement field at spatial position (s) and time t, calculated by the discrete element-time coupled model, u obs (s, t) is the measured displacement field at spatial position (s) and time t, measured by displacement monitoring equipment deployed in the target area, is the rate of change of the model parameter vector p over time, V represents the integration area, s represents the spatial position, and t represents the time.

10. A disaster status prediction system based on three-dimensional geological modeling, characterized in that: The system is applicable to the disaster status prediction method based on three-dimensional geological body modeling according to any one of claims 1 to 9, and is characterized in that the system comprises: a multi-source data acquisition and fusion module, a three-dimensional geological model construction module, a discrete element-time-effect model coupling module, a disaster dynamic prediction and deduction module, and a closed-loop correction and iterative update module; The multi-source data acquisition and fusion module collects underground geological body tomography data through seismic tomography and geological radar; simultaneously obtains surface morphology data through drone aerial photogrammetry and ground survey, simultaneously pre-processes the collected data, and uses a time-space consistency correction formula to unify the expression and construct a three-dimensional geological structure database; The three-dimensional geological model construction module: relying on the three-dimensional geological structure database, using geological modeling software to delineate the model space range and perform grid division, assign physical properties and lithology identifiers to the geological body, extract and define initial state parameters, and thus construct a three-dimensional geological body time-dependent degradation tensor model; The discrete element-time-effect model coupling module imports the 3D geological structure database into the discrete element software for discretization, quantifies the geological body's time-effect characteristics with the help of the 3D geological body time-effect degradation tensor model, and converts them into time-related parameters of the discrete element model, thereby establishing the discrete element-time-effect coupling model; The disaster dynamic prediction and deduction module uses the discrete element-time coupling model as initial input, sets simulation boundary conditions and time parameters, simulates the evolution of geological body mechanical behavior, monitors key mechanical parameters in real time and analyzes the progressive failure process, determines the three-dimensional spatial range of disaster occurrence based on particle displacement and failure state, calculates the probabilities of different disaster modes to determine the dominant failure mode, calculates the probability distribution of disaster occurrence at each time node considering the spatiotemporal influence and parameter uncertainty, and integrates and generates a three-dimensional disaster prediction matrix; The closed-loop correction and iterative update module deploys monitoring equipment to collect real-time data, compares it with simulation data to calculate deviations, drives the adaptive correction of model parameters through closed-loop correction adaptive control equations, and re-inputs the corrected parameters into the model for iterative simulation to update the three-dimensional disaster prediction matrix.

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