Geological disaster data analysis method and device based on finite element method and wave equation

Abstract

The present invention discloses a method and device for geological disaster data analysis based on the finite element method and wave equation. The method includes: arranging acquisition devices in a monitoring area to collect seismic data and geological environment data of the geological disaster monitoring area; setting an inversion equation including a data equation and a target equation based on the wave equation; obtaining the restored medium properties and overall wave field by alternately iteratively solving the inversion equation; deriving the medium elastic parameters, longitudinal wave velocity, transverse wave velocity, density and their background values from the seismic data according to the finite element method and the wave equation; and inputting the processed seismic data and the geological environment data into the inversion equation for inversion to obtain the geological disaster type, scale and location of the geological disaster monitoring area. By combining the wave equation with the finite element method, the present invention can handle complex geological structures and boundary conditions, and thus more accurately simulate and analyze geological disasters.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster data analysis, and in particular, to a method and device for geological disaster data analysis, and a computing device based on the finite element method and the wave equation. Background Art

[0002] Geological disasters (such as landslides, debris flows, etc.) pose a huge threat and loss to human society. By analyzing the development and influencing factors of different types of geological disasters, monitoring devices can be deployed on-site in a targeted manner for real-time monitoring to analyze abnormal deformation conditions such as tilting and vibration of disaster bodies. However, in many cases, the monitoring devices are interfered by environmental factors such as satellite signals, which are prone to false alarms and missed alarms, wasting manpower and material resources and causing adverse effects.

[0003] Existing geological monitoring methods mainly rely on means such as geological surveys, geological mapping, and manual monitoring. Geological disasters are analyzed by observing changes in geological phenomena, but this often takes a lot of time and effort and is difficult to capture minor changes in geological disasters. Or, by analyzing seismic waveform data to obtain the structure and physical properties inside the earth's crust to infer the possibility of geological disasters, but professional seismic equipment is required, and the cost is relatively high. Although some monitoring methods analyze the dynamic changes of the geological environment by monitoring geological environment data (such as precipitation, surface displacement, soil moisture content, etc.), since geological environment data is usually scattered, it is difficult to form a unified analysis model. Existing geological monitoring methods often only focus on a single data source (such as seismic data or geological environment data), while ignoring the correlation and complementarity between different data sources, resulting in incomplete data analysis results and being difficult to accurately reflect the true situation of geological disasters. Some geological disaster analysis methods (such as geophysical exploration methods) can provide relatively accurate results, but the model complexity is high, the calculation amount is large, and professional equipment and talent support are required.

[0004] To solve the above problems, the present invention proposes a method for geological disaster data analysis based on the finite element method and the wave equation. By combining the wave equation with the finite element method, it can handle complex geological structures and boundary conditions, and thus more accurately simulate and analyze geological disasters. Summary of the Invention

[0005] In view of the above problems, the present invention provides a method and device for geological disaster data analysis, and a computing device based on the finite element method and the wave equation.

[0006] According to one aspect of the present invention, there is provided a method for geological disaster data analysis based on the finite element method and the wave equation, including:

[0007] Deploy acquisition devices in the monitoring area to collect seismic data and geological environment data of the geological disaster monitoring area. Among them, the acquisition devices include rain gauges, displacement gauges, soil moisture gauges, meteorological monitoring stations, surface inclinometers, vibration sensors, anchor cable meters, and seismographs;

[0008] Based on the wave equation, set the inversion equation including the data equation and the target equation; solve the inversion equation by alternating iteration to obtain the restored medium properties and the overall wave field; among them, the inversion parameters of the inversion equation are the compression coefficient, shear compliance, and density contrast in the wave equation;

[0009] Derive the seismic data according to the finite element method and the wave equation to obtain the medium elastic parameters, longitudinal wave velocity, transverse wave velocity, density, and their background values;

[0010] Input the processed seismic data and the geological environment data into the inversion equation for inversion to obtain the geological disaster type, scale, and location of the geological disaster monitoring area.

[0011] In an optional manner, the seismic data includes seismic waveform data, ground vibration acceleration, vibration frequency, and vibration direction;

[0012] The geological environment data includes precipitation, surface displacement, displacement direction, soil water content, soil humidity, soil temperature, air temperature, air relative humidity, surface tilt angle, anchor cable stress, anchor cable strain, and anchor cable elongation.

[0013] In an optional manner, the expression of the data equation is:

[0014] ,

[0015] Among them, is the observed seismic wave field; is the theoretical wave field calculated from the wave equation; is the observation error; are the inhomogeneity coefficient and anisotropy coefficient of the medium respectively; , is the elastic modulus at the i-th discrete point, is the average value of; , is the shear modulus at the i-th discrete point, is the average value of; is the number of discrete points; is the space; is the time.

[0016] In an alternative manner, the expression of the target equation is:

[0017] ,

[0018] wherein, is the inversion parameter vector, , is the compressibility coefficient, is the shear compliance, is the density contrast; , , are the weight factors, which are respectively used to balance the contributions among the field value, the gradient value and the Laplacian value; is the integration region; , are the gradient operator and the Laplacian operator respectively.

[0019] In an alternative manner, the expression of the wave equation is:

[0020] ,

[0021] wherein, is the damping coefficient, , is the longitudinal wave velocity at the i-th discrete point, is the average value of the longitudinal wave velocity, is the number of discrete points; is the square of the wave velocity; is the result of the Laplacian operator acting on the seismic wave field u; is the external force source term, which is related to the geological environment data, , is the intensity of the j-th force source, is the position of the force source, is the Dirac function, is the number of force sources.

[0022] In an alternative manner, the derivation of the medium elastic parameters, the longitudinal wave velocity, the shear wave velocity, the density and their background values from the seismic data according to the finite element method and the wave equation further includes:

[0023] Dividing the monitoring area into multiple triangular finite elements, and each vertex of each finite element includes degrees of freedom in the x, y, and z directions;

[0024] Describing the displacement distribution within each finite element through a polynomial shape function, and specifying the elastic modulus, Poisson's ratio, and density for each finite element;

[0025] Solve the wave equation according to the implicit time integration method to obtain the nodal displacements at each time step;

[0026] Derive based on the nodal displacements to obtain the medium elastic parameters, longitudinal wave velocity, shear wave velocity, density, and their background values.

[0027] In an alternative manner, the expression for the implicit time integration is:

[0028] ,

[0029] where, is the mass matrix, , is the density, and are the shape functions, is the integration region; is the damping matrix; is the stiffness matrix, , is the double dot product; is the nodal displacement at the n+1th time step; is the nodal displacement at the nth time step; is the time step size; is the external force vector at the n+1th time step.

[0030] In an alternative manner, the inversion parameters of the inversion equation are:

[0031] ,

[0032] where, is the input parameter, including seismic data and geological environment data; is the weight coefficient; m is the number of parameters; is the type of geological disaster; is the scale of the geological disaster; is the location of the geological disaster; is the factor affecting the type of geological disaster.

[0033] According to another aspect of the present invention, there is provided a geological disaster data analysis device based on the finite element method and the wave equation, including:

[0034] A data acquisition module for deploying acquisition devices in a monitoring area to acquire seismic data and geological environment data of the geological disaster monitoring area, wherein the acquisition devices include a rain gauge, a displacement meter, a soil moisture meter, a meteorological monitoring station, a surface inclinometer, a vibration sensor, a cable anchor meter, and a seismograph;

[0035] An inversion equation setting module, configured to set an inversion equation including a data equation and a target equation based on a wave equation; and obtain a restored medium property and an overall wave field by alternately iteratively solving the inversion equation; wherein, an inversion parameter of the inversion equation is a compression coefficient, a shear compliance, and a density contrast in the wave equation.

[0036] A finite element processing module, configured to derive seismic data according to the finite element method and the wave equation to obtain medium elastic parameters, longitudinal wave velocity, transverse wave velocity, density, and their background values.

[0037] A geological disaster inversion module, configured to input the processed seismic data and the geological environment data into the inversion equation for inversion to obtain the type, scale, and location of geological disasters in the geological disaster monitoring area.

[0038] According to another aspect of the present invention, there is provided a computing device, including: a processor, a memory, a communication interface, and a communication bus, and the processor, the memory, and the communication interface complete communication with each other through the communication bus.

[0039] The memory is used to store at least one executable instruction, and the executable instruction causes the processor to execute operations corresponding to the above geological disaster data analysis method based on the finite element method and the wave equation.

[0040] According to the solution provided by the present invention, acquisition devices are deployed in the monitoring area to collect seismic data and geological environment data of the geological disaster monitoring area. Among them, the acquisition devices include a rain gauge, a displacement meter, a soil moisture meter, a meteorological monitoring station, a surface inclinometer, a vibration sensor, a cable anchor meter, and a seismograph; an inversion equation including a data equation and a target equation is set based on the wave equation; the inversion equation is solved by alternating iteration to obtain the restored medium properties and the overall wave field; wherein, the inversion parameters of the inversion equation are the compression coefficient, shear compliance, and density contrast in the wave equation; the seismic data is deduced according to the finite element method and the wave equation to obtain the medium elastic parameters, longitudinal wave velocity, transverse wave velocity, density, and their background values; the processed seismic data and the geological environment data are input into the inversion equation for inversion to obtain the geological disaster type, scale, and location of the geological disaster monitoring area. By deploying a variety of acquisition devices, the present invention can comprehensively collect seismic data and geological environment data of the geological disaster monitoring area. Through the fusion analysis of multi-source data, geological disasters can be analyzed more accurately. Setting the inversion equation based on the wave equation and solving it by alternating iteration can restore the properties of the medium and the overall wave field, which not only improves the inversion accuracy but also reveals the physical mechanism of geological disasters. By deducing the medium elastic parameters, longitudinal wave velocity, transverse wave velocity, density, and their background values through the finite element method and the wave equation, the process of geological disasters can be simulated more accurately. By adjusting the parameters of the inversion equation, it can adapt to different geological disaster types and monitoring areas.

[0041] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of the present invention more obvious and understandable, the following specifically illustrates the embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0043] Figure 1 shows a schematic flowchart of the geological disaster data analysis method based on the finite element method and the wave equation according to an embodiment of the present invention;

[0044] Figure 2 shows the layout schematic of the acquisition device according to an embodiment of the present invention Figure 1 ;

[0045] Figure 3Shows the layout schematic of the acquisition device according to an embodiment of the present invention Figure 2 ;

[0046] Figure 4 Shows the medium elastic parameters and background schematic diagram according to an embodiment of the present invention;

[0047] Figure 5 Shows the spectrum schematic diagram of seismic signals at different stages according to an embodiment of the present invention;

[0048] Figure 6 Shows the framework schematic diagram of the geological disaster data analysis device based on the finite element method and wave equation according to an embodiment of the present invention;

[0049] Figure 7 Shows the structural schematic diagram of the computing device according to an embodiment of the present invention. Detailed implementation manners

[0050] Hereinafter, exemplary embodiments of the present invention will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present invention can be more thoroughly understood and the scope of the present invention can be fully conveyed to those skilled in the art.

[0051] Figure 1 Shows the flow schematic diagram of the geological disaster data analysis method based on the finite element method and wave equation according to an embodiment of the present invention. Specifically, as Figure 1 shown, it includes the following steps:

[0052] Step S101, arranging acquisition devices in the monitoring area to acquire seismic data and geological environment data of the geological disaster monitoring area, wherein the acquisition devices include a rain gauge, a displacement meter, a soil moisture meter, a meteorological monitoring station, a surface inclinometer, a vibration sensor, a cable anchor meter, and a seismograph.

[0053] In this embodiment, by integrating various acquisition devices such as a rain gauge, a displacement meter, a soil moisture meter, a meteorological monitoring station, a surface inclinometer, a vibration sensor, a cable anchor meter, and a seismograph, and transmitting the acquired data to the central monitoring system, it is possible to comprehensively and real-time monitor the seismic data and geological environment data of the geological disaster monitoring area.

[0054] Specifically, as Figure 2 、 Figure 3As shown, acquisition devices are deployed according to the geological characteristics, historical disaster records, and meteorological conditions of the geological disaster monitoring area. For example, displacement gauges and surface inclinometers are deployed at the edge of the landslide, and rain gauges and soil moisture gauges are deployed in areas prone to waterlogging, etc. The acquisition devices transmit data to the central monitoring system via wired or wireless means. The wireless transmission method can use communication technologies such as 4G / 5G, LoRa, NB-IoT, etc. The deployment of the acquisition devices is shown in Table 1:

[0055] Table 1

[0056] Equipment type Installation location Quantity Model Rain gauge Areas prone to water accumulation upstream of the landslide 2 sets TR-525M (Automatic rain gauge station) Displacement gauge Edge of the landslide 4 sets LVDT (Linear Variable Differential Transformer) series Soil moisture gauge Inside and around the landslide 3 sets EC-5 (Soil moisture sensor) Meteorological monitoring station Open area near the landslide 1 set AWS310 (Automatic weather station) Surface inclinometer Inclined part of the landslide 2 sets MS5837-02BA03 (Dual-axis tilt sensor) Vibration sensor Inside and around the landslide 2 sets 4524B (Acceleration sensor) Anchorage meter Reinforcement engineering area of the landslide 1 set DVM600 (Digital anchorage dynamometer) Seismograph Near the landslide 1 set GS-11D (Broadband seismograph)

[0057] The 4524B acceleration sensor in Table 1 is used to monitor the vibration conditions inside the landslide, the DVM600 digital anchor cable dynamometer is used to monitor the stress of the anchor cables at the landslide reinforcement project site, and the GS-11D broadband seismograph is used to monitor the propagation of seismic waves.

[0058] In this embodiment, the seismic data includes seismic waveform data, ground vibration acceleration, vibration frequency, and vibration direction. The geological environment data includes precipitation, surface displacement, displacement direction, soil water content, soil humidity, soil temperature, air temperature, air relative humidity, surface inclination angle, anchor cable stress, anchor cable strain, and anchor cable elongation. Among them, the seismic data is obtained by shooting at the shot points. The shooting methods at the shot points include single-source excitation and multi-source mixed acquisition methods. Single-source excitation means that only one shot point is excited each time, and the underground geological information is obtained by recording and analyzing the reflected seismic wave signals. Multi-source mixed acquisition means that multiple shot points are excited simultaneously or almost simultaneously, and the seismic information is extracted by optimizing the sampling method.

[0059] Step S102, set an inversion equation including a data equation and a target equation based on the wave equation; solve the inversion equation by alternating iteration to obtain the restored medium properties and the overall wave field; where the inversion parameters of the inversion equation are the compression coefficient, shear compliance, and density contrast in the wave equation.

[0060] In this embodiment, due to the non-linear relationship between the seismic data and the underground medium properties, pre-stack seismic inversion based on the integral wave equation can effectively handle the non-linear relationship. Setting an inversion equation including a data equation and a target equation based on the wave equation and restoring the medium properties and the overall wave field by alternating iteration can directly invert the elastic parameters (such as the compression coefficient, shear compliance) and density contrast of the underground medium, and has higher imaging accuracy compared with traditional methods.

[0061] Specifically, an inversion equation including a data equation and a target equation is set based on the wave equation. The data equation is used to describe the relationship between the observed seismic data and the properties of the underground medium, and the target equation is used to constrain the inversion process to ensure the rationality of the inversion results. Determine the inversion parameters in the inversion equation, namely the compressibility coefficient, shear compliance, and density contrast in the wave equation, and interpret the underground geological structure according to the inversion results. For example, starting from a smooth low-frequency background field, alternately iteratively solve the data equation and the target equation, use the multiplicative regularization method to solve the inversion parameters in the conjugate gradient framework, and use the optimized scattering series Neumann sequence to obtain the overall wave field.

[0062] In an alternative manner, the expression of the data equation is:

[0063] ,

[0064] where is the observed seismic wave field; is the theoretical wave field calculated from the wave equation; is the observation error; are the inhomogeneity coefficient and anisotropy coefficient of the medium, respectively; , is the elastic modulus at the i-th discrete point, is the average value of; , is the shear modulus at the i-th discrete point, is the average value of; is the number of discrete points; is the space; is the time.

[0065] In this embodiment, by introducing the inhomogeneity coefficient and anisotropy coefficient of the medium, the underground medium can be described more precisely. Specifically, the theoretical wave field is calculated according to the wave equation and the medium parameters. According to the seismic wave field data and the theoretical wave field, the inhomogeneity coefficient and anisotropy coefficient of the medium are estimated through the data equation. The medium parameters (such as elastic modulus, shear modulus, etc.) are continuously updated by using the alternating iteration method until the observation error term meets the preset convergence condition or reaches the maximum number of iterations.

[0066] In an alternative manner, the expression of the target equation is:

[0067] ,

[0068] where is the inversion parameter vector, , is the compressibility coefficient, is the shear flexibility, is the density contrast; , , are weight factors, which are respectively used to balance the contributions among the field value, the gradient value, and the Laplacian value; is the integration region; , are the gradient operator and the Laplacian operator respectively.

[0069] In this embodiment, the objective equation realizes multi-scale constraints on the inversion parameters by integrating the errors of the field value (i.e., the seismic wave field, the gradient value, and the Laplacian value), and improves the stability of the inversion result. Specifically, the objective equation is solved according to the alternating direction method of multipliers (ADMM), the conjugate gradient method, or other optimization algorithms to update the inversion parameter vector. During the iteration process, the theoretical wave field is continuously calculated according to the current inversion parameter vector and compared with the observed wave field, and the inversion parameter vector is updated by minimizing the objective equation. According to the preset convergence condition (such as the decrease amount of the objective function value is less than a certain threshold or the number of iterations reaches the preset upper limit), it is judged whether the inversion process converges.

[0070] In an alternative manner, the expression of the wave equation is:

[0071] ,

[0072] where, is the damping coefficient, , is the longitudinal wave velocity at the i-th discrete point, is the average value of the longitudinal wave velocity, is the number of discrete points; is the square of the wave velocity; is the result of the Laplacian operator acting on the seismic wave field u; is the external force source term, which is related to the geological environment data, , is the intensity of the j-th force source, is the position of the force source, is the Dirac function, is the number of force sources.

[0073] In this embodiment, by introducing the damping coefficient, the wave equation can more accurately simulate the propagation and attenuation behavior of seismic waves in the formation, and improve the accuracy of seismic wave simulation. By introducing the external force source term, various external factors (such as faults, anomalies, etc.) in the geological environment can be considered, and the applicability of the model is enhanced.

[0074] Specifically, parameters such as the number of discrete points, longitudinal wave velocity, and the average value of longitudinal wave velocity are determined according to exploration data. The number of external force sources, the intensity and position of each force source are determined according to geological environment data. The determined parameters are substituted into the wave equation and solved by the finite element method to obtain the distribution of the seismic wave field.

[0075] Step S103, deduce the seismic data according to the finite element method and the wave equation to obtain the medium elastic parameters, longitudinal wave velocity, transverse wave velocity, density and their background values.

[0076] In this embodiment, through the deduction of the wave equation, multiple physical parameters of the medium can be inversely calculated simultaneously, including elastic parameters (such as Lame constants, shear modulus, etc.), longitudinal wave velocity, transverse wave velocity, density and their background values, etc.

[0077] Specifically, collect seismic exploration data, including information such as the propagation time, amplitude, and frequency of seismic waves. Establish an initial finite element model according to the geological background and geophysical characteristics of the exploration area. Discretize the wave equation by the finite element method to form a series of linear equations. Determine the size and shape of the finite element mesh according to the sampling interval and spatial distribution of the seismic data. Use the seismic data and the discretized wave equation to inversely calculate the elastic parameters, longitudinal wave velocity, transverse wave velocity, and density of the medium by the iterative solution method. During the iterative process, continuously adjust the parameters of the finite element model to minimize the difference between the simulated seismic wave field and the observed data. In the inversely calculated parameter distribution map, extract the background values of the medium parameters by methods such as statistical analysis or filtering. Among them, the background value represents the average physical properties of the formation, which is used to identify geological anomalies and evaluate the stability of the formation. Compare and verify the inversely calculated parameters with the known geological information, and interpret and predict the geological structure according to the inversely calculated parameter distribution map.

[0078] In an alternative manner, the deduction of the seismic data according to the finite element method and the wave equation to obtain the medium elastic parameters, longitudinal wave velocity, transverse wave velocity, density and their background values further includes:

[0079] Divide the monitoring area into multiple triangular finite elements, and the vertices of each finite element include degrees of freedom in the x, y, and z directions;

[0080] Describe the displacement distribution within each finite element by a polynomial shape function, and assign an elastic modulus, Poisson's ratio, and density to each finite element;

[0081] Solve the wave equation according to the implicit time integration method to obtain the nodal displacements at each time step;

[0082] Deduce according to the nodal displacements to obtain the medium elastic parameters, longitudinal wave velocity, transverse wave velocity, density and their background values.

[0083] In this embodiment, dividing the monitoring area into multiple triangular finite elements can more precisely capture the complexity and inhomogeneity of the geological structure. By describing the displacement distribution within each finite element using polynomial shape functions and solving the wave equation using an implicit time integration method, the background values of the medium parameters can be more accurately extracted.

[0084] Specifically, the monitoring area is divided into multiple triangular finite elements. Each vertex of the finite element has degrees of freedom in the x, y, and z directions to describe the propagation of seismic waves in three-dimensional space. Physical parameters such as elastic modulus, Poisson's ratio, and density are assigned to each finite element, and polynomial shape functions are used to describe the displacement distribution within each finite element to ensure the continuity and smoothness of the displacement. According to the implicit time integration method (such as the Newmark-β method), the wave equation is discretized and solved. At each time step, the displacement values of the nodes are obtained through iterative calculations. Using the obtained node displacement values and combining with the physical parameters of the finite element model, the elastic parameters, longitudinal wave velocity, transverse wave velocity, and density of the medium are obtained through an inversion algorithm. Then, as Figure 4 shown, the background values of these parameters are extracted through methods such as statistical analysis.

[0085] In an alternative approach, the expression for the implicit time integration is:

[0086] ,

[0087] where is the mass matrix, , is the density, and are the shape functions, is the integration region; is the damping matrix; is the stiffness matrix, , is the double dot product; is the nodal displacement at the (n + 1)-th time step; is the nodal displacement at the n-th time step; is the time step size; is the external force vector at the (n + 1)-th time step.

[0088] In this embodiment, the implicit time integration method is adopted, which can ensure the stability and efficiency of the solution process. Through the displacement information of multiple time steps, the high-frequency components in the wave equation can be better processed, avoiding numerical oscillations. The implicit time integration expression includes the mass matrix, damping matrix, and stiffness matrix, which can reflect the physical properties of the medium and the propagation law of seismic waves.

[0089] Specifically, according to the geological model and physical parameters of the monitoring area, a mass matrix, a damping matrix, a stiffness matrix, and the corresponding medium density, shape function, and integration region are constructed.

[0090] According to the propagation speed of seismic waves and the size of the monitoring area, a time step is set. The implicit time integration expression is transformed into a system of linear equations, and numerical solution methods (such as iterative methods, direct methods, etc.) are used to solve the nodal displacements at each time step. Using the obtained nodal displacements, the elastic parameters, longitudinal wave velocity, transverse wave velocity, density, and background value of the medium are inversed in combination with the physical parameters of the finite element model.

[0091] Step S104, input the processed seismic data and the geological environment data into the inversion equation for inversion to obtain the type, scale, and location of the geological disaster in the geological disaster monitoring area.

[0092] Specifically, an inversion equation for geological disasters in the geological disaster monitoring area is constructed according to the formation mechanism of geological disasters and the characteristics of the geological environment. The processed seismic data and geological environment data are input into the inversion equation for inversion calculation to solve the type (such as landslides, debris flows, etc.), scale (such as the volume of the landslide body, the flow rate of the debris flow, etc.) and location (such as longitude and latitude coordinates, altitude) of the geological disaster, and the inversion results are output in the form of graphics, tables, etc. to formulate geological disaster prevention and control measures and emergency plans. As Figure 5 shown, the above processing is carried out for a certain lake time period, and finally three stages of time-frequency spectra are obtained after dividing the time period, which are the three stages of dam break (before dam break, during dam break, and after dam break tending to be stable).

[0093] In an alternative manner, the inversion parameters of the inversion equation are:

[0094] ,

[0095] wherein, is the input parameter, including seismic data and geological environment data; is the weight coefficient; m is the number of parameters; is the type of geological disaster; is the scale of the geological disaster; is the location of the geological disaster; is the factor affecting the type of geological disaster.

[0096] In this embodiment, the inversion equation adopts the form of a logistic regression function, which can handle the non-linear relationship between the input parameters and the type, scale, and location of geological disasters.

[0097] According to the solution provided by the present invention, acquisition devices are arranged in a monitoring area to acquire seismic data and geological environment data of the geological disaster monitoring area. Among them, the acquisition devices include a rain gauge, a displacement meter, a soil moisture meter, a meteorological monitoring station, a surface inclinometer, a vibration sensor, a cable anchor meter, and a seismograph; an inversion equation including a data equation and a target equation is set based on the wave equation; the inversion equation is solved by alternating iteration to obtain the restored medium properties and the overall wave field; wherein, the inversion parameters of the inversion equation are the compression coefficient, shear compliance, and density contrast in the wave equation; the seismic data is derived according to the finite element method and the wave equation to obtain the medium elastic parameters, longitudinal wave velocity, transverse wave velocity, density, and their background values; the processed seismic data and the geological environment data are input into the inversion equation for inversion to obtain the geological disaster type, scale, and location of the geological disaster monitoring area. By arranging a variety of acquisition devices, the present invention can comprehensively collect seismic data and geological environment data of the geological disaster monitoring area. Through the fusion analysis of multi-source data, the geological disasters can be analyzed more accurately. By setting the inversion equation based on the wave equation and solving it by alternating iteration, the properties of the medium and the overall wave field can be restored, which not only improves the inversion accuracy but also reveals the physical mechanism of the occurrence of geological disasters. By deriving the medium elastic parameters, longitudinal wave velocity, transverse wave velocity, density, and their background values through the finite element method and the wave equation, the process of the occurrence of geological disasters can be more accurately simulated. By adjusting the parameters of the inversion equation, it can adapt to different geological disaster types and monitoring areas.

[0098] Figure 6 The structural schematic diagram of the geological disaster data analysis device based on the finite element method and the wave equation according to the embodiment of the present invention is shown. The geological disaster data analysis device based on the finite element method and the wave equation includes:

[0099] A data acquisition module 610, configured to arrange acquisition devices in a monitoring area to acquire seismic data and geological environment data of the geological disaster monitoring area. Among them, the acquisition devices include a rain gauge, a displacement meter, a soil moisture meter, a meteorological monitoring station, a surface inclinometer, a vibration sensor, a cable anchor meter, and a seismograph;

[0100] An inversion equation setting module 620, configured to set an inversion equation including a data equation and a target equation based on the wave equation; solve the inversion equation by alternating iteration to obtain the restored medium properties and the overall wave field; wherein, the inversion parameters of the inversion equation are the compression coefficient, shear compliance, and density contrast in the wave equation;

[0101] A finite element processing module 630, configured to derive the seismic data according to the finite element method and the wave equation to obtain the medium elastic parameters, longitudinal wave velocity, transverse wave velocity, density, and their background values;

[0102] The geological disaster inversion module 640 is configured to input the processed seismic data and the geological environment data into the inversion equation for inversion, so as to obtain the type, scale and location of the geological disasters in the geological disaster monitoring area.

[0103] Figure 7 FIG. shows a schematic structural diagram of an embodiment of the computing device of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the computing device.

[0104] As Figure 7 shown, the computing device may include: a processor 702, a communications interface 704, a memory 706, and a communication bus 708.

[0105] Wherein: the processor 702, the communications interface 704, and the memory 706 communicate with each other through the communication bus 708. The communications interface 704 is used to communicate with network elements of other devices such as clients or other servers. The processor 702 is configured to execute a program 710, and specifically may execute the relevant steps in the embodiment of the above-mentioned geological disaster data analysis method based on the finite element method and the wave equation.

[0106] Specifically, the program 710 may include program code, and the program code includes computer operation instructions.

[0107] The processor 702 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention. One or more processors included in the computing device may be of the same type of processor, such as one or more CPUs; or may be of different types of processors, such as one or more CPUs and one or more ASICs.

[0108] The memory 706 is used to store the program 710. The memory 706 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0109] According to the solution provided by the present invention, acquisition devices are deployed in the monitoring area to collect seismic data and geological environment data of the geological disaster monitoring area. Among them, the acquisition devices include a rain gauge, a displacement meter, a soil moisture meter, a meteorological monitoring station, a surface inclinometer, a vibration sensor, a cable anchor meter, and a seismograph. An inversion equation including a data equation and a target equation is set based on the wave equation. By alternately iteratively solving the inversion equation, the medium properties and the overall wave field are restored. Among them, the inversion parameters of the inversion equation are the compression coefficient, shear compliance, and density contrast in the wave equation. The seismic data is derived according to the finite element method and the wave equation to obtain the medium elastic parameters, longitudinal wave velocity, transverse wave velocity, density, and their background values. The processed seismic data and the geological environment data are input into the inversion equation for inversion to obtain the geological disaster type, scale, and location of the geological disaster monitoring area. By deploying a variety of acquisition devices, the present invention can comprehensively collect seismic data and geological environment data of the geological disaster monitoring area. Through the fusion analysis of multi-source data, geological disasters can be analyzed more accurately. By setting the inversion equation based on the wave equation and solving it by alternating iteration, the properties of the medium and the overall wave field can be restored, which not only improves the inversion accuracy but also reveals the physical mechanism of geological disasters. By deriving the medium elastic parameters, longitudinal wave velocity, transverse wave velocity, density, and their background values through the finite element method and the wave equation, the process of geological disasters can be simulated more accurately. By adjusting the parameters of the inversion equation, it can adapt to different geological disaster types and monitoring areas.

[0110] Those skilled in the art can understand that the modules in the devices in the embodiments can be adaptively changed and set in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition, they can be divided into multiple sub-modules or sub-units or sub-components. Except that at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all the features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all the processes or units of any method or device so disclosed. Unless otherwise explicitly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) can be replaced by an alternative feature that provides the same, equivalent, or similar purpose. In addition, those skilled in the art can understand that although some of the embodiments herein include certain features included in other embodiments rather than other features, the combination of the features of different embodiments means that it is within the scope of the present invention and forms different embodiments. For example, in the following claims, any one of the claimed embodiments can be used in any combination. The present invention can be implemented by means of hardware including several different elements and by means of a properly programmed computer. In the unit claims listing several devices, several of these devices can be embodied by the same hardware item. The steps in the above embodiments, unless otherwise specified, should not be construed as a limitation on the execution order.

Claims

1. A geological disaster data analysis method based on finite element method and wave equation, characterized in that: include: Deploy collection equipment in the monitoring area to collect seismic data and geological environment data in the geological disaster monitoring area, wherein the collection equipment includes a rain gauge, a displacement meter, a soil moisture meter, a meteorological monitoring station, a surface inclinometer, a vibration sensor, an anchor meter and a seismograph; An inversion equation including a data equation and a target equation is set based on the wave equation; medium properties and the overall wave field are restored by solving the inversion equation through alternating iterations; wherein the inversion parameters of the inversion equation are the compressibility coefficient, shear compliance and density contrast in the wave equation; The monitoring area is divided into a plurality of triangular finite elements, and the vertices of each finite element include the degrees of freedom in the x, y, and z directions; the displacement distribution in each finite element is described by a polynomial shape function, and the elastic modulus, Poisson's ratio, and density are specified for each finite element; the wave equation is solved according to the implicit time integration method to obtain the node displacement of each time step; the elastic parameters of the medium, the longitudinal wave velocity, the shear wave velocity, the density, and the background value thereof are derived according to the node displacement; The processed seismic data and the geological environment data are input into the inversion equation for inversion to obtain the type, scale and location of the geological disasters in the geological disaster monitoring area.

2. The geological disaster data analysis method based on finite element method and wave equation according to claim 1 is characterized in that: The seismic data includes seismic waveform data, ground vibration acceleration, vibration frequency and vibration direction; The geological environment data include precipitation, surface displacement, displacement direction, soil water content, soil humidity, soil temperature, air temperature, air relative humidity, surface inclination angle, anchor stress, anchor strain and anchor elongation.

3. The geological disaster data analysis method based on finite element method and wave equation according to claim 1 is characterized in that: The expression of the data equation is: in, is the observed seismic wave field; is the theoretical wave field calculated by the wave equation; is the observation error; are the inhomogeneity coefficient and anisotropy coefficient of the medium respectively; , is the elastic modulus at the ith discrete point, for The average value of , is the shear modulus at the ith discrete point, for The average value of is the number of discrete points; For space; For time.

4. The geological disaster data analysis method based on finite element method and wave equation according to claim 3 is characterized in that: The expression of the objective equation is: in, is the inversion parameter vector, , is the compression factor, is the shear flexibility, is the density contrast; , , are weight factors, which are used to balance the contributions among field value, gradient value and Laplace value; is the integration area; , are the gradient operator and the Laplace operator respectively.

5. The geological disaster data analysis method based on finite element method and wave equation according to claim 4 is characterized in that: The expression of the wave equation is: in, is the damping coefficient, , is the longitudinal wave velocity at the ith discrete point, is the average value of the longitudinal wave velocity, is the number of discrete points; is the square of the wave speed; is the result of Laplace operator acting on seismic wave field u; is the external force source term, which is related to the geological environment data. , is the strength of the jth force source, is the location of the force source, is the Dirac function, is the number of force sources.

6. The geological disaster data analysis method based on finite element method and wave equation according to claim 1 is characterized in that: The expression of the implicit time integral is: in, is the mass matrix, , is the density, and is the shape function of the nodes in the finite element mesh, is the integration region, i, j are the indices of the shape function, indicating the locations of the nodes in the finite element mesh; is the damping matrix; is the stiffness matrix, , It is a double point product; is the node displacement at the n+1th time step; is the node displacement at the nth time step; is the time step; is the external force vector at the n+1th time step.

7. The geological disaster data analysis method based on finite element method and wave equation according to claim 1 is characterized in that: The inversion parameters of the inversion equation are: in, As input parameters, it includes seismic data and geological environment data; is the weight coefficient; m is the number of parameters; It is the type of geological disaster; is the scale of geological disasters; For the location of geological hazards; Factors that affect the types of geological disasters.

8. A geological disaster data analysis device based on finite element method and wave equation, characterized in that: include: A data acquisition module is used to deploy acquisition equipment in the monitoring area to collect seismic data and geological environment data in the geological disaster monitoring area, wherein the acquisition equipment includes a rain gauge, a displacement meter, a soil moisture meter, a meteorological monitoring station, a surface inclinometer, a vibration sensor, an anchor meter and a seismograph; An inversion equation setting module is used to set an inversion equation including a data equation and a target equation based on a wave equation; by solving the inversion equation through alternating iterations, the medium properties and the overall wave field are restored; wherein the inversion parameters of the inversion equation are the compressibility coefficient, shear compliance and density contrast in the wave equation; The finite element processing module is used to divide the monitoring area into multiple triangular finite elements, and the vertices of each finite element include the degrees of freedom in the x, y, and z directions; the displacement distribution in each finite element is described by a polynomial shape function, and the elastic modulus, Poisson's ratio, and density are specified for each finite element; the wave equation is solved according to the implicit time integration method to obtain the node displacement of each time step; the elastic parameters, longitudinal wave velocity, shear wave velocity, density, and background values ​​of the medium are derived according to the node displacement; The geological disaster inversion module is used to input the processed seismic data and the geological environment data into the inversion equation for inversion to obtain the type, scale and location of the geological disasters in the geological disaster monitoring area.

9. A computing device comprising: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the geological disaster data analysis method based on the finite element method and the wave equation described in any one of claims 1-7.

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