Microseismic positioning and sensor layout optimization method and device based on probability density distribution field

Abstract

The invention provides a microseismic positioning and sensor layout optimization method and device based on a probability density distribution field, and relates to the technical field of underground engineering safety. According to the method, the determined probability density distribution field of the micro-seismic events can describe the probability density of occurrence of the micro-seismic events within a certain space range and time period. The micro-seismic event probability density distribution field can quantify and describe the distribution condition of errors, better corrects and compensates the errors in the micro-seismic positioning process, and plays an important role in knowing the spatial distribution rule of the micro-seismic events. By analyzing the micro-seismic event probability density distribution field, a high-risk area where a geological disaster possibly occurs can be recognized in advance, so that preventive measures are taken, and the risk of disaster occurrence is reduced. By analyzing the probability density of the micro-seismic events in different areas, the optimal arrangement position of the sensor can be determined, it is ensured that the monitoring system can capture the micro-seismic events more effectively, and the monitoring efficiency is improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of underground engineering safety technology, and in particular to a method and device for microseismic positioning and sensor layout optimization based on a probability density distribution field. Background Art

[0002] Microseismic monitoring technology is a three-dimensional real-time monitoring technology that uses seismic wave signals generated by micro-fractures inside the rock mass to study the stability of the rock mass. Its core lies in high-precision earthquake source positioning. Accurate earthquake source positioning is of great significance for delineating surrounding rock damage and preventing engineering geological disasters.

[0003] The microseismic source positioning process is affected by various uncertainties, including the dense distribution of underground engineering caverns, significant differences in the three-dimensional rock properties of the tunnel "axis, ring, and diameter", arrival time picking errors, sensor layout, etc. These uncertainties will lead to incorrect microseismic source locations. In addition, there is a lack of scientific sensor location and density allocation methods in the sensor layout design in underground engineering. This empirical layout method cannot ensure the optimal distribution of sensors, resulting in insufficient accuracy and reliability of the detection results, limiting the application effect of microseismic monitoring technology and failing to meet the high-precision monitoring requirements under complex rock engineering structures.

[0004] Therefore, there is an urgent need for an optimized microseismic positioning and sensor layout method to improve the microseismic positioning accuracy of large-volume complex rock masses in underground engineering and enhance the accuracy and reliability of rock microseismic monitoring in complex environments. Summary of the invention

[0005] The embodiment of the present invention provides a method and device for microseismic positioning and sensor layout optimization based on probability density distribution field.

[0006] A first aspect of an embodiment of the present invention provides a method for microseismic positioning and sensor layout optimization based on a probability density distribution field, the method comprising: Establishing a three-dimensional anisotropic wave velocity model for the rock mass in the area to be monitored, wherein the three-dimensional anisotropic wave velocity model includes a plurality of grid cells, each grid cell having a corresponding wave velocity value; Acquire sensor characteristic data of each sensor and microseismic events monitored by each sensor, wherein the characteristics of the sensor include sensor spatial coordinates and sensor spatial directions; Based on the waveforms of the microseismic events collected by the sensors, determining the elastic wave observation travel time from the sensors to the grid unit nodes; According to the sensor space coordinates and sensor space directions of each sensor, the position of any grid unit node is taken as the position of the hypothetical seismic source, and the elastic wave propagation path and theoretical forward travel time from each sensor to the hypothetical seismic source are calculated according to the three-dimensional anisotropic wave velocity model using the shortest path ray tracing algorithm, and the theoretical forward travel time from each sensor to the hypothetical seismic source is compared with the elastic wave observed travel time from each sensor to the hypothetical seismic source to determine the difference between the elastic wave observed travel time and the theoretical forward travel time; Adjusting the position of the hypothetical earthquake source, comparing the difference between the theoretical forward modeling travel time and the elastic wave observation travel time obtained at different hypothetical earthquake source positions, and determining a first preset number of grid unit nodes with the smallest difference between the elastic wave observation travel time and the theoretical forward modeling travel time as potential earthquake source points; Performing error analysis based on the location points of the plurality of potential earthquake source points to establish a probability density function of the potential earthquake source location error; Determine the probability density of the microseismic source of the microseismic event at each position to obtain the probability density distribution field of the microseismic event; Based on the probability density distribution field, determining a high probability area for the occurrence of the microseismic event; Based on the high probability area where the microseismic event occurs, sensor distribution adjustment information is output.

[0007] Optionally, the method further comprises: When multiple microseismic events are monitored, the probability density distribution field corresponding to each microseismic event is determined for each microseismic event; Superimposing the probability density distribution fields corresponding to the multiple microseismic events to obtain the overall probability density distribution field of all microseismic events; Determining a high probability area for the occurrence of the microseismic event based on the probability density distribution field includes: Based on the overall probability density distribution field, a high probability area for microseismic events to occur is determined.

[0008] Optionally, a second preset number of independent positioning simulation tests are performed on each of the potential earthquake source points to obtain a plurality of independent positioning simulation test results; Based on the position points of the plurality of potential earthquake source points, error analysis is performed to establish a probability density function of the potential earthquake source location error, including: Based on a plurality of independent positioning simulation test results corresponding to a plurality of the potential earthquake source points, an error analysis is performed to establish a probability density function of the potential earthquake source positioning error.

[0009] Optionally, the step of establishing a three-dimensional anisotropic wave velocity model for the rock mass in the area to be monitored includes: Obtain the test result data of three-dimensional anisotropic wave velocity measurement point test on the rock mass in the monitoring area of ​​underground engineering based on seismic wave reflection method and tunnel seismic exploration method, and determine the elastic wave velocity distribution data of the rock mass in the monitoring area; removing noise and outliers from the elastic wave velocity distribution data, filling in missing data using an interpolation method, and obtaining continuous elastic wave velocity distribution data for the entire region; The three-dimensional anisotropic wave velocity model is established according to the size of the monitoring area and the set grid step size, as well as the continuous elastic wave velocity distribution data.

[0010] Optionally, contour points of structural surfaces, layered rock masses in cavern areas, and excavation damage areas in the area to be monitored are obtained, and the contour points of structural surfaces, layered rock masses in cavern areas, and excavation damage areas in the area to be monitored are matched to grid unit nodes of a three-dimensional anisotropic wave velocity model, so as to identify structural surfaces, caverns, layered rock masses, and excavation damage areas in the monitoring area in the three-dimensional anisotropic wave velocity model; The wave velocity values ​​of the grid cells corresponding to the structural surface, cavern, layered rock mass and excavation damage area in the area to be monitored are respectively set to corresponding preset values.

[0011] Optionally, using the shortest path ray tracing algorithm, the elastic wave propagation path and theoretical forward travel time from each sensor to the assumed earthquake source are calculated according to the three-dimensional anisotropic wave velocity model, including: Through the three-dimensional anisotropic wave velocity model and i The waveform of the microseismic event collected by the first sensor determines the elastic wave along the first i The sensor space direction of the sensor passes through the first k Wave velocity values ​​of rock layers v i,k and propagation distance d i,k , determine the elastic wave along the i The propagation velocity from the sensor space direction of each sensor to the hypothetical earthquake source is: ; in, V i is the elastic wave along the i The propagation velocity from the sensor space direction of each sensor to the hypothetical earthquake source; D i For the i The spatial distance from each sensor to the hypothetical earthquake source; n is the elastic wave along the i The number of rock layers from the sensor direction to the hypothetical earthquake source; According to the elastic wave iThe propagation velocity from the sensor space direction of the first sensor to the hypothetical earthquake source and the i The elastic wave propagation path from the first sensor to the hypothetical earthquake source is determined i The theoretical forward travel time from a sensor to the hypothetical earthquake source.

[0012] A second aspect of an embodiment of the present invention provides a microseismic positioning and sensor layout optimization device based on a probability density distribution field, the device comprising: A model building module, used to build a three-dimensional anisotropic wave velocity model for the rock mass in the area to be monitored, wherein the three-dimensional anisotropic wave velocity model includes a plurality of grid units, each of which has a corresponding wave velocity value; An acquisition module, used to acquire sensor characteristic data of each sensor and microseismic events monitored by each sensor, wherein the characteristics of the sensor include sensor spatial coordinates and sensor spatial direction; A first determination module is used to determine the elastic wave observation travel time from each sensor to each grid unit node based on the waveform of the microseismic event collected by each sensor; A second determination module is used to take the position of any grid unit node as the position of the hypothetical seismic source according to the sensor space coordinates and sensor space directions of each sensor, adopt the shortest path ray tracing algorithm, calculate the elastic wave propagation path and theoretical forward travel time from each sensor to the hypothetical seismic source according to the three-dimensional anisotropic wave velocity model, compare the theoretical forward travel time from each sensor to the hypothetical seismic source with the elastic wave observed travel time from each sensor to the hypothetical seismic source, and determine the difference between the elastic wave observed travel time and the theoretical forward travel time; A third determination module is used to adjust the position of the hypothetical earthquake source, compare the difference between the theoretical forward modeling travel time and the elastic wave observation travel time obtained at different hypothetical earthquake source positions, and determine a first preset number of grid unit nodes with the smallest difference between the elastic wave observation travel time and the theoretical forward modeling travel time as potential earthquake source points; A function establishment module, used for performing error analysis based on the location points of a plurality of potential earthquake source points, and establishing a probability density function of the potential earthquake source location error; A fourth determination module is used to determine the probability density of the microseismic source of the microseismic event at each position to obtain a probability density distribution field of the microseismic event; A fifth determination module, configured to determine a high probability area for occurrence of the microseismic event based on the probability density distribution field; The output module is used to output sensor distribution adjustment information based on the high probability area of ​​the microseismic event.

[0013] Optionally, the device further comprises: A sixth determination module is used to determine the probability density distribution field corresponding to each microseismic event respectively for each microseismic event when multiple microseismic events are monitored; A seventh determination module is used to superimpose the probability density distribution fields corresponding to the multiple microseismic events to obtain an overall probability density distribution field of all microseismic events; The fifth determining module is specifically used for: Based on the overall probability density distribution field, a high probability area for microseismic events to occur is determined.

[0014] Optionally, the device further comprises: A testing module, used to perform a second preset number of independent positioning simulation tests on each of the potential earthquake source points to obtain a plurality of independent positioning simulation test results; The function establishment module is specifically used for: Based on a plurality of independent positioning simulation test results corresponding to a plurality of the potential earthquake source points, an error analysis is performed to establish a probability density function of the potential earthquake source positioning error.

[0015] Optionally, the model building module is specifically used to: Obtain the test result data of three-dimensional anisotropic wave velocity measurement point test on the rock mass in the monitoring area of ​​underground engineering based on seismic wave reflection method and tunnel seismic exploration method, and determine the elastic wave velocity distribution data of the rock mass in the monitoring area; removing noise and outliers from the elastic wave velocity distribution data, filling in missing data using an interpolation method, and obtaining continuous elastic wave velocity distribution data for the entire region; The three-dimensional anisotropic wave velocity model is established according to the size of the monitoring area and the set grid step size, as well as the continuous elastic wave velocity distribution data.

[0016] Optionally, the model building module is further used to: Acquire the contour points of the structural surface, the layered rock mass in the cavern area, and the excavation damage area in the area to be monitored, and correspond the contour points of the structural surface, the layered rock mass in the cavern area, and the excavation damage area in the area to be monitored to the grid unit nodes of the three-dimensional anisotropic wave velocity model, so as to identify the structural surface, the cavern, the layered rock mass, and the excavation damage area in the monitoring area in the three-dimensional anisotropic wave velocity model; The wave velocity values ​​of the grid cells corresponding to the structural surface, cavern, layered rock mass and excavation damage area in the area to be monitored are respectively set to corresponding preset values.

[0017] Optionally, the second determining module is specifically configured to: Through the three-dimensional anisotropic wave velocity model and i The waveform of the microseismic event collected by the first sensor determines the elastic wave along the firsti The sensor space direction of the sensor passes through the first k Wave velocity values ​​of rock layers v i,k and propagation distance d i,k , determine the elastic wave along the i The propagation velocity from the sensor space direction of each sensor to the hypothetical earthquake source is: ; in, V i is the elastic wave along the i The propagation velocity from the sensor space direction of each sensor to the hypothetical earthquake source; D i For the i The spatial distance from each sensor to the hypothetical earthquake source; n is the elastic wave along the i The number of rock layers from the sensor direction to the hypothetical earthquake source; According to the elastic wave i The propagation velocity from the sensor space direction of the first sensor to the hypothetical earthquake source and the i The elastic wave propagation path from the first sensor to the hypothetical earthquake source is determined i The theoretical forward travel time from a sensor to the hypothetical earthquake source.

[0018] A third aspect of an embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes, the method for microseismic positioning and sensor layout optimization based on a probability density distribution field as described in the first aspect of the present invention is implemented.

[0019] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the microseismic positioning and sensor layout optimization method based on probability density distribution field as described in the first aspect of the present invention.

[0020] A fifth aspect of an embodiment of the present invention provides a computer program product, including a computer program / instruction, which is used by a processor to implement the steps in the method for microseismic positioning and sensor layout optimization based on probability density distribution field as described in the first aspect of the present invention.

[0021] In the embodiment of the present invention, the determined probability density distribution field of microseismic events can describe the probability density of microseismic events within a certain spatial range and time period. In the embodiment of the present invention, by establishing a probability density distribution field of microseismic events, the distribution of these errors is quantified and described, so that errors can be better corrected and compensated during the positioning process, which is important for understanding the spatial distribution law of microseismic events. By analyzing the probability density distribution field of microseismic events, high-risk areas where geological disasters may occur can be identified in advance, so that preventive measures can be taken to reduce the risk of disasters.

[0022] In the embodiment of the present invention, by analyzing the probability density of microseismic events in different regions, the optimal placement of sensors can be determined to ensure that the monitoring system can more effectively capture microseismic events and improve monitoring efficiency. This strategy can ensure high measurement resolution and measurement accuracy while greatly reducing the number of sensors. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0024] Figure 1 is a flowchart of the steps of a method for microseismic positioning and sensor layout optimization based on a probability density distribution field provided by an embodiment of the present invention; Figure 2 It is a simplified technical route diagram of the microseismic positioning and sensor layout optimization method based on probability density distribution field provided by an embodiment of the present invention; Figure 3 This is an example diagram of an underground cavern group grid model obtained in the microseismic positioning and sensor layout optimization method based on probability density distribution field provided in an embodiment of the present invention; Figure 4 is an example diagram of a waveform diagram of a microseismic event obtained in the method for microseismic positioning and sensor layout optimization based on a probability density distribution field provided in an embodiment of the present invention; Figure 5 It is a schematic diagram of a forward ray route from a sensor to a potential earthquake source point obtained by taking a potential positioning result as a blasting event in a microseismic positioning and sensor layout optimization method based on a probability density distribution field provided by an embodiment of the present invention; Figure 6 It is a schematic diagram of the overall probability density distribution field obtained in the microseismic positioning and sensor layout optimization method based on the probability density distribution field provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0026] A flowchart of a method for microseismic positioning and sensor layout optimization based on a probability density distribution field provided by an embodiment of the present invention is shown in FIG. Figure 1 Specifically, the method comprises the following steps: S101, establishing a three-dimensional anisotropic wave velocity model for the rock mass in the area to be monitored, wherein the three-dimensional anisotropic wave velocity model includes a plurality of grid cells, each grid cell having a corresponding wave velocity value; S102, acquiring sensor characteristic data of each sensor and microseismic events monitored by each sensor, wherein the characteristics of the sensor include sensor spatial coordinates and sensor spatial direction; S103, determining elastic wave observation travel time from each sensor to each grid unit node based on the waveform of the microseismic event collected by each sensor; S104, according to the sensor space coordinates and sensor space directions of each sensor, taking the position of any grid unit node as the position of the hypothetical seismic source, using the shortest path ray tracing algorithm, calculating the elastic wave propagation path and theoretical forward travel time from each sensor to the hypothetical seismic source according to the three-dimensional anisotropic wave velocity model, comparing the theoretical forward travel time from each sensor to the hypothetical seismic source with the elastic wave observed travel time from each sensor to the hypothetical seismic source, and determining the difference between the elastic wave observed travel time and the theoretical forward travel time; S105, adjusting the position of the hypothetical earthquake source, comparing the difference between the theoretical forward modeling travel time and the elastic wave observation travel time obtained at different hypothetical earthquake source positions, and determining a first preset number of grid unit nodes with the smallest difference between the elastic wave observation travel time and the theoretical forward modeling travel time as potential earthquake source points; S106, performing error analysis based on the position points of the plurality of potential earthquake source points, and establishing a probability density function of the potential earthquake source location error; S107, determining the probability density of the microseismic source of the microseismic event at each position, and obtaining the probability density distribution field of the microseismic event; S108, determining a high probability area for occurrence of the microseismic event based on the probability density distribution field; S109: outputting sensor distribution adjustment information based on the high probability area where the microseismic event occurs.

[0027] In an embodiment of the present invention, in step S101, three-dimensional positioning imaging can be first performed on the rock mass in the monitored area, and the positioning imaging area can be divided into three-dimensional grid units according to a preset grid spacing. Based on the seismic wave reflection method TSP (Tunnel Seismic Prediction ahead) method, a three-dimensional anisotropic wave velocity measurement point test is carried out on the rock mass in the monitored area of ​​the underground engineering to determine the P-wave velocity distribution data of the rock mass in the monitored area, and further combined with the known engineering geological data of the rock mass in the monitored area to construct a three-dimensional anisotropic wave velocity model.

[0028] In the embodiment of the present invention, existing microseismic event data may be collected first, and the corresponding rock mass in the area to be monitored may be determined based on the existing microseismic event data.

[0029] In the embodiment of the present invention, the area to be monitored may also be divided into gradients according to the arrangement positions of the multiple sensors.

[0030] Specifically, in an optional implementation manner, based on the above embodiment, in the method for optimizing microseismic positioning and sensor layout based on probability density distribution field provided by the embodiment of the present invention, step S101 includes: S1011, obtaining test result data obtained by conducting a three-dimensional anisotropic wave velocity measurement point test on the rock mass in the monitoring area of ​​the underground project based on the seismic wave reflection method and the tunnel seismic exploration method, and determining elastic wave velocity distribution data of the rock mass in the monitoring area; S1012, removing noise and outliers in the elastic wave velocity distribution data, and filling in missing data using an interpolation method to obtain continuous elastic wave velocity distribution data for the entire region; S1013, establishing the three-dimensional anisotropic wave velocity model according to the size of the monitoring area and the set grid step size, as well as the continuous elastic wave velocity distribution data.

[0031] In the embodiment of the present invention, noise and outliers in the measured data can be removed to ensure the accuracy of the data. For areas with incomplete data, an interpolation method is used to fill in the missing data to ensure that the wave velocity data of the entire area is continuous.

[0032] In the embodiment of the present invention, in practical applications, a three-dimensional anisotropic wave velocity model including underground caverns and faults can be established according to the size of the monitoring area and the set grid step size, wherein each grid unit contains the rock mass wave velocity information of the area, and the wave velocity value will be different for different rock layers and structures.

[0033] In an optional implementation, based on the above embodiment, the microseismic positioning and sensor layout optimization method based on probability density distribution field provided by the embodiment of the present invention further includes: S1014, obtaining contour points of the structural surface, the layered rock mass in the cavern area, and the excavation damage area in the area to be monitored, and corresponding the contour points of the structural surface, the layered rock mass in the cavern area, and the excavation damage area in the area to be monitored to the grid unit nodes of the three-dimensional anisotropic wave velocity model, so as to identify the structural surface, the cavern, the layered rock mass, and the excavation damage area in the monitoring area in the three-dimensional anisotropic wave velocity model; S1015, setting the wave velocity values ​​of the grid cells corresponding to the structural surface, cavern, layered rock mass and excavation damage area in the area to be monitored to corresponding preset values.

[0034] In the embodiment of the present invention, the positions of tunnels and faults can be clearly identified in the three-dimensional anisotropic wave velocity model, and specifically, the contour points of the structural surface, the layered rock mass in the cavern area, and the excavation damage zone can be identified.

[0035] In the embodiment of the present invention, the coordinates of each control point (such as the sensor position, the contour point of the cavity area) in the three-dimensional anisotropic wave velocity model can be rounded so that these points can correspond to the grid unit nodes. The grid unit nodes can be grid vertices in the three-dimensional wave velocity model composed of multiple grid units.

[0036] In the embodiment of the present invention, the wave velocity in the tunnel can be set to the air wave velocity, and the wave velocity in the fault area can be set according to the specific situation. Based on this, the wave velocity values ​​of the grid units corresponding to the structural surface, cavern, layered rock mass and excavation damage area in the monitored area are respectively set to corresponding preset values. Specifically, for ease of understanding, the following is combined Figure 2 The microseismic positioning and sensor layout optimization method based on probability density distribution field provided by an embodiment of the present invention is further described. Figure 2 A simplified technical diagram of a method for microseismic positioning and sensor layout optimization based on a probability density distribution field provided by an embodiment of the present invention is shown.

[0037] like Figure 2 As shown, in an embodiment of the present invention, the underground cavern group model corresponding to the rock mass in the area to be monitored can be first obtained, which can be determined by known geological data or by on-site geological surveys. Then, a grid-unitized underground cavern group grid model is constructed based on the size of the rock mass in the area to be monitored and actual needs. Furthermore, it can be determined in combination with engineering geological data whether each grid unit corresponds to a structural surface, cavern, layered rock mass or excavation damage area, and the wave velocity values ​​of the grid units in different areas are assigned and set to corresponding preset values. Specifically, as Figure 3 As shown, it shows an example diagram of the underground cavern group grid model obtained in an embodiment of the present invention.

[0038] In an embodiment of the present invention, it is also necessary to obtain sensor characteristic data of each sensor, including the sensor spatial coordinates and the sensor spatial direction, so as to identify the sensor position and spatial direction in the three-dimensional anisotropic wave velocity model to facilitate the subsequent calculation of the travel time from each sensor to each grid unit node.

[0039] In the embodiment of the present invention, in step S104, the shortest path ray tracing algorithm may be used to calculate the propagation path and forward travel time from each sensor in the area to any node, and save them in the travel time database.

[0040] Then obtain the first arrival time of the microseismic event waveform in each channel t i , and calculate the objective function of each grid unit node in combination with the travel time database:

[0041] In the formula i =1, 2,…, l ; m = 1,2,…, j ; n =1, 2 … , k . N is the number of sensors in the monitoring area; r For the r Sensors; t s,r For the r sensors to the current grid cell node ( i , m , n )'s elastic wave observation travel time; For the r Sensors to grid unit nodes ( i , m , n )'s theory is being developed, l , j , k is the number of nodes in the three axes of the monitoring area.

[0042] For example, Figure 4 An example diagram of a waveform diagram of a microseismic event obtained in an embodiment of the present invention is shown.

[0043] In the embodiment of the present invention, the above objective function of each grid unit node is calculated to obtain a first preset number of grid unit nodes with the smallest difference between the elastic wave observed travel time and the theoretical forward modeling travel time as potential earthquake source points.

[0044] In an optional implementation, based on the above embodiment, in the method for microseismic positioning and sensor layout optimization based on probability density distribution field provided by the embodiment of the present invention, step S104 includes: S1041, through the three-dimensional anisotropic wave velocity model and i The waveform of the microseismic event collected by the first sensor determines the elastic wave along the first i The sensor space direction of the sensor passes through the first k Wave velocity values ​​of rock layers v i,k and propagation distance d i,k , determine the elastic wave along the i The propagation velocity from the sensor space direction of each sensor to the hypothetical earthquake source is: ; in, V i is the elastic wave along the i The propagation velocity from the sensor space direction of each sensor to the hypothetical earthquake source; D i For the i The spatial distance from each sensor to the hypothetical earthquake source; n is the elastic wave along the i The number of rock layers from the sensor direction to the hypothetical earthquake source; S1042, according to the elastic wave along the i The propagation velocity from the sensor space direction of the first sensor to the hypothetical earthquake source and the i The elastic wave propagation path from the first sensor to the hypothetical earthquake source is determined i The theoretical forward travel time from a sensor to the hypothetical earthquake source.

[0045] Specifically, in an embodiment of the present invention, all objective functions are sorted in ascending order, the coordinates of the first 10 grid unit nodes are taken as potential earthquake source points, and the coordinate values ​​of the potential earthquake source points are averaged as the corresponding positioning results; then, according to the principle of source interchangeability, the travel time and forward ray path from the potential earthquake source points in the monitoring area to the sensor are obtained for independent positioning simulation tests.

[0046] In an optional implementation, based on the above embodiment, the microseismic positioning and sensor layout optimization method based on probability density distribution field provided by the embodiment of the present invention further includes: S112, performing a second preset number of independent positioning simulation tests on each of the potential earthquake source points to obtain multiple independent positioning simulation test results.

[0047] In an embodiment of the present invention, step S112 is executed after step S105 and before step S106. In this case, S106 includes: performing error analysis based on multiple independent positioning simulation test results corresponding to multiple potential earthquake source points, and establishing a probability density function of potential earthquake source positioning errors.

[0048] Specifically, the following method can be used to implement an independent positioning simulation test for each potential earthquake source point: based on the pre-established three-dimensional anisotropic wave velocity model and the waveform of the acquired microseismic event, the potential earthquake source point is used as the occurrence point of the blasting event, and a forward simulation algorithm, such as a three-dimensional time domain finite difference algorithm, is used to generate synthetic microseismic event waveform data. Actual microseismic events are simulated based on the microseismic event waveform data, and used for subsequent independent positioning simulation tests. Based on the microseismic event waveform data, different earthquake source positioning algorithms are used to locate the synthetic microseismic events, and multiple independent positioning simulation test results are obtained. The earthquake source positioning algorithm can specifically adopt a hybrid optimization method, a wave velocity model method, and a method without prior velocity measurement.

[0049] In the embodiment of the present invention, the second preset number can be set according to actual needs, for example, it can be 200 times.

[0050] In the embodiment of the present invention, multiple independent positioning simulation tests are performed on each potential earthquake source point, and error analysis is performed based on the independent positioning simulation test results, which can ensure that the established microseismic probability density distribution model has statistical reliability and accuracy.

[0051] In the embodiment of the present invention, in step S106, the mean and standard deviation of all potential earthquake sources and the sample mean of microseismic location error can be calculated. μ x , μ y , μ z The calculation formula is: ; The standard deviation of all potential earthquake sources is: ; According to the close relationship between the mean and standard deviation of the normal distribution and the wave velocity distribution in the three-dimensional anisotropic wave velocity model and the relative relationship between the sensor and the source point, the probability density function of the microseismic source positioning error is established: .

[0052] in, μx , μy , μz are the means in the x, y and z directions, respectively, reflecting the systematic error, which is closely related to the wave velocity distribution and sensor layout in the three-dimensional anisotropic wave velocity model. σx , σy , σz are the standard deviations in the x, y and z directions, respectively, reflecting the magnitude of the random error, which is also affected by the wave velocity distribution and sensor layout in the three-dimensional anisotropic wave velocity model.

[0053] In the embodiment of the present invention, within a certain spatial range and time period, the integral of the probability density describes the probability that the total number of microseismic events falls within the spatial range, forming a probability density field of microseismic events. Based on this, in step S108, the high probability area of ​​microseismic events can be determined through the probability density distribution field.

[0054] In an optional implementation, based on the above embodiment, the microseismic positioning and sensor layout optimization method based on probability density distribution field provided by the embodiment of the present invention further includes: S110, when multiple microseismic events are monitored, determining a probability density distribution field corresponding to each microseismic event respectively for each microseismic event; S111, superimposing the probability density distribution fields corresponding to the multiple microseismic events to obtain the overall probability density distribution field of all microseismic events.

[0055] In the embodiment of the present invention, steps S110 to S111 are performed after step S107 and before step S108. In this case, step S108 includes: determining a high probability area for microseismic events to occur based on the overall probability density distribution field.

[0056] In the embodiment of the present invention, when multiple microseismic events occur, the probability density fields of each microseismic event are simply added together to obtain the overall probability density distribution field of all microseismic events. P total( x0 , y0 , z0 ): ; in, N is the total number of microseismic events, Pi ( x0 , y0 , z0 ) is the i The probability density field of microseismic events.

[0057] In the embodiment of the present invention, the overall probability density distribution field can also be P Total normalization ensures that the sum of the probability density field is 1, and the superposition result can be normalized.

[0058] In the embodiment of the present invention, the probability density field of each microseismic event can be offset and superimposed along each grid using an imaging function to obtain the overall probability density distribution field. Figure 6 As shown, it shows a schematic diagram of the overall probability density distribution field obtained in an embodiment of the present invention.

[0059] In the embodiment of the present invention, a plurality of existing microseismic event data may be collected to determine the overall probability density field of all microseismic events; using the probability density distribution field of microseismic events, high probability areas of microseismic events may be identified.

[0060] In the embodiment of the present invention, after determining the high probability area of ​​microseismic events, sensors can be arranged in the area in a dense distribution manner to improve the monitoring accuracy of these areas. In other areas, a relatively uniform distribution manner can be adopted to ensure coverage of the entire monitoring area.

[0061] In the embodiment of the present invention, the spacing and position of the sensors are reasonably arranged according to the technical parameters of the sensors, such as detection range, sensitivity, signal-to-noise ratio, etc. It is ensured that the coverage of each sensor can overlap with each other to avoid monitoring blind spots.

[0062] In the embodiment of the present invention, the layout of sensors can also be dynamically adjusted through real-time monitoring of microseismic events and data analysis. Specifically, sensors can be added, removed or moved according to the real-time distribution of microseismic events to optimize the monitoring effect.

[0063] For ease of understanding, the following combination Figure 2 The microseismic positioning and sensor layout optimization method based on probability density distribution field provided by the embodiment of the present invention is further described: In the embodiment of the present invention, in step S103, after calculating the elastic wave observation travel time from each sensor to each grid unit node based on a microseismic event, the above objective function is calculated for each grid unit based on the shortest path ray tracing algorithm, and the calculation results are statistically analyzed to obtain the microseismic positioning directions ( x, y, z ). Finally, the probability density distribution field corresponding to the current microseismic event in the monitored area is obtained by combining the spatial characteristics of the positioning sensor (sensor spatial coordinates and sensor spatial direction), and finally the high probability area of ​​microseismic event is obtained. The sensor distribution adjustment information is further output in combination with the known spatial characteristics of the positioning sensor (sensor spatial coordinates and sensor spatial direction), so as to dynamically adjust the sensor distribution so that the sensor coverage overlaps the high probability area.

[0064] Based on the same inventive concept, an embodiment of the present invention further provides a microseismic positioning and sensor layout optimization device based on a probability density distribution field, the device comprising: An acquisition module is used to acquire sensor data of the manipulator, wherein the sensor data includes linear acceleration and angular velocity in the x-, y-, and z-axis directions of each joint of the manipulator; The first input module is used to input the linear acceleration and angular velocity of each joint of the manipulator in the x-, y-, and z-axis directions into the first Fourier neural operator module to determine the current position state of each joint of the manipulator; A second input module is used to input the current position state of each joint of the manipulator and the position of the target object at the current moment into a second Fourier neural operator module to obtain the position state of each joint of the manipulator at the next moment, input the position state at the next moment and the position of the target object at the next moment into the second Fourier neural operator module again to obtain the position state of each joint of the manipulator at the next moment, and execute the second Fourier neural operator module in a loop until the tracking process sequence data of the manipulator for the target object is obtained, and the tracking process sequence data includes the position states of each joint of the manipulator at multiple consecutive moments; A control module is used to control the manipulator to track the target object according to the tracking process sequence data.

[0065] Based on the same inventive concept, an embodiment of the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes, the steps in the method for microseismic positioning and sensor layout optimization based on a probability density distribution field as described in any of the above embodiments are implemented.

[0066] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method for microseismic positioning and sensor layout optimization based on probability density distribution field described in any of the above embodiments.

[0067] Based on the same inventive concept, an embodiment of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps in the method for microseismic positioning and sensor layout optimization based on probability density distribution field described in any of the above embodiments.

[0068] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0069] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0070] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (apparatus), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0071] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0072] These computer program instructions can also be loaded onto a computer or other programmable terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable terminal device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0073] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0074] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0075] The above is a detailed introduction to a microseismic positioning and sensor layout optimization method based on a probability density distribution field provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A microseismic location and sensor layout optimization method based on probability density distribution field, characterized in that: The method comprises: Establishing a three-dimensional anisotropic wave velocity model for the rock mass in the area to be monitored, wherein the three-dimensional anisotropic wave velocity model includes a plurality of grid cells, each grid cell having a corresponding wave velocity value; Acquire sensor characteristic data of each sensor and microseismic events monitored by each sensor, wherein the characteristics of the sensor include sensor spatial coordinates and sensor spatial directions; Based on the waveforms of the microseismic events collected by the sensors, determining the elastic wave observation travel time from the sensors to the grid unit nodes; According to the sensor space coordinates and sensor space directions of each sensor, the position of any grid unit node is taken as the position of the hypothetical seismic source, and the elastic wave propagation path and theoretical forward travel time from each sensor to the hypothetical seismic source are calculated according to the three-dimensional anisotropic wave velocity model using the shortest path ray tracing algorithm, and the theoretical forward travel time from each sensor to the hypothetical seismic source is compared with the elastic wave observed travel time from each sensor to the hypothetical seismic source to determine the difference between the elastic wave observed travel time and the theoretical forward travel time; Adjusting the position of the hypothetical earthquake source, comparing the difference between the theoretical forward modeling travel time and the elastic wave observation travel time obtained at different hypothetical earthquake source positions, and determining a first preset number of grid unit nodes with the smallest difference between the elastic wave observation travel time and the theoretical forward modeling travel time as potential earthquake source points; Performing error analysis based on the location points of the plurality of potential earthquake source points to establish a probability density function of the potential earthquake source location error; Determine the probability density of the microseismic source of the microseismic event at each position to obtain the probability density distribution field of the microseismic event; Based on the probability density distribution field, determining a high probability area for the occurrence of the microseismic event; Based on the high probability area where the microseismic event occurs, sensor distribution adjustment information is output.

2. The microseismic location and sensor layout optimization method based on probability density distribution field according to claim 1 is characterized in that: The method further comprises: When multiple microseismic events are monitored, the probability density distribution field corresponding to each microseismic event is determined for each microseismic event; Superimposing the probability density distribution fields corresponding to the multiple microseismic events to obtain the overall probability density distribution field of all microseismic events; Determining a high probability area for the occurrence of the microseismic event based on the probability density distribution field includes: Based on the overall probability density distribution field, a high probability area for microseismic events to occur is determined.

3. The microseismic location and sensor layout optimization method based on probability density distribution field according to claim 1 is characterized in that: The method further comprises: Performing a second preset number of independent positioning simulation tests on each of the potential earthquake source points to obtain a plurality of independent positioning simulation test results; Based on the position points of the plurality of potential earthquake source points, error analysis is performed to establish a probability density function of the potential earthquake source location error, including: Based on a plurality of independent positioning simulation test results corresponding to a plurality of the potential earthquake source points, an error analysis is performed to establish a probability density function of the potential earthquake source positioning error.

4. The method for microseismic location and sensor layout optimization based on probability density distribution field according to claim 1, characterized in that: The three-dimensional anisotropic wave velocity model for the rock mass in the area to be monitored is established, including: Obtain the test result data of three-dimensional anisotropic wave velocity measurement point test on the rock mass in the monitoring area of ​​underground engineering based on seismic wave reflection method and tunnel seismic exploration method, and determine the elastic wave velocity distribution data of the rock mass in the monitoring area; removing noise and outliers from the elastic wave velocity distribution data, filling in missing data using an interpolation method, and obtaining continuous elastic wave velocity distribution data for the entire region; The three-dimensional anisotropic wave velocity model is established according to the size of the monitoring area and the set grid step size, as well as the continuous elastic wave velocity distribution data.

5. The method for microseismic location and sensor layout optimization based on probability density distribution field according to claim 4, characterized in that: The method further comprises: Acquire the contour points of the structural surface, the layered rock mass in the cavern area, and the excavation damage area in the area to be monitored, and correspond the contour points of the structural surface, the layered rock mass in the cavern area, and the excavation damage area in the area to be monitored to the grid unit nodes of the three-dimensional anisotropic wave velocity model, so as to identify the structural surface, the cavern, the layered rock mass, and the excavation damage area in the monitoring area in the three-dimensional anisotropic wave velocity model; The wave velocity values ​​of the grid cells corresponding to the structural surface, cavern, layered rock mass and excavation damage area in the area to be monitored are respectively set to corresponding preset values.

6. The method for microseismic location and sensor layout optimization based on probability density distribution field according to claim 5, characterized in that: The shortest path ray tracing algorithm is used to calculate the elastic wave propagation path and theoretical forward travel time from each sensor to the hypothetical earthquake source according to the three-dimensional anisotropic wave velocity model, including: Through the three-dimensional anisotropic wave velocity model and i The waveform of the microseismic event collected by the first sensor determines the elastic wave along the first i The sensor space direction of the sensor passes through the first k Wave velocity values ​​of rock layers v i,k and propagation distance d i,k , determine the elastic wave along the i The propagation velocity from the sensor space direction of each sensor to the hypothetical earthquake source is: ； in, V i is the elastic wave along the i The propagation velocity from the sensor space direction of each sensor to the hypothetical earthquake source; D i For the i The spatial distance from each sensor to the hypothetical earthquake source; n is the elastic wave along the i The number of rock layers from the sensor direction to the hypothetical earthquake source; According to the elastic wave i The propagation velocity from the sensor space direction of the first sensor to the hypothetical earthquake source and the i The elastic wave propagation path from the first sensor to the hypothetical earthquake source is determined i The theoretical forward travel time from a sensor to the hypothetical earthquake source.

7. A microseismic positioning and sensor layout optimization device based on probability density distribution field, characterized in that: The device comprises: A model building module, used to build a three-dimensional anisotropic wave velocity model for the rock mass in the area to be monitored, wherein the three-dimensional anisotropic wave velocity model includes a plurality of grid units, each of which has a corresponding wave velocity value; An acquisition module, used to acquire sensor characteristic data of each sensor and microseismic events monitored by each sensor, wherein the characteristics of the sensor include sensor spatial coordinates and sensor spatial direction; A first determination module is used to determine the elastic wave observation travel time from each sensor to each grid unit node based on the waveform of the microseismic event collected by each sensor; A second determination module is used to take the position of any grid unit node as the position of the hypothetical seismic source according to the sensor space coordinates and sensor space directions of each sensor, adopt the shortest path ray tracing algorithm, calculate the elastic wave propagation path and theoretical forward travel time from each sensor to the hypothetical seismic source according to the three-dimensional anisotropic wave velocity model, compare the theoretical forward travel time from each sensor to the hypothetical seismic source with the elastic wave observed travel time from each sensor to the hypothetical seismic source, and determine the difference between the elastic wave observed travel time and the theoretical forward travel time; A third determination module is used to adjust the position of the hypothetical earthquake source, compare the difference between the theoretical forward modeling travel time and the elastic wave observation travel time obtained at different hypothetical earthquake source positions, and determine a first preset number of grid unit nodes with the smallest difference between the elastic wave observation travel time and the theoretical forward modeling travel time as potential earthquake source points; A function establishment module, used for performing error analysis based on the location points of a plurality of potential earthquake source points, and establishing a probability density function of the potential earthquake source location error; A fourth determination module is used to determine the probability density of the microseismic source of the microseismic event at each position to obtain a probability density distribution field of the microseismic event; A fifth determination module, configured to determine a high probability area for occurrence of the microseismic event based on the probability density distribution field; The output module is used to output sensor distribution adjustment information based on the high probability area of ​​the microseismic event.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for microseismic positioning and sensor layout optimization based on probability density distribution field described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for microseismic positioning and sensor layout optimization based on probability density distribution field described in any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps in the method for microseismic positioning and sensor layout optimization based on probability density distribution field described in any one of claims 1 to 6 are implemented.

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