A mine microseismic source positioning method, device and medium

Abstract

This invention discloses a method, apparatus, and medium for locating microseismic sources in mines. The method includes: acquiring geological data, mining progress data, historical microseismic event data, and real-time seismic wave data of the target mine; constructing an initial three-dimensional anisotropic velocity model based on the geological data and the mining progress data, wherein the three-dimensional anisotropic velocity model is a velocity field model characterizing the propagation velocity of seismic waves in different propagation directions and the distribution of anisotropic parameters within the three-dimensional space of the mine; updating the anisotropic parameters of the initial three-dimensional anisotropic velocity model using an improved A* search algorithm based on the historical microseismic event data to obtain a target three-dimensional anisotropic velocity model; and determining the microseismic source of the target mine within a target time period based on the real-time seismic wave data and the target three-dimensional anisotropic velocity model. This invention improves the accuracy and precision of locating microseismic sources in target mines.

Description

Technical Field

[0001] This invention relates to the field of mine disaster monitoring and early warning technology, and in particular to a method, device and medium for locating mine microseismic sources. Background Technology

[0002] Mine dynamic disasters, such as rock bursts, mine tremors, and coal and gas outbursts, are major safety hazards restricting safe and efficient mine production. These disasters are often closely related to the redistribution of the formation stress field caused by mining activities, and are characterized by their suddenness, nonlinearity, and high destructiveness. Therefore, accurate monitoring and location of mine seismic waves is of great significance for ensuring the safety of underground workers and equipment.

[0003] Currently, most mine microseismic monitoring systems use uniform or layered velocity models for location calculations, resulting in poor accuracy in locating microseismic sources in deep and complex structural areas. Furthermore, existing technologies have low modeling accuracy for seismic wave propagation paths in anisotropic media, making it difficult to accurately reflect velocity differences in different propagation directions. This further increases the deviation between theoretical and actual travel times, thus affecting the location accuracy of microseismic event sources. Summary of the Invention

[0004] The purpose of this invention is to propose a method, device, and medium for locating microseismic sources in mines. An initial three-dimensional anisotropic velocity model is constructed based on the geological data and mining progress data of the target mine. An improved A* search algorithm is then used to update the initial three-dimensional anisotropic velocity model based on historical microseismic event data. Finally, real-time seismic wave data is combined to determine the microseismic source data of the target mine within the target time period, thereby improving the accuracy and precision of locating the microseismic sources in the target mine.

[0005] To achieve the above objectives, a first aspect of the present invention provides a method for locating microseismic sources in mines, the method comprising: Acquire geological data, mining progress data, historical microseismic event data, and real-time seismic wave data of the target mine; Based on the geological data and the mining progress data, an initial three-dimensional anisotropic velocity model is constructed. The three-dimensional anisotropic velocity model is a velocity field model that characterizes the propagation velocity of seismic waves in different propagation directions and the distribution of anisotropic parameters in the three-dimensional space of the mine. Based on the historical microseismic event data, the anisotropic parameters of the initial three-dimensional anisotropic velocity model are updated using an improved A* search algorithm to obtain the target three-dimensional anisotropic velocity model. Based on the real-time seismic wave data and the target three-dimensional anisotropic velocity model, the microseismic source of the target mine within the target time period is determined.

[0006] In this embodiment, by combining geological data, mining progress data, historical microseismic event data, and real-time seismic wave data of the target mine, a three-dimensional anisotropic velocity model is constructed and dynamically updated. Then, based on the target three-dimensional anisotropic velocity model, the microseismic source data of the target mine within the target time period is determined. This enables a more accurate characterization of the seismic wave propagation characteristics in the complex medium of the mine and improves the accuracy of the determination of the microseismic source data.

[0007] Furthermore, the step of updating the anisotropic parameters of the initial three-dimensional anisotropic velocity model based on the historical microseismic event data using an improved A* search algorithm to obtain the target three-dimensional anisotropic velocity model includes: Extract clearly defined P-wave and S-wave travel time data from the historical microseismic event data; Based on the P-wave travel time data and the S-wave travel time data, the anisotropic parameters of the initial three-dimensional anisotropic velocity model are updated using an improved A* search algorithm to obtain the target three-dimensional anisotropic velocity model.

[0008] In this embodiment, by extracting clear P-wave and S-wave travel time data from historical microseismic event data, and updating the anisotropic parameters of the initial three-dimensional anisotropic velocity model based on the P-wave and S-wave travel time data, and correcting the parameters of the velocity model based on high-quality historical samples, the reliability of the target three-dimensional anisotropic velocity model is improved.

[0009] Further, the step of updating the anisotropic parameters of the initial three-dimensional anisotropic velocity model using an improved A* search algorithm based on the P-wave travel time data and the S-wave travel time data includes: Based on the P-wave travel time data and the S-wave travel time data, an improved A* search algorithm is used to search for the theoretical ray path between the source location of each historical microseismic event and the location of each sensor in the initial three-dimensional anisotropic velocity model, and the theoretical travel time is calculated based on the theoretical ray path. Using the travel time residuals between the theoretical travel time and the P-wave and S-wave travel time data as the objective function, the anisotropic parameters in the initial three-dimensional anisotropic velocity model are inverted and updated.

[0010] In this embodiment, by searching for the theoretical ray paths corresponding to each historical microseismic event in the initial three-dimensional anisotropic velocity model and calculating the theoretical travel time, and then using the travel time residual between the theoretical travel time and the observed travel time as the objective function to invert and update the anisotropic parameters, the updated velocity model can be made closer to the actual propagation of seismic waves in the target mine, thereby improving the accuracy of the velocity model.

[0011] Furthermore, the improved A* search algorithm introduces the group velocity, propagation azimuth angle, and incident angle in anisotropic media, and constructs a heuristic function based on the group velocity, the propagation azimuth angle, and the incident angle. The heuristic function is used to calculate the expected propagation time from the current search node to the target node.

[0012] In this embodiment, by introducing the group velocity, propagation azimuth angle, and incident angle in anisotropic media into the improved A* search algorithm, and constructing a heuristic function accordingly, the calculation of the expected propagation travel time can better conform to the actual propagation law in anisotropic media, thereby improving the accuracy and efficiency of theoretical ray path search.

[0013] Furthermore, the objective function is:

[0014] in, Let the objective function be... N This represents the total number of historical micro-earthquake events. i These are the serial numbers of historical microseismic events. M i For the first i The number of valid time-to-time observations for historical microseismic events j To observe the sequence number, To observe the travel time, For the theory to be outdated, The anisotropic parameters are those of the initial three-dimensional anisotropic velocity model. μ The regularization coefficient is . This is the smoothness constraint term for the initial three-dimensional anisotropic velocity model.

[0015] In this embodiment, by simultaneously introducing the residual term between theoretical travel time and observed travel time, as well as the smoothness constraint term, into the objective function, it is possible to suppress excessive oscillations in the velocity model parameter update process while ensuring the accuracy of travel time fitting, thereby improving the stability and rationality of anisotropic parameter inversion update.

[0016] Further, determining the microseismic source of the target mine within the target time period based on the real-time seismic wave data and the target three-dimensional anisotropic velocity model includes: Phase identification is performed on the real-time seismic wave data to obtain the first arrival and arrival times of the P-waves and S-waves corresponding to each sensor; Based on the target three-dimensional anisotropic velocity model, several candidate seismic source locations are selected within the target mine, and the theoretical propagation travel time from each candidate seismic source location to each sensor location is calculated. Based on the theoretical propagation travel time corresponding to each candidate seismic source location and the first arrival and arrival times of the P-wave and S-wave corresponding to each sensor, a travel time residual objective function is constructed. By minimizing the travel time residual objective function, the microseismic source of the target mine within the target time period is determined.

[0017] In this embodiment, by performing phase identification on real-time seismic wave data, the first arrival times of P-waves and S-waves corresponding to each sensor are obtained. The travel time residual objective function is constructed by combining the target three-dimensional anisotropic velocity model to determine the microseismic source data of the target mine within the target time period. This enables precise localization of microseismic events in the target mine, thereby improving the accuracy of microseismic source data determination.

[0018] Furthermore, it also includes: Based on the microseismic source data of the target mine, the mining engineering data, the ground stress monitoring data, and the ground sound monitoring data, a mine microseismic risk early warning model is constructed. Based on the aforementioned mine microseismic risk early warning model, the microseismic risk early warning parameters of the target mine are determined, and the microseismic risk of the target mine is warned based on the aforementioned microseismic risk early warning parameters.

[0019] In this embodiment, a mine microseismic risk early warning model is constructed based on the seismic source data, mining engineering data, ground stress monitoring data, and ground sound monitoring data of the target mine. Based on this model, microseismic risk early warning parameters are determined and early warning is carried out. This enables comprehensive analysis and early warning of the microseismic risk of the target mine, thereby improving the accuracy and timeliness of mine microseismic risk early warning.

[0020] Furthermore, the microseismic risk early warning parameters include microseismic frequency anomaly parameters, energy release trend parameters, source concentration index parameters, stress concentration parameters, and dynamic response parameters; The method of issuing early warnings to the target mine based on the microseismic risk early warning parameters includes: Based on the microseismic frequency anomaly parameters, the energy release trend parameters, the seismic source concentration parameters, the stress concentration parameters, and the dynamic response parameters, a comprehensive risk value is determined; The comprehensive risk value is compared with a preset early warning threshold to determine the early warning level corresponding to the target mine, and micro-seismic early warning is issued to the target mine based on the early warning level.

[0021] In this embodiment, a comprehensive risk value is determined by integrating microseismic frequency anomaly parameters, energy release trend parameters, seismic source concentration parameters, stress concentration parameters, and dynamic response parameters. The warning level is then determined based on the comparison between the comprehensive risk value and the preset warning threshold. This enables the quantitative classification of the microseismic risk level of the target mine, thereby improving the accuracy of the microseismic risk warning results.

[0022] To achieve the above objectives, a second aspect of the present invention also provides a mine microseismic source location device for implementing the mine microseismic source location method described in any of the first aspects, the device comprising: The data acquisition module is used to acquire geological data, mining progress data, historical microseismic event data, and real-time seismic wave data of the target mine. The initial three-dimensional anisotropic velocity model generation module is used to construct an initial three-dimensional anisotropic velocity model based on the geological data and the mining progress data. The three-dimensional anisotropic velocity model is a velocity field model that characterizes the propagation velocity of seismic waves in different propagation directions and the distribution of anisotropic parameters in the three-dimensional space of the mine. The target three-dimensional anisotropic velocity model generation module is used to update the anisotropic parameters of the initial three-dimensional anisotropic velocity model based on the historical microseismic event data using an improved A* search algorithm, so as to obtain the target three-dimensional anisotropic velocity model. The microseismic source generation module is used to determine the microseismic source of the target mine within the target time period based on the real-time seismic wave data and the target three-dimensional anisotropic velocity model.

[0023] A third aspect of the present invention also provides a computer-readable storage medium comprising a stored computer program; wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform a mine microseismic source localization method as described in any of the first aspects above. Attached Figure Description

[0024] Figure 1 This is a flowchart of a preferred embodiment of a mine microseismic source location method provided in the first aspect of the present invention; Figure 2 This is a structural block diagram of a preferred embodiment of a mine microseismic source location device provided in the second aspect of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] It should be noted that the data involved in this invention (including but not limited to data used for analysis, data stored, data displayed, etc.) are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0027] In this embodiment of the invention, the words "exemplarily" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplarily" or "for example" in this invention should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of the words "exemplarily" or "for example" is intended to present the relevant concepts in a specific manner.

[0028] In this invention description, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In this invention description, unless otherwise stated, "a plurality of" means two or more. In this invention description, the term "comprising" and its variations are open-ended, meaning "including but not limited to." The term "based on" means "at least partially based on." The term "according to" means "at least partially according to." The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments."

[0029] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0030] In the description of this invention, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0031] The technical solution of the present invention will be further described below with reference to specific embodiments: The first aspect of this invention provides a method for locating the source of microseismic events in a mine, see [link to relevant documentation]. Figure 1 The diagram shown is a flowchart of a preferred embodiment of a mine microseismic source location method provided by the first aspect of the present invention. The method includes steps S1 to S4, as detailed below: Step S1: Obtain geological data, mining progress data, historical microseismic event data, and real-time seismic wave data of the target mine; In one example, a mine microseismic source location system deployed in a mine dispatch center, ground monitoring center, edge computing node, or cloud analysis platform establishes data interaction connections with a geological data management platform, a production dispatch system, a historical microseismic database, and underground seismic wave monitoring equipment, respectively. The geological data can be provided by the mine geological data management platform; the mining progress data can be provided by the production dispatch system, the mining equipment control system, or a manual input terminal; the historical microseismic event data can be provided by the historical microseismic database; and the real-time seismic wave data can be uploaded in real time by geophones, seismic sensors, distributed fiber optic sensing devices, or other wave monitoring devices deployed in different areas of the target mine. After receiving the above data, the mine microseismic source location system can perform time alignment, spatial coordinate unification, data integrity checks, and format conversion on various types of data to form a target input dataset for subsequent processing.

[0032] Step S2: Based on the geological data and the mining progress data, construct an initial three-dimensional anisotropic velocity model. The three-dimensional anisotropic velocity model is a velocity field model that characterizes the propagation velocity of seismic waves in different propagation directions and the distribution of anisotropic parameters in the three-dimensional space of the mine. In one example, the mine microseismic source localization system uses information such as the target mine's stratigraphic structure, lithological distribution, fault and fracture conditions, roadway layout, and working face advancement status to represent the target mine in a three-dimensional grid. It then assigns corresponding initial wave velocity and anisotropic parameters to different spatial regions, thus obtaining an initial three-dimensional anisotropic velocity model. This initial three-dimensional anisotropic velocity model reflects the characteristics of the underlying propagation medium at the current stage of the target mine, providing a basic model support for subsequent theoretical ray path searches and microseismic source localization.

[0033] Step S3: Based on the historical microseismic event data, the anisotropic parameters of the initial three-dimensional anisotropic velocity model are updated using the improved A* search algorithm to obtain the target three-dimensional anisotropic velocity model. In one example, the mine microseismic source location system extracts clear P-wave and S-wave travel time data from the historical microseismic event data. Based on the initial three-dimensional anisotropic velocity model, it uses an improved A-search algorithm to search for the theoretical ray paths from the source location of each historical microseismic event to the location of each sensor, and calculates the corresponding theoretical travel times. Then, based on the residuals between the theoretical and observed travel times, the anisotropic parameters in the initial three-dimensional anisotropic velocity model can be inverted and updated to obtain a target three-dimensional anisotropic velocity model that better reflects the actual propagation characteristics of the target mine.

[0034] Step S4: Based on the real-time seismic wave data and the target three-dimensional anisotropic velocity model, determine the microseismic source of the target mine within the target time period.

[0035] In one example, the real-time seismic wave data is first subjected to event trigger detection and phase identification to obtain the first arrival and arrival times of P-waves and S-waves corresponding to each sensor. Then, several candidate source locations are selected within the three-dimensional spatial range corresponding to the target mine, and the theoretical travel time from each candidate source location to each sensor location is calculated based on the target three-dimensional anisotropic velocity model. Further, a travel time residual objective function can be constructed based on the theoretical travel time corresponding to each candidate source location and the first arrival and arrival times of each sensor. By solving the travel time residual objective function, the microseismic sources of the target mine within the target time period are determined. The microseismic sources may include the spatial location corresponding to the microseismic event, and may further include the time of occurrence, the corresponding regional identifier, or other information that can characterize the spatial distribution of the microseismic sources.

[0036] It should be noted that the improved A* search algorithm in this embodiment is mainly used to realize the theoretical ray path search and theoretical travel time calculation in anisotropic media. Its specific implementation can be adjusted according to different mine conditions. As long as the path search can be realized by combining the propagation characteristics in anisotropic media, it can be applied to this invention.

[0037] It should be noted that the construction method, mesh generation method and parameter expression method of the initial three-dimensional anisotropic velocity model and the target three-dimensional anisotropic velocity model can be flexibly set according to the actual application scenario, and are not limited to a certain fixed modeling method.

[0038] It should be noted that the determination of the microseismic source in step S4 can be achieved by traversing the candidate source locations, or by iterative optimization, grid search or other equivalent solution methods. As long as the microseismic source of the target mine within the target time period can be determined based on real-time seismic wave data and the target three-dimensional anisotropic velocity model, it falls within the protection scope of this invention.

[0039] In this embodiment, by acquiring geological data, mining progress data, historical microseismic event data, and real-time seismic wave data of the target mine, an initial three-dimensional anisotropic velocity model is first constructed. Then, based on the historical microseismic event data, the initial three-dimensional anisotropic velocity model is updated to obtain a target three-dimensional anisotropic velocity model that better matches the actual propagation characteristics of the target mine. Finally, based on the real-time seismic wave data and the target three-dimensional anisotropic velocity model, the microseismic source of the target mine within the target time period is determined, thereby improving the accuracy and reliability of microseismic source location in mines under complex geological conditions.

[0040] In another preferred embodiment, the step of updating the anisotropic parameters of the initial three-dimensional anisotropic velocity model based on the historical microseismic event data using an improved A* search algorithm to obtain the target three-dimensional anisotropic velocity model includes: Extract clearly defined P-wave and S-wave travel time data from the historical microseismic event data; Based on the P-wave travel time data and the S-wave travel time data, the anisotropic parameters of the initial three-dimensional anisotropic velocity model are updated using an improved A* search algorithm to obtain the target three-dimensional anisotropic velocity model.

[0041] In one example, seismic wave data is acquired in real time via a distributed fiber optic acoustic sensing network. This data includes P-wave, S-wave, and surface wave data. Seismic phase identification involves inputting the seismic wave data into a pre-trained deep neural network model. This model employs an encoder-decoder architecture, incorporating multi-scale feature extraction and attention mechanisms. Through waveform feature extraction and weighted fusion, it identifies and extracts the travel time data of P-waves and S-waves, outputting the phase picking results. This deep neural network, combining multi-scale wavefield reconstruction with an attention mechanism, effectively identifies weak rupture signals submerged in strong noise, significantly improving the picking accuracy of P-waves and S-waves and laying a solid foundation for subsequent precise positioning.

[0042] In yet another preferred embodiment, the step of updating the anisotropic parameters of the initial three-dimensional anisotropic velocity model using an improved A* search algorithm based on the P-wave travel time data and the S-wave travel time data includes: Based on the P-wave travel time data and the S-wave travel time data, an improved A* search algorithm is used to search for the theoretical ray path between the source location of each historical microseismic event and the location of each sensor in the initial three-dimensional anisotropic velocity model, and the theoretical travel time is calculated based on the theoretical ray path. Using the travel time residuals between the theoretical travel time and the P-wave and S-wave travel time data as the objective function, the anisotropic parameters in the initial three-dimensional anisotropic velocity model are inverted and updated.

[0043] In one example, after acquiring the P-wave and S-wave travel times corresponding to historical microseismic events, the mine microseismic monitoring platform first reads the velocity parameters and anisotropic parameters of each grid cell in the initial three-dimensional anisotropic velocity model. Based on the source location and sensor locations of the historical microseismic events, it establishes a set of candidate propagation paths from the source location to the sensor location in the three-dimensional model space. Subsequently, using an improved A* search algorithm, it searches for theoretical ray paths event-by-event and sensor-by-sensor in the initial three-dimensional anisotropic velocity model to obtain the theoretical propagation path corresponding to each historical microseismic event under the current model parameter conditions. Based on the theoretical propagation paths, it further calculates the theoretical P-wave and theoretical S-wave travel times corresponding to each propagation path, and compares the theoretical P-wave and theoretical S-wave travel times with the actual P-wave and S-wave travel times of the historical microseismic events to form travel time residuals. Subsequently, with the goal of minimizing the overall travel time residual, the anisotropic parameters in the initial three-dimensional anisotropic velocity model are iteratively corrected. After reaching the preset convergence condition, the updated target three-dimensional anisotropic velocity model is output.

[0044] It should be noted that the anisotropy parameter update can be performed synchronously on all grid cells, or it can be performed locally only on the target area grid cells related to the propagation path of historical microseismic events.

[0045] It is understood that the inversion update process is not limited to a fixed iterative strategy. As long as the anisotropy parameters can be corrected based on the difference between the theoretical travel time and the observed travel time, it can be applied to this embodiment.

[0046] In this embodiment, by searching for the theoretical ray paths of each historical microseismic event in the initial three-dimensional anisotropic velocity model and updating the anisotropic parameters based on the travel time residuals, the updated velocity model can be made closer to the actual propagation law of seismic waves in the target mine, thereby improving the accuracy of subsequent microseismic source location.

[0047] In another preferred embodiment, the improved A* search algorithm introduces the group velocity, propagation azimuth, and incident angle in an anisotropic medium, and constructs a heuristic function based on the group velocity, the propagation azimuth, and the incident angle. The heuristic function is used to calculate the expected propagation travel time from the current search node to the target node.

[0048] In one example, the heuristic function for the improved A* search algorithm is: ; in, n For nodes, To estimate travel time, For spatial distance, It is the azimuth angle. Angle of incidence Group velocity.

[0049] Group speed This is obtained by solving the Christoffel equation for anisotropic media: ; in, The unit vector is the direction of the wavefront normal. For Christoffel matrix, For the density of the medium, The phase velocity is given.

[0050] Group speed ,Right now With phase velocity The relationship is: ; The node evaluation total cost function of the improved A* search algorithm f ( n )for: ; in, g ( n () represents the distance from the starting point to the current node. n The actual running time λ It is an anisotropic regulatory factor.

[0051] It should be noted that the azimuth angle can characterize the horizontal propagation direction of seismic waves in three-dimensional space, and the incident angle can characterize the incident direction characteristics of seismic waves relative to the local medium interface.

[0052] It should be noted that the group velocity is used to reflect the actual propagation velocity in the direction of seismic wave energy propagation. Therefore, compared with directly using a fixed velocity value under isotropic conditions, it can better reflect the true propagation characteristics in complex media.

[0053] It should be noted that the anisotropy regulating factor... λ The settings can be adjusted based on the anisotropy of the medium within the target mine to balance the impact of actual cumulative travel time and expected propagation travel time on node assessment.

[0054] It is understood that the specific mathematical expression of the heuristic function can be adjusted according to different anisotropic medium models. As long as the propagation direction information can be introduced into the node prediction travel time estimation process, it can be applied to this embodiment.

[0055] It is understood that the current search node and the target node can be understood as mesh nodes in a three-dimensional anisotropic velocity model, where the current search node is the node to be expanded during the search process, and the target node is the mesh node corresponding to the sensor position.

[0056] In this embodiment, by introducing group velocity, propagation azimuth angle, and incident angle into the improved A* search algorithm, and constructing a heuristic function based on these, the estimated propagation travel time can be made more consistent with the actual propagation law in anisotropic media, thereby improving the accuracy and efficiency of theoretical ray path search.

[0057] In yet another preferred embodiment, the objective function is: ; in, Let the objective function be... N This represents the total number of historical micro-earthquake events. i These are the serial numbers of historical microseismic events. M i For the first i The number of valid time-to-time observations for historical microseismic events j To observe the sequence number, To observe the travel time, For the theory to be outdated, The anisotropic parameters are those of the initial three-dimensional anisotropic velocity model. μ The regularization coefficient is . This is the smoothness constraint term for the initial three-dimensional anisotropic velocity model.

[0058] In one example, the theoretical travel time is calculated based on the theoretical ray path, and the minimum residual between the theoretical and observed travel times is used as the objective function to invert and update the anisotropic parameters of the initial three-dimensional velocity module, resulting in a dynamic three-dimensional anisotropic velocity model. The expression for the objective function is: ; in, Let be the objective function. N This represents the total number of microseismic events. i Mi is the sequence number of the microseismic event. i The number of hours observed for each microseismic event. j To observe the sequence number, To observe the travel time, For the theory to be outdated, This is the initial three-dimensional velocity module parameter vector. μ The regularization coefficient is . This is the initial smoothness constraint term for the 3D velocity module. During each parameter update cycle, the corresponding theoretical travel time is calculated based on the current anisotropic parameters, and the objective function value is obtained accordingly. If the objective function value of the current cycle decreases compared to the previous cycle, it indicates that the current parameter update direction is valid, and the execution entity continues to iterate along that update direction. If the change in the objective function value is lower than a preset threshold, or the number of iterations reaches a preset upper limit, the iteration stops, and the updated target 3D anisotropic velocity model is output.

[0059] It should be noted that the effective arrival number Mi typically corresponds to the number of effective P-wave arrival observations and / or effective S-wave arrival observations available for inversion calculation in the i-th historical microseismic event. It should also be noted that the smoothness constraint term... This is used to limit the abrupt changes in anisotropic parameters between adjacent grid cells, in order to avoid unreasonable local drastic jumps after model updates.

[0060] It should be noted that the regularization coefficient μ can be set according to the geological complexity of the target mine, the quality of historical data, and the requirements for inversion stability.

[0061] In this embodiment, by simultaneously considering the residual between theoretical and observed travel times and model smoothness constraints in the objective function, it is possible to improve the accuracy of travel time fitting while suppressing excessive oscillations during the anisotropic parameter update process, thereby improving the stability and rationality of velocity model inversion updates.

[0062] In yet another preferred embodiment, determining the microseismic source of the target mine within the target time period based on the real-time seismic wave data and the target three-dimensional anisotropic velocity model includes: Phase identification is performed on the real-time seismic wave data to obtain the first arrival and arrival times of the P-waves and S-waves corresponding to each sensor; Based on the target three-dimensional anisotropic velocity model, several candidate seismic source locations are selected within the target mine, and the theoretical propagation travel time from each candidate seismic source location to each sensor location is calculated. Based on the theoretical propagation travel time corresponding to each candidate seismic source location and the first arrival and arrival times of the P-wave and S-wave corresponding to each sensor, a travel time residual objective function is constructed. By minimizing the travel time residual objective function, the microseismic source of the target mine within the target time period is determined.

[0063] In one example, based on phase picking results and a dynamic three-dimensional anisotropic velocity module, a multi-event joint inversion method is used to finely locate microseismic events, obtaining the three-dimensional spatial coordinates of the source, the time of origin, and the focal mechanism parameters. The location objective function used in the multi-event joint inversion method is... The expression is: ; in, For the first i The coordinates of the epicenter location of the event. For the first i The moment when the event struck. Based on the location of the earthquake source When the earthquake occurred The theoretical travel time calculated using a dynamic three-dimensional anisotropic velocity model.

[0064] It should be noted that the travel time residual objective function can be constructed independently for a single microseismic event, or jointly for multiple microseismic events within the same target time period.

[0065] It is understood that the microseismic source may at least include the spatial location corresponding to the microseismic event, and in some embodiments, may further include the time of occurrence.

[0066] It is understood that the objective function for minimizing travel time residuals can be solved by grid search, iterative optimization, or other equivalent methods.

[0067] In this embodiment, a travel-time residual objective function is constructed based on real-time seismic wave data and a target three-dimensional anisotropic velocity model. Based on this function, the microseismic source of the target mine within the target time period is determined. This enables precise localization of microseismic events in the mine under complex geological conditions, thereby improving the accuracy and reliability of microseismic source localization.

[0068] In yet another preferred embodiment, it further includes: Based on the microseismic source data of the target mine, the mining engineering data, the ground stress monitoring data, and the ground sound monitoring data, a mine microseismic risk early warning model is constructed. Based on the aforementioned mine microseismic risk early warning model, the microseismic risk early warning parameters of the target mine are determined, and the microseismic risk of the target mine is warned based on the aforementioned microseismic risk early warning parameters.

[0069] In one example, after obtaining the microseismic source data of the target mine, further mining engineering data, ground stress monitoring data, and ground sound monitoring data are acquired. The mining engineering data may include mining engineering plan, roadway layout data, working face location data, mining progress data, and mining area boundary data. The ground stress monitoring data may include stress values, stress change trends, and stress anomaly information at different monitoring locations. The ground sound monitoring data may include ground sound event frequency, ground sound intensity, and ground sound temporal change information at different monitoring locations.

[0070] Specifically, a mine mining engineering plan can be extracted from the mine mining engineering data, and a three-dimensional analysis grid can be established by combining the spatial coordinates, depth, and stratigraphic information of the target mine. This three-dimensional analysis grid can be used to characterize the microseismic activity, mining disturbance, stress, and geosound activity states of different spatial regions of the target mine. In one example, the three-dimensional analysis grid can be divided according to a preset spatial resolution, for example, into regular grid units, or into non-uniform grid units based on structural zones, mining area boundaries, or key risk areas. For each grid unit, the executing entity can establish corresponding data associations, enabling microseismic source data, mining engineering data, geostress monitoring data, and geosound monitoring data to be uniformly mapped to the same spatial analysis framework.

[0071] Subsequently, based on the microseismic source data of the target mine, microseismic events within the target time period can be mapped to the three-dimensional analysis grid to obtain microseismic activity information corresponding to each grid cell. This microseismic activity information may include the frequency of microseismic events, the spatial distribution density of microseismic events, the cumulative energy value of events, and the degree of spatial concentration of seismic sources. Simultaneously, the executing entity can map the location of the mining area, the direction of face advance, and the progress of advance from the mine mining engineering data to the three-dimensional analysis grid to characterize the degree of impact of mining disturbances on each grid cell; it can also interpolate or match ground stress monitoring data to corresponding grid cells to characterize the stress state and stress changes of each grid cell; and it can associate ground sound monitoring data to corresponding grid cells to characterize the intensity and temporal evolution characteristics of ground sound activity in each grid cell. Based on the unified spatial mapping results of the above multi-source data, the mine microseismic risk early warning model is constructed.

[0072] In this embodiment, the mine microseismic risk early warning model can be understood as an analytical model used to characterize the correlation between the distribution of microseismic activity, mining disturbance state, stress response state, and intensity of ground sound activity in the three-dimensional space of the target mine. In other words, this model is not limited to a fixed algorithm form, but emphasizes the unified expression and comprehensive analysis of multi-source monitoring information related to microseismic risk within the target mine to support the subsequent determination of microseismic risk early warning parameters.

[0073] After constructing the mine microseismic risk early warning model, the microseismic risk early warning parameters of the target mine can be determined based on the model. Specifically, the microseismic risk early warning parameters may include microseismic frequency anomaly parameters, energy release trend parameters, source concentration parameters, stress concentration parameters, and dynamic response parameters.

[0074] The microseismic frequency anomaly parameter can be used to characterize the degree of deviation of the frequency of microseismic events in the current target time period from the historical normal time period. In one example, the number of microseismic events in each grid cell or key monitoring area in the current target time period can be counted and compared with the historical average number of microseismic events for the same period to determine the microseismic frequency anomaly parameter.

[0075] The energy release trend parameter can be used to characterize the changing trend of microseismic energy release levels within a target time period. In one example, the executing entity can statistically analyze the cumulative energy growth based on the energy information corresponding to microseismic events within the target time period, or analyze whether there is a continuous increasing trend in energy release by combining the frequency of microseismic events and energy distribution characteristics, thereby determining the energy release trend parameter.

[0076] The source concentration parameter can be used to characterize the spatial clustering of microseismic sources within a target time period. In one example, the executing entity can determine the source concentration parameter by statistically analyzing whether microseismic events occur in a certain area based on the distribution of microseismic sources in three-dimensional space, or by analyzing source density, spatial clustering degree, and migration of cluster centers.

[0077] The stress concentration parameters can be used to characterize the degree of stress anomaly in a local area of ​​the target mine. In one example, the implementing entity can determine the stress level and its changing trend at each monitoring location based on geostress monitoring data, and identify areas of stress anomaly concentration by combining the distribution of microseismic events, thereby determining the stress concentration parameters.

[0078] The dynamic response parameters can be used to characterize the response relationship between mining activity disturbances and microseismic activity. In one example, the implementing entity can combine mining progress data, ground sound monitoring data, and the spatiotemporal distribution of microseismic events to analyze whether microseismic activity shows synchronous enhancement, early response, or continuous activity as mining progresses, local ground sound intensifies, or stress changes, thereby determining the dynamic response parameters.

[0079] In a further implementation, a comprehensive risk value can be determined based on the microseismic frequency anomaly parameters, the energy release trend parameters, the source concentration parameters, the stress concentration parameters, and the dynamic response parameters. The comprehensive risk value can be obtained through a weighted fusion method or through a pre-trained classifier or prediction model. In one example, the classifier may include at least one of a random forest classifier, a support vector machine classifier, a gradient boosting tree classifier, and a neural network classifier. The executing entity compares the comprehensive risk value with a preset warning threshold to determine the warning level corresponding to the target mine, and issues a microseismic warning to the target mine based on the warning level.

[0080] In one example, the early warning result may include the hazard level, spatial coordinates of the hazard area, current values ​​of each microseismic risk early warning parameter, and early warning confidence level. Specifically, the hazard level can be divided into Level I, Level II, Level III, and Level IV, where Level I represents no hazard, Level II represents weak hazard, Level III represents moderate hazard, and Level IV represents strong hazard; the hazard area can be delineated based on the source concentration parameter and stress concentration parameter, outputting the corresponding three-dimensional spatial range; the early warning confidence level can be given by the probability value output by the classifier or the model evaluation value, used to reflect the reliability of this early warning result.

[0081] It should be noted that the construction method of the mine microseismic risk early warning model is not limited to the three-dimensional regular grid modeling method. It can also be implemented by irregular grid, partition modeling or other methods that can uniformly express the spatial relationship of multi-source monitoring data.

[0082] It should be noted that the types of microseismic risk warning parameters are not limited to those listed above. Without departing from the core idea of ​​this invention, other parameters that can characterize the degree of microseismic risk of the target mine can be added.

[0083] It should be noted that the preset early warning threshold can be set based on historical monitoring and statistical patterns, or it can be adaptively adjusted according to the actual working conditions, geological conditions, and risk level requirements of the mine.

[0084] It should be noted that the mine microseismic risk early warning model can be updated according to a preset time period, such as once every 24 hours, or it can be dynamically updated when there are significant changes in the mining progress, abnormally enhanced microseismic activity, or significant changes in the ground stress state.

[0085] It is understandable that the aforementioned early warning of microseismic risks in the target mine can be manifested in generating early warning levels and risk areas, or it can be further linked to output alarm information, dispatch prompts, or risk disposal suggestions.

[0086] In this embodiment, a mine microseismic risk early warning model is constructed based on the microseismic source data, mining engineering data, ground stress monitoring data, and ground sound monitoring data of the target mine. Based on the mine microseismic risk early warning model, microseismic risk early warning parameters and early warning levels are determined. This enables multi-source comprehensive analysis and graded early warning of microseismic risks in the target mine, thereby improving the accuracy, timeliness, and pertinence of microseismic risk early warning.

[0087] A second aspect of the present invention provides a mine microseismic source location device for implementing a mine microseismic source location method as described in any of the first aspects of the present invention. (See also...) Figure 2 The diagram shown is a structural block diagram of a preferred embodiment of a mine microseismic source location device provided in the second aspect of the present invention. The device includes: The data acquisition module is used to acquire geological data, mining progress data, historical microseismic event data, and real-time seismic wave data of the target mine. The initial three-dimensional anisotropic velocity model generation module is used to construct an initial three-dimensional anisotropic velocity model based on the geological data and the mining progress data. The three-dimensional anisotropic velocity model is a velocity field model that characterizes the propagation velocity of seismic waves in different propagation directions and the distribution of anisotropic parameters in the three-dimensional space of the mine. The target three-dimensional anisotropic velocity model generation module is used to update the anisotropic parameters of the initial three-dimensional anisotropic velocity model based on the historical microseismic event data using an improved A* search algorithm, so as to obtain the target three-dimensional anisotropic velocity model. The microseismic source generation module is used to determine the microseismic source of the target mine within the target time period based on the real-time seismic wave data and the target three-dimensional anisotropic velocity model.

[0088] It should be noted that the mine microseismic source location device provided in the second aspect embodiment of the present invention can realize all the processes of the mine microseismic source location method described in the first aspect above. The functions and technical effects of each module and unit in the device are the same as those of the mine microseismic source location method described in the first aspect above, and will not be repeated here.

[0089] A third aspect of the present invention also provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program; wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform a mine microseismic source localization method as described in any of the first aspects above.

[0090] Through the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary hardware platforms, and of course, it can also be implemented entirely by hardware. Based on this understanding, all or part of the technical solution of the present invention that contributes to the background technology can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM (Read-Only Memory) / RAM (Random Access Memory), magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0091] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for locating the source of microseismic events in a mine, characterized in that, include: Acquire geological data, mining progress data, historical microseismic event data, and real-time seismic wave data of the target mine; Based on the geological data and the mining progress data, an initial three-dimensional anisotropic velocity model is constructed. The three-dimensional anisotropic velocity model is a velocity field model that characterizes the propagation velocity of seismic waves in different propagation directions and the distribution of anisotropic parameters in the three-dimensional space of the mine. Based on the historical microseismic event data, the anisotropic parameters of the initial three-dimensional anisotropic velocity model are updated using an improved A* search algorithm to obtain the target three-dimensional anisotropic velocity model. Based on the real-time seismic wave data and the target three-dimensional anisotropic velocity model, the microseismic source of the target mine within the target time period is determined.

2. The method for locating a microseismic source in a mine as described in claim 1, characterized in that, The process of updating the anisotropic parameters of the initial three-dimensional anisotropic velocity model based on the historical microseismic event data using an improved A* search algorithm to obtain the target three-dimensional anisotropic velocity model includes: Extract clearly defined P-wave and S-wave travel time data from the historical microseismic event data; Based on the P-wave travel time data and the S-wave travel time data, the anisotropic parameters of the initial three-dimensional anisotropic velocity model are updated using an improved A* search algorithm to obtain the target three-dimensional anisotropic velocity model.

3. The method for locating a mine microseismic source as described in claim 2, characterized in that, The step of updating the anisotropic parameters of the initial three-dimensional anisotropic velocity model based on the P-wave travel time data and the S-wave travel time data using an improved A* search algorithm includes: Based on the P-wave travel time data and the S-wave travel time data, an improved A* search algorithm is used to search for the theoretical ray path between the source location of each historical microseismic event and the location of each sensor in the initial three-dimensional anisotropic velocity model, and the theoretical travel time is calculated based on the theoretical ray path. Using the travel time residuals between the theoretical travel time and the P-wave and S-wave travel time data as the objective function, the anisotropic parameters in the initial three-dimensional anisotropic velocity model are inverted and updated.

4. A method for locating a mine microseismic source as described in any one of claims 1-3, characterized in that, The improved A* search algorithm introduces the group velocity, propagation azimuth, and incident angle in anisotropic media, and constructs a heuristic function based on the group velocity, propagation azimuth, and incident angle. The heuristic function is used to calculate the expected propagation time from the current search node to the target node.

5. The method for locating a mine microseismic source as described in claim 3, characterized in that, The objective function is: in, Let the objective function be... N This represents the total number of historical micro-earthquake events. i This refers to the sequence number of historical microseismic events. M i For the first i The number of valid time-to-time observations for historical microseismic events j To observe the sequence number, To observe the travel time, For the theory to be outdated, The anisotropic parameters are those of the initial three-dimensional anisotropic velocity model. μ The regularization coefficient is . This is the smoothness constraint term for the initial three-dimensional anisotropic velocity model.

6. The method for locating a microseismic source in a mine as described in claim 1, characterized in that, The process of determining the microseismic source of the target mine within the target time period based on the real-time seismic wave data and the target three-dimensional anisotropic velocity model includes: Phase identification is performed on the real-time seismic wave data to obtain the first arrival and arrival times of the P-waves and S-waves corresponding to each sensor; Based on the target three-dimensional anisotropic velocity model, several candidate seismic source locations are selected within the target mine, and the theoretical propagation travel time from each candidate seismic source location to each sensor location is calculated. Based on the theoretical propagation travel time corresponding to each candidate seismic source location and the first arrival and arrival times of the P-wave and S-wave corresponding to each sensor, a travel time residual objective function is constructed. By minimizing the travel time residual objective function, the microseismic source of the target mine within the target time period is determined.

7. The method for locating a microseismic source in a mine as described in claim 1, characterized in that, Also includes: Based on the microseismic source data of the target mine, the mining engineering data, the ground stress monitoring data, and the ground sound monitoring data, a mine microseismic risk early warning model is constructed. Based on the aforementioned mine microseismic risk early warning model, the microseismic risk early warning parameters of the target mine are determined, and the microseismic risk of the target mine is warned based on the aforementioned microseismic risk early warning parameters.

8. The method for locating a mine microseismic source as described in claim 7, characterized in that, The microseismic risk warning parameters include microseismic frequency anomaly parameters, energy release trend parameters, source concentration index parameters, stress concentration parameters, and dynamic response parameters. The method of issuing early warnings to the target mine based on the microseismic risk early warning parameters includes: Based on the microseismic frequency anomaly parameters, the energy release trend parameters, the seismic source concentration parameters, the stress concentration parameters, and the dynamic response parameters, a comprehensive risk value is determined; The comprehensive risk value is compared with a preset early warning threshold to determine the early warning level corresponding to the target mine, and micro-seismic early warning is issued to the target mine based on the early warning level.

9. A mine microseismic source location device, used to implement the mine microseismic source location method as described in any one of claims 1 to 8, characterized in that, The device includes: The data acquisition module is used to acquire geological data, mining progress data, historical microseismic event data, and real-time seismic wave data of the target mine. The initial three-dimensional anisotropic velocity model generation module is used to construct an initial three-dimensional anisotropic velocity model based on the geological data and the mining progress data. The three-dimensional anisotropic velocity model is a velocity field model that characterizes the propagation velocity of seismic waves in different propagation directions and the distribution of anisotropic parameters in the three-dimensional space of the mine. The target three-dimensional anisotropic velocity model generation module is used to update the anisotropic parameters of the initial three-dimensional anisotropic velocity model based on the historical microseismic event data using an improved A* search algorithm, so as to obtain the target three-dimensional anisotropic velocity model. The microseismic source determination module is used to determine the microseismic source of the target mine within the target time period based on the real-time seismic wave data and the target three-dimensional anisotropic velocity model.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program; wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform a mine microseismic source location method as described in any one of claims 1 to 8.

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