Underground disaster dangerous area early warning method and device based on microseismic data

Abstract

The invention relates to the field of dangerous area early warning, in particular to an underground disaster dangerous area early warning method and device based on micro-seismic data, computer equipment and a storage medium, and aims to effectively extract feature information in micro-seismic data by adopting clustering analysis and multi-dimensional feature extraction. Quantitative analysis is carried out on the spatial and temporal distribution rule of the micro-seismic activity and the back driving factors thereof, and the early warning of the underground disaster dangerous area is realized efficiently and accurately.

Description

Technical Field

[0001] The present invention relates to the field of early warning of dangerous areas, and particularly to an early warning method, device, computer equipment and storage medium for underground disaster dangerous areas based on microseismic data. Background Art

[0002] The mining of metal ores is an important way to obtain metal resources. The causes of underground rock mass disasters in metal mines are complex and are often affected by both the original conditions of the underground geological structure and subsequent artificial excavation, blasting and other activities. Metal mines are often formed after hydrothermal erosion of underground cracks and joint surfaces. Therefore, the geological evolution process of metal mines is complex, the geological structure is rich, and there are extremely high requirements for the accuracy of underground stress field detection.

[0003] Secondly, the periodicity and multi-point nature of human activities have a strong disturbance on the frequency and intensity of underground rock mass disasters. Human activities include, but are not limited to, tunneling, blasting, excavation and backfilling, which will change the original ground stress field and form a real-time new ground stress field environment, thereby increasing the difficulty of early warning of rock mass disasters and posing a serious threat to miners' safety and production efficiency. In traditional microseismic monitoring systems, there is only a linear transmission process for real-time microseismic information, lacking the analysis of deep mechanisms and ignoring the spatio-temporal correlation and statistical characteristics of data, resulting in the inability to accurately predict the dangerous areas and times of mine tremors and lacking practical early warning functions. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to provide an early warning method, device, equipment and storage medium for underground disaster dangerous areas based on microseismic data, which uses clustering analysis and multi-dimensional feature extraction to effectively extract the feature information in the microseismic data, quantitatively analyze the spatio-temporal distribution law of microseismic activities and the driving factors behind them, and realize the early warning of underground disaster dangerous areas, which is efficient and accurate.

[0005] In a first aspect, an embodiment of the present application provides an early warning method for an underground disaster dangerous area based on microseismic data, including the following steps:

[0006] Obtain the microseismic data of the target area, process the microseismic data, and construct seismic catalog data, where the seismic catalog data includes the attribute data of several seismic events;

[0007] Based on the attribute data of several seismic events in the seismic catalog data, perform clustering analysis, divide the target area into sub-areas of several clustering types, and perform multi-dimensional feature extraction according to the sub-areas of several clustering types and the seismic catalog data to obtain multi-dimensional feature data of the sub-areas of several clustering types;

[0008] Using the Gutenberg-Richter law, B-value calculations are performed based on sub-regions of several of the clustering types and the seismic catalog data to obtain the B-values of the sub-regions of several clustering types;

[0009] Based on the multi-dimensional feature data and B-values of the sub-regions of several of the clustering types, early warnings for underground disaster-prone areas are carried out on the sub-regions of several of the clustering types to obtain the early warning results for the underground disaster-prone areas of the target region.

[0010] In a second aspect, an embodiment of the present application provides an underground disaster-prone area early warning device based on microseismic data, including:

[0011] A data acquisition module for acquiring microseismic data of a target region, processing the microseismic data, and constructing seismic catalog data, where the seismic catalog data includes attribute data of several seismic events;

[0012] A feature extraction module for performing clustering analysis based on the attribute data of several seismic events in the seismic catalog data, dividing the target region into sub-regions of several clustering types, and performing multi-dimensional feature extraction based on the sub-regions of several clustering types and the seismic catalog data to obtain multi-dimensional feature data of the sub-regions of several clustering types;

[0013] A B-value calculation module for using the Gutenberg-Richter law to perform B-value calculations based on the sub-regions of several of the clustering types and the seismic catalog data to obtain the B-values of the sub-regions of several clustering types;

[0014] A disaster-prone area early warning module for performing early warnings for underground disaster-prone areas on the sub-regions of several of the clustering types based on the multi-dimensional feature data and B-values of the sub-regions of several of the clustering types to obtain the early warning results for the underground disaster-prone areas of the target region.

[0015] In a third aspect, an embodiment of the present application provides a computer device, including: a processor, a memory, and a computer program stored on the memory and executable on the processor; when the computer program is executed by the processor, it implements the steps of the method for early warning of underground disaster-prone areas based on microseismic data as described in the first aspect.

[0016] In a fourth aspect, an embodiment of the present application provides a storage medium storing a computer program, and when the computer program is executed by a processor, it implements the steps of the method for early warning of underground disaster-prone areas based on microseismic data as described in the first aspect.

[0017] In an embodiment of the present application, a method, apparatus, computer device, and storage medium for warning underground disaster - prone areas based on micro - seismic data are provided. By using clustering analysis and multi - dimensional feature extraction, the characteristic information in micro - seismic data is effectively extracted, and the spatio - temporal distribution law of micro - seismic activities and the driving factors behind them are quantitatively analyzed, realizing the warning of underground disaster - prone areas, which is efficient and accurate.

[0018] For better understanding and implementation, the present invention will be described in detail below with reference to the accompanying drawings. Brief Description of the Drawings

[0019] Figure 1 A flowchart of a method for warning underground disaster - prone areas based on micro - seismic data provided in an embodiment of the present application;

[0020] Figure 2 A flowchart of S2 in the method for warning underground disaster - prone areas based on micro - seismic data provided in an embodiment of the present application;

[0021] Figure 3 A flowchart of S2 in the method for warning underground disaster - prone areas based on micro - seismic data provided in another embodiment of the present application;

[0022] Figure 4 A flowchart of S3 in the method for warning underground disaster - prone areas based on micro - seismic data provided in an embodiment of the present application;

[0023] Figure 5 A flowchart of S4 in the method for warning underground disaster - prone areas based on micro - seismic data provided in an embodiment of the present application;

[0024] Figure 6 A flowchart of S41 in the method for warning underground disaster - prone areas based on micro - seismic data provided in an embodiment of the present application;

[0025] Figure 7 A flowchart of S43 in the method for warning underground disaster - prone areas based on micro - seismic data provided in an embodiment of the present application;

[0026] Figure 8 A structural diagram of a device for warning underground disaster - prone areas based on micro - seismic data provided in an embodiment of the present application;

[0027] Figure 9 A structural diagram of a computer device provided in an embodiment of the present application. Detailed Embodiment

[0028] Exemplary embodiments will be described in detail herein, and examples thereof are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0029] The terms used in this application are for the purpose of describing particular embodiments only and are not intended to limit the present application. The singular forms "a", "the", and "said" used in this application and the appended claims are also intended to include the plural forms unless the context clearly dictates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0030] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" / "when" as used herein may be interpreted as "when" or "when" or "in response to determining".

[0031] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a method for warning underground disaster - prone areas based on micro - seismic data provided in the first embodiment of the present application. The method includes the following steps:

[0032] S1: Obtain the micro - seismic data of the target area, process the micro - seismic data, and construct seismic catalog data.

[0033] The execution subject of the method for warning underground disaster - prone areas based on micro - seismic data is a warning device for the method for warning underground disaster - prone areas based on micro - seismic data (hereinafter referred to as the warning device). In an alternative embodiment, the warning device may be a computer device, which may be a server, or a server cluster formed by combining multiple computer devices.

[0034] In this embodiment, the warning device can obtain the micro - seismic data of the target area through the IMS micro - seismic monitoring system. The IMS micro - seismic monitoring system is a digital and intelligent seismic monitoring system that can monitor the micro - seismic events generated during the deformation and failure process of rock masses in real - time and establish a comprehensive database. This database is used to store and manage micro - seismic data and related information, providing basic data support for subsequent analysis.

[0035] The early warning device processes the microseismic data to construct seismic catalog data. Among them, the processing steps include data cleaning, data formatting, and data construction. Specifically, the early warning device eliminates invalid data (such as noise interference and abnormal sensor records) and duplicate data in the microseismic data to ensure data quality, unifies the timestamp format (such as UTC time), the coordinate system (such as WGS84 coordinate system), and standardizes the magnitude unit (such as Richter magnitude ML). Using the double-difference location method or the Geiger algorithm, based on the P-wave and S-wave arrival time data recorded by stations in the microseismic data, it calculates the precise three-dimensional spatial position (longitude, latitude, depth) of the microseismic event and constructs seismic catalog data. Among them, the seismic catalog data includes attribute data of several seismic events, and the attribute data includes time, coordinates, and waveform amplitude. The time is used to indicate the occurrence time of the seismic event, the coordinates are used to indicate the hypocenter coordinates of the seismic event, and the magnitude is used to indicate the waveform amplitude or energy of the seismic event.

[0036] S2: Based on the attribute data of several seismic events in the seismic catalog data, perform clustering analysis, divide the target area into sub-areas of several clustering types, and perform multi-dimensional feature extraction according to the sub-areas of several clustering types and the seismic catalog data to obtain multi-dimensional feature data of the sub-areas of several clustering types.

[0037] In this embodiment, the early warning device uses the density-based spatial clustering algorithm (DBSCAN) to perform clustering analysis based on the attribute data of several seismic events in the seismic catalog data, divides the target area into sub-areas of several clustering types, and performs multi-dimensional feature extraction according to the sub-areas of several clustering types and the seismic catalog data to obtain multi-dimensional feature data of the sub-areas of several clustering types.

[0038] The multi-dimensional feature data includes time density features and spatial density features. Please refer to Figure 2 , Figure 2 which is a schematic flow chart of S2 in the underground disaster dangerous area early warning method based on microseismic data provided by an embodiment of the present application. The step S2 includes S201 to S202, specifically as follows:

[0039] S201: According to the time in the attribute data of several seismic events in the seismic catalog data, count the number of the first seismic events in several sub-areas of the clustering types within a preset unit time, and obtain the time density features of several sub-areas of the clustering types according to the number of the first seismic events and the unit time.

[0040] In this embodiment, the early warning device counts the number of first earthquake events in sub-regions of several clustering types within a preset unit time according to the time in the attribute data of several earthquake events in the earthquake catalog data, and divides the number of first earthquake events by the unit time according to the number of first earthquake events and the unit time to obtain the time density characteristics of sub-regions of several clustering types.

[0041] S202: Obtain the area data of sub-regions of several clustering types according to the coordinates in the attribute data of several earthquake events in the earthquake catalog data, count the number of second earthquake events in sub-regions of several clustering types within the area data, and obtain the spatial density characteristics of sub-regions of several clustering types according to the number of second earthquake events and the corresponding area data.

[0042] In this embodiment, the early warning device obtains the area data of sub-regions of several clustering types according to the coordinates in the attribute data of several earthquake events in the earthquake catalog data, counts the number of second earthquake events in sub-regions of several clustering types within the area data, and divides the number of second earthquake events by the corresponding area data according to the number of second earthquake events and the corresponding area data to obtain the spatial density characteristics of sub-regions of several clustering types.

[0043] The multi-dimensional feature data includes rock formation vulnerability characteristics and fault activation characteristics. Please refer to Figure 3 , Figure 3 which is a schematic flowchart of step S2 in the method for early warning of underground disaster dangerous areas based on microseismic data provided in another embodiment of this application. The step S2 includes S211 to S212, specifically as follows:

[0044] S211: Obtain the central coordinates of sub-regions of several clustering types, and obtain the rock mass strength interpolation of sub-regions of several clustering types according to the central coordinates and a preset three-dimensional compressive strength grid model; obtain the rock formation vulnerability characteristics of sub-regions of several clustering types according to the rock mass strength interpolation and a preset calculation algorithm for rock formation vulnerability characteristics.

[0045] In this embodiment, the early warning device obtains the central coordinates of sub-regions of several clustering types, and obtains the rock mass strength interpolation of sub-regions of several clustering types according to the central coordinates and a preset three-dimensional compressive strength grid model, where the three-dimensional compressive strength grid model is generated by Kriging interpolation based on borehole data (spacing 200m) to form a three-dimensional compressive strength grid model (50m×50m×10m).

[0046] The warning device obtains the rock stratum vulnerability characteristics of several sub-regions of the clustering type according to the rock mass strength interpolation and a preset calculation algorithm for the rock stratum vulnerability characteristics, where the calculation algorithm for the rock stratum vulnerability characteristics is as follows:

[0047] C_rock = (100 - RC) / 100 × (1 / KV)

[0048] In the formula, C_rock is the rock stratum vulnerability characteristic, RC is the rock mass strength interpolation, and KV is the rock mass integrity coefficient.

[0049] S212: According to the central coordinates, obtain the fault distances of several sub-regions of the clustering type, and according to the fault distances and a preset calculation algorithm for the fault activation characteristics, obtain the fault activation characteristics of several sub-regions of the clustering type.

[0050] In this embodiment, the warning device obtains the fault distances of several sub-regions of the clustering type according to the central coordinates. Specifically, the warning device obtains them on the central coordinates by using ground penetrating radar and seismic reflection exploration.

[0051] The warning device obtains the fault activation characteristics of several sub-regions of the clustering type according to the fault distances and a preset calculation algorithm for the fault activation characteristics, where the calculation algorithm for the fault activation characteristics is as follows:

[0052] D_fault = 1 / (1 + e (-0.01×(d-300)) )

[0053] In the formula, D_fault is the fault activation characteristic, and d is the fault distance.

[0054] S3: Adopt the Gutenberg-Richter law to calculate the B value according to several sub-regions of the clustering type and the seismic catalog data, and obtain the B values of several sub-regions of the clustering type.

[0055] In this embodiment, the warning device adopts the Gutenberg-Richter law to calculate the B value according to several sub-regions of the clustering type and the seismic catalog data, and obtain the B values of several sub-regions of the clustering type.

[0056] Please refer to Figure 4 , Figure 4 which is a schematic flowchart of S3 in the underground disaster dangerous area warning method based on microseismic data provided by an embodiment of the present application. The step S3 includes S31 to S32, specifically as follows:

[0057] S31: According to the waveform amplitudes in the attribute data of several earthquake events in the earthquake catalog data and a preset waveform amplitude threshold, count the number of third earthquake events in several sub-regions of the clustering types that are greater than the waveform amplitude threshold.

[0058] In this embodiment, the warning device counts the number of third earthquake events in several sub-regions of the clustering types that are greater than the waveform amplitude threshold according to the waveform amplitudes in the attribute data of several earthquake events in the earthquake catalog data and a preset waveform amplitude threshold.

[0059] S32: According to the number of third earthquake events in several sub-regions of the clustering types that are greater than the waveform amplitude threshold, the waveform amplitude threshold, and a preset B-value calculation algorithm, obtain the B-values of several sub-regions of the clustering types.

[0060] The B-value calculation algorithm is as follows:

[0061] log 10 N = a - B base ×M

[0062] In the formula, N is the number of earthquake events greater than the waveform amplitude threshold, a is a constant representing the overall activity of earthquake events, B base is the B-value used to describe the magnitude distribution characteristics of earthquake events, and M is the waveform amplitude threshold.

[0063] The B-value reflects the richness and energy distribution of earthquake activities and is an important parameter for analyzing earthquake activities. In this embodiment, the warning device obtains the B-values of several sub-regions of the clustering types according to the number of third earthquake events in several sub-regions of the clustering types that are greater than the waveform amplitude threshold, the waveform amplitude threshold, and a preset B-value calculation algorithm, so as to further understand the intensity and frequency characteristics of rock burst activities.

[0064] S4: According to the multi-dimensional feature data and B-values of several sub-regions of the clustering types, conduct early warnings on underground disaster risk areas for several sub-regions of the clustering types, and obtain the early warning results of underground disaster risk areas for the target area.

[0065] In this embodiment, the warning device conducts early warnings on underground disaster risk areas for several sub-regions of the clustering types according to the multi-dimensional feature data and B-values of several sub-regions of the clustering types, and obtains the early warning results of underground disaster risk areas for the target area. By using cluster analysis and multi-dimensional feature extraction, it effectively extracts the feature information in the microseismic data, quantitatively analyzes the spatio-temporal distribution law of microseismic activities and the driving factors behind them, and realizes the early warning of underground disaster risk areas, which is efficient and accurate.

[0066] Please refer toFigure 5 , Figure 5 is a schematic flowchart of step S4 in the underground disaster dangerous area warning method based on microseismic data provided by an embodiment of the present application. The steps S4 include S41 to S43, which are specifically as follows:

[0067] S41: According to the multi-dimensional feature data, correct the B values of the corresponding several sub-regions of the clustering types to obtain the corrected B values of the several sub-regions of the clustering types, and divide the B values of the corresponding sub-regions of the clustering types by the corrected B values to obtain the B value correction coefficients of the several sub-regions of the clustering types.

[0068] In this embodiment, the warning device corrects the B values of the corresponding several sub-regions of the clustering types according to the multi-dimensional feature data to obtain the corrected B values of the several sub-regions of the clustering types, and divides the B values of the corresponding sub-regions of the clustering types by the corrected B values to obtain the B value correction coefficients of the several sub-regions of the clustering types.

[0069] Please refer to Figure 6 , Figure 6 is a schematic flowchart of step S41 in the underground disaster dangerous area warning method based on microseismic data provided by an embodiment of the present application. The steps S4 include S411 to S413, which are specifically as follows:

[0070] S411: According to the time density feature, space density feature in the multi-dimensional feature data and a preset first B value correction algorithm, obtain the first corrected B values of the several sub-regions of the clustering types.

[0071] The first B value correction algorithm is:

[0072] B ajust = B base ×(1 + μ×S_density + ρ×T_density)

[0073] In the formula, B ajust is the first corrected B value, μ is the space density weight parameter, and ρ is the time density weight parameter.

[0074] In this embodiment, the warning device obtains the first corrected B values of the several sub-regions of the clustering types according to the time density feature, space density feature in the multi-dimensional feature data and the preset first B value correction algorithm.

[0075] S412: Traverse the strata vulnerability features and fault activation features in the multi-dimensional feature data. If the value of the strata vulnerability feature is greater than the preset strata vulnerability feature threshold, obtain the rock mass strength correction coefficient according to the strata vulnerability feature and the preset calculation algorithm for the rock mass strength correction coefficient; if the value of the fault activation feature is greater than the preset fault activation feature threshold, obtain the fault activation correction coefficient according to the fault activation feature and the preset calculation algorithm for the fault activation correction coefficient.

[0076] In this embodiment, the warning device traverses the strata vulnerability features and fault activation features in the multi-dimensional feature data. If the value of the strata vulnerability feature is greater than the preset strata vulnerability feature threshold, obtain the rock mass strength correction coefficient according to the strata vulnerability feature and the preset calculation algorithm for the rock mass strength correction coefficient, where the calculation algorithm for the rock mass strength correction coefficient is:

[0077] C ajust =(1 - 0.25×C_rock)

[0078] In the formula, C ajust is the rock mass strength correction coefficient;

[0079] If the value of the fault activation feature is greater than the preset fault activation feature threshold, the warning device obtains the fault activation correction coefficient according to the fault activation feature and the preset calculation algorithm for the fault activation correction coefficient, where the calculation algorithm for the fault activation correction coefficient is:

[0080] D ajust =(1 - 0.15×D_fault)

[0081] In the formula, d ajust is the fault activation correction coefficient.

[0082] S413: Obtain the formation types and corresponding formation type weight parameters of the sub-regions of several of the clustering types. According to the preliminary corrected B values, rock mass strength correction coefficients, fault activation correction coefficients, formation type weight parameters of the sub-regions of several of the clustering types, and the preset second B value correction algorithm, obtain the second corrected B values of the sub-regions of several of the clustering types as the corrected B values.

[0083] In this embodiment, the warning device obtains the formation types and corresponding formation type weight parameters of the sub-regions of several of the clustering types. Specifically, the warning device can determine the formation type corresponding to the central coordinates according to the central coordinates of the sub-regions of several of the clustering types, and obtain the formation type weight parameters according to the formation type and the preset correspondence between the formation type and the weight parameters.

[0084] The warning device obtains the second corrected B values of the sub-regions of several said clustering types as the corrected B values according to the preliminary corrected B values of the sub-regions of several said clustering types, the rock mass strength correction coefficient, the fault activation correction coefficient, the formation type weight parameter, and a preset second B value correction algorithm, where the second B value correction algorithm is:

[0085] B ajust ′ = B ajust × C ajust × D ajust × W strata

[0086] In the formula, B ajust ′ is the second corrected B value, and W strata is the formation type weight parameter.

[0087] S42: Obtain the comprehensive risk indexes of several said clustering types of sub-regions according to the multi-dimensional feature data of several said clustering types of sub-regions, a preset multi-dimensional feature coefficient, a B value correction coefficient, and a preset comprehensive risk index calculation algorithm.

[0088] In this embodiment, the warning device obtains the comprehensive risk indexes of several said clustering types of sub-regions according to the multi-dimensional feature data of several said clustering types of sub-regions, a preset multi-dimensional feature coefficient, a B value correction coefficient, and a preset comprehensive risk index calculation algorithm, where the multi-dimensional feature coefficient includes a microseismic anomaly coefficient, a correction coefficient, a rock mass strength coefficient, a fault influence coefficient, and a spatial density coefficient, and the comprehensive risk index calculation algorithm is:

[0089] CRI = α × (1 - B cf ) + β × C_rock + γ × D_fault + δ × S_density + ε × T_density

[0090] In the formula, CRI is the comprehensive risk index, α is the microseismic anomaly coefficient, B cf is the correction coefficient, β is the rock mass strength coefficient, γ is the fault influence coefficient, δ is the spatial density coefficient, S_density is the spatial density, ε is the time density coefficient, and T_density is the time density.

[0091] S43: Obtain the underground disaster dangerous area warning levels of several said clustering types of sub-regions according to the comprehensive risk indexes of several said clustering types of sub-regions, the B value correction coefficient, and corresponding preset thresholds, as the warning results of the underground disaster dangerous areas of the target area.

[0092] In this embodiment, the early warning device obtains the early warning levels of the underground disaster dangerous areas of several sub-regions of the clustering types according to the comprehensive risk index, B-value correction coefficient, and corresponding preset thresholds of several sub-regions of the clustering types, as the early warning results of the underground disaster dangerous areas of the target area.

[0093] Please refer to Figure 7 , Figure 7 which is a schematic flow chart of S43 in the method for early warning of underground disaster dangerous areas based on microseismic data provided by an embodiment of the present application. The steps S43 include S431 to S433, which are specifically as follows:

[0094] S431: If the comprehensive risk index of the sub-region of the clustering type is greater than or equal to the preset first comprehensive risk index threshold, and the B-value correction coefficient is less than the preset first B-value threshold, a first-level risk early warning level is obtained.

[0095] In this embodiment, according to the comprehensive risk index and the B-value correction coefficient, if the comprehensive risk index of the sub-region of the clustering type is greater than or equal to the preset first comprehensive risk index threshold, and the B-value correction coefficient is less than the preset first B-value threshold, a first-level risk early warning level is obtained. Specifically, the first comprehensive risk index threshold can be set to 0.8, the first B-value threshold is set to 0.7, and the first-level risk early warning level reflects that the current sub-region is a high-risk underground disaster dangerous area, and there is a high probability of underground disasters occurring.

[0096] S432: If the comprehensive risk index of the sub-region of the clustering type is less than the first comprehensive risk index threshold and greater than or equal to the preset second comprehensive risk index threshold, or the B-value correction coefficient is greater than or equal to the first B-value threshold and less than the preset second B-value threshold, a second-level risk early warning level is obtained.

[0097] In this embodiment, if the comprehensive risk index of the sub-region of the clustering type is less than the first comprehensive risk index threshold and greater than or equal to the preset second comprehensive risk index threshold, or the B-value correction coefficient is greater than or equal to the first B-value threshold and less than the preset second B-value threshold, the early warning device obtains a second-level risk early warning level. Specifically, the second comprehensive risk index threshold can be set to 0.6, the second B-value threshold is set to 0.9, and the second-level risk early warning level reflects that the current sub-region is a low-risk underground disaster dangerous area, and underground disasters may occur.

[0098] S433: If the comprehensive risk index of the sub-region of the clustering type is less than the second comprehensive risk index threshold, or the B-value correction coefficient is greater than the second B-value threshold, a third-level risk early warning level is obtained.

[0099] In this embodiment, if the comprehensive risk index of the sub-region of the clustering type is less than the second comprehensive risk index threshold, or the B-value correction coefficient is greater than the second B-value threshold, the warning device obtains a third-level risk warning level, where the third-level risk warning level reflects that the current sub-region is a safe region.

[0100] Please refer to Figure 8 , Figure 8 FIG. is a schematic structural diagram of an underground disaster dangerous area warning device based on microseismic data provided by an embodiment of the present application. The device can implement all or part of the underground disaster dangerous area warning device based on microseismic data through software, hardware, or a combination of both. The device 8 includes:

[0101] A data acquisition module 81, configured to acquire microseismic data of a target area, process the microseismic data, and construct seismic catalog data, where the seismic catalog data includes attribute data of a number of seismic events;

[0102] A feature extraction module 82, configured to perform clustering analysis based on the attribute data of a number of seismic events in the seismic catalog data, divide the target area into several sub-regions of clustering types, and perform multi-dimensional feature extraction according to the several sub-regions of clustering types and the seismic catalog data to obtain multi-dimensional feature data of the several sub-regions of clustering types;

[0103] A B-value calculation module 83, configured to calculate the B-value according to the several sub-regions of clustering types and the seismic catalog data by using the Gutenberg-Richter law to obtain the B-values of the several sub-regions of clustering types;

[0104] A dangerous area warning module 84, configured to perform underground disaster dangerous area warning on the several sub-regions of clustering types according to the multi-dimensional feature data and B-values of the several sub-regions of clustering types to obtain an underground disaster dangerous area warning result of the target area.

[0105] In the embodiment of the present application, through the data acquisition module, microseismic data of the target area is acquired, and the microseismic data is processed to construct earthquake catalog data, wherein the earthquake catalog data includes attribute data of several earthquake events; through the feature extraction module, clustering analysis is performed based on the attribute data of several earthquake events in the earthquake catalog data, the target area is divided into sub-areas of several clustering types, and multi-dimensional feature extraction is performed according to the sub-areas of several clustering types and the earthquake catalog data to obtain multi-dimensional feature data of the sub-areas of several clustering types; through the B-value calculation module, using the Gutenberg-Richter law, B-value calculation is performed according to the sub-areas of several clustering types and the earthquake catalog data to obtain the B-values of the sub-areas of several clustering types; through the dangerous area warning module, according to the multi-dimensional feature data and B-values of the sub-areas of several clustering types, early warning of underground disaster dangerous areas is carried out on the sub-areas of several clustering types, and the early warning result of the underground disaster dangerous area of the target area is obtained. By using clustering analysis and multi-dimensional feature extraction, the feature information in the microseismic data is effectively extracted, the spatio-temporal distribution law of microseismic activities and the driving factors behind them are quantitatively analyzed, and early warning of underground disaster dangerous areas is realized, which is efficient and accurate.

[0106] Please refer to Figure 9 , Figure 9 which is a schematic structural diagram of a computer device provided by an embodiment of the present application. The computer device 9 includes: a processor 91, a memory 92, and a computer program 93 stored in the memory 92 and executable on the processor 91; the computer device can store multiple instructions, and the instructions are suitable for being loaded and executed by the processor 91 to perform the method steps of the above Figures 1 to 7 shown embodiment. The specific execution process can be referred to the specific description of the Figures 1 to 7 shown embodiment, and details are not described herein.

[0107] Among them, the processor 91 may include one or more processing cores. The processor 91 uses various interfaces and lines to connect various parts within the server. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 92, and by invoking data in the memory 92, it executes various functions of the underground disaster hazard area warning device 8 based on microseismic data and processes data. Optionally, the processor 91 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 91 may integrate a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the touch display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 91 and may be implemented separately by a single chip.

[0108] Among them, the memory 92 may include a random access memory (RAM) and may also include a read-only memory (ROM). Optionally, the memory 92 includes a non-transitory computer-readable storage medium. The memory 92 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 92 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch instructions, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store the data involved in the above-mentioned method embodiments. Optionally, the memory 92 may also be at least one storage device located far from the aforementioned processor 91.

[0109] The embodiment of the present application also provides a storage medium, which can store multiple instructions, and the instructions are suitable for being loaded and executed by a processor to perform the method steps of the above Figures 1 to 7 shown embodiments. The specific execution process can refer to the Figures 1 to 7 specific description of the shown embodiments and will not be elaborated here.

[0110] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of the functional units and modules are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0111] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0112] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0113] In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal device and method can be implemented in other ways. For example, the device / terminal device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.

[0114] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0115] In addition, in each embodiment of the present invention, each functional unit may be integrated in a processing unit, may exist separately as individual physical units, or two or more units may be integrated in one unit. The above integrated units may be implemented in the form of hardware or in the form of software functional units.

[0116] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, all or part of the processes in the above-described embodiment methods of the present invention may also be completed by instructing relevant hardware through a computer program. The computer program may be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described method embodiments may be implemented. Among them, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file, or some intermediate form, etc.

[0117] The present invention is not limited to the above-described embodiments. If various modifications or deformations of the present invention do not depart from the spirit and scope of the present invention, and if these modifications and deformations fall within the scope of the claims of the present invention and equivalent technical scope, then the present invention also intends to include these modifications and deformations.

Claims

1. An early warning method for underground disaster - prone areas based on micro - seismic data, characterized in that, Including the following steps: Obtain microseismic data of the target area, process the microseismic data, and construct seismic catalog data, where the seismic catalog data includes attribute data of a number of seismic events; Based on the attribute data of a number of seismic events in the seismic catalog data, perform clustering analysis, divide the target area into sub-areas of several clustering types, and perform multi-dimensional feature extraction according to the sub-areas of several clustering types and the seismic catalog data to obtain multi-dimensional feature data of the sub-areas of several clustering types; Adopt the Gutenberg-Richter law, calculate the B value according to the sub-areas of several clustering types and the seismic catalog data to obtain the B values of the sub-areas of several clustering types; According to the multi-dimensional feature data and B values of the sub-areas of several clustering types, perform early warning on the underground disaster-prone areas of the sub-areas of several clustering types to obtain the early warning result of the underground disaster-prone areas of the target area.

2. The method for warning underground disaster dangerous areas based on microseismic data according to claim 1, wherein: The attribute data includes time and coordinates; the multi-dimensional feature data includes time density feature and spatial density feature; The performing multi-dimensional feature extraction according to the sub-areas of several clustering types and the seismic catalog data to obtain multi-dimensional feature data of the sub-areas of several clustering types includes the steps of: According to the time in the attribute data of a number of seismic events in the seismic catalog data, count the number of first seismic events in the sub-areas of several clustering types within a preset unit time, and obtain the time density feature of the sub-areas of several clustering types according to the number of first seismic events and the unit time; According to the coordinates in the attribute data of a number of seismic events in the seismic catalog data, obtain the area data of the sub-areas of several clustering types, count the number of second seismic events in the area data of the sub-areas of several clustering types, and obtain the spatial density feature of the sub-areas of several clustering types according to the number of second seismic events and the corresponding area data.

3. The method for warning underground disaster danger areas based on microseismic data according to claim 2, characterized in that, The multi-dimensional feature data further includes rock layer vulnerability feature and fault activation feature; The performing multi-dimensional feature extraction according to the sub-areas of several clustering types and the seismic catalog data to obtain multi-dimensional feature data of the sub-areas of several clustering types further includes the steps of: Obtain the central coordinates of the sub-areas of several clustering types, obtain the rock mass strength interpolation of the sub-areas of several clustering types according to the central coordinates and a preset three-dimensional compressive strength grid model; obtain the rock layer vulnerability feature of the sub-areas of several clustering types according to the rock mass strength interpolation and a preset rock layer vulnerability feature calculation algorithm; obtain the fault distance of the sub-areas of several clustering types according to the central coordinates, and obtain the fault activation feature of the sub-areas of several clustering types according to the fault distance and a preset fault activation feature calculation algorithm.

4. The underground disaster dangerous area warning method based on microseismic data according to claim 3, characterized in that The attribute data further includes waveform amplitude; Performing B - value calculation based on the sub - regions of several of the clustering types and the earthquake catalog data to obtain the B - values of the sub - regions of several clustering types, including the steps: According to the waveform amplitudes in the attribute data of several earthquake events in the earthquake catalog data and a preset waveform amplitude threshold, counting the number of third - type earthquake events in several of the sub - regions of the clustering types that are greater than the waveform amplitude threshold; Based on the number of third - type earthquake events in several of the sub - regions of the clustering types that are greater than the waveform amplitude threshold, the waveform amplitude threshold, and a preset B - value calculation algorithm, obtaining the B - values of the sub - regions of several clustering types.

5. The method for warning underground disaster danger areas based on microseismic data according to claim 4, characterized in that Performing underground disaster - prone area early warning on several of the sub - regions of the clustering types according to the multi - dimensional feature data and B - values of several of the sub - regions of the clustering types, and obtaining the underground disaster - prone area early warning result of the target area, including the steps: According to the multi - dimensional feature data, correcting the B - values of several of the sub - regions of the corresponding clustering types to obtain the corrected B - values of several of the sub - regions of the clustering types, and dividing the B - values of the corresponding sub - regions of the clustering types by the corrected B - values to obtain the B - value correction coefficients of several of the sub - regions of the clustering types; According to the multi - dimensional feature data of several of the sub - regions of the clustering types, a preset multi - dimensional feature coefficient, the B - value correction coefficient, and a preset comprehensive risk index calculation algorithm, obtaining the comprehensive risk indices of several of the clustering types; According to the comprehensive risk indices of several of the sub - regions of the clustering types, the B - value correction coefficient, and the corresponding preset thresholds, obtaining the underground disaster - prone area early warning levels of several of the sub - regions of the clustering types as the underground disaster - prone area early warning result of the target area.

6. The method for warning underground disaster risk areas based on microseismic data according to claim 5, characterized in that, The step of correcting the B - values of several of the sub - regions of the corresponding clustering types according to the multi - dimensional feature data to obtain the corrected B - values of several of the sub - regions of the clustering types includes the steps: According to the time - density feature, space - density feature in the multi - dimensional feature data and a preset first B - value correction algorithm, obtaining the first corrected B - values of several of the sub - regions of the clustering types; Traversing the rock - layer vulnerability feature and fault activation feature in the multi - dimensional feature data. If the value of the rock - layer vulnerability feature is greater than a preset rock - layer vulnerability feature threshold, obtaining a rock - mass strength correction coefficient according to the rock - layer vulnerability feature and a preset rock - mass strength correction coefficient calculation algorithm; if the value of the fault activation feature is greater than a preset fault activation feature threshold, obtaining a fault activation correction coefficient according to the fault activation feature and a preset fault activation correction coefficient calculation algorithm; Obtaining the formation types of several of the sub - regions of the clustering types and the corresponding formation - type weight parameters, and according to the preliminary corrected B - values, rock - mass strength correction coefficient, fault activation correction coefficient, formation - type weight parameters of several of the sub - regions of the clustering types and a preset second B - value correction algorithm, obtaining the second corrected B - values of several of the sub - regions of the clustering types as the corrected B - values.

7. The method for warning of underground disaster risk areas based on microseismic data according to claim 6, characterized in that, Obtaining the early warning levels of the underground disaster risk areas for several sub-areas of the clustering types based on the comprehensive risk index, B-value correction coefficient, and corresponding preset thresholds of several sub-areas of the clustering types, including the steps: If the comprehensive risk index of the sub-area of the clustering type is greater than or equal to the preset first comprehensive risk index threshold, and the B-value correction coefficient is less than the preset first B-value threshold, obtaining a first-level risk early warning level; If the comprehensive risk index of the sub-area of the clustering type is less than the first comprehensive risk index threshold and greater than or equal to the preset second comprehensive risk index threshold, or the B-value correction coefficient is greater than or equal to the first B-value threshold and less than the preset second B-value threshold, obtaining a second-level risk early warning level; If the comprehensive risk index of the sub-area of the clustering type is less than the second comprehensive risk index threshold, or the B-value correction coefficient is greater than the second B-value threshold, obtaining a third-level risk early warning level.

8. An early warning device for underground disaster - prone areas based on micro - seismic data, characterized in that, Including: A data acquisition module, configured to acquire microseismic data of a target area, process the microseismic data, and construct seismic catalog data, where the seismic catalog data includes attribute data of several seismic events; A feature extraction module, configured to perform clustering analysis based on the attribute data of several seismic events in the seismic catalog data, divide the target area into several sub-areas of clustering types, and perform multi-dimensional feature extraction based on the several sub-areas of the clustering types and the seismic catalog data to obtain multi-dimensional feature data of several sub-areas of the clustering types; A B-value calculation module, configured to calculate the B-value according to the Gutenberg-Richter law based on several sub-areas of the clustering types and the seismic catalog data to obtain the B-values of several sub-areas of the clustering types; A risk area early warning module, configured to perform early warning of the underground disaster risk areas for several sub-areas of the clustering types based on the multi-dimensional feature data and B-values of several sub-areas of the clustering types, and obtain the early warning result of the underground disaster risk areas of the target area.

9. A computer device, characterized in that, Including: A processor, a memory, and a computer program stored on the memory and executable on the processor; when the computer program is executed by the processor, the steps of the method for early warning of underground disaster risk areas based on microseismic data according to any one of claims 1 to 7 are implemented.

10. A storage medium, characterized in that, The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for early warning of underground disaster risk areas based on microseismic data according to any one of claims 1 to 7 are implemented.

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