Micro-seismic multichannel data processing method and system based on DBSCAN algorithm

Abstract

The invention discloses a micro-seismic multichannel data processing method and system based on a DBSCAN algorithm, and belongs to the technical field of micro-seismic monitoring. According to the invention, through feature vector construction and optimization distance calculation, the clustering reliability is enhanced; a multi-dimensional feature vector Xi containing P-wave refinement arrival time ti, an amplitude Ai and space coordinates (xi, yi, zi) is constructed, distance calculation is performed by combining a normalized cosine distance with wave velocity constraint, a micro-seismic wave propagation rule is fully utilized, the discrimination capability of DBSCAN clustering on physical consistency is enhanced, and the robustness of a screening result is improved, so that noise interference can be effectively processed in a complex environment, and the accuracy and the robustness of the screening result are improved. And the stability and the reliability of a micro-seismic positioning result under different geological conditions are ensured.

Description

Technical Field

[0001] The invention relates to the technical field of microseismic monitoring, and in particular to a microseismic multi-channel data processing method based on a DBSCAN algorithm. Background Art

[0002] Microseismic monitoring technology is an important means of analyzing geological activities or evaluating engineering safety by recording underground tiny fracture signals. It is widely used in fields such as mine safety monitoring, oil and gas exploration, and tunnel engineering. In actual monitoring, multiple sensors are usually distributed in different locations to collect multi-channel microseismic data. By analyzing the P-wave arrival time of these channels, microseismic positioning is performed using the propagation law of seismic waves. However, due to noise interference, the complexity of geological media, and the inconsistency between multi-channel data, errors often occur in picking up P-wave arrival times. For example, some channels may misjudge the arrival time due to noise, while other channels may not be correctly picked up due to weak signals. If this unreasonable arrival time data is used directly for positioning, it will lead to deviations in the results. Therefore, how to accurately pick up and screen the P-wave arrival time from multi-channel microseismic data is a key link in microseismic monitoring technology.

[0003] In the process of microseismic data processing, existing technologies mostly focus on the analysis of single-channel data, ignoring the possible correlation between multi-channel data. Even if there are technologies that utilize the correlation between multiple channels, they are usually based on simple time differences or geometric constraints, and do not really conduct joint analysis based on the overall distribution or consistency of multi-channel data. As a result, when faced with complex geological environments or noise interference, it is difficult to effectively distinguish microseismic signals from noise, and the picking results are unstable, affecting the accuracy of microseismic positioning results. Summary of the invention

[0004] In view of the above problems, a microseismic multi-channel data processing method based on the DBSCAN algorithm is provided to solve the problem that the traditional method is difficult to eliminate erroneous data and has low microseismic event positioning accuracy due to the independent processing of a single channel in the microseismic P wave arrival time picking. This method combines the STA / LTA and AIC algorithms for preliminary arrival time picking, and introduces the DBSCAN algorithm to perform joint clustering analysis on multi-channel arrival time data to screen out effective channel data, thereby improving the positioning accuracy and stability of microseismic events.

[0005] To achieve the above object, the technical solution adopted by the present invention is.

[0006] A microseismic multi-channel data processing method based on DBSCAN algorithm includes the following steps: 1. Acquire multi-channel microseismic signals, perform noise reduction preprocessing on the microseismic signals of each channel through a bandpass filter of a preset frequency band, and output a preprocessed signal. S i ( t ); Step 2: Preprocess the signal of each channel S i ( t ) respectively execute the waveform triggering algorithm and the arrival time refinement algorithm to generate an initial P wave arrival time set T; Step 3: Based on the initial P-wave arrival time set T, construct a multidimensional feature vector set X including spatiotemporal features, amplitude features and spatial features; Step 4: Perform joint clustering analysis on the multidimensional feature vector set X by using the DBSCAN algorithm to remove abnormal data and screen out valid arrival data clusters; Step 5: Perform microseismic event location calculation using the P-wave arrival time data in the effective arrival time data cluster to accurately locate the spatial position of the microseismic source.

[0007] Preferably, the preset frequency band of the bandpass filter is 10 Hz to 500 Hz.

[0008] Preferably, the waveform triggering algorithm adopts a short-time energy to long-time energy ratio analysis algorithm, i.e., a STA / LTA algorithm, comprising the following steps: Step S1: Preprocess the signal through each channel S i ( t ) to calculate the short-time energy stay ( t ) and long-term energy stay ( t ):

[0009] in, t For time; stay ( t ) is the time t At t is the end of the window, and the average signal energy in the short-time window is taken forward; LTA ( t ) is the time t At t is the end of the window, and the average signal energy in the long-term window is taken forward; Step S2: Calculate characteristic function CF ( t ): ; Step S3: Set the trigger threshold to 2.5. CF ( t )>2.5, record this moment t For potential time t0i .

[0010] Preferably, the short-time window is 60ms, corresponding to the number of sampling points. N STA = sampling rate × STA duration; the long time window is 120ms, corresponding to the number of sampling points L STA = sampling rate × STA duration.

[0011] Preferably, the potential time t 0i Deviating from the true P wave fluctuation, at the potential time t 0i A time window is selected nearby, and the arrival time refinement algorithm is used to accurately locate the true P wave position; the arrival time refinement algorithm adopts the AIC algorithm, and its formula is as follows: AIC ( k )= k log ( cousin ( S i [1: k ]))+( N - K ) ·log ( cousin ( S i [ k +1: N ])) in, k is the possible P wave arrival time in the window, N is the total number of window points, cousin is the variance, AIC ( k ) is the smallest value k The corresponding signal changes from noise to P wave, that is, the time t i .

[0012] Preferably, the multidimensional feature vector set X={X1,X2...X i ...X n}, X i =( t i , A i , x i , y i , z i ), where X i For the i The feature vector of each channel;n is the total number of channels; t i For the i When the P wave of each channel is refined; A i is the normalized amplitude after standardization; ( x i , y i , z i ) is the i The spatial coordinates of the sensor.

[0013] Preferably, the normalized amplitude A i The standardization steps are as follows: Step S1: Get the time for refinement t i The absolute maximum value of the signal within 0.05 seconds before and after: ; Step S2: Refine the time t i The absolute maximum value is normalized A i = MAX ( A i - A min ) / ( A max - A min ),in, A min and A max is the minimum and maximum amplitude of all channels.

[0014] Preferably, the DBSCAN algorithm comprises the following steps: Step S1: randomly selecting a point to be visited from the multidimensional feature vector set X X i As a starting point; Step S2: Calculate the cosine distance combined with the time-space constraint penalty X i The distance d to other points ( X i , X j ), if d( X i , X j )≤0.3, then X jFor its neighbors, count the number of points in the neighborhood N e ( X i ) is to adapt to the characteristics of microseismic multi-channel data; Step S3: If N e ( X i )≥3, then X i As core points, create a new cluster C k ,Will X i and its domain points to join the new cluster C k ,right C k Repeat the neighborhood query for each core point within, and continue to expand C k , until no new points can be added; Step S4: If N e ( X i )<3, and X i The neighborhood that does not belong to any cluster is marked as a noise point and added to the set N; Step S5: Repeat the above steps for all unvisited points in X until all points are processed and the clustering results are output. C ={ C 1, C 2, ..., C k} and noise point set N; Step S6: Select the cluster containing the most points from C C 1, which represents the effective arrival time data of the same microseismic event, and then C 1 Extract the corresponding arrival data and obtain the effective arrival data cluster T={t'1, t'2, ..., t' m},in m ≤ n .

[0015] Preferably, the formula of the cosine distance combined with the time-space constraint penalty is: Where Vp is the velocity of the P wave.

[0016] A microseismic multi-channel data processing system comprises a signal acquisition module for acquiring multi-channel microseismic signals distributed in the three-dimensional spatial position of a monitoring area; a signal processing module connected to the signal acquisition module and executing the microseismic multi-channel data processing method; a positioning inversion module connected to the signal processing module and executing the position coordinate solution of the microseismic source according to the effective arrival time data cluster in the signal processing module, and finally accurately outputting the spatial position of the microseismic source; and a dynamic adjustment module connected to the signal processing module and adaptively adjusting the domain parameters and wave velocity constraint weights in the DBSCAN algorithm according to the real-time signal-to-noise ratio and the number of effective channels in the signal processing module.

[0017] Due to the adoption of the above technical solution, the present invention has the following beneficial effects.

[0018] (1) Based on the principle of multi-channel joint analysis, the present invention uses DBSCAN clustering technology to eliminate unreasonable arrival noise data, accurately identify and extract P-wave arrival time, and improve the accuracy of microseismic positioning. In view of the limitations of traditional single-channel independent picking, the DBSCAN algorithm is introduced to perform joint clustering analysis on multi-channel arrival data, effectively identifying and eliminating noise misjudgment or abnormal channel arrival data, thereby improving data consistency and positioning accuracy.

[0019] (2) Based on the principle of consistency between time and space, the present invention enhances clustering reliability by constructing feature vectors and optimizing distance calculation; constructing a P-wave refined arrival time t i , Amplitude A i and spatial coordinates ( x i , y i , z i )’s multidimensional feature vector X i , and the normalized cosine distance combined with wave velocity constraints is used for distance calculation, which makes full use of the propagation law of microseismic waves, enhances the ability of DBSCAN clustering to distinguish physical consistency, and improves the robustness of the screening results, so that it can effectively deal with noise interference in complex environments and ensure the stability and reliability of microseismic positioning results under different geological conditions.

[0020] (3) Based on the STA / LTA and AIC synergy principle, the present invention optimizes the arrival quality by combining preliminary picking with multi-channel screening. It also performs preliminary picking of P-wave arrivals on each channel by combining STA / LTA with AIC, and then uses the DBSCAN algorithm to perform joint clustering analysis on multi-channel arrival data to screen out effective channel data and fully explore the synergy between data. Compared with traditional methods, it can more comprehensively consider the temporal and spatial correlation of signals, thereby improving positioning accuracy. That is, on the basis of single-channel picking, multi-channel joint discrimination can significantly reduce erroneous arrivals, improve positioning stability in complex environments, and thus improve the positioning accuracy and stability of microseismic events.

[0021] (4) The present invention uses the DBSCAN algorithm to perform joint clustering analysis on multi-channel feature vectors (including P-wave refined arrival time, amplitude and spatial coordinates), which can identify consistent patterns from the overall distribution characteristics of the data and infer which channels' arrival times conform to the laws of microseismic events, thereby more accurately eliminating abnormal data misjudged by noise, thereby improving the accuracy and stability of microseismic positioning in complex environments.

[0022] (5) Compared with traditional methods, the DBSCAN algorithm of the present invention can reduce the dependence on preset models when processing multi-channel data, reduce computational complexity, and improve the speed and efficiency of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The following is a detailed discussion of the making and application of the preferred embodiments of the present invention. However, it should be understood that the present invention provides many applicable inventive concepts that can be embodied in various specific environments. The specific embodiments discussed are only for the purpose of illustrating the specific ways of making and using the present invention, and do not limit the scope of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative work.

[0024] Figure 1 The figure is a flow chart of the data processing method of the present invention. DETAILED DESCRIPTION

[0025] The following is a detailed discussion of the making and application of the preferred embodiments of the present invention. However, it should be understood that the present invention provides many applicable inventive concepts that can be embodied in various specific environments. The specific embodiments discussed are only intended to illustrate specific ways to make and use the present invention and do not limit the scope of the present invention.

[0026] The present invention provides a microseismic multi-channel data processing method and system based on the DBSCAN algorithm, aiming to solve the problem that the traditional method is difficult to eliminate erroneous data and insufficient positioning accuracy in the microseismic P wave arrival time picking due to the independent processing of a single channel. The method is based on the STA / LTA and AIC collaborative principle, and optimizes the arrival quality by combining preliminary picking with multi-channel screening; and performs preliminary picking of the P wave arrival time of each channel by combining STA / LTA with AIC, and then uses the DBSCAN algorithm to perform joint clustering analysis on the multi-channel arrival time data, screens out the effective channel data, and fully explores the synergistic effect between the data. Compared with the traditional method, it can more comprehensively consider the spatiotemporal correlation of the signal, thereby improving the positioning accuracy. That is, on the basis of single-channel picking, the multi-channel joint discrimination significantly reduces the erroneous arrival time, improves the positioning stability in complex environments, and thus improves the positioning accuracy and stability of microseismic events. Example 1

[0027] The following is combined with Figure 1 Further elaboration.

[0028] Step 1: Acquisition and preprocessing of microseismic multi-channel data. Microseismic signals are collected through multiple sensors (channels) to obtain multi-channel time series data. Assume that there are n channels, denoted as S={S1,S2,...S i ...,S n}, where S i ( i =1,2,...,n) is the i The sampling frequency is usually 1000 Hz or higher. To reduce the influence of background noise, the signal of each channel is preprocessed.

[0029] Specifically, a bandpass filter is used to filter the signal, and the filter frequency range is usually set to 10 Hz to 500 Hz (which can be adjusted according to the actual microseismic signal characteristics) to retain the main frequency band of the P-wave signal and remove low-frequency drift and high-frequency noise. The waveform data after filtering, i.e., the preprocessed signal, is recorded as S i ( t ),in t For time.

[0030] Step 2: Preliminary picking of the P wave arrival time of each single channel by waveform triggering algorithm and arrival time refinement algorithm to obtain the initial P wave arrival time set. S i ( t ) ( i=1,2,...,n), the STA / LTA algorithm combined with AIC (Akaike Information Criterion) is used to preliminarily pick the P-wave arrival time and obtain the initial arrival time set T.

[0031] The waveform trigger algorithm adopts the short-time energy and long-time energy ratio analysis algorithm, namely the STA / LTA algorithm. The STA / LTA algorithm detects the arrival time of the P wave by comparing the ratio of the short-time energy and the long-time energy of the signal. The specific steps are as follows: Step S1: Pre-process the signal of each channel S i ( t ) to calculate the short-time energy stay ( t ) and long-term energy stay ( t ): in, t For time; stay ( t ) is the time t At t The signal energy average value in the short-time window is taken as the end of the window. The short-time window is 60ms, corresponding to the number of sampling points. N STA = sampling rate × STA duration; LTA ( t ) is the time t At t The signal energy average value in the long-term window is taken as the end of the window. The long-term window is 120ms, corresponding to the number of sampling points. L STA = sampling rate × STA duration.

[0032] Step S2: Calculate characteristic function CF ( t ), characteristic function CF ( t )yes stay ( t )and LTA ( t ) ratio: .

[0033] Step S3: Set the trigger threshold to 2.5. CF ( t )>2.5, it is considered that the P wave may arrive and this moment is recorded t For potential time t 0i .

[0034] The potential time t 0i Deviating from the true P wave fluctuation, at the potential time t 0i A time window is selected nearby, and this embodiment uses 0.5 seconds and 500 points. The arrival time refinement algorithm is used to accurately locate the true P wave position; the arrival time refinement algorithm uses the AIC algorithm, and its formula is as follows: AIC ( k )= k log ( cousin ( S i [1: k ]))+( N - K ) ·log ( cousin ( S i [ k +1: N ])) in, k is the possible P wave arrival time in the window, N is the total number of window points, cousin is the variance, AIC ( k ) is the smallest value k The corresponding signal changes from noise to P wave, that is, the time t i .

[0035] The DBSCAN algorithm is used to jointly discriminate the multi-channel arrival data, screen out valid channels and eliminate erroneous channels. The core of this step is to use the distribution characteristics of multi-channel data in time, amplitude and space to identify consistent patterns and eliminate abnormal data.

[0036] Step 3: Construct a multidimensional feature vector set. Based on the initial P-wave arrival time set T, construct a multidimensional feature vector set X containing spatiotemporal features, amplitude features, and spatial features.

[0037] In order to realize the joint discrimination of multi-channel data, the picking result of each channel is converted into a feature vector, and a feature data set X={X1,X2...X i ...X n}, X i =( t i , A i , x i , y i , zi ), where X i For the i The feature vector of each channel; n is the total number of channels; t i For the i When the P wave of each channel is refined; A i is the normalized amplitude after standardization; ( x i , y i , z i ) is the i The spatial coordinates of the sensors. A i The standardization processing steps are as follows: Step S1: Take the refined time t i The absolute maximum value of the signal within 0.05 seconds before and after: .

[0038] Step S2: Refine the time t i The absolute maximum value is normalized A i = MAX ( A i - A min ) / ( A max - A min ),in, A min and A max is the minimum and maximum amplitude of all channels.

[0039] Step 4: Perform joint clustering analysis on the multi-dimensional feature vector set using the DBSCAN algorithm to obtain effective arrival time data clusters.

[0040] The multidimensional feature vector set X is subjected to joint clustering analysis by the DBSCAN algorithm to remove abnormal data and screen out valid arrival data clusters. The DBSCAN algorithm comprises the following steps: Step S1: Randomly select a point to be visited from the multidimensional feature vector set X X i As a starting point.

[0041] Step S2: Calculate the cosine distance combined with the time-space constraint penalty X i The distance d to other points (X i , X j ), if d( X i , X j )≤0.3, then X j For its neighbors, count the number of points in the neighborhood N e ( X i ) is adapted to the characteristics of microseismic multi-channel data.

[0042] Step S3: If N e ( X i )≥3, then X i As core points, create a new cluster C k ,Will X i and its domain points to join the new cluster C k , for the new cluster C k Repeat the neighborhood query for each core point within, and continue to expand C k , until no new points can be added.

[0043] Step S4: If N e ( X i )<3, and X i The neighborhood that does not belong to any cluster is marked as a noise point and added to the set N.

[0044] Step S5: Repeat the above steps for all unvisited points in X until all points are processed and the clustering results are output. C ={ C 1, C 2, ..., C k} and a set of noise points N.

[0045] Step S6: Select the cluster containing the most points from C C 1, which represents the effective arrival time data of the same microseismic event, and then C 1 Extract the corresponding arrival data and obtain the effective arrival data cluster T={t'1, t'2, ..., t' m},in m ≤ n .

[0046] The formula of the cosine distance combined with the space-time constraint penalty is: Where Vp is the velocity of the P wave.

[0047] Step 5: Perform microseismic event location calculation using the effective arrival time data cluster to obtain the spatial position of the microseismic source. The microseismic event location calculation can be performed using the P wave arrival time data in the effective arrival time data cluster to accurately locate the spatial position of the microseismic source.

[0048] The microseismic multi-channel data processing method of the present invention solves the bottleneck problem of insufficient effectiveness judgment and low positioning accuracy under noise interference caused by single-channel analysis in existing microseismic monitoring technology through the synergistic technical means of multi-dimensional feature vector fusion, two-stage (i.e., STA / LTA algorithm and AIC algorithm) collaborative noise reduction process and DBSCAN algorithm. The specific technical features work together as follows.

[0049] (1) Constructing the distance metric between multi-dimensional features and wave velocity constraints: The spatial coordinates of each sensor in the sensor array ( x i , y i , z i ) and the normalized amplitude A i , No. i The P wave of each channel is refined to the time t i Constructing a joint feature vector, that is, constructing a multi-dimensional feature that is jointly related to spatiotemporal features, amplitude features, and spatial features, so that multi-channel spatiotemporal information forms geometric association constraints.

[0050] In addition, the time difference deviation calculation is corrected by the wave velocity Vp, and the physical law of microseismic propagation is superimposed on the amplitude distribution matching of cosine similarity, so that the DBSCAN clustering can simultaneously meet the signal intensity similarity and spatiotemporal propagation consistency. That is, in view of the limitations of traditional single-channel independent picking, the DBSCAN algorithm is introduced to perform joint clustering analysis on multi-channel arrival data, which can effectively identify and eliminate noise misjudgment or abnormal channel arrival data, and improve data consistency and positioning accuracy.

[0051] (2) Two-stage collaborative noise reduction process: The STA / LTA algorithm is used to quickly trigger the initial picking of the P wave arrival time, and the AIC algorithm is used for local precise positioning to significantly reduce the initial screening error of each single channel; through multi-channel joint clustering (rather than single-channel isolated processing), the density correlation characteristics of effective data clusters are used to further eliminate random noise. The first stage filters out obvious abnormal points, and the second stage solves the local optimal trap of a single channel through joint analysis, reducing the global calculation complexity, which significantly reflects the gain characteristics of the two-stage collaboration.

[0052] (3) Wave velocity Vp and number of channels n Dynamic adjustment, the minimum feasible data volume threshold ≥ 3, can ensure clustering sensitivity in different deployment scenarios; A i The standardization unifies the cross-channel dimensions, improves the comparability of cosine similarity, and combines with space-time constraints to give the distance criterion multi-dimensional physical meaning.

[0053] (4) Based on the principle of consistency between time and space, the present invention enhances clustering reliability by constructing feature vectors and optimizing distance calculation; constructing P-wave refined arrival time t i , Amplitude A i and spatial coordinates ( x i , y i , z i )’s multidimensional feature vector X i , and the normalized cosine distance combined with wave velocity constraints is used for distance calculation, which makes full use of the propagation law of microseismic waves, enhances the ability of DBSCAN algorithm clustering to distinguish physical consistency, and improves the robustness of the screening results, so that it can effectively deal with noise interference in complex environments and ensure the stability and reliability of microseismic positioning results under different geological conditions. Example 2

[0054] A microseismic multi-channel data processing system comprises a signal acquisition module for acquiring multi-channel microseismic signals distributed in the three-dimensional spatial position of a monitoring area; a signal processing module connected to the signal acquisition module and executing the microseismic multi-channel data processing method; a positioning inversion module connected to the signal processing module and executing the position coordinate solution of the microseismic source according to the effective arrival time data cluster in the signal processing module, and finally accurately outputting the spatial position of the microseismic source; and a dynamic adjustment module connected to the signal processing module and adaptively adjusting the domain parameters and wave velocity constraint weights in the DBSCAN algorithm according to the real-time signal-to-noise ratio and the number of effective channels in the signal processing module.

[0055] The microseismic multi-channel data processing system adopted by the present invention realizes the acquisition of multi-channel microseismic signals through the three-dimensional spatial distribution of the sensor array in the microseismic monitoring area, and then preliminarily picks up the P wave arrival time of each channel through STA / LTA combined with AIC, and then uses the DBSCAN algorithm to perform joint clustering analysis on the multi-channel arrival time data, screens out the effective channel data, and fully explores the synergistic effect between the data. The dynamic adjustment module can adaptively adjust the domain parameters and wave velocity constraint weights in the DBSCAN algorithm according to the real-time signal-to-noise ratio and the number of effective channels in the signal processing module, and the adaptive dynamic parameter adjustment of the neighborhood parameters driven by the signal-to-noise ratio and the number of effective channels realizes the optimization of calculation efficiency.

[0056] Although the specification has been described in detail, it should be understood that various changes, substitutions and modifications may be made without departing from the spirit and scope of the invention as defined by the appended claims. In addition, the specific embodiments described are not intended to limit the scope of the invention, and those of ordinary skill in the art can easily understand based on the present invention that currently existing or later to be developed processes, machines, manufactures, material compositions, means, methods, or steps can perform substantially the same functions as the embodiments of the present invention or obtain substantially the same results. Therefore, the appended claims are intended to include such processes, machines, manufactures, material compositions, means, methods or steps within their scope.

Claims

1. A microseismic multi-channel data processing method based on DBSCAN algorithm, characterized by: The method comprises the following steps:

1. obtaining multi-channel microseismic signals, performing noise reduction preprocessing on the microseismic signals of each channel through a bandpass filter of a preset frequency band, and outputting the preprocessed signals. S i ( t ); Step 2: Preprocess the signal of each channel S i ( t ) respectively execute the waveform triggering algorithm and the arrival time refinement algorithm to generate an initial P wave arrival time set T; Step 3: Based on the initial P-wave arrival time set T, construct a multidimensional feature vector set X including spatiotemporal features, amplitude features and spatial features; Step 4: Perform joint clustering analysis on the multidimensional feature vector set X by using the DBSCAN algorithm to remove abnormal data and screen out valid arrival data clusters; Step 5: Perform microseismic event location calculation using the P-wave arrival time data in the effective arrival time data cluster to accurately locate the spatial position of the microseismic source.

2. The microseismic multi-channel data processing method based on the DBSCAN algorithm according to claim 1, characterized in that: The preset frequency band of the bandpass filter in step 1 is 10 Hz to 500 Hz.

3. The microseismic multi-channel data processing method based on the DBSCAN algorithm according to claim 1, characterized in that: The waveform triggering algorithm in step 2 adopts a short-time energy and long-time energy ratio analysis algorithm, namely, a STA / LTA algorithm, and includes the following steps: Step S1: Preprocess the signal through each channel S i ( t ) to calculate the short-time energy STA ( t ) and long-term energy STA ( t ): in, t For time; STA ( t ) is the time t At t is the end of the window, and the average signal energy in the short-time window is taken forward; LTA ( t ) is the time t At t is the end of the window, and the average signal energy in the long-term window is taken forward; Step S2: Calculate characteristic function CF ( t ): Step S3: Set the trigger threshold to 2.

5. CF ( t )>2.5, record this moment t For potential time t 0i .

4. The microseismic multi-channel data processing method based on the DBSCAN algorithm according to claim 3, characterized in that: The short-time window is 60ms, corresponding to the number of sampling points N STA = sampling rate × STA duration; the long time window is 120ms, corresponding to the number of sampling points L STA = sampling rate × STA duration.

5. The microseismic multi-channel data processing method based on the DBSCAN algorithm according to claim 3, characterized in that: The potential time t 0i Deviating from the true P wave fluctuation, at the potential time t 0i A time window is selected nearby, and the arrival time refinement algorithm in step 2 is used to accurately locate the true P wave position; the arrival time refinement algorithm adopts the AIC algorithm, and its formula is as follows: AIC ( k )= k·log ( var ( S i [1: k ]))+( N - K ) ·log ( var ( S i [ k +1: N ])) in, k is the possible P wave arrival time in the window, N is the total number of window points, var is the variance, AIC ( k ) is the smallest value k The corresponding signal changes from noise to P wave, that is, the time t i .

6. The microseismic multi-channel data processing method based on the DBSCAN algorithm according to claim 1, characterized in that: The multidimensional feature vector set X={X1,X2...X i ...X n }, X i =( t i , A i , x i , y i , z i ), where X i For the i The feature vector of each channel; n is the total number of channels; t i For the i When the P wave of each channel is refined; A i is the normalized amplitude after standardization; ( x i , y i , z i ) is the i The spatial coordinates of the sensor.

7. The microseismic multi-channel data processing method based on the DBSCAN algorithm according to claim 6, characterized in that: The normalized amplitude A i The standardization steps are as follows: Step S1: Get the time for refinement t i The absolute maximum value of the signal within 0.05 seconds before and after ； Step S2: Refine the time t i The absolute maximum value is normalized A i = max ( A i - A min ) / ( A max - A min ),in, A min and A max is the minimum and maximum amplitude of all channels.

8. The microseismic multi-channel data processing method based on the DBSCAN algorithm according to claim 1, characterized in that: The DBSCAN algorithm includes the following steps: Step S1: Randomly select a point to be visited from the multidimensional feature vector set X X i As a starting point; Step S2: Calculate the cosine distance combined with the time-space constraint penalty X i The distance d to other points ( X i , X j ), if d( X i , X j )≤0.3, then X j For its neighbors, count the number of points in the neighborhood N e ( X i ) is to adapt to the characteristics of microseismic multi-channel data; Step S3: If N e ( X i )≥3, then X i As core points, create a new cluster C k ,Will X i and its domain points to join the new cluster C k ,right C k Repeat the neighborhood query for each core point within, and continue to expand C k , until no new points can be added; Step S4: If N e ( X i )<3, and X i The neighborhood that does not belong to any cluster is marked as a noise point and added to the set N; Step S5: Repeat the above steps for all unvisited points in X until all points are processed and the clustering results are output. C ={ C 1, C 2, ..., C k } and noise point set N; Step S6: From C Select the cluster containing the most points C 1, which represents the effective arrival time data of the same microseismic event, and then C 1 Extract the corresponding arrival data and obtain the effective arrival data cluster T={t'1, t'2, ..., t' m },in m ≤ n .

9. The microseismic multi-channel data processing method based on the DBSCAN algorithm according to claim 8, characterized in that: The formula of the cosine distance combined with the time-space constraint penalty is: Wherein, Vp is the velocity of P wave.

10. A microseismic multi-channel data processing system, characterized in that: It includes a signal acquisition module for collecting multi-channel microseismic signals distributed in the three-dimensional spatial position of the monitoring area; a signal processing module connected to the signal acquisition module and executing the microseismic multi-channel data processing method as described in any one of claims 1 to 9; a positioning inversion module connected to the signal processing module and executing the position coordinate solution of the microseismic source according to the effective arrival time data cluster in the signal processing module, and finally accurately outputting the spatial position of the microseismic source; and a dynamic adjustment module connected to the signal processing module and adaptively adjusting the domain parameters and wave velocity constraint weights in the DBSCAN algorithm according to the real-time signal-to-noise ratio and the number of effective channels in the signal processing module.

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