Mine earthquake p-wave picking method, system and device based on adaptive characteristic function

Abstract

The application discloses a mine earthquake P wave picking method, system and device based on an adaptive characteristic function, relates to the technical field of microseismic signal processing, and is based on an original mine earthquake record, adopts an ICEEMDAN algorithm, determines a mode function component without noise and a mode function component with noise, adopts wavelet threshold processing on the mode function component with noise to obtain a mode function component after noise reduction, obtains a mine earthquake record after noise reduction based on the mode function component without noise and the mode function component after noise reduction, obtains an adaptive characteristic function sequence based on the mine earthquake record after noise reduction and calculates a moving standard deviation sequence of the sequence, determines an AIC window length based on the moving standard deviation sequence and a preset picking threshold value, calculates an AIC value of the adaptive characteristic function sequence corresponding to the AIC window length, and regards a time corresponding to the minimum AIC value as a P wave arrival time of the original mine earthquake record. The application improves the accuracy and reliability of microseismic monitoring.

Description

Technical Field

[0001] This application relates to the field of microseismic signal processing technology, and in particular to a method, system and device for picking up mine seismic P-waves based on adaptive characteristic functions. Background Technology

[0002] Microseismic monitoring technology, as a reliable and effective monitoring tool, has been widely used in various fields such as rockburst monitoring, hydraulic fracturing monitoring, and mine safety. The accuracy of microseismic event arrival time identification directly affects the accuracy of microseismic location results, while high-quality arrival time estimation can significantly improve the efficiency of microseismic monitoring. Currently, several mature pickups are available for evaluating high-quality mine seismic record arrival times, including the Akaike Information Criterion (AIC) and other improved pickups. Furthermore, various methods have been developed to suppress noise in noisy mine seismic records, and a joint pickup method has been designed based on these methods.

[0003] However, existing technologies still face numerous challenges and limitations in practical applications. First, digital filters typically require specific parameter settings based on different dominant frequencies of microseismic data, increasing operational complexity and potentially leading to instability and suboptimal performance in filtering. Second, in high-noise environments, existing technologies have limited ability to predict the dominant frequency of mine seismic records, resulting in decreased signal processing accuracy. Particularly in complex coal mine environments, traditional machine learning methods rely on manually designed features, which fail to fully capture key information from the data, thus affecting model accuracy and practicality. Deep learning models in coal mine environments are limited by scarce high-quality training data, impacting their generalization ability under different conditions and exhibiting instability when processing high-noise, non-stationary signals. Although models have improved in picking accuracy, in some cases, their performance still fails to surpass that of human experts.

[0004] In summary, existing technologies in microseismic monitoring and phase acquisition, especially in coal mine environments, face challenges such as the accuracy of signal arrival time identification, the complexity of filter parameter settings, the limitations of feature design in machine learning methods, the computational resource requirements of deep learning models, and their dependence on high-quality data. Therefore, there is an urgent need to develop a technical solution capable of efficiently and stably extracting distortion-free seismic records in complex and noisy coal mine environments, thereby improving the application effectiveness and reliability of microseismic monitoring. Summary of the Invention

[0005] The purpose of this application is to provide a method, system, and device for picking up mine seismic P-waves based on adaptive characteristic functions, which improves the accuracy and reliability of microseismic monitoring.

[0006] To achieve the above objectives, this application provides the following solution:

[0007] Firstly, this application provides a method for picking up mine seismic P-waves based on adaptive feature functions, including:

[0008] Obtain the original seismic records;

[0009] Based on the original seismic records, the ICEEMDAN algorithm was used to determine the noise-free mode function components and the noisy mode function components.

[0010] Wavelet thresholding is used to denoise the noisy modal function components to obtain the denoised modal function components;

[0011] The denoised seismic record is obtained based on the noise-free modal function components and the denoised modal function components.

[0012] Based on the denoised seismic records and adaptive feature functions, an adaptive feature function sequence is obtained; the adaptive feature functions are constructed based on the amplitude sequence of the denoised seismic records.

[0013] Calculate the moving standard deviation of the adaptive feature function sequence to obtain the corresponding moving standard deviation sequence;

[0014] The corresponding AIC window length is determined based on the moving standard deviation sequence and the preset picking threshold;

[0015] Within the AIC window length, the AIC value corresponding to the adaptive feature function sequence is calculated, and the time corresponding to the minimum AIC value is taken as the P-wave arrival time of the original seismic record.

[0016] Optionally, based on the original seismic records, the ICEEMDAN algorithm is used to determine the noise-free modal function components and the noisy modal function components, specifically including:

[0017] The ICEEMDAN algorithm was used to decompose the original seismic record and obtain multiple intrinsic mode function components;

[0018] Calculate the sample entropy value of each intrinsic mode function component;

[0019] Determine whether the sample entropy value of each intrinsic mode function component is greater than a preset threshold;

[0020] If so, the corresponding intrinsic mode function component is taken as the noisy mode function component;

[0021] If not, the corresponding intrinsic mode function component is taken as a noise-free mode function component.

[0022] Optionally, based on the noise-free modal function components and the denoised modal function components, a denoised seismic record is obtained, specifically including:

[0023] Calculate the sample entropy value of each noise-reduced modal function component;

[0024] Determine whether the sample entropy value of each denoised modal function component is greater than a preset threshold;

[0025] If so, the corresponding denoised modal function components are discarded;

[0026] If not, the corresponding denoised modal function components and the noise-free modal function components are reconstructed to obtain the denoised seismic record.

[0027] Optionally, an adaptive feature function is constructed, specifically including:

[0028] Based on the amplitude sequence of the denoised seismic records, the weight parameters corresponding to each sampling point are determined.

[0029] Determine the relative time series based on the weight parameters;

[0030] The relative energy coefficient is determined based on the relative time series and weight parameters;

[0031] An adaptive feature function is constructed based on the relative time series and the relative energy coefficient.

[0032] Optionally, the expression for the weight parameter W is:

[0033]

[0034] Where W(i) represents the weight parameter of the i-th sampling point; x(j) represents the amplitude value of the j-th sampling point in the amplitude sequence of the seismic record;

[0035] The expression for the relative time series S is:

[0036] S(i)=|x(i) / X max |;

[0037] Where S(i) represents the relative time series of the i-th sampling point; X max It is the maximum absolute amplitude in the amplitude sequence of the mine seismic record;

[0038] The expression for the relative energy coefficient α is:

[0039]

[0040] The expression for the adaptive feature function ACF is:

[0041] ACF(i) = x α (i)+W·[x(i)-x(i-1)] 2 .

[0042] Optionally, the corresponding AIC window length is determined based on the moving standard deviation sequence and a preset picking threshold, specifically including:

[0043] The time interval is determined based on the moving standard deviation sequence and a preset picking threshold.

[0044] The length of the corresponding AIC window is determined based on the time interval.

[0045] Optionally, the formula for calculating the moving standard deviation is:

[0046]

[0047] Where M(i) represents the moving standard deviation of the i-th sampling point; N represents the size of the sliding window; ACF(j) represents the corresponding value of the j-th sampling point in the adaptive feature function; u i It is the moving average value of the i-th sampling point within the sliding window.

[0048] Optionally, the expression for the AIC function is:

[0049] AIC(i)=(i-2)log(var(ACF[1,i]))+(L-2-i)log(var(ACF[1,i]));

[0050] Where AIC(i) represents the AIC value of the i-th sampling point; var(ACF[1,i]) represents the variance of the ACF from the 1-th sampling point to the i-th sampling point; and L represents the AIC window length.

[0051] Secondly, this application provides a mine seismic P-wave pickup system based on an adaptive feature function. The mine seismic P-wave pickup system based on the adaptive feature function is used to implement the aforementioned mine seismic P-wave pickup method based on an adaptive feature function. The mine seismic P-wave pickup system based on the adaptive feature function includes:

[0052] The data acquisition unit is used to acquire raw seismic records.

[0053] The modal function component partitioning unit is used to determine the noise-free modal function components and the noisy modal function components based on the original seismic records and using the ICEEMDAN algorithm.

[0054] The noise reduction unit is used to perform noise reduction processing on the noisy modal function components using wavelet thresholding to obtain the noise-reduced modal function components.

[0055] The denoised seismic record determination unit is used to obtain the denoised seismic record based on the noise-free modal function components and the denoised modal function components.

[0056] An adaptive feature function sequence determination unit is used to obtain an adaptive feature function sequence based on the denoised seismic record and the adaptive feature function; the adaptive feature function is constructed based on the amplitude sequence of the denoised seismic record.

[0057] The moving standard deviation sequence determination unit is used to calculate the moving standard deviation of the adaptive feature function sequence to obtain the corresponding moving standard deviation sequence;

[0058] The AIC window length determination unit is used to determine the corresponding AIC window length based on the moving standard deviation sequence and the preset picking threshold.

[0059] The P-wave arrival time determination unit is used to calculate the AIC value corresponding to the adaptive feature function sequence within the AIC window length range, and take the time corresponding to the minimum AIC value as the P-wave arrival time of the original seismic record.

[0060] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the mine seismic P-wave pickup method based on adaptive characteristic functions as described in any of the preceding claims.

[0061] According to the specific embodiments provided in this application, this application has the following technical effects:

[0062] This application discloses a method, system, and device for P-wave acquisition in seismic events based on adaptive characteristic functions. The method is based on the original seismic records and uses the ICEEMDAN-SamEn-WTD method to denoise the original seismic records, which significantly improves the accuracy of subsequent P-wave acquisition. Then, the MSD-ACF-AIC method is used to acquire the P-wave arrival time of the denoised seismic records, which significantly improves the accuracy of P-wave arrival time acquisition and lays a good foundation for subsequent microseismic event monitoring. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 A schematic flowchart of a mine seismic P-wave picking method based on adaptive feature function provided in an embodiment of this application;

[0065] Figure 2A schematic diagram of IMF components obtained by decomposing the original seismic record using the ICEEMDAN method, as provided in an embodiment of this application;

[0066] Figure 3 A schematic diagram comparing the original seismic record and the denoised seismic record provided in an embodiment of this application;

[0067] Figure 4 A schematic diagram comparing the results of P-wave arrival time picking of synthetic seismic records based on the M-AIC picking method, provided as an embodiment of this application;

[0068] Figure 5 A schematic diagram comparing the original seismic record and the denoised seismic record provided for another embodiment of this application;

[0069] Figure 6 A schematic diagram illustrating the distribution of P-wave pickup bias provided in another embodiment of this application;

[0070] Figure 7 A schematic diagram of the functional modules of a mine seismic P-wave pickup system based on an adaptive feature function provided in an embodiment of this application;

[0071] Figure 8 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application.

[0072] Figure label:

[0073] Data acquisition unit-1, modal function component division unit-2, noise reduction unit-3, noise-reduced seismic record determination unit-4, adaptive characteristic function sequence determination unit-5, moving standard deviation sequence determination unit-6, AIC window length determination unit-7, P-wave arrival time determination unit-8. Detailed Implementation

[0074] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0075] This application provides a method, system, and device for P-wave picking in seismic data based on adaptive feature functions, aiming to improve the accuracy of P-wave picking in high-noise seismic records. The method mainly consists of two aspects: denoising and P-wave signal extraction. Specifically, the denoising aspect employs a combination of improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Sample Entropy (SamEn), and Wavelet Threshold Denoising (WTD) to extract undistorted useful signals from high-noise microseismic data. This significantly improves the accuracy of P-wave picking and ensures effective extraction of key seismic records even in high-noise environments. In P-wave signal extraction, a relative time series and relative energy coefficient were introduced to construct an improved adaptive characteristic function (ACF). To further improve the picking accuracy, a moving standard deviation (MSD) was creatively introduced to calculate the improved adaptive characteristic function. The window length of the autoregressive Akaike Information Criterion (AIC) was determined by the picking threshold. Then, the AIC within the window length was calculated, and the minimum value was determined as the initial P-wave arrival time of the original seismic record, which further improved the accuracy of P-wave picking.

[0076] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0077] In one exemplary embodiment, such as Figure 1 As shown, a method for picking up mine seismic P-waves based on adaptive feature functions is provided, including the following steps S1 to S6. Wherein:

[0078] Step S1: Obtain the original seismic records.

[0079] Step S2: Based on the original seismic records, the ICEEMDAN algorithm is used to determine the noise-free mode function components and the noisy mode function components.

[0080] Step S3: Use wavelet thresholding to denoise the noisy modal function components to obtain the denoised modal function components.

[0081] Step S4: Based on the noise-free modal function components and the denoised modal function components, the denoised seismic record is obtained.

[0082] Step S5: Based on the denoised seismic records and the adaptive feature function, an adaptive feature function sequence is obtained; the adaptive feature function is constructed based on the amplitude sequence of the denoised seismic records.

[0083] Step S6: Calculate the moving standard deviation of the adaptive feature function sequence to obtain the corresponding moving standard deviation sequence.

[0084] Step S7: Determine the corresponding AIC window length based on the moving standard deviation sequence and the preset picking threshold.

[0085] Step S8: Within the AIC window length range, calculate the AIC value corresponding to the adaptive feature function sequence, and take the time corresponding to the minimum AIC value as the P-wave arrival time of the original seismic record.

[0086] Specifically, in microseismic monitoring, determining the arrival time of the P-wave is a crucial step in identifying the location of the seismic source and determining its parameters. By accurately identifying the arrival time of the P-wave, the spatial location, source intensity, and activity patterns of the microseismic event can be further calculated. Using the arrival time of the P-wave and the seismic wave propagation velocity, the location of the seismic source can be calculated using the finite difference method or other algorithms. The source location calculated using the P-wave arrival time, combined with other source information such as the arrival time and amplitude of the S-wave, can be further calculated to determine parameters such as the magnitude and stress release of the source. These data can then be used to analyze the activity characteristics and potential risks of the source. By continuously monitoring the arrival time of the P-wave, real-time monitoring of microseismic events can be achieved. Once microseismic activity is identified and its location and intensity are determined, the monitoring system can issue timely warnings to avoid potential accidents. The collected P-wave arrival time data, after accumulation, can be used to conduct in-depth analysis of the stress state and fault activity in mining or engineering areas, identify potential risk areas, and provide a reference for subsequent operations.

[0087] As an optional implementation, step S2 specifically includes:

[0088] Step S21: The original seismic record is decomposed using the ICEEMDAN algorithm to obtain multiple Intrinsic Mode Function (IMF) components, as well as multiple residual components.

[0089] Step S22: Calculate the sample entropy values ​​of each intrinsic mode function component. After obtaining the IMF components using ICEEMDAN decomposition, it is necessary to determine which IMF components are substantially helpful in reconstructing the new seismic record. Specifically, the smaller the sample entropy, the higher the self-similarity of the signal and the lower the noise; conversely, the larger the sample entropy, the greater the noise.

[0090] Step S23: Determine whether the sample entropy value of each intrinsic mode function component is greater than the preset threshold; if yes, proceed to step S24; otherwise, proceed to step S25.

[0091] Step S24: The corresponding intrinsic mode function component is taken as the noisy mode function component.

[0092] Step S25: The corresponding intrinsic mode function component is taken as a noise-free mode function component.

[0093] Specifically, the ICEEMDAN algorithm is used to decompose the original seismic records. ICEEMDAN is an advanced signal processing method that improves upon traditional Empirical Mode Decomposition (EMD) techniques and their variants (such as EEMD and CEEMDAN), aiming to more accurately decompose complex nonlinear and non-stationary signals. Compared to the mode aliasing problem in EEMD, ICEEMDAN adaptively adds noise, making the separation of each intrinsic mode function (IMF) clearer, significantly improving the accuracy and stability of signal processing. Therefore, ICEEMDAN can provide more accurate and robust results when processing complex signals, effectively reducing mode aliasing and making signal decomposition more precise.

[0094] As an optional implementation, step S21 specifically includes:

[0095] Step S211: Set the noise amplitude parameter ε0, generate the noise sequence n(i), and add Gaussian white noise to the original seismic record to obtain X(i).

[0096] X(i)=X+ε0E1(n(i)),i=1,2,...,G (1)

[0097] Where X is the original seismic record, E1(·) is the function for calculating IMF1, and i is the sampling point number.

[0098] Step S212, obtain the first residual component R1:

[0099]

[0100] Where A(·) is the local average value of the original seismic record generated by EMD, and G is the number of time series.

[0101] Step S213, define k as the k-th intrinsic mode function component generated by ICEEMDAN decomposition. When k=1, the first intrinsic mode function component F1 is obtained:

[0102]

[0103] When k = 2, 3, 4, ..., K, calculate the k-th residual component R. k and the k-th intrinsic mode function component F k :

[0104]

[0105] F k =R k-1 -R k (5)

[0106] Among them, E k (·) is a function that generates the EMD decomposition model.

[0107] Step S214: Repeat the calculation to obtain all IMF components until the final residual component R. n Satisfying monotonicity, it cannot be further decomposed; the original seismic record is decomposed into two parts:

[0108]

[0109] Furthermore, to verify the effectiveness of the ICEEMDAN decomposition, a seismic record with a sampling rate of 500 and a sample size of 2000 was synthesized using the Ricker wavelet, and then 70% random noise was randomly added to it. The ICEEMDAN method was used to decompose it into 9 IMF components. The decomposed IMF components, in descending order, correspond to different frequency compositions of the original seismic record. The decomposed IMF components and residual components are shown below. Figure 2 As shown, from Figure 2 It can be seen that ICEEMDAN decomposition can help extract useful signals from complex seismic records.

[0110] As an optional implementation, step S22 specifically includes:

[0111] Step S221, assume C f (i) represents the IMF sequence obtained using ICEEMDAN decomposition, where i = 1, 2, 3, ..., G, and f represents the total number of IMFs. Using C... f (i) Construct an m-dimensional block sequence Y(i):

[0112] Y(i)=|C f (i),C f (i+1),...,C f (i+m-1)| (7)

[0113] Where i = 1, 2, ..., k, and k = G - m + 1.

[0114] Step S222: Calculate the distance between sequence Y(i) and all sequences Y(j), where the distance is defined as:

[0115] d ij =max|C f (i+l)-C f (j+l)| (8)

[0116] Where i≠j, l=0,1,2,...,m-1.

[0117] Step S223: Define T = r × SD, where r ranges from 0.1 to 0.25, and SD represents the standard deviation of the calculated sequence. Then calculate d. ij The number of quantities greater than T is denoted as T.

[0118] Step S224, Define This represents the mean of the i-th m-dimensional block sequence, used to calculate the average similarity of sample sequences in the current dimension.

[0119]

[0120] Step S225, all The average value is defined as φ m , representing the overall average similarity in the current dimension.

[0121]

[0122] In step S226, let m = m + 1, and then repeat steps S221 to S225. That is, during the calculation process, all relevant distances and average values ​​will be calculated again.

[0123] Step S227, define the sample entropy SamEn as:

[0124] SamEn=lnφ m -lnφ m+1 (11)

[0125] Where, φ m and φ m+1 It is the average value calculated from m-dimensional and m+1-dimensional calculations.

[0126] Here, m and T are two important parameters. We set m to 1 and T to 0.2 times the standard deviation of the IMF to distinguish the different characteristics of the decomposed IMFs. Subsequently, the SampEn values ​​of different IMFs were calculated to illustrate their respective complexities. To extract useful IMF components, a reasonable threshold needs to be determined to remove redundant IMF components. By judging the noise characteristics of the signal through randomness, the threshold was set to 0.2.

[0127] As an optional implementation, step S4 specifically includes:

[0128] Step S41: Calculate the sample entropy value of each denoised modal function component.

[0129] Step S42: Determine whether the sample entropy value of each denoised modal function component is greater than the preset threshold; if yes, proceed to step S43; if no, proceed to step S44.

[0130] Step S43: Discard the corresponding denoised modal function components.

[0131] Step S44: Reconstruct the corresponding denoised mode function components and the noise-free mode function components to obtain the denoised seismic record.

[0132] Specifically, since the original seismic records contain some mid-to-high frequency components, the original ICEEMDAN algorithm and sample entropy denoising mode directly discard significant IMF components, resulting in the loss of useful signals while reducing noise. Therefore, this application improves upon this by performing wavelet threshold denoising on IMF components that exceed a preset threshold, and then calculating the sample entropy value again for the denoised IMF components. If the sample entropy value is higher than the preset threshold, it is discarded; otherwise, it is retained. Figure 2 The sample entropy values ​​of IMF components 1 to 4 are all greater than 0.2. Wavelet threshold denoising is performed on IMF components 1 to 4, and the sample entropy values ​​of the denoised IMF components are less than 0.2. Finally, the noise-free IMF components and the denoised IMF components are reconstructed to obtain the effective seismic record, as shown in the schematic diagram below. Figure 3 As shown, Figure 3 The blue curve represents the original seismic record, and the red curve represents the denoised seismic record. This method is named ICEEMDAN-SamEn-WTD, and the denoised seismic record obtained using this method is used as input for subsequent P-wave picking methods.

[0133] As an optional implementation, step S5 involves constructing an adaptive feature function, specifically including:

[0134] Step S511: Based on the amplitude sequence of the denoised seismic record, determine the weight parameters corresponding to each sampling point.

[0135] Step S512: Determine the relative time series based on the weight parameters.

[0136] Step S513: Determine the relative energy coefficient based on the relative time series and weight parameters.

[0137] Step S514: Construct an adaptive feature function based on the relative time series and relative energy coefficient.

[0138] Specifically, the arrival time of P-waves in mine-induced earthquakes can be indicated by amplifying and analyzing changes in amplitude and frequency within the earthquake sequence. This is the primary task of the characteristic function. Establishing a suitable and sensitive characteristic function will further improve the resolution between signal and noise, thereby enhancing the accuracy of the arrival time. In this application, relative time series and relative energy coefficients are introduced to construct an adaptive characteristic function.

[0139] As an optional implementation, in step S511, the expression for the weight parameter W is:

[0140]

[0141] Where W(i) represents the weight parameter of the i-th sampling point, and parameter W is a weight parameter that varies with the sampling rate and stationary noise characteristics; x(j) represents the amplitude value of the j-th sampling point in the amplitude sequence of the seismic record.

[0142] The expression for the relative time series S is:

[0143] S(i)=|x(i) / X max | (13)

[0144] Where S(i) represents the relative time series of the i-th sampling point; X max It is the maximum absolute amplitude in the amplitude sequence of the seismic record, used to adjust the time series in the seismic record.

[0145] The expression for the relative energy coefficient α is:

[0146]

[0147] The expression for the adaptive feature function ACF is:

[0148] ACF(i) = x α (i)+W·[x(i)-x(i-1)] 2 (15)

[0149] The adaptive feature function ACF is adapted from Allen's feature function.

[0150] As an optional implementation, step S5 specifically includes:

[0151] The waveform record obtained after denoising the above-mentioned noisy synthetic seismic record is used as the input for P-wave arrival picking. The ACF is calculated using formula (15) to obtain the adaptive feature function sequence, such as... Figure 4 As shown in (c) in the figure, Figure 4 (a) in the image represents the original seismic record. Figure 4 (b) in the image represents the denoised seismic record, from... Figure 4 As can be seen from (a) to (c), the obtained adaptive feature function sequence further suppresses the noise in the seismic signal.

[0152] As an optional implementation, step S6 specifically includes:

[0153] Calculate the moving standard deviation of each sampling point in the adaptive feature function sequence. To further improve the picking accuracy of P-wave arrival, the MSD and ACF-AIC methods are combined. MSD is used for ACF, and the sliding window and picking threshold E0 are manually set. In this application, the sliding window is set to 0.25s, and the picking threshold M0 is set to 0.5. The formula for calculating the moving standard deviation of each sampling point is:

[0154]

[0155] Where M(i) represents the moving standard deviation of the i-th sampling point; N represents the size of the sliding window; ACF(j) represents the corresponding value of the j-th sampling point in the adaptive feature function; u i It is the moving average value of the i-th sampling point within the sliding window.

[0156] As an optional implementation, step S7 specifically includes:

[0157] Step S71: Determine the time interval based on the moving standard deviation sequence and a preset picking threshold. A basic time interval L0 is determined using M(i) and M0, as follows: Figure 4 As shown in (d), the length between the two black dashed lines is the basic time interval L0. The basic time interval L0 is determined by the times t1 and t2 corresponding to the two points where the picking threshold M0 and M(i) intersect, represented by the red dashed lines. In addition, the maximum value of M(i) is also included in this interval.

[0158] Step S72: Determine the corresponding AIC window length based on the time interval.

[0159] The AIC window length is determined based on t1 and the basic time interval L0, as determined by MSD. The AIC window length should be large enough to encompass prominent waveforms. In this application, the AIC window L is controlled by N*L0. This application sets N to 2, thus the AIC calculation window is... Figure 4 The distance L between the two black dashed lines shown in (c).

[0160] The AIC function is modified using ACF to obtain the modified AIC function, which is then used as the final AIC function. Its expression is:

[0161] AIC(i)=(i-2)log(var(ACF[1,i]))+(L-2-i)log(var(ACF[1,i])) (18)

[0162] Where AIC(i) represents the AIC value of the i-th sampling point; var(ACF[1,i]) represents the variance of the ACF from the 1st sampling point to the i-th sampling point, i = 1, 2, ..., L; L represents the AIC window length.

[0163] Further, in step S8, based on the aforementioned AIC function, within the AIC window length range, the AIC value corresponding to the adaptive feature function sequence is calculated, such as... Figure 4 As shown by the red dashed line in (e), the time corresponding to the minimum AIC value is taken as the P-wave arrival time of the original seismic record.

[0164] The above-mentioned method for automatic P-wave acquisition is named MSD-ACF-AIC. Figure 4 The image shows a comparison between the results obtained by applying the MSD-ACF-AIC method to the reconstructed denoised seismic record using the ICEEMDAN-SamEn-WTD method and the results obtained by using the conventional M-AIC picker. Figure 4 In (f), Ori represents the noisy mine seismic composite signal, and the blue solid line represents the M-AIC pickup result of the noisy signal; Figure 4 In (g), Pre represents the denoised seismic signal obtained by a conventional M-AIC pickup, and the green solid line represents the pickup result of the M-AIC pickup on the denoised signal. By comparison, it can be found that the reconstructed denoised seismic record can reliably replace the original seismic signal for P-wave first arrival pickup. Especially... Figure 4 (f) and Figure 4 As can be seen from (g), the denoising strategy of ICEEMDAN-SamEn-WTD in this application significantly improves the accuracy of P-wave arrival pickup in high-noise environments. Furthermore, Figure 4 The MSD-ACF-AIC method shown in (b) is more accurate than the traditional M-AIC method, indicating that the MSD-ACF-AIC method has a significant advantage in phase picking, especially in P-wave first arrival picking. Therefore, the MSD-ACF-AIC method can provide higher accuracy and robustness in complex noise environments, while the ICEEMDAN-SamEn-WTD denoising strategy effectively improves the signal quality, making the denoised signal better suited for subsequent seismic signal analysis.

[0165] Furthermore, the technical solution of this application will be further verified by applying the mine seismic P-wave picking method based on adaptive feature function to the manually picked mine seismic dataset (a total of 343 mine seismic records) of Dongtan Coal Mine.

[0166] First, the ICEEMDAN-SamEn-WTD method of this application is used to denoise the manually picked seismic dataset from the Dongtan Coal Mine, resulting in denoised seismic records. Figure 5 The text displays mine seismic records before and after noise reduction. Figure 5 In the image, (a) represents the seismic record before denoising (i.e., the original seismic record). Figure 5 (b) in the figure shows the denoised seismic record. It can be seen that, compared with the original seismic record, the noise of the denoised seismic record obtained by using the ICEEMDAN-SamEn-WTD method is significantly reduced, and the signal-to-noise ratio is increased from 5.9dB in the original seismic record to 10.8dB. This indicates that the denoising algorithm can effectively enhance the signal quality, thereby improving the picking accuracy of P-wave arrival.

[0167] Furthermore, the MSD-ACF-AIC method was used to perform P-wave arrival time picking on the denoised seismic records, and the autocorrelation coefficient sequence obtained after calculating ACF is as follows: Figure 5 As shown in (c) above, the diagram of P-wave arrival time manually calibrated by professionals is as follows. Figure 5 As shown in (d) in the figure, the arrival time of the P-wave, automatically calibrated using the MSD-ACF-AIC method, is as follows: Figure 5 As shown in (e), it can be seen that the arrival time of the P wave automatically calibrated using the MSD-ACF-AIC method (i.e., Figure 5 (e) The red dashed line) and the P-wave arrival time manually marked by professionals (i.e. Figure 5 The error between the green dashed lines in (d) is zero, indicating that the accuracy of the automated method in this embodiment has reached a level comparable to that of manual calibration.

[0168] Furthermore, the test results showed that in 94.7% of the data (i.e., 325 records), the error between the automatically picked P-wave arrival time and the manually calibrated time was within 0 to 3 sample intervals. Specifically, these errors ranged from + / -0 ms to + / -6 ms. Figure 6 As shown. Figure 6 The distribution of P-wave pickup errors was further demonstrated, with most errors concentrated around 0 ms, and the distribution of errors exhibiting a typical normal distribution. This result shows that the method has extremely high accuracy and stability in automatically picking up P-wave arrival times.

[0169] Overall, the experimental results verify that the mine seismic P-wave picking method based on adaptive characteristic function proposed in this application not only performs well in noise suppression, but also accurately estimates the arrival time of noisy mine seismic records, greatly improving the efficiency and accuracy of mine seismic monitoring. This provides a solid technical foundation for the automated application in mine seismic data processing and also demonstrates its broad application prospects in coal mine safety monitoring under complex geological conditions.

[0170] Based on the same inventive concept, this application also provides a mine seismic P-wave pickup system based on adaptive characteristic functions for implementing the aforementioned mine seismic P-wave pickup method. The solution provided by this system is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more mine seismic P-wave pickup system embodiments based on adaptive characteristic functions provided below can be found in the limitations of the mine seismic P-wave pickup method based on adaptive characteristic functions described above, and will not be repeated here.

[0171] In one exemplary embodiment, such as Figure 7 As shown, a mine seismic P-wave pickup system based on adaptive characteristic functions is provided, including:

[0172] Data acquisition unit 1 is used to acquire raw seismic records.

[0173] Modal function component partitioning unit 2 is used to determine noise-free modal function components and noisy modal function components based on the original seismic records and using the ICEEMDAN algorithm.

[0174] The noise reduction unit 3 is used to perform noise reduction processing on the noisy modal function components using wavelet thresholding to obtain the noise-reduced modal function components.

[0175] The noise-reduced seismic record determination unit 4 is used to obtain the noise-reduced seismic record based on the noise-free modal function components and the noise-reduced modal function components.

[0176] The adaptive feature function sequence determination unit 5 is used to obtain an adaptive feature function sequence based on the denoised seismic record and the adaptive feature function; the adaptive feature function is constructed based on the amplitude sequence of the denoised seismic record.

[0177] The moving standard deviation sequence determination unit 6 is used to calculate the moving standard deviation of the adaptive feature function sequence to obtain the corresponding moving standard deviation sequence.

[0178] The AIC window length determination unit 7 is used to determine the corresponding AIC window length based on the moving standard deviation sequence and the preset picking threshold.

[0179] The P-wave arrival time determination unit 8 is used to calculate the AIC value corresponding to the adaptive feature function sequence within the AIC window length range, and take the time corresponding to the minimum AIC value as the P-wave arrival time of the original seismic record.

[0180] In one exemplary embodiment, a computer device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a mine seismic P-wave pickup method based on an adaptive feature function.

[0181] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 8 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a mine seismic P-wave pickup method based on adaptive characteristic functions.

[0182] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0183] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0184] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0185] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0186] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0187] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for picking up mine seismic P-waves based on adaptive characteristic functions, characterized in that, The mine seismic P-wave picking method based on adaptive feature functions includes: Obtain the original seismic records; Based on the original seismic records, the ICEEMDAN algorithm was used to determine the noise-free mode function components and the noisy mode function components. Wavelet thresholding is used to denoise the noisy modal function components to obtain the denoised modal function components; The denoised seismic record is obtained based on the noise-free modal function components and the denoised modal function components. Based on the denoised seismic records and adaptive feature functions, an adaptive feature function sequence is obtained; the adaptive feature functions are constructed based on the amplitude sequence of the denoised seismic records. Calculate the moving standard deviation of the adaptive feature function sequence to obtain the corresponding moving standard deviation sequence; The corresponding AIC window length is determined based on the moving standard deviation sequence and the preset picking threshold; Within the AIC window length, the AIC value corresponding to the adaptive feature function sequence is calculated, and the time corresponding to the minimum AIC value is taken as the P-wave arrival time of the original seismic record.

2. The method for picking up mine seismic P-waves based on adaptive feature functions according to claim 1, characterized in that, Based on the original seismic records, the ICEEMDAN algorithm was used to determine the noise-free and noisy modal function components, specifically including: The original seismic record was decomposed using the ICEEMDAN algorithm to obtain multiple intrinsic mode function components; Calculate the sample entropy value of each intrinsic mode function component; Determine whether the sample entropy value of each intrinsic mode function component is greater than a preset threshold; If so, the corresponding intrinsic mode function component is taken as the noisy mode function component; If not, the corresponding intrinsic mode function component is taken as a noise-free mode function component.

3. The method for picking up mine seismic P-waves based on adaptive feature functions according to claim 1, characterized in that, Based on the noise-free modal function components and the denoised modal function components, the denoised seismic record is obtained, specifically including: Calculate the sample entropy value of each noise-reduced modal function component; Determine whether the sample entropy value of each denoised modal function component is greater than a preset threshold; If so, the corresponding denoised modal function component is discarded; If not, the corresponding denoised modal function components and the noise-free modal function components are reconstructed to obtain the denoised seismic record.

4. The method for picking up mine seismic P-waves based on adaptive characteristic functions according to claim 1, characterized in that, Constructing adaptive feature functions specifically includes: Based on the amplitude sequence of the denoised seismic records, the weight parameters corresponding to each sampling point are determined. Determine the relative time series based on the weight parameters; The relative energy coefficient is determined based on the relative time series and weight parameters; An adaptive feature function is constructed based on the relative time series and the relative energy coefficient.

5. The method for picking up mine seismic P-waves based on adaptive characteristic functions according to claim 4, characterized in that, The expression for the weight parameter W is: Where W(i) represents the weight parameter of the i-th sampling point; x(j) represents the amplitude value of the j-th sampling point in the amplitude sequence of the seismic record; The expression for the relative time series S is: S(i)=|x(i) / X max |; Where S(i) represents the relative time series of the i-th sampling point, X max It is the maximum absolute amplitude in the amplitude sequence of the mine seismic record; The expression for the relative energy coefficient α is: The expression for the adaptive feature function ACF is: ACF(i)=x α (i)+W(i)·[x(i)-x(i-1)] 2 。 6. The method for picking up mine seismic P-waves based on adaptive characteristic functions according to claim 1, characterized in that, Based on the moving standard deviation sequence and a preset picking threshold, the corresponding AIC window length is determined, specifically including: The time interval is determined based on the moving standard deviation sequence and a preset picking threshold. The length of the corresponding AIC window is determined based on the time interval.

7. The method for picking up mine seismic P-waves based on adaptive characteristic functions according to claim 6, characterized in that, The formula for calculating the moving standard deviation is: Where M(i) represents the moving standard deviation of the i-th sampling point; N represents the size of the sliding window; ACF(j) represents the corresponding value of the j-th sampling point in the adaptive feature function; u i It is the moving average value of the i-th sampling point within the sliding window.

8. The method for picking up mine seismic P-waves based on adaptive characteristic functions according to claim 6, characterized in that, The expression for the AIC function is: AIC(i)=(i-2)log(var(ACF[1,i]))+(L-2-i)log(var(ACF[1,i])); Where AIC(i) represents the AIC value of the i-th sampling point; var(ACF[1,i]) represents the variance of the ACF from the 1-th sampling point to the i-th sampling point; and L represents the AIC window length.

9. A mine seismic P-wave pickup system based on adaptive characteristic functions, characterized in that, The adaptive feature function-based P-wave acquisition system is used to implement the adaptive feature function-based P-wave acquisition method for mine seismic activity as described in any one of claims 1-8. The adaptive feature function-based P-wave acquisition system comprises: The data acquisition unit is used to acquire raw seismic records. The modal function component partitioning unit is used to determine the noise-free modal function components and the noisy modal function components based on the original seismic records and using the ICEEMDAN algorithm. The noise reduction unit is used to perform noise reduction processing on the noisy modal function components using wavelet thresholding to obtain the noise-reduced modal function components. The denoised seismic record determination unit is used to obtain the denoised seismic record based on the noise-free modal function components and the denoised modal function components. An adaptive feature function sequence determination unit is used to obtain an adaptive feature function sequence based on the denoised seismic record and the adaptive feature function; the adaptive feature function is constructed based on the amplitude sequence of the denoised seismic record. The moving standard deviation sequence determination unit is used to calculate the moving standard deviation of the adaptive feature function sequence to obtain the corresponding moving standard deviation sequence; The AIC window length determination unit is used to determine the corresponding AIC window length based on the moving standard deviation sequence and the preset picking threshold. The P-wave arrival time determination unit is used to calculate the AIC value corresponding to the adaptive feature function sequence within the AIC window length range, and take the time corresponding to the minimum AIC value as the P-wave arrival time of the original seismic record.

10. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the mine seismic P-wave pickup method based on adaptive characteristic functions as described in any one of claims 1-8.

Images