A method for detecting the first arrival time of ground microseismic monitoring data

Abstract

The present invention belongs to the technical field of microseismic monitoring for oil and gas exploration and development, and specifically relates to a method for detecting the first arrival time of ground microseismic monitoring data. The present invention provides a method for detecting the first arrival time of ground microseismic monitoring data, which comprises the following steps: obtaining ground microseismic monitoring data, and performing noise reduction processing by using a time-frequency domain filtering method; selecting a time window, and extracting each microseismic record in the time window; calculating the time difference between each two microseismic data from four aspects: waveform, frequency, phase, and amplitude value; calculating the relative arrival time of each microseismic event; performing time difference correction on each microseismic record; obtaining a reference track using the microseismic record after time difference correction; detecting the absolute arrival time of the microseismic of the reference track; and calculating the absolute first arrival time of each microseismic event. The method of the present invention effectively overcomes the adverse effects of the first arrival polarity reversal and low signal-to-noise ratio on the linear superposition of commonly used microseismic arrival records, and improves the detection accuracy of the first arrival time.

Description

Technical Field

[0001] The invention belongs to the technical field of microseismic monitoring in oil and gas exploration and development, and in particular relates to a method for detecting the first arrival time of ground microseismic monitoring data. Background Art

[0002] Hydraulic fracturing microseismic monitoring technology is a technology that monitors the development of underground fractures and evaluates the effect of fracturing by detecting microseismic events caused by underground rock fractures. Microseismic positioning is the core issue of microseismic monitoring technology, and accurate detection of the initial arrival of microseismic events is the key link in microseismic positioning.

[0003] Manually picking up microseismic first arrival time data from microseismic monitoring data is labor-intensive and inefficient, so the automatic detection method of microseismic first arrival time has been widely studied. For example, the STA / LTA method and its improved method mainly use the ratio of energy in long and short time windows to determine the first arrival time of microseismic events. It is easy to implement and has high computational efficiency, but it is not very effective for microseismic data with low signal-to-noise ratio.

[0004] Microseismic events with similar source locations show similar waveform characteristics on different seismograph records. Therefore, in recent years, an automatic detection method for the first arrival of microseismic events based on the similar waveform characteristics in microseismic records has emerged, which improves the detection accuracy of the first arrival of microseismic events. However, the signal-to-noise ratio of ground microseismic data is often very low, and the polarity of the first arrival of microseismic event signals depends on the source mechanism. For example, the double-couple source mechanism makes the positive and negative polarities of the first arrival of microseismic waves received by the ground detectors distributed in four quadrants. The first arrival polarities of the same microseismic event received by seismographs at different locations are both positive and negative, and the first arrival polarity reversal phenomenon occurs, which reduces the signal-to-noise ratio of the reference track obtained by direct waveform superposition, affecting the detection accuracy of the first arrival of microseismic events.

[0005] Chinese invention patent CN106896407B discloses a method for picking up the first arrival of microseismic signals based on approximate negative entropy. The method designs a suitable approximate negative entropy function according to the characteristics of the microseismic signal itself, sets a threshold value in combination with the weighted function of the extreme value difference of the negative entropy spectrum curve, and then realizes the accurate division of the signal area and the noise area and the accurate picking up of the first arrival of the microseismic signal. The beneficial effect of the invention is that according to the characteristics of the microseismic signal itself, a new method for picking up the first arrival of microseismic signals based on approximate negative entropy is proposed by citing the approximate negative entropy theory in information theory. Compared with the existing picking method, the new method can realize the accurate picking up of the first arrival time under the condition of low signal-to-noise ratio, and the signal-to-noise ratio can be as low as -12dB, and the calculation process is simple, the speed is fast, and the accuracy is high.

[0006] Chinese patent application CN111337975A discloses a method for automatically identifying microseismic events based on variance fractal dimension, using the first arrival time to calculate the time difference time. The subscript j∈[1, N] represents the jth perforation data, and N represents the total number of microseismic channels; using the time difference time, the correction time is obtained for microseismic data. The correction time is used to correct multiple microseismic data; the model channel SM(t) is obtained by superimposing the corrected microseismic data; the envelope signal E(t) is obtained for the model channel SM(t), and the energy envelope E(t) is transformed using the Hilbert transform; and VFD is used to identify microseismic events. This method has a high ability to identify microseismic events in microseismic monitoring data, and solves the problem that the microseismic magnitude energy is small and the signal-to-noise ratio is low, which seriously restricts the identification and first arrival picking of microseismic events.

[0007] However, the detection accuracy of the first arrival of microseismic earthquakes still needs to be further improved, and the similar characteristics of the waveform, frequency, phase, and amplitude values ​​of microseismic signals on different recording tracks should be fully utilized to overcome the adverse effects of weak signal energy, polarity reversal, and noise interference. Summary of the invention

[0008] The main purpose of the present invention is to provide a method for detecting the first arrival time of ground microseismic monitoring data. The method of the present invention is suitable for detecting the first arrival time of microseismic records with first arrival polarity reversal and low signal-to-noise ratio, thereby improving the detection accuracy of the first arrival time of microseismic records.

[0009] To achieve the above object, the present invention adopts the following technical solutions:

[0010] The present invention provides a method for detecting the first arrival time of ground microseismic monitoring data, which comprises the following steps:

[0011] Acquire ground microseismic monitoring data and use time-frequency domain filtering method to perform noise reduction processing; select a time window and extract each microseismic record in the time window; calculate the time difference between every two microseismic data from four aspects: waveform, frequency, phase, and amplitude value; calculate the relative arrival time of each microseismic event; perform time difference correction on each microseismic record; obtain a reference track using the microseismic record after time difference correction; detect the absolute arrival time of the microseismic event on the reference track; calculate the first absolute arrival time of each microseismic event.

[0012] Among them, noise reduction processing of microseismic records refers to filtering the microseismic monitoring data. The filtering adopts the time-frequency domain filtering method to remove interference signals from the two dimensions of time and frequency.

[0013] Furthermore, from the four aspects of waveform, frequency, phase, and amplitude, the characteristics of similar signals and different noises at different monitoring locations are utilized, and the time difference between every two microseismic data is calculated using the following formula:

[0014]

[0015] Where W is the length of the microseismic record, x i (w) and x j (w) are the similar amplitudes of the i-th and j-th record tracks in the waveform record at the sampling point w. The similar amplitudes are composed of the amplitude value, the main frequency value, and the instantaneous phase value. and x i (w) and x j The first-order derivative formula of (w) increases the frequency band of the data while ensuring that the phase value remains unchanged, and the calculated time difference is more precise. Specifically, the numerical difference is used for calculation, Δt i,j is the arrival time difference between the i-th and j-th microearthquake records.

[0016] Furthermore, according to the arrival time difference Δt between two microseismic events in each recording channel, i,j =t i -t j , calculate the relative arrival time of each microseismic event, give full play to the similarity of microseismic events in waveform, frequency, phase and amplitude value, and minimize the impact of noise interference.

[0017] Furthermore, if there are N microseismic records involved in the calculation, the following equation can be obtained:

[0018] At=Δt (2)

[0019] Where A is a sparse coefficient matrix composed of 0, 1 and -1, Δt is the arrival time difference vector, and t is the relative arrival time vector. The least squares solution of formula (2) can be obtained by the following formula:

[0020] t=(A T A) -1 A T Δt (3)

[0021] Among them, A is a sparse coefficient matrix composed of 0, 1 and -1, A T is the transposed matrix of A, Δt is the arrival time difference vector, and t is the relative arrival time vector.

[0022] Furthermore, the zero-delay similar amplitude cross-correlation calculation of adjacent traces is performed on the microseismic records after time difference correction, taking advantage of the similarity of the waveform, frequency, phase, and amplitude values ​​of the microseismic events, and the zero-delay similar amplitude cross-correlation results are summed and superimposed. The adverse effects of noise are suppressed by superposition processing to obtain the reference trace; the calculation formula for the zero-delay similar amplitude cross-correlation and superposition of adjacent traces is:

[0023]

[0024] Where X is the adjacent microseismic waveform record x after time difference correction i With x i+1 The reference trace record of the zero-delay similar amplitude cross-correlation function superimposed, N is the number of microseismic record traces, W is the length of the microseismic record, w is the w-th sampling point in the microseismic waveform record, is the similar amplitude of the recording track i at the sampling point w after time difference correction. The similar amplitude consists of amplitude value, main frequency value and instantaneous phase value, which ensures the accuracy of calculation from three dimensions.

[0025] Furthermore, the weighted long-short time window energy ratio method is used to detect the first arrival absolute time T0 of the reference track; the calculation formula is:

[0026]

[0027] Where X(w) is the adjacent microseismic record x after time difference correction. i With x i+1 The zero-delay similar amplitude cross-correlation superposition trace record, w is the sample point number, W L and W S Represent the lengths of the long window and the short window respectively. and are the similar amplitude means of microseismic records in the short time window and the long time window, respectively, m is the sampling point number of the microseismic record, and R(m) is the weighted energy ratio of the long and short time windows. The time corresponding to the maximum value of R(m) is the absolute arrival time T0 of the reference trace.

[0028] Furthermore, a weighted long-short time window energy ratio algorithm is used to detect the first arrival time of the reference trace. When the energy ratio R(m) of the weighted short-time window to the long-time window exceeds a given threshold, it means that the first arrival time of the reference trace has been detected. The threshold is calculated comprehensively based on the microseismic waveform records and can reflect the proportion of effective signals and noise interference in the data.

[0029] Furthermore, the relative arrival time of each channel is added to the first arrival absolute arrival time of the reference channel to obtain the first arrival absolute arrival time of each channel, that is:

[0030] T k =T0+t k (6)

[0031] Where T0 is the first arrival time of the superimposed reference trace, t k is the relative arrival time of each channel, T k The absolute arrival time of the first arrival of each trace is gradually decomposed into relative arrival time, absolute arrival time and reference trace arrival time, which further improves the accuracy of microseismic event arrival detection.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The method of the present invention can more accurately determine the time difference of microseismic arrival between two microseismic records. By performing zero-delay similar amplitude cross-correlation superposition processing on the microseismic records after time difference correction, a superposition reference track with a high signal-to-noise ratio is obtained, and a weighted long-short time window energy ratio algorithm is used to improve the detection accuracy of the absolute arrival time of microseismic in the reference track. Therefore, the adverse effects of the first-arrival polarity reversal and low signal-to-noise ratio on the linear superposition of commonly used microseismic records are effectively overcome, and the detection accuracy of the first-arrival time of microseismic events is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0035] Figure 1 A flowchart of a method for detecting the first arrival time of ground microseismic monitoring data according to a specific embodiment of the present invention;

[0036] Figure 2 A noise-free microseismic record image synthesized according to a specific embodiment of the present invention;

[0037] Figure 3 A microseismic record with random noise added as described in a specific embodiment of the present invention;

[0038] Figure 4 A microseismic record after time difference correction according to a specific embodiment of the present invention;

[0039] Figure 5 It is a microseismic record diagram of adjacent zero-delay similar amplitude cross-correlation according to a specific embodiment of the present invention;

[0040] Figure 6 is a reference track and a weighted long-short time window energy ratio curve according to a specific embodiment of the present invention, wherein Figure 6 (a) is the reference trace record obtained by cross-correlating and superimposing adjacent traces with similar amplitudes at zero delay. Figure 6 (b) is the weighted long-short time window energy ratio curve;

[0041] Figure 7 A comparison diagram of the initial arrival time and the theoretical arrival time of each channel detected in a specific embodiment of the present invention;

[0042] Figure 8 is a graph of absolute errors of the first arrival times of each track detected in a specific embodiment of the present invention;

[0043] Fig. 9 is a reference track and a long-short time window energy ratio curve of a conventional method described in a specific embodiment of the present invention, wherein Fig. 9(a) is the reference trace record obtained by linear superposition, Fig. 9 (b) is the energy ratio curve of the long and short time windows and the first arrival time. DETAILED DESCRIPTION

[0044] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0045] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations and / or combinations thereof.

[0046] In order to enable those skilled in the art to more clearly understand the technical solution of the present invention, the technical solution of the present invention will be described in detail below in conjunction with specific embodiments.

[0047] Example 1

[0048] like Figure 1 As shown, the method for detecting the first arrival time of ground microseismic monitoring data includes the following steps:

[0049] Step 1: Input the microseismic monitoring data of each recording channel and perform noise reduction on the monitoring data. The noise reduction adopts the time-frequency domain filtering method to remove the interference signal from the two dimensions of time and frequency;

[0050] Step 2: Given a time window, extract the microseismic monitoring data of the time window, and calculate the time difference between every two microseismic data from four aspects: waveform, frequency, phase, and amplitude. The calculation formula is as follows:

[0051]

[0052] Where W is the length of the microseismic record, x i (w) and x j (w) are the similar amplitudes of the i-th and j-th waveform records at the sampling point w. The similar amplitudes are composed of amplitude value, main frequency value and instantaneous phase value. and x i (w) and x j The first-order derivative formula of (w) increases the frequency band of the data while ensuring that the phase remains unchanged, and the calculated time difference is more precise. Specifically, the numerical difference is used for calculation, Δt i,jis the arrival time difference between the i-th and j-th microearthquake records.

[0053] Step 3: Based on the arrival time difference Δt of microseismic events between two recording channels i,j =t i -t j , calculate the relative arrival time of each microseismic event, give full play to the similarity of microseismic events in waveform, frequency, phase and amplitude, and minimize the influence of noise interference. If there are N microseismic records involved in the calculation, the following equation can be obtained:

[0054] At=Δt (2)

[0055] Where A is a sparse coefficient matrix consisting of 0, 1 and -1, Δt is the arrival time difference vector, and t is the relative arrival time vector. The least squares solution of formula (2) can be obtained by the following formula:

[0056] t=(A T A) -1 A T Δt (3)

[0057] Among them, A is a sparse coefficient matrix composed of 0, 1 and -1, A T is the transposed matrix of A, Δt is the arrival time difference vector, and t is the relative arrival time vector.

[0058] Step 4: Use the relative arrival time t of each channel k Perform time difference correction on each microseismic record.

[0059] Step 5: Perform zero-delay similar amplitude cross-correlation calculation on adjacent traces of microseismic records after time difference correction, take advantage of the similar waveform of microseismic events, and sum and superimpose the zero-delay similar amplitude cross-correlation results. The adverse effects of noise are suppressed by superposition processing to obtain the reference trace. The calculation formula for zero-delay similar amplitude cross-correlation and superposition of adjacent traces is:

[0060]

[0061] Where X is the adjacent microseismic waveform record x after time difference correction i With x i+1 The reference trace record of zero-delay similar amplitude cross-correlation function superposition, N is the number of seismic record traces, W is the length of microseismic record, w is the wth sampling point in the microseismic waveform record, is the similar amplitude of the recording track i at the sampling point w after time difference correction. The similar amplitude is composed of amplitude value, main frequency value and instantaneous phase, which ensures the accuracy of calculation from three dimensions.

[0062] Step 6: Use the weighted long-short time window energy ratio method to detect the first arrival absolute time T0 of the reference track. The calculation formula is:

[0063]

[0064] Where X(w) is the adjacent microseismic record x after time difference correction. i With x i+1 The zero-delay similar amplitude cross-correlation superposition trace record, w is the sample point number, W L and W S Represent the lengths of the long window and the short window respectively. and are the similar amplitude means of microseismic records in the short time window and the long time window, respectively, m is the sampling point number of the microseismic record, and R(m) is the weighted energy ratio of the long and short time windows. The time corresponding to the maximum value of R(m) is the absolute arrival time T0 of the reference trace.

[0065] The long-short time window energy ratio algorithm is used to detect the first arrival time of the reference trace. When the energy ratio R(m) of the weighted short time window to the long time window exceeds the given threshold, it means that the first arrival time of the reference trace has been detected. The threshold is calculated comprehensively based on the microseismic waveform records and can reflect the proportion of effective signals and noise interference in the data.

[0066] Step 7: Set the relative arrival time t of each channel k , added to the reference channel’s first arrival absolute time T0, we get the first arrival absolute time T of each channel. k ,Right now:

[0067] T k =T0+t k (6)

[0068] Where T0 is the first arrival time of the superimposed reference trace, t k is the relative arrival time of each channel, T k The absolute arrival time of the first arrival of each trace is gradually decomposed into relative arrival time, absolute arrival time and reference trace arrival time, which further improves the accuracy of microseismic event arrival detection.

[0069] In order to further illustrate the implementation idea and process of this method and to demonstrate the effectiveness of the method, synthetic microseismic records were used for testing and compared with conventional methods.

[0070] S1: Figure 2This is a synthetic microseismic monitoring record containing a microseismic event, with a total of 16 waveform records and a time series length of 450ms. Among them, the first arrival polarity of the microseismic event is positive in the 1st to 7th tracks and negative in the 8th to 16th tracks. The waveform record polarity can reflect the moment tensor characteristics of rock fracture, that is, the rock fracture can be inverted through the waveform record to obtain shear fracture or tensile fracture. Figure 3 It is a microseismic record with random noise added, and the microseismic events are buried in the noise.

[0071] S2: After obtaining the relative arrival time of each microseismic event based on the similar amplitude cross-correlation function of each pair of waveform records, the relative arrival time is used to perform time difference correction on each microseismic record. Figure 4 It is a microseismic record after time difference correction. The position of each microseismic event on the time axis is consistent. This is because the time difference caused by the difference in propagation distance and propagation speed has been eliminated.

[0072] S3: The zero-delay similar amplitude cross-correlation calculation of adjacent microseismic waveform records after time difference correction is performed to obtain the waveform records of adjacent zero-delay similar amplitude cross-correlation records. The similarity advantages of microseismic events in waveform, frequency, phase and amplitude are fully utilized through similar amplitude cross-correlation, the effective signal is enhanced, and the noise interference is suppressed. Figure 5 In these cross-correlation traces, the extreme value of the 8th trace is negative, and the extreme values ​​of the remaining traces are all positive. The superposition of the traces will greatly enhance the recognition of microseismic events and further suppress random noise, thereby achieving the purpose of improving the signal-to-noise ratio.

[0073] S4: Superimpose the adjacent zero-delay similar amplitude cross-correlation record data to obtain the reference track record, such as Figure 6 (a) shows that the energy of the first arrival of the microseismic event on the reference trace is enhanced and the noise is suppressed. The reference trace is processed using the weighted energy ratio algorithm of the short-time window and the long-time window, and the obtained weighted energy ratio curve of the short-time window and the long-time window is shown in Figure 6 As shown in (b), the first arrival time of the reference track can be automatically and efficiently identified. The lengths of the long and short time windows are 80 and 20 sampling points respectively.

[0074] S5: According to the relative arrival time of each trace and the first arrival time of the reference trace, the first arrival absolute arrival time of the microseismic event in each trace is obtained, such as Figure 7 As shown in the figure, the absolute error curve between the time when similar amplitude cross-correlation is automatically detected and the time when it is theoretically detected is as follows Figure 8 As shown. Figure 8 It can be seen that the overall error of the detection of the absolute arrival time of the first arrival of the present invention is small, within ±3ms, and the effect is very ideal.

[0075] S6: In order to compare with the conventional method, the microseismic records after time difference correction are directly stacked without similar amplitude cross-correlation processing. The reference traces are as follows: Fig. 9 As shown in (a). Due to the polarity reversal and strong noise interference of microseismic events, the positive and negative amplitude values ​​cancel each other out after superposition, and the similar advantages of microseismic events in waveform, frequency, phase, and amplitude value are not brought into play. The signal-to-noise ratio of the reference track is low, and the microseismic events are not obvious, making it difficult to effectively identify. The energy ratio curve of the short-time window and long-time window of the reference track obtained by the energy ratio algorithm of the short-time window and the long-time window is shown in Fig. 9 As shown in (b), the extreme value near the first arrival time is not obvious, and it is difficult to automatically detect the first arrival time position.

[0076] It can be seen that the present invention can more accurately obtain the difference in microseismic arrival time between two microseismic recording tracks, and the obtained reference track has an obvious microseismic signal peak. The energy ratio of the weighted short-time window and the long-time window can then more accurately detect the first arrival time of the reference track. However, the conventional method reduces the signal-to-noise ratio of the reference track after waveform superposition due to the first arrival polarity inversion, and it is also difficult to detect the first arrival time of the microseismic event using the energy ratio method of the weighted short-time window and the long-time window.

[0077] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A method for detecting the first arrival time of ground microseismic monitoring data, characterized in that: It includes the following steps: Acquire ground microseismic monitoring data and use time-frequency domain filtering method to reduce noise; select a time window and extract each microseismic record within the time window; calculate the time difference between every two microseismic data from four aspects: waveform, frequency, phase, and amplitude value; Calculate the relative arrival time of each microseismic event; perform time difference correction on each microseismic record; and obtain a reference trace using the microseismic record after time difference correction; Detect the absolute arrival time of microseismic events in the reference channel; calculate the absolute arrival time of the first arrival of microseismic events in each channel; The time difference between every two microseismic data is calculated using the following formula from four aspects: waveform, frequency, phase, and amplitude: Where W is the length of the microseismic record, x i (w) and x j (w) are the similar amplitudes of the i-th and j-th waveform records at the sampling point w. The similar amplitudes are composed of amplitude value, main frequency value and instantaneous phase. and x i (w) and x j The first-order derivative formula of (w) increases the frequency band of the data while ensuring that the phase remains unchanged, and the calculated time difference is more precise. Specifically, numerical difference calculation is used, Δt i,j is the arrival time difference between the i-th and j-th microseismic traces; According to the arrival time difference Δt of microseismic events recorded between two channels i,j =t i -t j , calculate the relative arrival time of each microseismic event; If there are N microseismic records involved in the calculation, the following equation is obtained: At=Δt (2) Where A is a sparse coefficient matrix composed of 0, 1 and -1, Δt is the arrival time difference vector, and t is the relative arrival time vector. The least squares solution of formula (2) is obtained by the following formula: t=(A T A) -1 A T Δt (3) Among them, A is a sparse coefficient matrix composed of 0, 1 and -1, A T is the transposed matrix of A, Δt is the arrival time difference vector, and t is the relative arrival time vector; The zero-delay similar amplitude cross-correlation calculation of adjacent traces of the time-difference corrected microseismic records is performed to take advantage of the similar waveforms of microseismic events, and the zero-delay similar amplitude cross-correlation results are superimposed. The adverse effects of noise are suppressed through superposition processing to obtain reference traces. Add the relative arrival time of each channel to the absolute arrival time of the reference channel to get the absolute arrival time of each channel, that is: T k =T0+t k (7) Where T0 is the first arrival time of the superimposed reference trace, t k is the relative arrival time of each channel, T k It is the absolute arrival time of each path.

2. The method according to claim 1, characterized in that: The calculation formula for the cross-correlation and superposition of zero-delay similar amplitudes of adjacent channels is: Where X is the adjacent microseismic waveform record x after time difference correction i With x i+1 The reference trace record is a superposition of the zero-delay similar amplitude cross-correlation function, N is the number of microseismic record traces, W is the length of the microseismic record, w is the w-th sampling point in the microseismic waveform record, It is the similar amplitude of the recording track i at the sampling point w after time difference correction. The similar amplitude is composed of amplitude value, main frequency value and instantaneous phase, which ensures the accuracy of calculation from three dimensions.

3. The method according to claim 1, characterized in that: The weighted long-short time window energy ratio method is used to detect the first arrival absolute time T0 of the reference track; the calculation formula is: Where X(w) is the adjacent microseismic record x after time difference correction. i With x i+1 The zero-delay similar amplitude cross-correlation superposition trace record, w is the sample point number, W L and W S Represent the lengths of the long window and the short window respectively. and are the mean values ​​of similar amplitudes of microseismic records in the short time window and the long time window, respectively; m is the sampling point number of the microseismic record; R(m) is the weighted energy ratio of the long and short time windows; the time corresponding to the maximum value of R(m) is the absolute arrival time T0 of the reference trace.

4. The method according to claim 3, characterized in that: The weighted long-short time window energy ratio algorithm is used to detect the first arrival time of the reference trace. When the energy ratio R(m) of the weighted short-time window to the long-time window exceeds a given threshold, it means that the first arrival time of the reference trace has been detected. The threshold is calculated comprehensively based on the microseismic waveform records and can reflect the proportion of effective signals and noise interference in the data.

Images