Micro-seismic signal first arrival pickup method based on sparse generalized S transformation

Abstract

The invention belongs to the technical field of geophysical signal processing and mine micro-seismic monitoring, and relates to a micro-seismic signal first arrival pickup method based on sparse generalized S transformation. Aiming at the problems of low signal-to-noise ratio, complex noise type, weak first-arrival wave energy and difficulty in accurately picking up first arrival of mine micro-seismic signals, the method comprises the following steps of: firstly, performing time-frequency analysis on the micro-seismic signals by utilizing sparse generalized S-transform, then extracting a maximum energy point in a time-frequency domain, performing connected domain decomposition on a time-frequency spectrum, screening a time-frequency connected region containing main energy, and finally extracting the maximum energy point from the time-frequency domain; and irrelevant noise interference is suppressed. And on the basis, performing energy superposition and normalization processing on the main energy time-frequency block in a constraint frequency band, and combining an energy peak position and an energy transition characteristic in a time window to realize accurate judgment of the arrival time of the microseismic signal first arrival wave.

Description

Technical Field

[0001] This invention belongs to the field of geophysical signal processing and mine microseismic monitoring technology, and relates to a method for picking up the first arrival of microseismic signals based on sparse generalized S-transform, which belongs to the interdisciplinary field of time-frequency analysis and signal analysis. Background Technology

[0002] Microseismic monitoring in mines is a crucial technical means for early warning of dynamic disasters and assessment of rock mass stability in coal mines. The arrival time of the first arrival wave of microseismic signals is a key parameter in subsequent source location and rupture mechanism analysis, so its acquisition accuracy directly affects the reliability of subsequent analysis results. However, due to the complex environment of underground mines, which is often affected by various types of noise, such as random noise, mechanical interference, and single-frequency interference, the signal-to-noise ratio of microseismic signals is usually low. Currently used first arrival acquisition methods mainly include STA / LTA and AIC methods, but these conventional methods are sensitive to noise, have poor stability, and are difficult to apply to complex underground mine environments. Therefore, there is an urgent need for a robust microseismic signal first arrival acquisition method that can be stably applied to complex underground mine environments. The sparse generalized S-variance can optimize time-frequency resolution, accurately characterize first arrival features, and has strong noise resistance, adapting to complex monitoring environments. This method, combined with a microseismic signal first arrival acquisition method based on energy structure characteristics, can improve the acquisition accuracy and stability under complex noise environments. Summary of the Invention

[0003] The purpose of this invention is to overcome the problems of low accuracy and poor noise resistance in the prior art for picking up the first arrival time of microseismic signals. It proposes a method for picking up the first arrival time of microseismic signals based on sparse generalized S-transform, achieving high-precision, automated, and robust picking of the arrival time of the first arrival wave of microseismic signals. This invention includes the following steps:

[0004] 1. A method for picking up the first arrival of microseismic signals based on sparse generalized S-transform, characterized by the following specific steps:

[0005] S1. Import the microseismic signal x(t), and perform time-frequency analysis on it using the sparse generalized S-transform algorithm to obtain the high-resolution time spectrum TFR(f,t) of the microseismic signal. The specific formula is as follows:

[0006]

[0007] In the formula, t is time, f is frequency, TFR(f,t) is the time spectrum of the signal obtained by using the sparse generalized S-transform, and SGST[﹒ ] represents the sparse generalized S-transform of the data;

[0008] S2. Find the maximum energy point in the spectrum of the sparse generalized S-transformation of the signal. The specific formula is as follows:

[0009]

[0010] In the formula, f0 is the frequency corresponding to the maximum energy point, and t0 is the corresponding time. (﹒) indicates searching for the maximum value;

[0011] S3. Perform connected component segmentation on the time spectrum of the signal, using the following formula:

[0012]

[0013] In the formula, B(f,t) is the result of connected component partitioning of the signal's time spectrum. This means adding all C values ​​from k=1 to k. k Subsets merged, C k This represents the k-th time-frequency connected region;

[0014] S4. Then select the connected component containing the maximum energy, using the following formula:

[0015]

[0016] In the formula, C * This represents the connected component containing the maximum energy. Indicates that it belongs to the symbol;

[0017] S5. Preserve the connected component containing the maximum energy, as shown in the following formula:

[0018]

[0019] In the formula, The time-frequency block of the main energy. This indicates that it is not a symbol;

[0020] S6. Square the time-frequency block within a 20Hz frequency band before and after the main energy frequency, and then normalize the resulting energy. The specific formula is as follows:

[0021]

[0022]

[0023] In the formula, f1 is the lower limit of the frequency band, which is the first 10 Hz of the main energy; f2 is the upper limit of the frequency band, which is the last 10 Hz of the main energy; and E(t) is the time-frequency block superposition energy of the main energy under the frequency band constraint. This represents all frequencies f in the range from f1 to f2. Stacked together, Let E(t) be the energy after normalization, and max(﹒) represent finding the maximum value;

[0024] S7. Determine the location of the moment of maximum energy using the following formula:

[0025]

[0026] In the formula, t max Normalized energy curve The peak time corresponds to;

[0027] S8. Set the time window length Δt, and then find the position of the maximum energy transition within the window. The specific formula is as follows:

[0028]

[0029]

[0030]

[0031] In the formula, Δt is the time window length, typically set to 0.2 s, τ is the time interval, ΔE(t) is the first-order energy difference catastrophe operator, and t p The time corresponding to the maximum energy transition within the window;

[0032] S9. Find the time point t at which the signal energy first appears in the connected component containing the maximum energy. left If the time difference between the time point corresponding to the first occurrence of signal energy and the time corresponding to the maximum energy transition within the window is greater than ε, then t is calculated. left With t p average t final The specific formula is as follows:

[0033]

[0034]

[0035]

[0036]

[0037]

[0038] In the formula, i is the frequency index, j is the time index, and min{﹒} represents finding the minimum value. This indicates the existence of some i such that (i,j) belongs to set P, where P is the set of coordinates of all non-zero time-frequency points, and j left For the leftmost time index, t left The time point at which the first signal energy appears is defined, ε is the time difference between the two methods, typically set to 0.02 s, and t is the time point at which the signal energy first appears. final This represents the arrival time of the first arrival wave of the microseismic signal.

[0039] The microseismic signal first arrival pickup method based on sparse generalized S-transform of the present invention has the following advantages:

[0040] (1) The sparse constraint makes the energy of the first arrival wave more prominent in the time and frequency domain, and can still stably identify the first arrival even under low signal-to-noise ratio conditions;

[0041] (2) The original "point judgment mode" for initial arrival picking has been upgraded to "structure judgment mode". Since the original traditional method only relies on a single feature point, the present invention makes judgment based on the structural features of the time-frequency connected domain, which is more in line with the initial arrival picking of microseismic signals in the complex environment of mines.

[0042] (3) By combining the determination of the maximum energy point, the energy transition point and the time point when the first signal energy appears, misjudgment caused by noise mutation or subsequent strong waves can be effectively avoided. Attached Figure Description

[0043] To more clearly illustrate the technical solutions and embodiments of the present invention, the accompanying drawings used in the technical description and embodiments are briefly introduced below. The drawings are provided to further understand the embodiments of the present invention and constitute a part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention.

[0044] Figure 1 This is a flowchart of the microseismic signal first arrival picking method based on sparse generalized S-transform;

[0045] Figure 2 This is a schematic diagram of the initial arrival pickup of the synthesized signal without noise.

[0046] Figure 3 This is a schematic diagram of the initial arrival pickup of a synthesized signal with a signal-to-noise ratio of -5dB. Detailed Implementation

[0047] To more clearly illustrate the technical advantages of this invention, taking synthesized microseismic signals as an example, and in conjunction with the accompanying drawings, the embodiments of this invention will be further described in detail. The specific implementation methods of this invention are as follows:

[0048] S1. Import the microseismic signal x(t), and perform time-frequency analysis on it using the sparse generalized S-transform algorithm to obtain the high-resolution time spectrum TFR(f,t) of the microseismic signal, such as... Figure 2 (b-1);

[0049] S2. Find the maximum energy point in the spectrum of the sparse generalized S-transformation of the signal;

[0050] S3. Perform connected component segmentation on the time spectrum of the signal;

[0051] S4. Then select the connected component containing the maximum energy;

[0052] S5. Preserve the connected component containing the maximum energy, such as Figure 2 (c-1);

[0053] S6. Square the time-frequency block under the frequency band constraint of 20Hz before and after the frequency of the main energy, and then normalize the obtained energy.

[0054] S7. Determine the location of the moment when energy is at its maximum;

[0055] S8. Set the time window length Δt, and then find the position of the maximum energy transition within the window;

[0056] S9. Find the time point t at which the signal energy first appears in the connected component containing the maximum energy. left If the time difference between the time point corresponding to the first occurrence of signal energy and the time corresponding to the maximum energy transition within the window is greater than ε, then t is calculated. left With t p average t final ,like Figure 2 (c-2).

[0057] Implementation examples of the present invention:

[0058] Figure 1 This is a flowchart of the microseismic signal first arrival picking method based on sparse generalized S-transform;

[0059] Figure 2 The diagrams show the initial arrival pickup of the synthesized signal without noise. Figure (a-1) is the time-domain diagram of the noiseless signal, showing a time range of 0 to 3 seconds with clear initial arrivals. Figure (a-2) is the frequency-domain diagram of the noiseless signal. Figure (b-1) is the time-spectrum diagram of the noiseless signal, showing clear energy distribution and concentrated main energy. Figure (b-2) is a three-dimensional time-spectrum diagram of the noiseless signal, showing that the main energy is closely connected to other energies (including the initial arrival energy). Figure (c-1) is the time-spectrum diagram retaining the connected domain containing the maximum energy, showing that the time-frequency block from the initial arrival to the maximum energy is retained, and other interfering energies have been removed. Figure (c-2) is the initial arrival pickup diagram of the noiseless signal, showing that this method picks up 813 initial arrival points in 1.6260 seconds with zero error, indicating accurate initial arrival pickup.

[0060] Figure 3The first arrival images are shown for a synthesized signal with a signal-to-noise ratio (SNR) of -5dB. Image (a-1) is the time-domain plot of the synthesized signal with an SNR of -5dB. As can be seen from the image, the signal is obscured by noise, and the first arrival is unclear. Image (a-2) is the frequency-domain plot of the synthesized signal with an SNR of -5dB. Image (b-1) is the time-spectrum plot of the synthesized signal with an SNR of -5dB. As can be seen from the image, the time-spectrum has a cluttered background, and the area near the main energy level is obscured by noise. Image (b-2) is the first arrival image for a synthesized signal with an SNR of -5dB. The three-dimensional time-frequency spectrum of the synthesized signal with a signal-to-noise ratio of dB is shown in Figure (c-1), which is the time-frequency spectrum of the connected region where the maximum energy is retained. As can be seen from the figure, the method retains the time-frequency block from the first arrival to the main energy to the greatest extent. Figure (c-2) is the first arrival picking diagram with a signal-to-noise ratio of -5dB. As can be seen from the figure, the method picks up 812 first arrival points in 1.6240s with an error of only 0.123%, proving that the method can accurately pick up the first arrival even under the influence of significant noise.

[0061] The above embodiments are only used to illustrate the present invention. The implementation steps of the method can be varied. Any equivalent transformations and improvements made on the basis of the technical solution of the present invention should not be excluded from the protection scope of the present invention.

Claims

1. A method for picking up the first arrival of microseismic signals based on sparse generalized S-transform, characterized in that... The following specific steps are adopted: S1. Import the microseismic signal x(t), and perform time-frequency analysis on it using the sparse generalized S-transform algorithm to obtain the high-resolution time spectrum TFR(f,t) of the microseismic signal. The specific formula is as follows: ； In the formula, t is time, f is frequency, TFR(f,t) is the time spectrum of the signal obtained by using the sparse generalized S-transform, and SGST[﹒ ] represents the sparse generalized S-transform of the data; S2. Find the maximum energy point in the spectrum of the sparse generalized S-transformation of the signal. The specific formula is as follows: ； In the formula, f0 is the frequency corresponding to the maximum energy point, and t0 is the corresponding time. (﹒) indicates searching for the maximum value; S3. Perform connected component segmentation on the time spectrum of the signal, using the following formula: ； In the formula, B(f,t) is the result of connected component partitioning of the signal's time spectrum. This means adding all C values ​​from k=1 to k. k Subsets merged, C k This represents the k-th time-frequency connected region; S4. Then select the connected component containing the maximum energy, using the following formula: ； In the formula, C * This represents the connected component containing the maximum energy. Indicates that it belongs to the symbol; S5. Preserve the connected component containing the maximum energy, as shown in the following formula: ； In the formula, The time-frequency block of the main energy. This indicates that it is not a symbol; S6. Square the time-frequency block within a 20Hz frequency band before and after the main energy frequency, and then normalize the resulting energy. The specific formula is as follows: ； ； In the formula, f1 is the lower limit of the frequency band, which is the first 10 Hz of the main energy; f2 is the upper limit of the frequency band, which is the last 10 Hz of the main energy; and E(t) is the time-frequency block superposition energy of the main energy under the frequency band constraint. This represents all frequencies f in the range from f1 to f2. Stacked together, Let E(t) be the energy after normalization, and max(﹒) represent finding the maximum value; S7. Determine the location of the moment of maximum energy using the following formula: ； In the formula, t max Normalized energy curve The peak time corresponds to; S8. Set the time window length Δt, and then find the position of the maximum energy transition within the window. The specific formula is as follows: ； ； ； In the formula, Δt is the time window length, typically set to 0.2 s, τ is the time interval, ΔE(t) is the first-order energy difference catastrophe operator, and t p The time corresponding to the maximum energy transition within the window; S9. Find the time point t at which the signal energy first appears in the connected component containing the maximum energy. left If the time difference between the time point corresponding to the first occurrence of signal energy and the time corresponding to the maximum energy transition within the window is greater than ε, then t is calculated. left With t p average t final The specific formula is as follows: ； ； ； ； ； In the formula, i is the frequency index, j is the time index, and min{﹒} represents finding the minimum value. This indicates the existence of some i such that (i,j) belongs to set P, where P is the set of coordinates of all non-zero time-frequency points, and j left For the leftmost time index, t left The time point at which the first signal energy appears is defined, ε is the time difference between the two methods, typically set to 0.02 s, and t is the time point at which the signal energy first appears. final This represents the arrival time of the first arrival wave of the microseismic signal.

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