A method for enhancing the response characteristics of small faults in coal and rock by integrating wavelet transform

Abstract

The present invention discloses a method for enhancing the response characteristics of small coal-rock faults by fusing wavelet transforms. The method belongs to a seismic signal processing and interpretation technology, which includes obtaining seismic data and performing certain preprocessing; using discrete wavelet transforms to perform multi-resolution decomposition of the seismic signals; screening multi-level detailed signals and selecting detailed signals in the main frequency band of seismic excitation as characteristic signals; adding the characteristic signals to low-frequency approximate signals to reconstruct time-domain seismic data; and extracting response characteristic attributes from the reconstructed seismic data. By utilizing multi-resolution analysis of wavelet transforms, the present invention can directly highlight the main frequency information of seismic data, suppress data noise, and thereby enhance the response characteristics of small faults, improve the accuracy of small fault feature identification, and thus make coal mining safer and more efficient.

Description

Technical Field

[0001] The present invention relates to a seismic signal processing and interpretation technology, and more specifically, to a method for enhancing the response characteristics of small coal rock faults by fusing wavelet transform. Background Art

[0002] With the rapid development of coal mining technology, coal mining companies are now demanding higher accuracy in fault identification. To further improve coal mining safety and increase the efficiency of coal resource extraction, it is necessary to further enhance fault detection accuracy and proactively prevent safety issues, especially when identifying small faults. Currently, the most common method for fault detection is to obtain high-precision seismic data through seismic exploration, and then identify faults using interpretation techniques. In theory, high-precision three-dimensional seismic exploration technology can be used to identify small faults in coal mines. However, in actual production, we have found that due to the influence of complex geological structures, the characteristics of many small faults are obscured by noise and the strong characteristics of larger faults. As a result, small faults cannot be accurately identified, which in turn affects the safe and efficient mining of coal mines. Therefore, it is necessary to further research and improve the processing and interpretation methods of seismic data obtained from three-dimensional exploration. Summary of the Invention

[0003] One of the purposes of the present invention is to address the above-mentioned shortcomings and provide a method for enhancing the response characteristics of small faults in coal and rock by fusing wavelet transform, in order to solve the technical problems in the existing technology that are affected by complex geological structures, the characteristics of many small faults will be masked by noise and the strong characteristics of large faults, resulting in the inability to accurately identify small faults, affecting the efficient mining of coal mines, etc.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0005] The present invention provides a method for enhancing the response characteristics of small coal-rock faults by fusing wavelet transform, the method comprising the following steps:

[0006] Step A: Obtain interpretable seismic data obtained from seismic exploration.

[0007] Step B: Use discrete wavelet transform to perform multi-resolution analysis on the seismic signal from time domain to frequency domain to obtain detailed signals and approximate signals in different frequency bands; the multi-resolution analysis from time domain to frequency domain is to perform continuous high-pass and low-pass filtering on the time domain signal, thereby decomposing the time domain signal into different frequency bands.

[0008] Step C: Select the detailed signal in the frequency band where the earthquake main frequency is located from the detailed signals and approximate signals in different frequency bands, and combine it with the low-frequency approximate signal to form an earthquake characteristic signal in the frequency domain.

[0009] Step D: Reconstruct the seismic characteristic signal in the frequency domain into new seismic data in the time domain through inverse wavelet transform. The formula of the inverse wavelet transform is:

[0010]

[0011] Where W(a, b) represents the wavelet coefficients, a and b represent the scale parameter and translation parameter, respectively. f(t) is the original signal, ψ(t) is the wavelet basis function, and dt represents the integral differential over the time variable t, i.e., the integral over time. The principle of wavelet transform can be explained by its formula. For the inverse transform, the formula can be expressed as:

[0012]

[0013] Where f(t) is the original signal in time. W(a, b) is the coefficient obtained by wavelet transform, which depends on the scale parameter a and the translation parameter b. ψ(t) is the wavelet basis function. ψ is a normalization constant related to the wavelet basis function ψ, which ensures that the wavelet transform is reversible. represents the integration over all possible scales a and translations b. is the scale factor, which is used to adjust the expansion and contraction of the wavelet function. da and db are integral differentials, corresponding to the integral of the scale parameter and location parameter, respectively.

[0014] Step F: Extracting fault response characteristic attributes from the new seismic data; the fault response characteristics of the new seismic data are characterized by calculating the variances using the following formula.

[0015]

[0016] In the above formula, δ 2 The variance value of the current sampling point, t is the time position of the current sampling point, j is the spatial position of the current sampling point, w j-t is the triangle weight function, w ij is the weight of the jth sampling point in the window, i is the index of the sampling point, x ij is the i-th amplitude value of the j-th sampling point, is the average amplitude value of the jth sampling point, L is the length of the time window, and I is the amplitude value of each sampling point. is the amplitude value x at the jth sampling point in the window ij The square of the corresponding weight w ij .

[0017] Preferably, a further technical solution is: in step A, the interpretable seismic data obtained from seismic exploration are also preprocessed, and the preprocessing is to perform high-resolution prestack migration seismic data processing on high-density seismic single-shot records collected in the field, so as to obtain processed interpretable seismic data; the high-resolution prestack migration seismic data processing is to enhance the signal-to-noise ratio and resolution of the seismic signal by signal processing, and eliminate interference factors other than geological factors in the seismic signal.

[0018] A further technical solution is that the discrete wavelet of the discrete wavelet transform in the above step B is a suitable mother wavelet, and the maximum degree of the discrete wavelet transform decomposition is proportional to the length of the seismic signal.

[0019] A further technical solution is that the length of the seismic signal in the above step B is a power of 2.

[0020] A further technical solution is: the seismic characteristic signal in the frequency domain in the above step C is reconstructed into a new detailed signal and a new approximate signal of the new seismic data through the above inverse wavelet transform formula.

[0021] A further technical solution is that the new detailed signal is a detailed signal of a wavelet frequency band that is consistent with the main frequency of the earthquake, and the new approximate signal is an approximate signal at the maximum scale.

[0022] A further technical solution is: the variance calculation is centered on the sampling point, and sampling points of 1 / 2 time window length are selected in all directions to calculate the sum of the variances of the amplitude value of the sampling point and the amplitude mean of the selected sample points.

[0023] A further technical solution is: the variance value in step F is obtained by multiplying the sum of the variances by the above weight function and then normalizing it.

[0024] Compared with the prior art, the beneficial effects of the present invention are at least one of the following: using wavelet transform to transform seismic data from the time domain to the frequency domain, and taking advantage of the multi-resolution analysis of wavelet transform to retain the time information of the signal while obtaining the frequency characteristics.

[0025] By integrating and deleting frequency information, the main frequency information of the earthquake is retained, thereby highlighting the characteristics of small faults and filtering out noise and other interference signals.

[0026] By amplifying the characteristics of small faults in the frequency domain and suppressing the characteristics of noise in the frequency domain, the characteristics of small faults can be highlighted, and then combined with seismic attribute display to achieve the purpose of enhancing the characteristic response of small faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The figure is a flow chart of a method according to an embodiment of the present invention.

[0028] Figure 2 The original seismic data before partial feature enhancement in one embodiment of the present invention.

[0029] Figure 3 for Figure 2 Seismic data after multiresolution analysis using discrete wavelet transform.

[0030] Figure 4 This is seismic data after feature enhancement after main frequency extraction and inverse wavelet transformation reconstruction in one embodiment of the present invention.

[0031] Figure 5 for Figure 4 Variance attribute extraction plot of . DETAILED DESCRIPTION

[0032] The present invention will be further described below with reference to the accompanying drawings.

[0033] One embodiment of the present invention is a method for enhancing the response characteristics of small faults in coal and rock by integrating wavelet transform. The general process of the method is as follows: Figure 1 As shown, the specific implementation steps are as follows:

[0034] P1: Obtain seismic data and perform certain preprocessing.

[0035] High-density single-shot seismic records collected in the field are processed with high-resolution prestack migration seismic data to obtain interpretable seismic data, such as Figure 2 As shown,

[0036] High-resolution prestack migration seismic data processing, as described above, involves enhancing the signal-to-noise ratio and resolution of seismic signals through a series of signal processing methods. Through basic processing methods such as static correction, surface consistency analysis, dynamic correction, and amplitude compensation, interference from the signal other than geological factors is minimized.

[0037] P2: Use discrete wavelet transform to perform multi-resolution decomposition of seismic signals, as follows: Figure 3 shown.

[0038] In the time domain, we can use different mathematical calculations to obtain phase differences and relationships between data, thereby highlighting the different properties and characteristics of geological bodies. The data obtained are collectively referred to as seismic attributes, which further characterize the response characteristics of the fault.

[0039] The purpose of the wavelet transform is to represent a changing signal (i.e., a time or position with a certain value) in order to reorganize the information contained in the signal and reveal some attributes that are not clearly displayed in the initial representation. It is worth noting that after the wavelet transform decomposes the time signal into a frequency signal, we can observe the dominant frequency of the signal in the time-frequency space and how the dominant frequency changes over time. This makes it very suitable for approximating data with obvious discontinuities. Within the variable time window of the wavelet transform, we can extract information from different frequency bands simultaneously according to different needs.

[0040] The discrete wavelet transform analyzes the signal in different frequency bands at different resolutions by decomposing it into approximate information and detailed information. The discrete wavelet transform mainly consists of a scaling function and a wavelet function, which are associated with a low-pass filter and a high-pass filter, respectively. The time domain signal can be decomposed into different frequency bands by simply performing continuous high-pass and low-pass filtering on the time domain signal. The original signal x[n] first passes through a half-band high-pass filter g[n] and a low-pass filter h[n]. After filtering, half of the samples can be eliminated according to the Nyquist criterion, and the highest frequency of the signal is π / 2 radians. Therefore, the signal can be sampled twice by simply discarding all other samples. This constitutes a level of decomposition, which can be expressed mathematically as follows:

[0041]

[0042] Where y high[ k] and y low [k] are the outputs of the high-pass filter g[2k-n] and the low-pass filter h[2k-n] after downsampling. x[n] is the discrete time series of the original signal, where n represents the time point. k represents the output sequence y high [k] and y low [k] The time index.

[0043] After decomposition in this way, the current signal has only half the number of samples as the original signal, so the time resolution of the signal is halved, but the frequency resolution is doubled. Because the current frequency band now spans only half of the original frequency band, the frequency uncertainty is effectively reduced by half. This process is also called subband coding, and it can be repeated for further decomposition.

[0044] The dominant frequencies of the original signal will appear as high amplitudes in the DWT signal containing these specific frequencies, unlike the Fourier transform, in that the temporal locality of these frequencies is not lost. However, the resolution of this temporal locality depends on the level at which they occur.

[0045] P3: Filter the multi-level detailed signals and select the detailed signals in the main frequency band of earthquake excitation as the characteristic signals.

[0046] During the DWT decomposition process, the number of samples decreases as the frequency decreases. The time resolution of the signal decreases as the frequency decreases. At the same time, the frequency interval decreases as the frequency decreases, so the frequency resolution of the signal increases. Of course, due to the continuous two-time downsampling, the length of the signal must be a power of 2, or at least a multiple of a power of 2, in order for this scheme to be effective. At the same time, the length of the signal also determines the number of layers into which the signal can be decomposed.

[0047] Assume that the original signal x[n] has 32 samples spanning the frequency band from 0 to 32 MHz. The original signal passes through a high-pass filter g[n] and a low-pass filter h[n], and its output is downsampled twice. The output of the high-pass filter is the first-stage DWT coefficient, with 16 samples (thus half the time resolution) representing the signal in the frequency range of 16 to 32 MHz (thus twice the frequency resolution). The output of the low-pass filter also has 16 samples, but it represents the other half of the signal in the frequency range of 0 to 16 MHz. The signal is then further decomposed by passing it through the same low-pass and high-pass filters. The output of the second high-pass filter, after downsampling, is the second-stage DWT coefficient, with 8 samples representing the signal in the frequency range of 8 to 16 MHz. The output of the second low-pass filter, after downsampling, also has 8 samples, again representing the other half of the signal in the frequency range of 0 to 8 MHz. This signal now has half the time resolution of the first-stage signal, but twice the frequency resolution. This means that compared to the original signal, the time resolution is reduced by a quarter, while the frequency resolution is increased by a quarter. The low-pass filter output is then further decomposed again. This process continues until only one DWT coefficient can be calculated at the 5th layer.

[0048] P4: Add the characteristic signal to the low-frequency approximate signal to reconstruct the time domain seismic data, such as Figure 4 shown.

[0049] An important property of the discrete wavelet transform is that the high-pass and low-pass filters are not independent of each other, but can be related by the formula:

[0050] g[L-1-n]=(-1) n ·h[n]

[0051] Where g[n] is a high-pass filter, h[n] is a low-pass filter, L is the filter length (in samples), and n represents the time step. These two filters are inverse versions of each other with alternating odd indices, and the low-pass to high-pass transition is represented by (-1) n The two filters and the downsampling operation have the same formula as above:

[0052]

[0053] Reconstruction is very easy in this case because the half-band filters form an orthonormal basis. The above steps are performed in reverse order. The signal at each level is upsampled twice and passed through the synthesis filter g′ [n] and h′ [n] (high pass and low pass respectively) and then add them together. Therefore, the reconstruction formula for each layer is:

[0054]

[0055] P5: Extract response feature attributes from reconstructed seismic data, such as Figure 5 shown.

[0056] There are many ways to express the fault response characteristics of seismic data. The most common and widely accepted method is to calculate the variance volume to characterize the fault response characteristics. The larger the variance value of a point, the greater the difference between it and the surrounding area, and the higher the possibility of it being a fault. Therefore, in order to characterize the fault response characteristics of the reconstructed data, it is necessary to quantify it by calculating the variance. The calculation formula is as follows:

[0057]

[0058] Where, δ 2 The variance value of the current sampling point, t is the time position of the current sampling point, j is the spatial position of the current sampling point, w j-t is the triangle weight function, w ij is the weight of the jth sampling point in the window, i is the index of the sampling point, x ij is the i-th amplitude value of the j-th sampling point, is the average amplitude value of the jth sampling point, L is the length of the time window, and I is the amplitude value of each sampling point. is the amplitude value x at the jth sampling point in the window ij The square of the corresponding weight w ij .

[0059] It can be seen from the above embodiments that the present invention can directly highlight the main frequency information of seismic data and suppress data noise by utilizing multi-resolution analysis of wavelet transform, thereby enhancing the response characteristics of small faults and improving the accuracy of small fault feature identification, thereby making coal mining safer and more efficient.

[0060] In addition to the above, it should be noted that references to "one embodiment," "another embodiment," "an embodiment," and the like in this specification refer to specific features, structures, or characteristics described in conjunction with that embodiment as included in at least one embodiment generally described in this application. The appearance of the same expression in multiple places in the specification does not necessarily refer to the same embodiment. Furthermore, when a specific feature, structure, or characteristic is described in conjunction with any embodiment, it is intended that such feature, structure, or characteristic, when implemented in conjunction with other embodiments, also falls within the scope of the present invention.

[0061] Although the present invention has been described herein with reference to a number of illustrative embodiments thereof, it will be understood that numerous other modifications and implementations may be devised by those skilled in the art that fall within the scope and spirit of the principles disclosed herein. More specifically, within the scope of the present disclosure, the drawings, and the claims, numerous variations and modifications may be made to the components and / or layout of the subject combination arrangement. In addition to variations and modifications to the components and / or layout, other uses will also be apparent to those skilled in the art.

Claims

1. A method for enhancing the response characteristics of small faults in coal and rock by integrating wavelet transform, characterized in that The method comprises the following steps: Obtain interpretable seismic data from seismic exploration; Using discrete wavelet transform to perform multi-resolution analysis on the seismic data from the time domain to the frequency domain, thereby obtaining detailed signals and approximate signals in different frequency bands; the multi-resolution analysis from the time domain to the frequency domain is to perform continuous high-pass and low-pass filtering on the time domain signal, thereby decomposing the time domain signal into different frequency bands; Among the detailed signals and approximate signals in the different frequency bands, the detailed signal in the frequency band where the main frequency of the earthquake is located is selected, and combined with the low-frequency approximate signal to form a frequency-domain earthquake characteristic signal; Reconstructing the seismic characteristic signal in the frequency domain into new seismic data in the time domain through inverse wavelet transformation; Extracting fault response characteristic attributes from the new seismic data; the fault response characteristics of the new seismic data are characterized by calculating the variances of the following formulas respectively; In the above formula, δ 2 The variance value of the current sampling point, t is the time position of the current sampling point, j is the spatial position of the current sampling point, w j-t is the triangle weight function, w ij is the weight of the jth sampling point in the window, i is the index of the sampling point, x ij is the i-th amplitude value of the j-th sampling point, is the average amplitude value of the jth sampling point, L is the length of the time window, and I is the amplitude value of each sampling point. is the amplitude value x at the jth sampling point in the window ij The square of the corresponding weight w ij .

2. The method for enhancing the response characteristics of small coal-rock faults by integrating wavelet transform according to claim 1, characterized in that: The method also includes preprocessing the interpretable seismic data obtained from seismic exploration, wherein the preprocessing is to perform high-resolution prestack migration seismic data processing on high-density seismic single-shot records collected in the field, thereby obtaining processed interpretable seismic data; the high-resolution prestack migration seismic data processing is to enhance the signal-to-noise ratio and resolution of the seismic data signal through signal processing, and eliminate interference factors other than geological factors in the seismic data signal.

3. The method for enhancing the response characteristics of small coal-rock faults by fusing wavelet transform according to claim 1, characterized in that: The discrete wavelet of the discrete wavelet transform is a suitable mother wavelet, and the maximum degree of the discrete wavelet transform decomposition is proportional to the length of the seismic data signal.

4. The method for enhancing the response characteristics of small coal-rock faults by fusing wavelet transform according to claim 3, characterized in that: The length of the seismic data signal is a power of 2.

5. The method for enhancing the response characteristics of small coal-rock faults by fusing wavelet transform according to claim 1, characterized in that: The seismic characteristic signal in the frequency domain is reconstructed into a new detailed signal and a new approximate signal of new seismic data through an inverse wavelet transform formula.

6. The method for enhancing the response characteristics of small coal-rock faults by fusing wavelet transform according to claim 5, characterized in that: The new detailed signal is a detailed signal of a wavelet frequency band that is consistent with the main frequency of the earthquake, and the new approximate signal is an approximate signal at the maximum scale.

7. The method for enhancing the response characteristics of small coal-rock faults by fusing wavelet transform according to claim 1, characterized in that: The variance calculation is centered on the sampling point, and the number of sample points with a length of 1 / 2 time window are selected in all directions. The sum of the variances of the amplitude value of the sampling point and the amplitude mean of the selected sample points is calculated.

8. The method for enhancing the response characteristics of small coal-rock faults by fusing wavelet transform according to claim 1 or 7, characterized in that: The variance value is obtained by multiplying the variance sum by the above weight function and then normalizing it.

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