A coal seam floor water-conducting fracture identification method based on microseismic Krauklis wave

Abstract

The application discloses a coal seam floor water-conducting crack identification method based on microseismic Krauklis waves, and belongs to the technical field of coal seam floor water damage detection. The method comprises the following steps: collecting microseismic original signals and performing segmented processing, sequentially performing continuous wavelet transform and time-frequency domain conversion on each signal segment to obtain a time-frequency spectrum; separating and extracting time-frequency features of Krauklis waves according to the time-frequency spectrum; determining an energy extreme point and a Krauklis wave energy packet arrival time corresponding to each frequency; then obtaining a dispersion curve by calculating the phase velocity of each frequency, and then calculating the slope of the dispersion curve at different positions; based on the propagation theory of Krauklis waves in a flat crack, a quantitative mapping model between the crack opening and the slope of the dispersion curve is established; the slope is substituted into the quantitative mapping model for inversion, so that the average opening of the water-conducting crack in the current detection area is accurately obtained, thereby providing data support for realizing accurate monitoring and early warning of water inrush from the coal seam floor in deep mining.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of coal seam floor water damage detection, and particularly relates to a coal seam floor water-conducting fracture identification method based on microseismic Krauklis waves. BACKGROUND

[0002] Coal seam floor water damage is a major safety threat faced by many coalfields with complex hydrogeological conditions. The core cause lies in the floor failure zone formed by the mining-induced fracture of the working face floor rock mass under the multi-field and multi-phase coupling engineering geological conditions of geological structure activation and intensified seepage field, which is easy to connect with the Ordovician limestone aquifer to form a high-pressure water inrush channel, and further cause heavy or severe water inrush and well flooding accidents. Therefore, accurate detection of the floor water-conducting fracture is the key to water damage prevention.

[0003] At present, the field mainly relies on microseismic monitoring methods, which can infer the crack propagation and water inrush channel incubation, but it is difficult to identify the size and water-bearing property of the channel. In recent years, it has been found that the dispersion characteristics of Krauklis waves (a special slow wave of crack medium response) have clear physical correlations with crack length, opening and fluid properties. However, under the coupling of coal measure strata structure and mining damage engineering geological conditions, the propagation characteristics of Krauklis waves generated by the working face floor rock crack and its multi-crack coupling response mechanism are unknown, which leads to the current inability to judge the opening of the coal seam floor water-conducting fracture through Krauklis waves.

[0004] Therefore, how to provide a new coal seam floor water-conducting fracture identification method, which can quantitatively judge the opening of the coal seam floor water-conducting fracture, so as to provide data support for the accurate monitoring and early warning of deep mining coal seam floor water inrush, is the research direction required by the application. SUMMARY

[0005] In view of the problems existing in the prior art, the application provides a coal seam floor water-conducting fracture identification method based on microseismic Krauklis waves, which establishes a mapping relationship between the opening difference of the coal seam floor water-conducting fracture and the dispersion curve of Krauklis waves, so as to accurately determine the opening of the coal seam floor water-conducting fracture according to the dispersion curve characteristics of Krauklis waves obtained after collection and extraction, thereby providing data support for the accurate monitoring and early warning of deep mining coal seam floor water inrush.

[0006] In order to achieve the above purpose, the technical scheme adopted by the application is as follows: a coal seam floor water-conducting fracture identification method based on microseismic Krauklis waves, comprising the following steps:

[0007] Step one, microseismic original signal collection: obtaining the microseismic original signal of the coal seam floor in a period of time.

[0008] Step two, time-frequency feature extraction of Krauklis wave: segment the collected microseismic original signal, and perform continuous wavelet transform and time-frequency domain conversion on each signal segment to obtain a time-frequency spectrum, and extract the time-frequency feature of Krauklis wave according to the time-frequency spectrum.

[0009] Step three, dispersion curve extraction: in the time-frequency feature of Krauklis wave, for each frequency, determine the energy extreme point and Krauklis wave energy packet arrival time corresponding to the frequency; then calculate the phase velocity of each frequency to obtain the dispersion curve.

[0010] Step four, calculate the slope of the dispersion curve: calculate the slope of the dispersion curve at different positions according to the phase velocity of each frequency;

[0011] Step five, establish a crack opening degree mapping model: based on the propagation theory of Krauklis wave in a flat crack, a quantitative mapping model between crack opening degree and slope of dispersion curve is established.

[0012] Step six, determine the average opening degree of the water-conducting crack in the coal seam floor: substitute the slope calculated in step four into the quantitative mapping model in step five to obtain the average opening degree of the water-conducting crack in the current detection area.

[0013] Further, the microseismic original signal in step one includes P wave, S wave and Krauklis wave radiated from the tip of the water-conducting crack.

[0014] Further, the step two is specifically: dividing the microseismic original signal into multiple signal segments, and processing each signal segment as follows:

[0015] S1, perform continuous wavelet transform on the signal segment:

[0016]

[0017] wherein, represents the result of continuous wavelet transform of the signal segment; s(t) represents the input signal; a represents a scale parameter (a>0) for controlling the stretching of the wavelet; represents a translation parameter for controlling the position of the wavelet on the time axis, thereby realizing local analysis of different time segments of the signal; represents a wavelet mother function, which needs to satisfy the admissibility condition: ; wherein, is the Fourier transform of the wavelet mother function, is the angular frequency of the wavelet mother function; represents a conjugate.

[0018] S2, convert the result of continuous wavelet transform of the signal segment into time-frequency domain:

[0019]

[0020] where F a represents the frequency corresponding to the scale parameter a, represents the sampling interval, F c represents the center frequency of the wavelet mother function; a smaller scale corresponds to a compressed wavelet, which is used to analyze the high-frequency components of the signal, and the time resolution is good; a larger scale corresponds to a stretched wavelet, which is used to analyze the low-frequency components of the signal, and the frequency resolution is good.

[0021] S3, selecting a wavelet mother function:

[0022]

[0023] where T represents the width of the Gaussian envelope, represents the center angular frequency of the wavelet mother function, , t is a time variable, and j is an imaginary unit; the time-frequency domain of each signal segment is obtained through the above processing, thereby obtaining a time-frequency spectrum of the microseismic original signal.

[0024] S4, time window extraction: in the time-frequency spectrum, according to the time-frequency characteristics of the evolution of the Krauklis wave energy group with time, a time window containing the Krauklis wave signal is intercepted, and the time-frequency feature extraction of the Krauklis wave is completed.

[0025] Further, the step three is specifically:

[0026] I. for each frequency f i in the Krauklis wave time-frequency characteristics , find the energy extreme point corresponding to the frequency in the time-frequency matrix:

[0027]

[0028] where, represents the time-frequency spectrum amplitude value at f i , time in the time-frequency matrix; represents the operation of finding the maximum value of the function; represents the time corresponding to the arrival time of the Krauklis wave energy group when the energy extreme point is located.

[0029] II. assuming that the offset distance between the detector and the seismic source is x, then the frequency f i corresponding phase velocity is:

[0030]

[0031] The dispersion curve v(f) is obtained by traversing all frequencies in the Krauklis wave time-frequency characteristics.

[0032] Further, the specific formula of the fourth step is:

[0033]

[0034] Wherein, k represents the slope of the current position of the dispersion curve, f represents the frequency value of the current position of the dispersion curve, and v represents the phase velocity corresponding to the frequency value f.

[0035] Further, the quantitative mapping model in the fifth step is:

[0036] The relationship formula of the quantitative mapping model of the dispersion curve slope extracted according to the plurality of crack openings is fitted:

[0037]

[0038] Wherein k represents the slope of the dispersion curve, and d represents the crack opening.

[0039] Compared with the prior art, the present application has the following advantages:

[0040] 1. Method innovation: the present application applies the dispersion characteristics of Krauklis wave to the opening identification of the coal seam floor water-conducting crack, specifically: the dispersion curve (the change relationship of phase velocity with frequency) of Krauklis wave is used to quantitatively invert the crack opening, so as to realize the quantitative estimation of the floor water-conducting crack opening, and overcome the limitation that the traditional method can only be qualitatively judged.

[0041] 2. Technical scheme innovation: the present application establishes a complete and operable technical process from the microseismic original signal to the crack opening, the Krauklis wave time-frequency signal is accurately extracted by using continuous wavelet transform and time-frequency domain conversion; then the dispersion curve is extracted by time-frequency analysis, the slopes at different positions of the dispersion curve are calculated, and the quantitative mapping model of the slope and the water-conducting crack opening is established to accurately obtain the crack opening condition, which converts the theoretical physical relationship into a field usable engineering technical scheme, and the scheme has wide applicability.

[0042] 3. Model innovation: the present application constructs the quantitative mapping model between the crack opening and the slope of the dispersion curve based on the dispersion characteristics of Krauklis wave, which makes it possible to directly evaluate the water passing capacity of the water-conducting channel by using geophysical observation data, and provides data support for the accurate evaluation and early warning of the floor water inrush risk. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 It is the overall flowchart of the present application.

[0044] Figure 2 is a time-frequency spectrogram in an embodiment of the present application.

[0045] Figure 3 is a dispersion curve in an embodiment of the present application.

[0046] Figure 4 is a quantitative mapping model diagram in an embodiment of the present application. DETAILED DESCRIPTION

[0047] The present application will be further described below.

[0048] As shown in Figure 1 , the present application comprises the following steps:

[0049] Step one, microseismic original signal collection: obtaining microseismic original signals in a period of time under the coal seam floor, including P wave, S wave and Krauklis wave radiated by water- conducted crack tip.

[0050] Step two, time-frequency feature extraction of Krauklis wave: segmenting the collected microseismic original signals, and sequentially performing continuous wavelet transform and time- frequency domain conversion on each signal segment to obtain a time-frequency spectrogram, and extracting the time- frequency features of Krauklis wave according to the time- frequency spectrogram, specifically: dividing the microseismic original signals into multiple signal segments, and performing the following processing on each signal segment:

[0051] S1, performing continuous wavelet transform on the signal segment:

[0052]

[0053] wherein, represents the result of continuous wavelet transform of the signal segment; s(t) represents the input signal; a represents a scale parameter (a>0) for controlling the stretching and shrinking of the wavelet; represents a translation parameter for controlling the position of the wavelet on the time axis, thereby realizing local analysis of different time periods of the signal; represents a wavelet mother function, which needs to satisfy the admissibility condition: ; wherein, is the Fourier transform of the wavelet mother function, is the angular frequency of the wavelet mother function; represents conjugate.

[0054] S2, converting the result of continuous wavelet transform of the signal segment into time- frequency domain:

[0055]

[0056] wherein, F a represents the frequency corresponding to the scale parameter a, denotes the sampling interval, F c denotes the center frequency of the mother wavelet; smaller scales correspond to compressed wavelets, which are used to analyze the high frequency components of the signal with good time resolution; larger scales correspond to stretched wavelets, which are used to analyze the low frequency components of the signal with good frequency resolution.

[0057] S3, selecting a wavelet mother function:

[0058]

[0059] wherein T denotes the width of the Gaussian envelope, denotes the center angular frequency of the wavelet mother function, , t is a time variable, and j is an imaginary unit; the time-frequency domain of each signal segment is obtained through the above processing, so that the time-frequency spectrum of the microseismic original signal is obtained as shown in Figure 2 .

[0060] S4, time window extraction: in the time-frequency spectrum, according to the time-frequency characteristics of the evolution of the Krauklis wave energy group with time, a time window containing the Krauklis wave signal is manually or automatically intercepted, and the time-frequency characteristics extraction of the Krauklis wave is completed.

[0061] Step three, dispersion curve extraction: in the time-frequency characteristics of the Krauklis wave, for each frequency, the corresponding energy extreme point and the Krauklis wave energy packet arrival time are determined; then the phase velocity of each frequency is calculated, so that the dispersion curve is obtained as shown in Figure 3 .

[0062] I. For each frequency f i in the time-frequency characteristics of the Krauklis wave, , the energy extreme point corresponding to the frequency in the time-frequency matrix is found:

[0063]

[0064] wherein, denotes the time-frequency spectrum amplitude value at the time i , and the time in the time-frequency matrix, the frequency is f denotes the operation of finding the maximum value of the function; denotes the Krauklis wave energy packet arrival time corresponding to the time when the energy extreme point is located.

[0065] II. Assuming that the offset distance between the detector and the source is x, then the phase velocity corresponding to the frequency f i is:

[0066]

[0067] All frequencies in the time-frequency feature of Krauklis wave are traversed to obtain a dispersion curve v(f).

[0068] Step four, calculating the slope of the dispersion curve: the slope of different positions of the dispersion curve is calculated according to the phase velocity of each frequency, and the specific formula is:

[0069]

[0070] Wherein, k represents the slope of the current position of the dispersion curve, f represents the frequency value of the current position of the dispersion curve, and v represents the phase velocity corresponding to the frequency value f.

[0071] Step five, establishing a crack opening mapping model: based on the propagation theory of Krauklis wave in the crack of the plate, a quantitative mapping model between the crack opening d and the slope k of the dispersion curve is established as shown in Figure 4 , and specifically:

[0072] According to the slope of the dispersion curve extracted from multiple crack openings, the relationship formula of the quantitative mapping model of the two is fitted:

[0073]

[0074] Wherein, k represents the slope of the dispersion curve, and d represents the crack opening.

[0075] Step six, determining the crack opening of the coal seam floor water-conducting crack: the slope k calculated in step four is substituted into the quantitative mapping model of step five to obtain the opening d of the water-conducting crack at different positions, and the average value is obtained after the average value is obtained. The average opening of the water-conducting crack in the current detection area is obtained.

[0076] The above only describes the preferred embodiments of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the present application.

Claims

1. A microseismic Krauklis wave-based coal seam floor water-conducting fracture identification method, characterized in that, The method comprises the following steps: Step one, microseismic original signal collection: obtaining microseismic original signals in a period of time under the coal seam floor; Step two, time-frequency feature extraction of Krauklis wave: segmenting the collected microseismic original signals, and sequentially performing continuous wavelet transform and time-frequency domain conversion on each signal segment to obtain a time-frequency spectrum, and extracting the time-frequency feature of Krauklis wave according to the time-frequency spectrum; Step three, dispersion curve extraction: in the time-frequency feature of Krauklis wave, determining the energy extreme point and Krauklis wave energy packet arrival time corresponding to each frequency; then calculating the phase velocity of each frequency to obtain a dispersion curve; Step four, calculation of dispersion curve slope: calculating the slope of different positions of the dispersion curve according to the phase velocity of each frequency; Step five, establishment of crack opening degree mapping model: based on the propagation theory of Krauklis wave in a flat crack, a quantitative mapping model between the crack opening degree and the slope of the dispersion curve is established; Step six, determination of the coal seam floor water-conducting crack opening degree: the slope calculated in step four is substituted into the quantitative mapping model in step five to obtain the average opening degree of the water-conducting crack in the current detection area.

2. The coal seam floor water conducting fracture identification method based on microseismic Krauklis wave according to claim 1, characterized in that, The microseismic original signal in the step one comprises P wave, S wave and Krauklis wave radiated by the water-conducting crack tip.

3. The coal seam floor water conducting fracture identification method based on microseismic Krauklis wave according to claim 1, characterized in that, The step two specifically comprises: dividing the microseismic original signal into multiple signal segments, and performing the following processing on each signal segment: S1, performing continuous wavelet transform on the signal segment: wherein denotes the result of the continuous wavelet transform of a signal segment; s(t) denotes the input signal; a denotes a scale parameter; denotes a translation parameter; denotes a wavelet mother function, which has to satisfy an admissibility condition: wherein is the Fourier transform of the wavelet mother function, is the angular frequency of the wavelet mother function; denotes the conjugate; S2, converting the continuous wavelet transform result of the signal segment into a time-frequency domain: where F a denotes the frequency corresponding to the scale parameter a, denotes the sampling interval, F c denotes the center frequency of the wavelet mother function; S3, selecting a wavelet mother function: Wherein, T represents the width of the control Gaussian envelope, The center angular frequency of the wavelet mother function is represented by , t is a time variable, and j is an imaginary unit; The time-frequency domain of each signal segment is obtained through the above processing, thereby obtaining the time-frequency spectrum of the microseismic original signal; S4, time window extraction: in the time-frequency spectrum, according to the time-frequency feature of the evolution of the Krauklis wave energy group with time, the time window containing the Krauklis wave signal is intercepted to complete the time-frequency feature extraction of the Krauklis wave.

4. The coal seam floor water conducting fracture identification method based on microseismic Krauklis wave according to claim 1, characterized in that, The step three specifically comprises: I. For each frequency f in the time-frequency characteristics of Krauklis wave i and Find the energy extreme point corresponding to the frequency in the time-frequency matrix: wherein, represents the time-frequency spectrum amplitude value at frequency f i and time in the time-frequency matrix; represents finding the operation that maximizes the value of the following function; represents the Krauklis wave energy packet arrival time corresponding to the time at which the energy extreme point is located; II. If the offset between the receiver and the source is x, then the frequency f i The corresponding phase velocity is: Traversing all frequencies in the time-frequency feature of the Krauklis wave to obtain a dispersion curve v(f).

5. The coal seam floor water conducting fracture identification method based on microseismic Krauklis wave according to claim 1, characterized in that, The specific formula of the step four is: Wherein, k represents the slope of the current position of the dispersion curve, f represents the frequency value of the current position of the dispersion curve, and v represents the phase velocity corresponding to the frequency value f.

6. The coal seam floor water conducting fracture identification method based on microseismic Krauklis wave according to claim 1, characterized in that, The quantitative mapping model in the step five is specifically: Fitting the relationship formula of the quantitative mapping model according to the dispersion curve slopes extracted from multiple crack opening degrees: Wherein, k represents the slope of the dispersion curve, and d represents the crack opening degree.

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