Acoustic emission identification method for rock stratum fracture development under mining-induced stress disturbance

Abstract

The invention discloses an acoustic emission identification method for rock stratum fracture development under mining-induced stress disturbance, and relates to the technical field of acoustic exploration. Comprising the following steps: determining a mining-induced stress field in a mining activity, arranging sensors in a mining-induced influence range, and obtaining collected data; preprocessing the collected data to obtain preprocessed data; acquiring acquired data of a standard part in the mining activity as standard data, and acquiring and calculating interval transit time porosity parameters based on the standard data; and determining a rock stratum fracture development index of the non-standard part based on the interval transit time porosity parameter, and judging a development fracture based on the rock stratum fracture development index. Secondary disturbance to the rock stratum can be avoided, and the rock stratum gap can be accurately displayed.

Description

Technical Field

[0001] This invention relates to the field of acoustic exploration technology, and in particular to an acoustic emission identification method for rock fracture development under mining stress disturbance. Background Technology

[0002] In underground engineering, mining-induced stress disturbances can lead to a redistribution of stress within rock strata, resulting in the development of fractures. These fractures can become channels for groundwater seepage and even cause safety accidents such as landslides. Research on acoustic long-range sounding technology is based on methods for evaluating the quality of raw long-range sounding data from target blocks and interpretation models for karst fracture-vuggy reservoirs. However, current processing procedures and parameter selections have not been specifically optimized for rock fractures, nor have they adequately considered noise removal from the block data.

[0003] Therefore, providing an acoustic emission identification method for rock fracture development under mining stress disturbance to overcome the difficulties of existing technologies is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the present invention provides an acoustic emission identification method for rock strata fracture development under mining stress disturbance, which can avoid secondary disturbance to the rock strata and accurately display rock strata fractures.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] An acoustic emission identification method for rock fracture development under mining stress disturbance includes the following steps: determining the mining stress field in mining activities, deploying sensors within the mining influence range, and obtaining collected data;

[0007] The collected data is preprocessed to obtain preprocessed data;

[0008] Acquire standard data from the standard portion of mining activities as standard data, and calculate the acoustic transit porosity parameter based on the standard data;

[0009] The rock fracture development index of the non-standard part is determined based on the acoustic time-difference porosity parameter, and the development of fractures is judged based on the rock fracture development index.

[0010] Optional, preprocessing includes:

[0011] The collected data is filtered to remove burr interference unrelated to the formation properties, reduce interference factors in automatic correction, and obtain filtered data.

[0012] Wavefield separation is performed on the filtered data to extract the reflected transverse waves and obtain the preprocessed data.

[0013] Optionally, the acoustic transit time porosity parameter can be obtained as follows:

[0014] Based on density porosity and combined with the sonic transit time logging values ​​of the preprocessed data, the sonic transit time logging values ​​of the target layer in the standard section of the rock formation are obtained;

[0015] The parameters required to calculate the acoustic transit porosity are expressed as follows:

[0016]

[0017] Among them, C p Δt is the formation compaction coefficient, and Δt is the sonic transit time logging value of the target formation. f Let Δt be the acoustic transit time of the formation fluid. ma Acoustic transit time of mudstone skeleton, ф ac For acoustic transit porosity, ф den This refers to density logging porosity.

[0018] Optionally, density logging porosity is determined based on rock skeleton density value, density logging value, and formation fluid density value, wherein the rock skeleton density value is determined based on the rock composition and material composition ratio of the mudstone formation in the standard well.

[0019] Optionally, the expression for the rock fracture development index can be determined as follows:

[0020] Among them, ф ac (Standard) refers to the porosity of the standard segment acoustic transit time, ф ac (To be tested) is the acoustic transit porosity of the section to be tested. If the calculated r>1, then other rock strata have developed cracks; otherwise, they do not have developed cracks.

[0021] Optionally, when r > 1, the preprocessed data undergoes imaging processing, including:

[0022] The preprocessed data is enhanced by overlaying common center points;

[0023] The processing results are subjected to offset imaging to determine whether the imaging results are clear. If they are not clear, the imaging parameters are adjusted until the imaging results are clear.

[0024] The crack image is obtained by denoising the imaging results.

[0025] As can be seen from the above technical solution, compared with the prior art, the present invention provides an acoustic emission identification method for rock stratum fracture development under mining stress disturbance, which has the following beneficial effects: 1) The present invention uses non-contact monitoring to obtain rock stratum data, without the need for additional drilling, avoiding secondary disturbance to the rock stratum, and realizing accurate identification of rock stratum fractures under mining stress disturbance; 2) The present invention can perform acoustic imaging processing and can accurately display rock stratum fractures. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0027] Figure 1 This is a flowchart of an acoustic emission identification method for rock fracture development under mining stress disturbance disclosed in this invention;

[0028] Figure 2 This is a schematic diagram of the acoustic emission identification method for rock fracture development under mining stress disturbance disclosed in this invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Reference Figure 1 As shown, this invention discloses an acoustic emission identification method for rock fracture development under mining-induced stress disturbance, comprising the following steps:

[0031] Determine the stress field caused by mining activities, deploy sensors within the range of mining influence, and collect data.

[0032] The collected data is preprocessed to obtain preprocessed data;

[0033] Acquire standard data from the standard portion of mining activities as standard data, and calculate the acoustic transit porosity parameter based on the standard data;

[0034] The rock fracture development index of the non-standard part is determined based on the acoustic time-difference porosity parameter, and the development of fractures is judged based on the rock fracture development index.

[0035] Specifically, the distribution characteristics of the mining-induced stress field directly determine the development range and degree of rock fractures; therefore, accurately determining the mining-induced stress field is the prerequisite for the entire identification method. In mining activities, the mining-induced stress field is affected by multiple factors, including mining methods (such as longwall mining and room-and-pillar mining), coal seam depth, and the physical and mechanical properties of the rock strata (such as elastic modulus and Poisson's ratio). The mining-induced stress field is usually determined by combining numerical simulation with on-site monitoring. The determination methods include:

[0036] A geological model is established using numerical software such as FLAC3D. The physical and mechanical parameters of the rock strata obtained through laboratory tests and in-situ tests are input to simulate the stress field evolution at different mining stages (initial mining, normal pushing mining, and final mining). The influence range of the advance support pressure, stress concentration coefficient, distribution and transfer law of high and low stress zones are initially identified, and the mining influence range is initially delineated (generally an area of ​​1-3 times the mining height around the goaf).

[0037] By combining the on-site monitoring data from stress sensors (such as fiber optic stress sensors), the simulation results are corrected, and key areas such as stress concentration zones and stress reduction zones are finally determined.

[0038] Furthermore, when deploying sensors within the defined mining impact area, piezoelectric acoustic emission sensors can be selected, as they are highly sensitive to high-frequency stress waves generated by rock fissures. The deployment density needs to be adjusted according to the mining impact area: denser deployment is used in stress concentration areas, while the density can be appropriately relaxed in stress-stable areas. Deployment locations should avoid large equipment vibration sources (such as coal mining machines and scraper conveyors), and ensure close coupling between the sensors and the rock surface to reduce signal transmission loss. The data collected by the sensors includes information such as the amplitude, frequency, and arrival time of the stress waves.

[0039] Further, it also includes comparing monitoring data with numerical simulation results, using Kalman filtering or ensemble Kalman filtering methods, taking the monitoring data as the true value, and reversing and calibrating the boundary conditions and parameters of the numerical model to make the simulated stress field more accurate.

[0040] Further preprocessing includes:

[0041] The collected data is filtered to remove burr interference unrelated to the formation properties, reduce interference factors in automatic correction, and obtain filtered data.

[0042] Wavefield separation is performed on the filtered data to extract the reflected transverse waves and obtain the preprocessed data.

[0043] Furthermore, the core objective of filtering is to retain the effective stress wave signals associated with rock fractures while removing noise. Interference signals at mining sites can be categorized into two types: one is high-frequency glitches, such as transient pulses generated by poor sensor contact; these signals typically have frequencies above 500kHz and are unrelated to geological formation properties. The other is low-frequency automatic correction interference, such as steady-state vibrations generated by the deformation of support equipment, with frequencies mostly below 20Hz. To address these two types of interference, a combined filtering strategy is required: first, a high-pass filter is used to filter low-frequency vibration interference; then, a low-pass filter is used to suppress high-frequency glitches; and finally, an adaptive filtering algorithm is used to further reduce the remaining random interference.

[0044] Furthermore, it also includes determining whether the filtered data meets the preset requirements based on the signal-to-noise ratio.

[0045] Furthermore, wave field separation methods include linear prediction, FK filtering, median filtering, etc. In the actual process of sound wave testing, due to the different propagation paths, the travel times of direct waves and reflected waves differ greatly. The linear prediction wave field separation method is proposed based on this premise.

[0046] Specifically, the filtered data stream still contains various wave types, including direct waves, reflected P-waves, and reflected S-waves. Among these, reflected S-waves are most sensitive to rock fissures, therefore wavefield separation is required to extract them. Linear prediction methods are suitable for scenarios where the travel times of direct and reflected waves differ significantly.

[0047] In mining operations, direct waves propagate along the surface of rock strata with a short travel time, while reflected shear waves need to penetrate the rock strata and be reflected back through fissures, resulting in a longer travel time. This method establishes a mathematical prediction model for direct waves, subtracts the predicted direct wave signal from the original data, and the remaining portion is the reflected wave.

[0048] FK filtering is based on the difference in propagation speed of different wave types: direct waves propagate quickly and exhibit high wavenumber characteristics in the frequency-wavenumber domain, while reflected shear waves propagate slowly and correspond to low wavenumber characteristics. By setting a filtering window in the frequency-wavenumber domain, reflected shear waves can be separated. This method is particularly effective for wavefield separation in layered rock formations, but in heterogeneous formations such as fracture zones, it needs to be combined with median filtering to eliminate pulse interference. Median filtering is a nonlinear filtering method that excels at handling sudden interference. Its principle is to use a sliding window to take the median value to replace the original data, which can effectively remove abnormal pulses while preserving the waveform characteristics of reflected shear waves. In practical applications, median filtering is often used as a preprocessing step for FK filtering to remove strong interference before wavefield separation, thereby improving the extraction accuracy of reflected shear waves.

[0049] Furthermore, by subtracting the direct wave from the corresponding receiver in the measured waveform obtained from the linear predictive wavefield separation method, the remaining portion is the reflected wave, and the corresponding expression is:

[0050]

[0051] Among them, W n (ω) represents the measured wave, n represents the number of measurements, n = 1, 2, ..., N, A l (ω) represents the direct wave, and l represents the number of reflections.

[0052] Furthermore, the acoustic transit time porosity parameter is obtained as follows:

[0053] Based on density porosity and the preprocessed data, the sonic transit time logging value of the target layer in the standard section of the rock formation is obtained;

[0054] The parameters required to calculate the acoustic transit porosity are expressed as follows:

[0055]

[0056] Among them, C p Δt is the formation compaction coefficient, and Δt is the sonic transit time logging value of the target formation. f Let Δt be the acoustic transit time of the formation fluid. ma Acoustic transit time of mudstone skeleton, ф ac For acoustic transit porosity, ф den For density logging porosity, its expression is:

[0057]

[0058] Where, ρ ma ρ is the density of the rock skeleton. b ρ is the density logging value. f This represents the density of the formation fluid.

[0059] Furthermore, density logging porosity is determined based on rock skeleton density value, density logging value, and formation fluid density value. Among them, rock skeleton density value is determined based on the rock composition and material composition ratio of mudstone formation in standard wells.

[0060] Specifically, the expression for calculating the density value of the rock skeleton is: ρ ma =Σ(ρ i ×V i ), where ρ i Let V be the density of the i-th type of rock mineral. i Let be the volume fraction of the i-th rock mineral in the total volume of the rock.

[0061] Furthermore, the expression for the rock fracture development index is determined as follows: Among them, ф ac (Standard) refers to the porosity of the standard segment acoustic transit time, ф ac (To be tested) is the acoustic transit porosity of the section to be tested. If the calculated r>1, then other rock strata have developed cracks; otherwise, they do not have developed cracks.

[0062] Furthermore, when r > 1, imaging processing is performed on the preprocessed data, including:

[0063] The preprocessed data is enhanced by overlaying common center points;

[0064] The processing results are subjected to offset imaging to determine whether the imaging results are clear. If they are not clear, the imaging parameters are adjusted until the imaging results are clear.

[0065] The crack image is obtained by denoising the imaging results.

[0066] Specifically, common center point superposition includes superimposing signals from the same reflection point collected by different sensors to enhance the energy of the reflected transverse wave. The more times the signals are superimposed, the stronger the signal becomes. During the superposition process, it is necessary to ensure the accuracy of the common center point.

[0067] Migration imaging is the process of repositioning the superimposed signal to the actual reflection point. The key is to establish an accurate velocity model. If the velocity model has a large error, the imaging result will be blurry or misaligned. The velocity model needs to be adjusted by trial and error until the crack boundary is clear.

[0068] Noise reduction is used to eliminate residual noise after imaging, including wavelet denoising: the imaging result is decomposed into wavelet coefficients of different frequencies, the wavelet coefficients corresponding to noise are suppressed by thresholding, and then the image is reconstructed. After noise reduction, the detailed features of the crack must be preserved to avoid excessive noise reduction leading to the loss of crack information. Finally, a crack image containing the location, length, and width of the crack is generated.

[0069] Furthermore, it also includes using image segmentation algorithms to extract cracks from crack images.

[0070] Specifically, the crack image is converted to grayscale to determine the grayscale digital image; the grayscale digital image is then processed using nonlocal mean filtering to obtain a filtered digital image; the filtered digital image is then processed using a homomorphic filtering algorithm to obtain a second digital image; dilation is performed on the second digital image to connect the discontinuous cracks, thus determining a continuous second digital image; the continuous second digital image is then segmented to remove noise, resulting in a denoised second digital image; the skeleton of the denoised second digital image is extracted to obtain the cracks, and the geometric parameters of the cracks are quantized to determine the crack type.

[0071] In one specific embodiment, refer to Figure 2As shown, a geological model was constructed using FLAC3D numerical software. The model's dimensions were: strike length 300m, dip width 150m, and vertical depth 100m. With the corresponding parameters input, 20 fiber optic stress sensors were deployed within a 100m advance range along the working face's advancing direction to monitor stress changes in real time. Monitoring data showed stress concentration in the 50m advance region of the working face, and a stress reduction zone within 30m behind the goaf. Based on the monitoring data, the numerical model was revised, ultimately determining that the stress concentration area was mainly distributed in the 20-50m advance region of the working face. An RSM-SY8 piezoelectric sensor was used. One acoustic emission sensor is placed every 5m in the stress concentration area and one every 10m in the advance area of ​​50-100m. The corresponding sensor data is acquired, and the wave field is separated by a combination of median filtering and FK filtering. The original rock strata section of the working face that has not been affected by mining is selected as the standard section. The developed cracks are determined by combining the acquired data. The reflection signals of four sensors at the same reflection point are superimposed to establish a velocity model. The model parameters are adjusted by trial and error until the crack boundary is clear. The image is reconstructed after the noise coefficient is suppressed by soft thresholding, and the crack image is finally generated.

[0072] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An acoustic emission identification method for rock fracture development under mining-induced stress disturbance, comprising the following steps: Determine the stress field caused by mining activities, deploy sensors within the range of mining influence, and collect data. The collected data is preprocessed to obtain preprocessed data; Acquire standard data from the standard portion of mining activities as standard data, and calculate the acoustic transit porosity parameter based on the standard data; The rock fracture development index of the non-standard part is determined based on the acoustic time-difference porosity parameter, and the development of fractures is judged based on the rock fracture development index.

2. The acoustic emission identification method for rock fracture development under mining-induced stress disturbance according to claim 1, characterized in that, Preprocessing includes: The collected data is filtered to remove burr interference unrelated to the formation properties, reduce interference factors in automatic correction, and obtain filtered data. Wavefield separation is performed on the filtered data to extract the reflected transverse waves and obtain the preprocessed data.

3. The acoustic emission identification method for rock fracture development under mining-induced stress disturbance according to claim 1, characterized in that, The method for obtaining the acoustic transit time porosity parameter is as follows: Based on density porosity and the preprocessed data, the sonic transit time logging value of the target layer in the standard section of the rock formation is obtained; The parameters required to calculate the acoustic transit porosity are expressed as follows: Among them, C p Δt is the formation compaction coefficient, and Δt is the sonic transit time logging value of the target formation. f Let Δt be the acoustic transit time of the formation fluid. ma Acoustic transit time of mudstone skeleton, ф ac For acoustic transit porosity, ф den This refers to density logging porosity.

4. The acoustic emission identification method for rock fracture development under mining-induced stress disturbance according to claim 3, characterized in that, Density logging porosity is determined based on rock skeleton density value, density logging value, and formation fluid density value. Among them, the rock skeleton density value is determined based on the rock composition and material composition ratio of the mudstone formation in the standard well.

5. The acoustic emission identification method for rock fracture development under mining stress disturbance according to claim 3, characterized in that, The expression for the rock fracture development index is determined as follows: Among them, ф ac (Standard) refers to the porosity of the standard segment acoustic transit time, ф ac (To be tested) is the acoustic transit porosity of the section to be tested. If the calculated r>1, then other rock strata have developed cracks; otherwise, they do not have developed cracks.

6. The acoustic emission identification method for rock fracture development under mining stress disturbance according to claim 5, characterized in that, When r > 1, the preprocessed data undergoes imaging processing, including: The preprocessed data is enhanced by overlaying common center points; The processing results are subjected to offset imaging to determine whether the imaging results are clear. If they are not clear, the imaging parameters are adjusted until the imaging results are clear. The crack image is obtained by denoising the imaging results.

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