Slope crack passive source seismic wave imaging method

Abstract

The invention provides a side slope crack passive source seismic wave imaging method, which adopts a multi-wave multi-component seismograph to simultaneously collect a plurality of seismic wave signals on three components, including surface waves, body waves and the like. According to the design, the richness of the data is remarkably improved, so that the physical characteristics of various seismic wave signals can be comprehensively utilized for inversion analysis in data processing. Through combined processing and constraint of different wave types, the problem of multiplicity of solutions in geophysical inversion can be effectively reduced, and the stability and reliability of a result are improved. In addition, due to acquisition of multi-component data, imaging is more comprehensive, and particularly, the method has obvious advantages in description of fracture directivity and geometrical characteristics.

Description

Technical Field

[0001] The invention relates to the technical field of crack development detection in open-pit mine dumps, and in particular to a passive source seismic wave imaging method for slope cracks. Background Art

[0002] The development of cracks in the spoil dump is a common safety hazard in the process of open-pit mining. The formation of cracks is closely related to the stacking method, geological conditions, hydrological environment and external disturbance factors of the spoil dump. With the increase of stacking height, rainfall infiltration and external forces such as earthquake vibration, the cracks gradually expand, causing the spoil dump to crack, and finally leading to geological disasters such as landslides and collapses in the spoil dump. In order to ensure the long-term safety and stability of the spoil dump, effective technical means are needed to master the laws of crack development in order to better formulate corresponding spoil dump safety management measures. The existing crack detection method for the spoil dump is mainly the ground penetrating radar method. However, its actual effect and accuracy are often limited by the differences in geological conditions in the mining area and the limitations of the method. In addition, due to the continuous increase in the height of the spoil dump in recent years and the complexity of the crack development in the spoil dump, the theoretical detection depth and accuracy of this method cannot meet the requirements for accurate detection of crack development inside the complete spoil dump. An efficient and accurate crack monitoring technology is urgently needed to timely discover and effectively manage the cracks in the spoil dump and ensure the engineering safety of the spoil dump.

[0003] Passive source seismic imaging is a new seismic wave detection method based on environmental noise. It extracts surface wave and body wave signals from background noise and uses surface wave dispersion inversion, HVSR (horizontal vertical component spectral ratio) imaging and body wave imaging to image underground structures. In recent years, with the application of dense array observation technology and three-component node seismic detectors, this method has developed into multi-wave and multi-component high-density imaging technology. Its application range has expanded from the deep lithosphere to shallow mines, with a detection depth of tens to hundreds of meters. The imaging accuracy is close to that of active source exploration, and no artificial seismic source is required, which makes it have the significant advantages of short construction period, low cost and environmental friendliness.

[0004] When the cover of the spoil pile is deformed and damaged, crack development areas and loose deposit areas will be formed. The propagation speed of seismic waves in different stratum media is different. Generally speaking, the higher the medium density, the higher the propagation speed. The seismic wave velocity in bedrock is higher than that in sandy soil layers, crack development areas, and loose deposit areas. Using this characteristic, a passive source seismic wave imaging method for slope cracks is proposed to detect the development of cracks inside the spoil pile. The method uses multiple waves (P waves, S waves and surface waves) and multiple components (three-component data in the X, Y, and Z directions), and comprehensively constrains the three-dimensional shear wave velocity model obtained by surface wave dispersion inversion, HVSR imaging and body wave imaging, HVSR depth domain imaging profiles, and seismic reflection wave migration profiles, and cross-validates, thereby effectively improving the detection capability of complex structures such as underground cracks and anisotropy. When imaging the development of cracks in the spoil dump, three sets of node seismometers are evenly buried around the spoil pile, on the top and bottom of the slope according to the actual geological conditions of the survey area to form a dense observation system, ensuring comprehensive monitoring of the entire spoil dump area and capturing the seismic wave propagation characteristics at different locations and depths to evaluate the internal defects of the entire spoil dump area. Summary of the invention

[0005] In order to make up for the deficiencies of the prior art, the present invention provides a slope fissure passive source seismic wave imaging method.

[0006] The present invention is implemented by the following technical solution: a slope crack passive source seismic wave imaging method, specifically comprising the following steps: Step S1: setting node seismograph acquisition parameters according to the actual geological conditions of the spoil pile; and performing consistency test on the instrument; Step S2: Analyze the step distribution, the geometric characteristics of the top and bottom areas of the spoil dump, and evenly bury node seismometers on the steps, top and bottom of the spoil dump according to the actual geological conditions of the survey area; Step S3: After the acquisition is completed, the original data is downloaded, sorted, checked, and processed. The specific processing flow is as follows: Step S3-1: Data processing flow of background noise surface wave dispersion inversion method Before the formal dispersion inversion, the collected raw data are preprocessed, including removing instrument response, removing mean, removing trend, unit length cutting and suppressing interference signals; the processed waveform is subjected to spectrum whitening in the required frequency band, and then the spatial autocorrelation function between the two stations is calculated every 2 minutes using the frequency domain waveform spatial autocorrelation method; Assume that the distance between stations A and B is r, and express their spatial autocorrelation in the form of Bessel function: (1) In the formula is the average spectral density of the wave field, and the Green function of the Rayleigh surface wave in the frequency domain is written in the form of Neumann function and Bessel function: (2) Since the seismic background noise is not white noise, the spectral coherence of the seismic background waveform needs to be normalized, that is, (4) (5) The uniform background noise source is satisfied by spatial and temporal averaging of stations at different azimuths during micromotion array observations. The imaginary part is equal to 0. In summary, after obtaining the autocorrelation function between stations, the dispersion information of Rayleigh surface wave phase velocity at different frequencies can be obtained according to the following formula: (6) Where f is the frequency, r is the distance between stations, C is the Rayleigh surface wave phase velocity, and k is the wave number. Rayleigh wave phase velocities of different periods are obtained by fitting the zero-order Bessel function or determining the zero poles. A reliable Rayleigh surface wave phase velocity dispersion curve under the array is obtained by combining the spatial and temporal averages between different arrays. Subsequently, the surface wave direct inversion method based on ray tracing is used to invert the surface wave dispersion data of all paths into a three-dimensional shear wave velocity model. Step S3-2: HVSR imaging data processing flow The three-component background noise data collected were used to remove short-term interference using the STA / LTA algorithm. The retained steady-state micromotion data were divided into several 50-s time windows, and the HVSR curve of each time window was calculated. (7) Among them, NS, EW, and V are the Fourier spectra of the north-south, east-west, and vertical components of the micromotion, respectively. The HVSR curve of each time window is smoothed using a Pazen window with a width of 12, and the HVSR curves of all time windows are averaged to obtain the final HVSR curve and its corresponding standard deviation; When the study area is a simple single-layer structure, the thickness of the sediment layer h is equivalent to 1 / 4 wavelength λ of the fundamental resonant seismic wave; (8) in is the resonance frequency, when n=1 it is the fundamental resonance, is the average S-wave velocity of the sedimentary layer. The surface wave dispersion curve is used to guide the depth conversion of the HVSR curve to obtain the HVSR depth domain imaging profile; Step S3-3: Passive source body wave imaging method data processing flow Based on the continuous waveform data recorded by the array, the frequency domain signal-to-noise ratio method is used to separate surface waves and body waves. The frequency domain signal-to-noise ratio calculation formula can be expressed as: (9) When surface waves dominate, the signal-to-noise ratio of the time window data is lower than that when body waves dominate. Based on this feature, the data segment dominated by body waves is selected, and the mutual interference algorithm is used for interference operation to extract the Green function between two detection points, and then obtain the seismic virtual shot gather record; the mutual interference in the frequency domain is expressed as: (10) After obtaining the virtual source shot gather records dominated by body waves through the seismic interferometry algorithm, conventional processing methods including geometric diffusion compensation, trace equalization, deconvolution, velocity analysis, dynamic correction and common center point stacking are used to enhance the reflection wave information. Finally, the seismic migration imaging methods such as post-stack depth migration are used to obtain the three-dimensional seismic imaging results of the work area. Step S4: By comprehensively constraining the three-dimensional shear wave velocity model, HVSR depth domain imaging profile and seismic reflection wave migration profile, the fracture development zone inside the spoil dump can be effectively located and interpreted; the results of the three methods are spatially complemented: the low-speed anomaly area in the shear wave velocity model corresponds to the wave velocity reduction characteristics of the fracture development area, the low-frequency anomaly area in the HVSR imaging profile reflects the resonance frequency reduction phenomenon caused by the fracture development, and the phase axis breakage or strong scattering area in the seismic reflection profile reveals the influence of fractures on seismic wave propagation.

[0007] As a preferred solution, the node seismograph acquisition parameters in step S1 include observation system, sampling time, sampling interval, acquisition mode, track spacing, and measurement point spacing.

[0008] As a preferred solution, the specific arrangement method of evenly burying node seismometers on the steps, top and bottom of the soil dump in step S2 is: Step S2-1 Slope top area: node seismometers are evenly arranged on the slope top, and the point spacing is selected taking into account both data density and layout efficiency, and is set to 10 meters; Step S2-2: Step edge: node seismometers are evenly arranged at each step edge to form a closed layout around the step, with the point spacing set to 10 meters; Step S2-3 Slope bottom area: The node seismometers at the slope bottom are arranged in a circle around the slope bottom, with a point spacing of 10 meters to cover the potential fracture development zone in the slope bottom area; Step S2-4: Numbering: Marking is performed by combining the step number and the node number.

[0009] Due to the adoption of the above technical solution, the present invention has the following beneficial effects compared with the prior art: This patent uses a multi-wave and multi-component seismograph to simultaneously collect multiple seismic wave signals on three components, including surface waves, body waves, etc. This design significantly improves the richness of the data, making it possible to comprehensively utilize the physical characteristics of multiple seismic wave signals for inversion analysis in data processing. By jointly processing and constraining different wave types, the multi-solution problem in geophysical inversion can be effectively reduced, and the stability and reliability of the results can be improved. In addition, the acquisition of multi-component data makes imaging more comprehensive, especially in the characterization of fracture directionality and geometric characteristics.

[0010] In the design of the observation system, this technology proposes a scientific node layout plan: multi-wave and multi-component seismometers are evenly and densely buried on the top of the spoil dump, on the edge of the steps and at the bottom, to form an observation network that fully covers the spoil dump. The existing spoil dump crack detection technology does not have such full coverage of the spoil dump. This full coverage layout mode from top to bottom significantly improves the comprehensiveness of the observation and ensures the detection accuracy under complex terrain conditions.

[0011] Additional aspects and advantages of the present invention will become apparent from the following description or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which: Figure 1 Schematic diagram of the placement of node seismometers (circles are node seismometers); Figure 2 Schematic diagram of the dump section for placing node seismometers (the inverted triangle is the node seismometer); Figure 3 This is a flow chart of the multi-wave and multi-component passive source seismic imaging method. DETAILED DESCRIPTION

[0013] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0014] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0015] Combine the following Figures 1 to 3 The passive source seismic wave imaging method for slope fractures according to an embodiment of the present invention is described in detail.

[0016] The present invention proposes a slope crack passive source seismic wave imaging method, which specifically includes the following steps: Step S1: Before starting the data collection, ensure that the node seismometer has sufficient power to collect data for a long time; set the node seismometer acquisition parameters including observation system, sampling time, sampling interval, acquisition mode, track spacing, and measurement point spacing according to the actual geological conditions of the spoil pile; perform consistency test on the instrument to ensure that the node seismometer works normally after arriving at the site; Step S2: Conduct a comprehensive survey of the dumping site, analyze the step distribution, the geometric characteristics of the top and bottom areas of the dumping site, and evenly bury node seismometers ( Figure 1 , Figure 2 ); the specific layout method is: Step S2-1 Slope top area: Node seismometers are evenly arranged on the slope top to ensure that the slope top area can be fully covered and complete seismic wave signals can be collected. The selection of point spacing takes into account both data density and layout efficiency and is set to 10 meters; Step S2-2 Step edge: node seismometers are evenly arranged at the edge of each step to form a closed layout around the step, and the point spacing is set to 10 meters. If the step is wide, additional node instruments can be added inside the step to further improve the imaging accuracy. For example, if there is a 15m wide step, an additional circle of node seismometers can be added inside the step, 10m away from the outer circle nodes. Multiple circles of node instruments can be added according to the specific width of the step; Step S2-3 Slope bottom area: The node seismometers at the slope bottom are arranged in a circle around the slope bottom, with a point spacing of 10 meters to cover the potential fracture development zone in the slope bottom area; When burying nodes, the seismographs should be flat, stable, straight, tight, and all nodes should be on the same plane. When the terrain conditions are complex and the height difference between nodes is large, the buried coordinates and elevation of the nodes should be measured and corrected during processing. When the wind is too strong, a pit should be dug for burial, and the surface should be covered with thin soil of about 10 cm.

[0017] The instrument management software based on the mobile platform is used to control the normal indicators and normal operation of the node instruments; the acquisition time is calculated after the last node is laid out. The location of the measuring point, the corresponding node number, the start and end time of the acquisition, and the possible impact of the on-site occurrence on the acquisition are recorded.

[0018] Step S2-4: Numbering: Marking is done by combining the step number and the node number. For example, the nodes at the bottom of the slope are numbered T0-1-1, T0-1-2, T0-1-3, etc.; the nodes on the first step are numbered T1-1-1, T1-1-2, T1-1-3, etc. Assuming that the first step is wide enough, node seismometers are added within a distance of 10m, numbered T1-2-1, T1-2-2, T1-2-3, etc. The nodes on the second step are numbered T2-1-1, T2-1-2, T2-1-3, etc. and so on. The nodes on the top of the slope are numbered according to a certain point in a certain column, such as D3-2 represents the second node seismometer in the third column of the top of the slope.

[0019] Step S3: After the acquisition is completed, download, organize, and check the original data, and process the data after confirming that there are no problems with the data ( Figure 3 ), the specific processing flow is as follows: Step S3-1: Data processing flow of background noise surface wave dispersion inversion method Before the formal dispersion inversion, the collected raw data are preprocessed, including removing instrument response, removing mean, removing trend, unit length cutting and suppressing interference signals; the processed waveform is subjected to spectrum whitening in the required frequency band, and then the spatial autocorrelation function between the two stations is calculated every 2 minutes using the frequency domain waveform spatial autocorrelation method; Assume that the distance between stations A and B is r, and express their spatial autocorrelation in the form of Bessel function: (1) In the formula is the average spectral density of the wave field, and the Green function of the Rayleigh surface wave in the frequency domain is written in the form of Neumann function and Bessel function: (2) Since the seismic background noise is not white noise, the spectral coherence of the seismic background waveform needs to be normalized, that is, (4) (5) The uniform background noise source is satisfied by spatial and temporal averaging of stations at different azimuths during micromotion array observations. The imaginary part is equal to 0. In summary, after obtaining the autocorrelation function between stations, the dispersion information of Rayleigh surface wave phase velocity at different frequencies can be obtained according to the following formula: (6) Where f is the frequency, r is the distance between stations, C is the Rayleigh surface wave phase velocity, and k is the wave number. Rayleigh wave phase velocities of different periods are obtained by fitting the zero-order Bessel function or determining the zero poles. A reliable Rayleigh surface wave phase velocity dispersion curve under the array is obtained by combining the spatial and temporal averages between different arrays. Subsequently, the surface wave direct inversion method based on ray tracing is used to invert the surface wave dispersion data of all paths into a three-dimensional shear wave velocity model. Step S3-2: HVSR imaging data processing flow The three-component background noise data collected were used to remove short-term interference using the STA / LTA algorithm. The retained steady-state micromotion data were divided into several 50-s time windows, and the HVSR curve of each time window was calculated. (7) Among them, NS, EW, and V are the Fourier spectra of the north-south, east-west, and vertical components of the micromotion, respectively. The HVSR curve of each time window is smoothed using a Pazen window with a width of 12, and the HVSR curves of all time windows are averaged to obtain the final HVSR curve and its corresponding standard deviation; The peak frequency of the HVSR curve is approximately the resonance frequency of the sedimentary layer, but the stratum interface and velocity information cannot be directly obtained from the HVSR curve; when the study area is a simple single-layer structure, the thickness of the sedimentary layer h is equivalent to 1 / 4 wavelength λ of the fundamental resonant seismic wave; (8) in is the resonance frequency, when n=1 it is the fundamental resonance, is the average S-wave velocity of the sedimentary layer. The surface wave dispersion curve is used to guide the depth conversion of the HVSR curve to obtain the HVSR depth domain imaging profile; Step S3-3: Passive source body wave imaging method data processing flow Since body waves are weak signals in the collected signals, in order to improve the imaging signal-to-noise ratio of body wave signals, it is necessary to separate body waves from surface waves. Based on the continuous waveform data recorded by the array, the frequency domain signal-to-noise ratio method is used to separate surface waves and body waves. The frequency domain signal-to-noise ratio calculation formula can be expressed as: (9) When surface waves dominate, the signal-to-noise ratio of the time window data is lower than that when body waves dominate. Based on this feature, the data segment dominated by body waves is selected, and the mutual interference algorithm is used for interference operation to extract the Green function between two detection points, and then obtain the seismic virtual shot gather record; the mutual interference in the frequency domain is expressed as: (10) After obtaining the virtual source shot gather records dominated by body waves through the seismic interferometry algorithm, conventional processing methods including geometric diffusion compensation, trace equalization, deconvolution, velocity analysis, dynamic correction and common center point stacking are used to enhance the reflection wave information. Finally, the seismic migration imaging methods such as post-stack depth migration are used to obtain the three-dimensional seismic imaging results of the work area. Step S4: By comprehensively constraining the three-dimensional shear wave velocity model, HVSR depth domain imaging profile and seismic reflection wave migration profile, the fracture development zone inside the spoil dump is effectively located and interpreted; the results of the three methods are spatially complemented: the low-speed anomaly area in the shear wave velocity model corresponds to the wave velocity reduction characteristics of the fracture development area, the low-frequency anomaly area in the HVSR imaging profile reflects the resonance frequency reduction phenomenon caused by fracture development, and the phase axis fracture or strong scattering area in the seismic reflection profile reveals the impact of fractures on seismic wave propagation; through the unification of depth calibration and cross-validation of spatial distribution, these anomaly features are superimposed in three-dimensional space to achieve a detailed characterization of the geometric morphology and physical properties of the fracture development zone, and further improve the accuracy of imaging results and the reliability of interpretation.

[0020] In the description of this specification, the description of the terms "one embodiment", "some embodiments", "specific embodiments", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0021] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A passive source seismic wave imaging method for slope cracks, characterized in that , specifically including the following steps: Step S1: setting node seismograph acquisition parameters according to the actual geological conditions of the spoil pile; and performing consistency test on the instrument; Step S2: Analyze the step distribution, the geometric characteristics of the top and bottom areas of the spoil dump, and evenly bury node seismometers on the steps, top and bottom of the spoil dump according to the actual geological conditions of the survey area; Step S3: After the acquisition is completed, the original data is downloaded, sorted, checked, and processed. The specific processing flow is as follows: Step S3-1: Data processing flow of background noise surface wave dispersion inversion method Before the formal dispersion inversion, the collected raw data are preprocessed, including removing instrument response, removing mean, removing trend, unit length cutting and suppressing interference signals; the processed waveform is subjected to spectrum whitening in the required frequency band, and then the spatial autocorrelation function between the two stations is calculated every 2 minutes using the frequency domain waveform spatial autocorrelation method; Assume that the distance between stations A and B is r, and express their spatial autocorrelation in the form of Bessel function: （1） In the formula is the average spectral density of the wave field, and the Green function of the Rayleigh surface wave in the frequency domain is written in the form of Neumann function and Bessel function: （2） Since the seismic background noise is not white noise, the spectral coherence of the seismic background waveform needs to be normalized, that is, （4） （5） The uniform background noise source is satisfied by spatial and temporal averaging of stations at different azimuths during micromotion array observations. The imaginary part of is equal to 0; In summary, after obtaining the autocorrelation function between stations, the dispersion information of Rayleigh surface wave phase velocity at different frequencies can be obtained according to the following formula: （6） Where f is the frequency, r is the distance between stations, C is the Rayleigh surface wave phase velocity, and k is the wave number. Rayleigh wave phase velocities of different periods are obtained by fitting the zero-order Bessel function or determining the zero poles. A reliable Rayleigh surface wave phase velocity dispersion curve under the array is obtained by combining the spatial and temporal averages between different arrays. Subsequently, the surface wave direct inversion method based on ray tracing is used to invert the surface wave dispersion data of all paths into a three-dimensional shear wave velocity model. Step S3-2: HVSR imaging data processing flow The three-component background noise data collected were used to remove short-term interference using the STA / LTA algorithm. The retained steady-state micromotion data were divided into several 50-s time windows, and the HVSR curve of each time window was calculated. （7） Among them, NS, EW, and V are the Fourier spectra of the north-south, east-west, and vertical components of the micromotion, respectively; the HVSR curve of each time window is smoothed using a Pazen window with a width of 12, and the HVSR curves of all time windows are averaged to obtain the final HVSR curve and its corresponding standard deviation; When the study area is a simple single-layer structure, the thickness of the sediment layer h is equivalent to 1 / 4 wavelength λ of the fundamental resonant seismic wave; （8） in is the resonance frequency, when n=1 it is the fundamental resonance, is the average S-wave velocity of the sedimentary layer. The surface wave dispersion curve is used to guide the depth conversion of the HVSR curve to obtain the HVSR depth domain imaging profile; Step S3-3: Passive body wave imaging method data processing flow Based on the continuous waveform data recorded by the array, the frequency domain signal-to-noise ratio method is used to separate surface waves and body waves. The frequency domain signal-to-noise ratio calculation formula can be expressed as: （9） When surface waves dominate, the signal-to-noise ratio of the time window data is lower than that when body waves dominate. Based on this feature, the data segment dominated by body waves is selected, and the mutual interference algorithm is used for interference operation to extract the Green function between two detection points, and then obtain the seismic virtual shot gather record; the mutual interference in the frequency domain is expressed as: （10） After obtaining the virtual source shot gather records dominated by body waves through the seismic interferometry algorithm, conventional processing methods including geometric diffusion compensation, trace equalization, deconvolution, velocity analysis, dynamic correction and common center point stacking are used to enhance the reflection wave information. Finally, the seismic migration imaging methods such as post-stack depth migration are used to obtain the three-dimensional seismic imaging results of the work area. Step S4: By comprehensively constraining the three-dimensional shear wave velocity model, HVSR depth domain imaging profile and seismic reflection wave migration profile, the fracture development zone inside the spoil dump can be effectively located and interpreted; the results of the three methods are spatially complemented: the low-speed anomaly area in the shear wave velocity model corresponds to the wave velocity reduction characteristics of the fracture development area, the low-frequency anomaly area in the HVSR imaging profile reflects the resonance frequency reduction phenomenon caused by the fracture development, and the phase axis breakage or strong scattering area in the seismic reflection profile reveals the influence of fractures on seismic wave propagation.

2. A slope crack passive source seismic wave imaging method according to claim 1, characterized in that ,The node seismograph acquisition parameters in step S1 include observation system, sampling time, sampling interval, ,acquisition mode, track spacing, and measurement point spacing.

3. A slope crack passive source seismic wave imaging method according to claim 1, characterized in that The specific arrangement method of evenly burying node seismometers on the steps, top and bottom of the soil pile in step S2 is: Step S2-1 Slope top area: node seismometers are evenly arranged on the slope top, and the point spacing is selected taking into account both data density and layout efficiency, and is set to 10 meters; Step S2-2: Step edge: node seismometers are evenly arranged at each step edge to form a closed layout around the step, with the point spacing set to 10 meters; Step S2-3 Slope bottom area: The node seismometers at the slope bottom are arranged in a circle around the slope bottom, with a point spacing of 10 meters to cover the potential fracture development zone in the slope bottom area; Step S2-4: Numbering: Marking is performed by combining the step number and the node number.

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