A method for slope fracture passive seismic wave imaging

Abstract

The application provides a kind of side slope fissure passive source seismic wave imaging method, the patent adopts multi-wave multi-component seismograph, simultaneously gathers multiple seismic wave signals on three components, including surface wave, body wave and the like.This design significantly improves the richness of data, so that in data processing, a variety of seismic wave signals can be comprehensively utilized for inversion analysis.By joint processing and constraint of different wave types, the multi-solution problem in geophysical inversion can be effectively reduced, and the stability and reliability of the results are improved.In addition, the acquisition of multi-component data makes the imaging more comprehensive, especially in the characterization of fissure directionality and geometric features, which has obvious advantages.

Description

Technical Field

[0001] This invention relates to the field of crack development detection technology in open-pit mine spoil heaps, and more specifically, to a passive source seismic wave imaging method for slope cracks. Background Technology

[0002] The development of fissures in spoil heaps is a common safety hazard during open-pit mining. The formation of fissures is closely related to the spoil heap's accumulation method, geological conditions, hydrological environment, and external disturbances. With increasing accumulation height, rainfall infiltration, and external forces such as earthquakes, fissures gradually enlarge, leading to cracking of the spoil heap and ultimately geological disasters such as landslides and collapses. To ensure the long-term safety and stability of spoil heaps, effective technical means are needed to understand the patterns of fissure development in order to better formulate corresponding safety management measures. Existing methods for detecting fissures in spoil heaps mainly rely on ground-penetrating radar (GPR). However, its actual effectiveness and accuracy are often limited by the differences in geological conditions in mining areas and the limitations of the method itself. Furthermore, due to the continuous increase in spoil heap height and the complexity of fissure development in recent years, the theoretical detection depth and accuracy of this method cannot meet the requirements for accurate detection of fissure development within a complete spoil heap. There is an urgent need for an efficient and accurate fissure monitoring technology to promptly detect and effectively manage fissures in spoil heaps, ensuring the engineering safety of spoil heaps.

[0003] Passive-source seismic imaging is a novel seismic wave detection method based on ambient noise. It extracts surface wave and volume wave signals from the background noise and utilizes techniques such as surface wave dispersion inversion, HVSR (horizontal-vertical component spectral ratio) imaging, and volume wave imaging to image subsurface structures. In recent years, with the application of dense array observation technology and three-component nodal seismic detectors, this method has evolved into a multi-wave, multi-component high-density imaging technique. Its application range has expanded from the deep lithosphere to shallow mines, with detection depths reaching tens to hundreds of meters. The imaging accuracy is close to that of active-source exploration, and the absence of artificial seismic sources gives it significant advantages such as short construction period, low cost, and environmental friendliness.

[0004] Deformation and damage to the overburden layer can create areas of fracture development and loose deposits. Seismic waves propagate at different speeds in different geological media; generally, the higher the density of the medium, the higher the propagation speed. Seismic wave velocities in bedrock are higher than those in sandy soil layers, fracture development zones, and loose deposits. Utilizing this characteristic, a passive source seismic wave imaging method for slope fractures is proposed to detect fracture development within the overburden pile. This method uses multiple waves (P-waves, S-waves, and surface waves) and multiple components (three-component data in the X, Y, and Z directions). It comprehensively constrains and cross-validates the three-dimensional shear wave velocity model obtained from surface wave dispersion inversion, HVSR imaging, and volume wave imaging, along with HVSR depth domain imaging profiles and seismic reflection wave migration profiles. This effectively improves the detection capability for complex structures such as underground fractures and anisotropy. When imaging the development of fissures in the spoil heap, three-dimensional nodal seismographs are evenly buried around the spoil heap, at the top and bottom of the slope, according to the actual geological conditions of the survey area, to form a dense observation system. This ensures comprehensive monitoring of the entire spoil heap area and captures the seismic wave propagation characteristics at different locations and depths, so as to assess the internal defects of the entire spoil heap area. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, this invention provides a passive source seismic wave imaging method for slope fissures.

[0006] This invention is achieved through the following technical solution: a passive source seismic wave imaging method for slope fissures, specifically including the following steps:

[0007] Step S1: Set the nodal seismograph acquisition parameters according to the actual geological conditions of the spoil heap; conduct a consistency test on the instrument;

[0008] Step S2: Analyze the distribution of steps, the geometric characteristics of the top and bottom areas of the spoil heap, and uniformly bury nodal seismographs at the steps, top, and bottom of the spoil heap according to the actual geological conditions of the survey area.

[0009] Step S3: After data collection, download, organize, and check the raw data, and perform data processing. The specific processing flow is as follows:

[0010] Step S3-1: Data Processing Flow of Background Noise Surface Wave Dispersion Inversion Method

[0011] Before performing the dispersion inversion, the collected raw data is preprocessed, including removing instrument response, removing mean, removing trend, unit length cutting, and suppressing interference signals. The processed waveform is then subjected to spectral whitening within the required frequency band. Then, the spatial autocorrelation function between the two stations is calculated every 2 minutes using the frequency domain waveform spatial autocorrelation method.

[0012] Let the distance between stations A and B be r. Express their spatial autocorrelation in the form of a Bessel function:

[0013] (1)

[0014] In the formula Given the average spectral density of the wave field, the Green's function in the frequency domain of the Rayleigh surface wave can be written in the form of the Neumann function and the Bessel function:

[0015] (2)

[0016] Since earthquake background noise is not white noise, the spectral coherence of the earthquake background waveform also needs to be normalized, i.e.,

[0017] (4)

[0018] (5)

[0019] By averaging the spatial and temporal data from stations at different azimuths during micro-motion array observations, a uniform background noise source can be achieved. The imaginary part is equal to 0. In summary, after obtaining the autocorrelation function between stations, the Rayleigh surface wave phase velocity dispersion information at different frequencies is obtained according to the following formula:

[0020] (6)

[0021] In the formula, f is the frequency, r is the distance between stations, C is the Rayleigh surface wave phase velocity, and k is the wave number. By fitting the zero-order Bessel function or determining the zero poles, the Rayleigh wave phase velocities of different periods are obtained. By combining the spatial and temporal averaging between different arrays, a reliable Rayleigh surface wave phase velocity dispersion curve below the array is obtained. Subsequently, a 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.

[0022] Step S3-2: HVSR Imaging Data Processing Flow

[0023] Using the acquired three-component background noise data, the STA / LTA algorithm was employed to remove short-term interference. The remaining steady-state micro-motion data was divided into several 50-second time windows, and the HVSR curve for each time window was calculated.

[0024] (7)

[0025] Wherein, NS, EW, and V are the Fourier spectra of the north-south, east-west, and vertical components of the micro-motion, respectively. The HVSR curve of each time window is smoothed using a Pazen window with a width of 12. The mean of the HVSR curves of all time windows is calculated to obtain the final HVSR curve and its corresponding standard deviation.

[0026] When the study area has a simple single-layer structure, the thickness h of the sedimentary layer is equivalent to 1 / 4 wavelength λ of the basic resonant seismic wave;

[0027] (8)

[0028] in The resonant frequency is the fundamental frequency, and when n=1, it is the fundamental resonance. The average S-wave velocity of the sediment layer is used. The depth conversion of the HVSR curve is guided by the surface wave dispersion curve to obtain the HVSR depth domain imaging profile.

[0029] Step S3-3: Data Processing Flow of Passive Source Volume Wave Imaging Method

[0030] Based on continuous waveform data recorded by the array, surface waves and volume waves are separated using the frequency domain signal-to-noise ratio (SNR) method. The formula for calculating the frequency domain SNR can be expressed as:

[0031] (9)

[0032] 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 characteristic, the data segment where body waves dominate is selected, and interferometry is performed using a mutual coherence algorithm to extract the Green's function between two receiver points, thereby obtaining the seismic virtual shot gather record; the mutual coherence in the frequency domain is represented as:

[0033] (10)

[0034] After obtaining the virtual source shot gather record with body wave dominance after 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 reflected wave information. Finally, seismic migration imaging methods such as post-stack depth migration are used to obtain the three-dimensional seismic imaging results of the work area.

[0035] 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 heap is effectively located and interpreted; the results of the three methods are spatially complementary: the low-velocity anomaly area in the shear wave velocity model corresponds to the wave velocity reduction characteristics in the fracture development area, the low-frequency anomaly area in the HVSR imaging profile reflects the phenomenon of reduced resonance frequency caused by fracture development, and the in-phase axis fracture or strong scattering area in the seismic reflection profile reveals the influence of fractures on seismic wave propagation.

[0036] As a preferred option, the nodal seismograph acquisition parameters in step S1 include the observation system, sampling time, sampling interval, acquisition mode, trace spacing, and measuring point spacing.

[0037] As a preferred option, the specific arrangement of the nodal seismometers evenly buried at the top and bottom of the soil dump steps in step S2 is as follows:

[0038] Step S2-1 Slope Top Area: The nodal seismographs are evenly distributed on the slope top. The point spacing is set to 10 meters, taking into account both data density and deployment efficiency.

[0039] Step S2-2 Step Edge: Arrange nodal seismographs evenly at the edge of each step to form a closed layout around the step, with a point spacing of 10 meters;

[0040] Step S2-3 Slope bottom area: The slope bottom node seismometers are arranged to surround the slope bottom with a point spacing of 10 meters to cover the potential fracture development zone in the slope bottom area;

[0041] Step S2-4 Numbering: Mark the steps and nodes by combining the step number and node number.

[0042] By employing the above technical solutions, this invention has the following beneficial effects compared to existing technologies:

[0043] This patent employs a multi-wave, multi-component seismograph to simultaneously acquire various seismic wave signals across three components, including surface waves and body waves. This design significantly enhances data richness, enabling the comprehensive utilization of the physical properties of multiple seismic wave signals for inversion analysis during data processing. By jointly processing and constraining different wave types, the problem of multiple solutions in geophysical inversion can be effectively reduced, improving the stability and reliability of the results. Furthermore, the acquisition of multi-component data provides more comprehensive imaging, particularly offering significant advantages in characterizing fracture directionality and geometric features.

[0044] In terms of observation system design, this technology proposes a scientific node layout scheme: multi-wave multi-component seismometers are evenly and densely buried at the top, edge, and bottom of the spoil heap to form a comprehensive observation network covering the spoil heap. Existing spoil heap crack detection technologies do not provide such comprehensive coverage of the spoil heap. This top-to-bottom comprehensive layout significantly improves the comprehensiveness of the observation and ensures the detection accuracy under complex terrain conditions.

[0045] Additional aspects and advantages of the invention will become apparent in the following description or may be learned by practice of the invention. Attached Figure Description

[0046] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0047] Figure 1 A schematic diagram of the placement of nodal seismographs (circles represent nodal seismographs).

[0048] Figure 2 A schematic diagram of the spoil heap profile for placing nodal seismographs (the inverted triangle represents the nodal seismograph).

[0049] Figure 3 This is a flowchart of a multi-wave, multi-component passive source seismic imaging method. Detailed Implementation

[0050] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0051] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0052] The following is combined Figures 1 to 3 The passive source seismic wave imaging method for slope fissures according to embodiments of the present invention will be described in detail.

[0053] This invention proposes a passive source seismic wave imaging method for slope cracks, which specifically includes the following steps:

[0054] Step S1: Before starting data acquisition, ensure that the nodal seismograph has sufficient power to perform long-term data acquisition; set the nodal seismograph acquisition parameters according to the actual geological conditions of the spoil heap, including the observation system, sampling time, sampling interval, acquisition mode, trace spacing, and measuring point spacing; conduct a consistency test on the instrument to ensure that the nodal seismograph works normally after arriving at the site.

[0055] Step S2: Conduct a comprehensive reconnaissance of the spoil heap, analyze the distribution of steps, and the geometric characteristics of the top and bottom areas. Based on the actual geological conditions of the survey area, evenly bury nodal seismographs at the steps, top, and bottom of the spoil heap. Figure 1 , Figure 2 The specific deployment method is as follows:

[0056] Step S2-1: Slope Top Area: Nodal seismographs are evenly distributed at the slope top to ensure full coverage of the area and acquisition of complete seismic signals. The point spacing is set to 10 meters, balancing data density and deployment efficiency.

[0057] Step S2-2: Edge of the Step: Distribute nodal seismographs evenly along the edge of each step to form a closed array around the step, with a spacing of 10 meters between the nodal points. If the step is wide, additional nodal instruments can be added inside the step to further improve imaging accuracy. For example, for a 15m wide step, an additional ring of nodal seismographs can be added inside the step, 10m apart from the outer ring of nodal instruments. Depending on the specific width of the step, multiple rings of nodal instruments can be added.

[0058] Step S2-3 Slope bottom area: The slope bottom node seismometers are arranged to surround the slope bottom with a point spacing of 10 meters to cover the potential fracture development zone in the slope bottom area;

[0059] When burying seismograph nodes, ensure they are level, stable, upright, straight, and tightly fitted, with all nodes preferably on the same plane. In complex terrain conditions with significant elevation differences between nodes, measure the node burial coordinates and elevations, and make corrections during processing. In areas with excessive wind, dig pits for burial, covering the surface with approximately 10cm of soil.

[0060] The instrument management software, based on a mobile platform, ensured that the instrument parameters at each control node were normal and that the system was functioning correctly. Data acquisition time was calculated after the last node was deployed. The location of each measurement point, its corresponding node number, the start and end times of data acquisition, and any potential on-site factors that might affect the acquisition were recorded.

[0061] Step S2-4 Numbering: Mark the nodes by combining the step number and node sequence number. For example: the nodes at the bottom of the slope are numbered T0-1-1, T0-1-2, T0-1-3...; the nodes of the first step are numbered T1-1-1, T1-1-2, T1-1-3... Assuming the first step is wide enough, add a node seismograph within 10m and number it T1-2-1, T1-2-2, T1-2-3...; the nodes of the second step are numbered T2-1-1, T2-1-2, T2-1-3... and so on; the nodes at the top of the slope are numbered by a certain point in a certain column, such as D3-2 representing the second node seismograph in the third column at the top of the slope.

[0062] Step S3: After data collection, download, organize, and check the raw data. After confirming that the data is correct, proceed with data processing. Figure 3 The specific processing procedure is as follows:

[0063] Step S3-1: Data Processing Flow of Background Noise Surface Wave Dispersion Inversion Method

[0064] Before performing the dispersion inversion, the collected raw data is preprocessed, including removing instrument response, removing mean, removing trend, unit length cutting, and suppressing interference signals. The processed waveform is then subjected to spectral whitening within the required frequency band. Then, the spatial autocorrelation function between the two stations is calculated every 2 minutes using the frequency domain waveform spatial autocorrelation method.

[0065] Let the distance between stations A and B be r. Express their spatial autocorrelation in the form of a Bessel function: (1)

[0066] In the formula Given the average spectral density of the wave field, the Green's function in the frequency domain of the Rayleigh surface wave can be written in the form of the Neumann function and the Bessel function:

[0067] (2)

[0068] Since earthquake background noise is not white noise, the spectral coherence of the earthquake background waveform also needs to be normalized, i.e.,

[0069] (4)

[0070] (5)

[0071] By averaging the spatial and temporal data from stations at different azimuths during micro-motion array observations, a uniform background noise source can be achieved. The imaginary part is equal to 0. In summary, after obtaining the autocorrelation function between stations, the Rayleigh surface wave phase velocity dispersion information at different frequencies is obtained according to the following formula:

[0072] (6)

[0073] In the formula, f is the frequency, r is the distance between stations, C is the Rayleigh surface wave phase velocity, and k is the wave number. By fitting the zero-order Bessel function or determining the zero poles, the Rayleigh wave phase velocities of different periods are obtained. By combining the spatial and temporal averaging between different arrays, a reliable Rayleigh surface wave phase velocity dispersion curve below the array is obtained. Subsequently, a 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.

[0074] Step S3-2: HVSR Imaging Data Processing Flow

[0075] Using the acquired three-component background noise data, the STA / LTA algorithm was employed to remove short-term interference. The remaining steady-state micro-motion data was divided into several 50-second time windows, and the HVSR curve for each time window was calculated.

[0076] (7)

[0077] Wherein, NS, EW, and V are the Fourier spectra of the north-south, east-west, and vertical components of the micro-motion, respectively. The HVSR curve of each time window is smoothed using a Pazen window with a width of 12. The mean of the HVSR curves of all time windows is calculated to obtain the final HVSR curve and its corresponding standard deviation.

[0078] The peak frequency of the HVSR curve is approximately the resonance frequency of the sedimentary layer, but the stratigraphic interface and velocity information cannot be directly obtained from the HVSR curve. When the study area has a simple single-layer structure, the thickness of the sedimentary layer h is equivalent to 1 / 4 wavelength λ of the basic resonant seismic wave.

[0079] (8)

[0080] in The resonant frequency is the fundamental frequency, and when n=1, it is the fundamental resonance. The average S-wave velocity of the sediment layer is used. The depth conversion of the HVSR curve is guided by the surface wave dispersion curve to obtain the HVSR depth domain imaging profile.

[0081] Step S3-3: Data Processing Flow of Passive Source Volume Wave Imaging Method

[0082] Since volume waves are weak signals in the acquired signals, it is necessary to separate them from surface waves to improve the imaging signal-to-noise ratio (SNR) of volume wave signals. Based on the continuous waveform data recorded by the array, the frequency domain SNR method is used to separate surface waves and volume waves. The formula for calculating the frequency domain SNR can be expressed as:

[0083] (9)

[0084] 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 characteristic, the data segment where body waves dominate is selected, and interferometry is performed using a mutual coherence algorithm to extract the Green's function between two receiver points, thereby obtaining the seismic virtual shot gather record; the mutual coherence in the frequency domain is represented as:

[0085] (10)

[0086] After obtaining the virtual source shot gather record with body wave dominance after 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 reflected wave information. Finally, seismic migration imaging methods such as post-stack depth migration are used to obtain the three-dimensional seismic imaging results of the work area.

[0087] 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 within the spoil heap is effectively located and interpreted. The results of the three methods are spatially complementary: the low-velocity anomaly in the shear wave velocity model corresponds to the wave velocity reduction characteristics in the fracture development area; the low-frequency anomaly in the HVSR imaging profile reflects the resonance frequency reduction phenomenon caused by fracture development; and the in-phase axis fracture or strong scattering area in the seismic reflection profile reveals the influence of fractures on seismic wave propagation. Through unified depth calibration and cross-validation of spatial distribution, these anomaly features are superimposed in three-dimensional space to achieve a fine characterization of the geometric morphology and physical properties of the fracture development zone, further improving the accuracy of imaging results and the reliability of interpretation.

[0088] In the description of this specification, the terms "one embodiment," "some embodiments," "specific embodiment," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0089] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method of passive seismic imaging of a fracture in a slope, characterized in that ,comprising the following steps: Step S1: setting node seismograph acquisition parameters according to the actual geological conditions of the dump; testing the consistency of the instrument; Step S2: analyzing the step distribution, slope top area and slope bottom area of the dump, and uniformly burying node seismographs in the steps, slope top and slope bottom of the dump according to the actual geological conditions of the survey area; Step S3: after the acquisition is completed, download, organize and check the original data, and perform data processing, the specific processing procedure being as follows: Step S3-1: data processing procedure of background noise surface wave dispersion inversion method Before formally performing dispersion inversion, the original data collected are preprocessed, including removing instrument response, removing mean value, removing trend, unit length cutting and suppressing interference signals; the processed waveforms are subjected to spectral whitening processing in the required frequency band, then the spatial autocorrelation function between two stations is calculated every 2 minutes by using the frequency domain waveform spatial autocorrelation method; Let the distance between stations A and B be r, and their spatial autocorrelation be expressed in the form of the Bessel function: (1) where The Green's function for the Rayleigh surface wave frequency domain is written in terms of Neumann and Bessel functions, where is the average spectral density of the wave field. （2） Since the seismic background noise is not white noise, the spectral coherence of the seismic background waveform also needs to be normalized, that is, （4） （5） The spatial and temporal average of different azimuthal stations observed by the microtremor array satisfies the homogeneous background noise source, at this time the imaginary part of the complex amplitude is equal to 0; in summary, after obtaining the autocorrelation function between stations, the Rayleigh surface wave phase velocity dispersion information at different frequencies is obtained according to the following formula: （6） In the formula, f is the frequency, r is the distance between stations, C is the Rayleigh wave phase velocity, and k is the wave number; the Rayleigh wave phase velocity of different periods is obtained by fitting the zero-order Bessel function or determining the zero-pole; in combination with the spatial and temporal averages between different arrays, the reliable Rayleigh wave phase velocity dispersion curve under the array is obtained; 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 procedure Using the collected three-component background noise data, the STA / LTA algorithm is used to remove short-time interference, the remaining steady micro-motion data is divided into several 50s time windows, and the HVSR curve of each time window is calculated, （7） In the formula, NS, EW and V are the Fourier spectra of the north-south, east-west and vertical components of the micro-motion; the HVSR curve of each time window is smoothed by using a 12-width Parzen window, and the mean value of the HVSR curves of all time windows is calculated to obtain the final HVSR curve and the corresponding standard deviation; When the study area is a simple single-layer structure, the thickness h of the sedimentary layer is equivalent to 1 / 4 of the wavelength of the base resonant seismic wave; （8） wherein is the resonance frequency, n = 1, 3, 5..., and when n = 1, it is the fundamental resonance, is the average S-wave velocity of the deposition layer, and the depth conversion of the HVSR curve is guided by the surface wave dispersion curve to obtain the HVSR depth domain imaging profile; Step S3-3: data processing procedure of passive source body wave imaging method 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, and the frequency domain signal-to-noise ratio calculation formula can be expressed as: （9） The signal-to-noise ratio of the time window data when surface waves are dominant is lower than that when body waves are dominant, based on this feature, the data segment with dominant body waves is selected, the mutual coherence algorithm is used for interference operation, the Green function between two geophones is extracted, and then the seismic virtual shot record is obtained; the mutual coherence interference in the frequency domain is expressed as: （10） After obtaining the virtual seismic source shot record with dominant body waves by the seismic interference algorithm, the conventional processing methods including geometric diffusion compensation, trace equalization, deconvolution, velocity analysis, moveout correction and common midpoint stacking are used to enhance the reflection wave information, and finally the post-stack depth migration seismic migration imaging method is used to obtain the three-dimensional seismic imaging result of the work area; Step S4: The fissure development zone inside the dump is effectively located and interpreted by comprehensively constraining the three-dimensional shear wave velocity model, the HVSR depth domain imaging profile and the seismic reflection wave migration profile; the results of the three methods complement each other in space: the low-velocity anomaly area in the shear wave velocity model corresponds to the wave velocity reduction characteristics of the crack development area, the low-frequency anomaly area of the HVSR imaging profile reflects the resonance frequency reduction phenomenon caused by the fissure development, and the broken phase axis or strong scattering area in the seismic reflection profile reveals the influence of the cracks on the propagation of the seismic wave.

2. The method of claim 1, wherein The node seismograph acquisition parameters in the step S1 include an observation system, a sampling time, a sampling interval, an acquisition mode, a trace interval and a station interval.

3. The method of claim 1, wherein The specific layout mode of the node seismograph uniformly buried at the steps, the top of the slope and the bottom of the slope in the step S2 is as follows: Step S2-1: The top of the slope: the node seismographs are uniformly arranged at the top of the slope, and the point interval is set to 10 meters by considering the data density and the layout efficiency; Step S2-2: The edge of the step: the node seismographs are uniformly arranged at the edge of each step to form a closed layout around the step, and the point interval is set to 10 meters; Step S2-3: The bottom of the slope: the node seismographs are arranged around the bottom of the slope, and the point interval is set to 10 meters to cover the potential fissure development zone of the bottom of the slope; Step S2-4: Numbering: the marking is performed in combination with the step number and the node serial number.

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