A non-destructive and efficient method for geological structure investigation of waste rock piles

Abstract

The invention discloses a non-destructive and efficient method for investigating the geological structure of a waste rock mountain, which belongs to the technical field of investigating the geological structure of a waste rock mountain and solves the problem of how to arrange node seismographs in the form of irregular arrays to carry out non-destructive and efficient investigation of the geological structure of a waste rock mountain. The technical scheme of the invention collects micro-motion signals by arranging node seismographs in the form of irregular arrays on the target slope of the waste rock mountain. The micro-motion signal data collected by the node seismographs are processed, dispersion curves are extracted by an extended autocorrelation method, and shear wave velocity structures are inverted by a genetic algorithm, so as to obtain a two-dimensional velocity profile of the slope of the waste rock mountain. The positions of void areas, weak layers and water-saturated areas are determined according to the distribution of the two-dimensional velocity profile, and prevention and control measures are formulated for the positions of void areas, weak layers and water-saturated areas. There is no need to arrange observation arrays of complex shapes, which saves a lot of manpower and material resources, has high data processing efficiency, and realizes non-destructive and efficient investigation of the geological structure of the waste rock mountain.

Description

Technical Field

[0001] The invention belongs to the technical field of geological structure investigation of waste rock piles, and relates to a non-destructive and efficient geological structure investigation method of waste rock piles. Background Art

[0002] Waste rock heaps are formed by the open-air accumulation of rock and soil materials discarded during coal mining and washing and processing. They are typical artificial accumulations of loose bodies. The accumulation materials of waste rock heaps are complex, have small near-sphericity, and have different strengths. In the accumulation process, they are prone to form unfavorable geological bodies such as void areas, weak layers, and water-saturated areas. Due to their poor structural strength, when they are disturbed by external factors (artificial excavation, explosions, and heavy rains), they are prone to induce geological disasters such as collapse, landslides, and mud-rock flows in waste rock heaps, threatening the personal and property safety of nearby residents. Therefore, it is necessary to investigate the internal geological structure of waste rock heaps, explore the distribution of unfavorable geological bodies, and implement targeted prevention and control measures for corresponding dangerous areas to ensure the safety and stability of waste rock heaps.

[0003] At present, the most commonly used method for geological structure survey is drilling, which can directly obtain accurate information on strata and lithology, but the information obtained is limited and the construction process is destructive, so it is not suitable for use on waste rock heaps. Although traditional geophysical exploration (such as electrical method, radar exploration, etc.) can obtain overall geological structure information, it also has shortcomings such as shallow detection depth and susceptibility to interference, and cannot meet the requirements for effective detection of the geological structure of waste rock heaps.

[0004] The microseismic survey method is a passive surface wave exploration technology. This method does not require active excitation of the seismic source. It receives weak vibration signals from the ground to image the underground space. It has the advantages of safe seismic source and large detection depth, and is widely used in exploration at various engineering scales. However, the traditional microseismic survey method often requires the deployment of complex observation arrays. An observation array is generally composed of 4 to 10 detectors, which are connected by cables. The observation radius ranges from a few meters to tens of meters. It has high site requirements, cumbersome deployment, and difficult movement, resulting in high labor costs and low collection efficiency in its detection project.

[0005] The node seismograph is an integrated seismic data acquisition tool. Although it is small in size, it can realize single-body GPS timing, GPS positioning and autonomous data recording. It gets rid of the problem of fixed point positions of connected instruments in the absence of GPS, and can be flexibly deployed in areas with complex terrain. Summary of the invention

[0006] The technical problem to be solved by the present invention is: how to use irregular arrays to arrange node seismographs to conduct non-destructive and efficient investigation of the geological structure of a waste rock mountain.

[0007] The present invention solves the above technical problems through the following technical solutions:

[0008] A non-destructive and efficient method for investigating geological structures of waste rock heaps comprises the following steps:

[0009] S1. Arrange node seismographs in an irregular array on the target slope of the waste rock mountain to collect micro-motion signals;

[0010] S2. Process the micro-seismic signal data collected by the node seismometer, extract the dispersion curve using the extended autocorrelation method, and invert the shear wave velocity structure using the genetic algorithm to obtain the two-dimensional velocity profile of the waste rock hill slope;

[0011] S3. Determine the locations of void areas, weak layers and water-saturated areas based on the distribution of two-dimensional velocity profiles, and formulate prevention and control measures for the locations of void areas, weak layers and water-saturated areas.

[0012] The technical solution of the present invention applies node seismometers to the micro-seismic investigation method. There is no need to deploy observation arrays of complex shapes. A node seismometer is a data acquisition station with a small deployment area and no need for cable connection, which saves a lot of manpower and material resources. For irregular arrays, the extended autocorrelation method and genetic algorithm are used to process the data collected by the node seismometers on the waste rock pile. The data processing efficiency is high, and a non-destructive and efficient waste rock pile geological structure investigation is realized. The investigation results can provide reference materials for the safety prevention and control of waste rock piles.

[0013] Furthermore, the node seismograph comprises: a wireless communication module (10), a time synchronization module (11), a central control module (12), a data storage module (13), a signal acquisition module (14), a power module (15), and a detector (16); the node seismograph comprises: a wireless communication module (10), a time synchronization module (11), a central control module (12), a data storage module (13), a signal acquisition module (14), a power module (15), and a detector (16); the wireless communication module (10), the time synchronization module (11), the data storage module (13), the signal acquisition module (14), and the power module (15) are respectively connected to the central control module (12), and the detector (16) is connected to the signal acquisition module (14).

[0014] Furthermore, the method described in step S1 for arranging node seismometers on the target slope of the waste rock mountain to collect micro-motion signals is as follows: first, a plurality of node seismometers are arranged on the target slope of the waste rock mountain, parameters are set and the node seismometers are activated; then, the GPS signal is received through the time synchronization module (11) to synchronize the time of each node seismometer; then, the detector (16) starts to continuously receive the micro-motion signal and convert it into an electrical signal; then, the collected electrical signal is converted into a digital signal through the built-in A / D converter of the signal acquisition module (14), and the data storage module (13) stores the converted digital signal in the built-in SD card; finally, after the acquisition work is completed, the node seismometers are uniformly recovered.

[0015] Furthermore, the specific method of extracting the dispersion curve using the extended autocorrelation method described in step S2 is as follows:

[0016] S2.1. Obtaining the Autocorrelation Coefficient

[0017] Assuming that the micromotion signals recorded by any two node seismometers S1 and S2 are u1(t) and u2(t), the calculation formula of the spatial autocorrelation coefficient ρ of the signal is as follows:

[0018]

[0019] Among them, S(r,θ,f) is the cross spectrum of node seismometers S1 and S2, S(0,f) and S(r,f) are the power spectra of node seismometers S1 and S2, respectively, and r is the distance between adjacent node seismometers;

[0020] S2.2. Extracting dispersion curves

[0021] First, fix the frequency f0 and obtain a sequence r~ρ(r,f0) consisting of different lengths r and corresponding spatial autocorrelation coefficients ρ; then, fit it with the zero-order Bessel function J0 through the least squares method to obtain the Bessel function variable x=2πf0r / c(f0), and then calculate the Rayleigh wave phase velocity c(f0) corresponding to the frequency f0; repeat the above steps to calculate the Rayleigh wave phase velocity c(f) of other frequencies f, and finally obtain the dispersion curve at this node.

[0022] Furthermore, the calculation formula of the cross spectrum of the node seismometers S1 and S2 is as follows:

[0023] Among them, R 1,2 (τ)=∑u1(t)u2(t+τ) (2)

[0024] The calculation formula of the power spectrum of the node seismometers S1 and S2 is as follows:

[0025] Among them, R1,1 (τ)=∑u1(t)u1(t+τ) (3)

[0026] Among them, R 2,2 (τ)=∑u2(t)u2(t+τ) (4)

[0027] Among them, R(τ)=∑u(t)u(t+τ) is the autocorrelation function of the signal u(t).

[0028] Furthermore, the least square method and the zero-order Bessel function J0 are fitted with the following formula:

[0029] ρ(r,f)=J0(2πfr / c(f)) (5)

[0030] Var=∑[ρ(r)-J0(r)] 2 (6)

[0031] Among them, Var represents the fit between the spatial autocorrelation coefficient function ρ(r) and the zero-order Bessel function J0(r).

[0032] Furthermore, the specific method of using genetic algorithm to invert the shear wave velocity structure described in step S2 is as follows: using the half-wavelength theory to convert the extracted dispersion curve into a one-dimensional shear wave velocity structure model; then using the shear wave velocity as the inversion target, setting the objective function to perform velocity structure inversion; repeating the above steps until the genetic algorithm model population update algebra reaches a limited number of times, thereby obtaining the inverted shear wave velocity.

[0033] Furthermore, the calculation formula for converting the extracted dispersion curve into a one-dimensional shear wave velocity structure model using the half-wavelength theory is as follows:

[0034] D = 0.5 × c / f, where c = (0.9-0.95) V s (7)

[0035] Where D is the apparent depth of Rayleigh wave propagation, V s is the shear wave velocity;

[0036] The objective function formula is as follows:

[0037]

[0038] Among them, E(m j ) is the jth generation model m j The corresponding objective function, N is the number of frequency points, V the (i) is the theoretical V at the ith frequency point s Speed, V obs (i) is the observed V at the ith frequency points speed.

[0039] Furthermore, the specific method of inverting the shear wave velocity structure using the genetic algorithm described in step S2 is as follows:

[0040] The specific process of the genetic algorithm is as follows:

[0041] 1) Binary encoding of the velocity model, where each bit of the binary code is equivalent to a "gene" and can take a value of 1 or 0;

[0042] 2) Set the number of initial models, encoding length, and number of evolutions, and then randomly generate models to form an initial model population so that they are evenly distributed in the entire model space;

[0043] 3) Based on probability To select the model, P s The probability of a model individual with a high value being selected is high, P s The probability of a model individual with a low probability of being selected is low until all subsets in the model space are successfully paired. m represents the model, M is the model space;

[0044] 4) Set an exchange probability P for each encoding parameter of the paired model x , to exchange;

[0045] 5) Set a mutation probability P m , randomly change a "gene" in the selected parameter code;

[0046] 6) After the new model is obtained by mutation, the degree of fit of the objective function of the new and old models is calculated. If the new model has a low degree of fit, then according to a certain update probability P u , replace the new model with the model in the previous generation; repeat the above steps until the model group update algebra reaches a limited number of times, and the inverted shear wave velocity is obtained.

[0047] Furthermore, the specific method for obtaining the two-dimensional velocity profile of the slope of the waste rock mountain described in step S2 is as follows: the inverted one-dimensional shear wave velocity structure of each node seismometer is combined and gridded, and the velocity of the blank nodes between the velocity structures is assigned using the nearest neighbor interpolation method, that is, the velocity value closest to the blank node in the horizontal-vertical coordinate system is assigned to the blank node, and finally the two-dimensional velocity profile under the side line can be obtained.

[0048] The advantages of the present invention are:

[0049] Currently, the commonly used methods for geological survey of waste rock hills (such as drilling and traditional geophysical methods) are destructive or have shallow detection depths and are easily disturbed, and are not suitable for surveys on waste rock hills. The traditional micro-seismic survey method has complex and cumbersome array layout and high site requirements, and cannot be carried out on the complex and steep terrain of waste rock hills. The technical solution of the present invention applies node seismometers to the micro-seismic survey method, and there is no need to deploy observation arrays of complex shapes. A node seismometer is a data acquisition station with a small deployment area and no cable connection, which saves a lot of manpower and material resources. For irregular arrays, the extended autocorrelation method and genetic algorithm are used to process the data collected by the node seismometers on the waste rock hills, thereby realizing non-destructive and efficient geological structure surveys of waste rock hills. The survey results can provide reference materials for the safety prevention and control of waste rock hills. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of a non-destructive and efficient method for investigating geological structures of waste rock heaps according to the first embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram of the principle of a node seismograph in Embodiment 1 of the present invention to investigate the geological structure of a waste rock mountain;

[0052] Figure 3 is a structural schematic diagram of a node seismograph according to the first embodiment of the present invention;

[0053] Figure 4 is a flow chart of extracting dispersion curves using the extended autocorrelation method according to the first embodiment of the present invention;

[0054] Figure 5 This is a flow chart of the genetic algorithm inversion of shear wave velocity structure according to the first embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and specific embodiments:

[0057] Embodiment 1

[0058] like Figure 1 As shown, a non-destructive and efficient method for investigating the geological structure of a waste rock dump of the present invention comprises the following steps:

[0059] 1. Deploy node seismometers on the target slope of the waste rock mountain to collect micro-motion signals

[0060] When there is no earthquake, the surface of the earth is in a weak vibration state everywhere. This continuous weak vibration (amplitude 10 -2 ~10 -4 Micromotion (mm) is called micromotion. It is generally caused by human activities and natural phenomena, and comes from all directions of the observation point, carrying rich information about the earth's interior. Micromotion signals have different propagation speeds in different earth media. They propagate quickly in hard rocks, but slowly in soft rock layers, gaps or aquifers. Therefore, geological conditions such as stratum lithology and geological structure can be indirectly inferred based on the speed of micromotion signals.

[0061] like Figure 2 As shown, firstly, multiple node seismometers are arranged on the target slope of the waste rock mountain, parameters are set and the node seismometers are activated; then the GPS signal is received by the time synchronization module 11 to synchronize the time of each node seismometer; then the detector 16 starts to continuously receive micro-motion signals (such as Figure 2 The collected electrical signal is converted into a digital signal by the built-in A / D converter of the signal acquisition module 14, and the data storage module 13 stores the converted digital signal in the built-in SD card; finally, after the collection work is completed, the node seismometers are collected uniformly; during the collection period, the central control module 12 controls the coordinated work between the various modules, the power supply module 15 provides power for the node seismometers, and the wireless communication module 10 transmits the test results and collection status information to the user device to check the abnormality of the data collection status, and timely remove bad sectors and adjust the instrument.

[0062] The node seismograph includes: a wireless communication module 10, a time synchronization module 11, a central control module 12, a data storage module 13, a signal acquisition module 14, a power module 15, and a detector 16; the wireless communication module 10, the time synchronization module 11, the data storage module 13, the signal acquisition module 14, and the power module 15 are respectively connected to the central control module 12, and the detector 16 is connected to the signal acquisition module 14.

[0063] 2. Use the extended autocorrelation method and genetic algorithm to process the micro-seismic signal data collected by the node seismometer to obtain the two-dimensional velocity profile of the slope of the waste rock mountain;

[0064] 2.1. Extended autocorrelation method to extract dispersion curves

[0065] like Figure 4 The figure shows the flow chart of extracting dispersion curve by extended autocorrelation method, which includes the following steps:

[0066] (1) Obtaining the autocorrelation coefficient

[0067] Assuming that the micromotion signals recorded by any two node seismometers S1 and S2 are u1(t) and u2(t), the calculation formula of the spatial autocorrelation coefficient ρ of the signal is as follows:

[0068]

[0069] Among them, S(r,θ,f) is the cross spectrum of node seismometers S1 and S2, S(0,f) and S(r,f) are the power spectra of node seismometers S1 and S2, respectively, and r is the distance between adjacent node seismometers;

[0070] The calculation formula of the cross spectrum of the node seismometers S1 and S2 is as follows:

[0071] Among them, R 1,2 (τ)=∑u1(t)u2(t+τ) (2)

[0072] The calculation formula of the power spectrum of the node seismometers S1 and S2 is as follows:

[0073] Among them, R 1,1 (τ)=∑u1(t)u1(t+τ) (3)

[0074] Among them, R 2,2 (τ)=∑u2(t)u2(t+τ) (4)

[0075] Among them, R(τ)=∑u(t)u(t+τ) is the autocorrelation function of the signal u(t).

[0076] (2) Extracting dispersion curves

[0077] First, fix the frequency f0 and obtain a sequence r~ρ(r,f0) consisting of different distances r and corresponding spatial autocorrelation coefficients ρ; then, fit it with the zero-order Bessel function J0 through the least squares method to obtain the Bessel function variable x=2πf0r / c(f0), and then calculate the Rayleigh wave phase velocity c(f0) corresponding to the frequency f0; repeat the above steps to calculate the Rayleigh wave phase velocity c(f) of other frequencies f, and finally obtain the dispersion curve at this node.

[0078] The formula for fitting the least square method with the zero-order Bessel function J0 is as follows:

[0079] ρ(r,f)=J0(2πfr / c(f)) (5)

[0080] Var=∑[ρ(r)-J0(r)]2 (6)

[0081] Since the extended autocorrelation method extracts the dispersion curve by keeping the frequency f unchanged, for different r, the Bessel function variable 2πf0r / c(f0) is linearly related to r, so the autocorrelation coefficients at different distances r can be combined, making it possible to process irregular arrays.

[0082] 2.2 Inversion of shear wave velocity structure using genetic algorithm

[0083] like Figure 5 The figure shows the flow chart of genetic algorithm inversion of shear wave velocity structure. The extracted dispersion curve is transformed into a one-dimensional shear wave velocity structure model using the half-wavelength theory. The calculation formula is as follows:

[0084] D = 0.5 × c / f, where c = (0.9-0.95) V s (7)

[0085] Where D is the apparent depth of Rayleigh wave propagation, V s is the shear wave velocity;

[0086] Then, the shear wave velocity is taken as the inversion target, and the objective function is set to perform velocity structure inversion, so that the inverted shear wave velocity structure can more accurately reflect the real situation of the underground medium;

[0087] The objective function formula is as follows:

[0088]

[0089] Among them, E(m j ) is the jth generation model m j The corresponding objective function, N is the number of frequency points, V the (i) is the theoretical V at the ith frequency point s Speed, V obs (i) is the observed V at the ith frequency point s speed;

[0090] Repeat the above steps until the genetic algorithm model population update generation reaches a limited number of times, thereby obtaining the inverted shear wave velocity.

[0091] The specific process of the genetic algorithm is as follows:

[0092] 1) Binary encoding of the velocity model, where each bit of the binary code is equivalent to a "gene" and can take a value of 1 or 0;

[0093] 2) Set the number of initial models, encoding length, and number of evolutions, and then randomly generate models to form an initial model population so that they are evenly distributed in the entire model space;

[0094] 3) Based on probability To select the model, P s The probability of a model individual with a high value being selected is high, P s The probability of a model individual with a low probability of being selected is low until all subsets in the model space are successfully paired. m represents the model, M is the model space;

[0095] 4) Set an exchange probability P for each encoding parameter of the paired model x , to exchange;

[0096] 5) Set a mutation probability P m , randomly change a "gene" in the selected parameter code;

[0097] 6) After the new model is obtained by mutation, the degree of fit of the objective function of the new and old models is calculated. If the new model has a low degree of fit, then according to a certain update probability P u , replace the new model with the model in the previous generation; repeat the above steps until the model group update algebra reaches a limited number of times, and the inverted shear wave velocity is obtained.

[0098] 2.3 Two-dimensional velocity structure imaging

[0099] The inverted one-dimensional shear wave velocity structures of the seismometers at each node are combined and gridded, and the velocity of the blank nodes between the velocity structures is assigned using the nearest neighbor interpolation method, that is, the velocity value closest to the blank node in the horizontal-vertical coordinate system is assigned to the blank node, and finally the two-dimensional velocity profile under the lateral line can be obtained.

[0100] 3. Determine the location of unfavorable geological bodies (void areas, weak layers, water-saturated areas, etc.) based on the distribution of low-velocity bodies in the two-dimensional velocity profile, and then formulate and implement targeted prevention and control measures for the corresponding dangerous areas based on the location of the unfavorable geological bodies.

[0101] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A non-destructive and efficient method for investigating the geological structure of a waste rock dump, characterized in that: The following steps are involved: S1. Arrange node seismographs in an irregular array on the target slope of the waste rock mountain to collect micro-motion signals; S2. Process the micro-seismic signal data collected by the node seismometer, extract the dispersion curve using the extended autocorrelation method, and invert the shear wave velocity structure using the genetic algorithm to obtain the two-dimensional velocity profile of the waste rock hill slope; The specific method of extracting the dispersion curve using the extended autocorrelation method is as follows: S2.

1. Obtaining the Autocorrelation Coefficient Assuming that the micromotion signals recorded by any two node seismometers S1 and S2 are u1(t) and u2(t), the calculation formula of the spatial autocorrelation coefficient ρ of the signal is as follows: Among them, S(r,θ,f) is the cross spectrum of node seismometers S1 and S2, S(0,f) and S(r,f) are the power spectra of node seismometers S1 and S2, respectively, and r is the distance between adjacent node seismometers; S2.

2. Extracting dispersion curves First, fix the frequency f0 and obtain a sequence r~ρ(r, f0) composed of different lengths r and corresponding spatial autocorrelation coefficients ρ; then, fit it with the zero-order Bessel function J0 through the least squares method to obtain the Bessel function variable x=2πf0r / c(f0), and then calculate the Rayleigh wave phase velocity c(f0) corresponding to the frequency f0; repeat the above steps to calculate the Rayleigh wave phase velocity c(f) of other frequencies f, and finally obtain the dispersion curve at this node; The specific method of inverting the shear wave velocity structure using the genetic algorithm is as follows: using the half-wavelength theory to convert the extracted dispersion curve into a one-dimensional shear wave velocity structure model; then taking the shear wave velocity as the inversion target, setting the objective function to perform velocity structure inversion; repeating the above steps until the genetic algorithm model population update algebra reaches a limited number of times, thereby obtaining the inverted shear wave velocity; The specific process of the genetic algorithm is as follows: 1) Binary code the velocity model. Each bit of the binary code is equivalent to a "gene" and can take a value of 1 or 0. 2) Set the number of initial models, encoding length, and number of evolutions, and then randomly generate models to form an initial model population so that they are evenly distributed in the entire model space; 3) Based on probability To select the model, P s The probability of a model individual with a high value being selected is high, P s The probability of a model individual with a low probability of being selected is low until all subsets in the model space are successfully paired; among them, m represents the model, M is the model space; 4) Set an exchange probability P for each encoding parameter of the paired model x , to exchange; 5) Set a mutation probability P m , randomly change a "gene" in the selected parameter code; 6) After the new model is obtained by mutation, the fitness of the objective function of the new and old models is calculated; if the fitness of the new model is small, then according to a certain update probability P u , replace the new model with the model in the previous generation; repeat the above steps until the model population update algebra reaches a limited number of times, and the inverted shear wave velocity is obtained; The specific method for obtaining the two-dimensional velocity profile of the slope of the waste rock mountain is as follows: the inverted one-dimensional shear wave velocity structure of each node seismometer is combined and gridded, and the velocity of the blank node between the velocity structures is assigned using the nearest neighbor interpolation method, that is, the velocity value closest to the blank node is assigned to the blank node in the horizontal-vertical coordinate system, and finally the two-dimensional velocity profile under the lateral line can be obtained; S3. Determine the locations of void areas, weak layers and water-saturated areas based on the distribution of two-dimensional velocity profiles, and formulate prevention and control measures for the locations of void areas, weak layers and water-saturated areas.

2. A non-destructive and efficient method for investigating geological structures of waste rock heaps according to claim 1, characterized in that: The node seismograph comprises: a wireless communication module (10), a time synchronization module (11), a central control module (12), a data storage module (13), a signal acquisition module (14), a power module (15), and a detector (16); the node seismograph comprises: a wireless communication module (10), a time synchronization module (11), a central control module (12), a data storage module (13), a signal acquisition module (14), a power module (15), and a detector (16); the wireless communication module (10), the time synchronization module (11), the data storage module (13), the signal acquisition module (14), and the power module (15) are respectively connected to the central control module (12), and the detector (16) is connected to the signal acquisition module (14).

3. A non-destructive and efficient method for investigating geological structures of waste rock heaps according to claim 2, characterized in that: The method described in step S1 for arranging node seismometers on the target slope of the waste rock mountain to collect micro-motion signals is as follows: first, a plurality of node seismometers are arranged on the target slope of the waste rock mountain, parameters are set and the node seismometers are activated; then, the GPS signal is received through the time synchronization module (11) to synchronize the time of each node seismometer; then, the detector (16) starts to continuously receive the micro-motion signal and convert it into an electrical signal; then, the collected electrical signal is converted into a digital signal through the built-in A / D converter of the signal acquisition module (14), and the data storage module (13) stores the converted digital signal in the built-in SD card; finally, after the acquisition work is completed, the node seismometers are uniformly recovered.

4. A non-destructive and efficient method for investigating geological structures of waste rock heaps according to claim 3, characterized in that: The calculation formula of the cross spectrum of the node seismometers S1 and S2 is as follows: Among them, R 1,2 (τ)=∑u1(t)u2(t+τ)(2) The calculation formula of the power spectrum of the node seismometers S1 and S2 is as follows: Among them, R 1,1 (τ)=∑u1(t)u1(t+τ)(3) Among them, R 2,2 (τ)=∑u2(t)u2(t+τ)(4)wherein, R(τ)=∑u(t)u(t+τ) is the autocorrelation function of the signal u(t).

5. A non-destructive and efficient method for investigating geological structures of waste rock heaps according to claim 4, characterized in that: The formula of the least square method and the zero-order Bessel function J0 is as follows: ρ(r,f)=J0(2πfr / c(f)) (5) Var=∑[ρ(r)-J0(r)] 2 (6) Among them, Var represents the fit between the spatial autocorrelation coefficient function ρ(r) and the zero-order Bessel function J0(r).

6. A non-destructive and efficient method for investigating geological structures of waste rock heaps according to claim 5, characterized in that: The calculation formula for converting the extracted dispersion curve into a one-dimensional shear wave velocity structure model using the half-wavelength theory is as follows: D = 0.5 × c / f, where c = (0.9-0.95) V s (7) Where D is the apparent depth of Rayleigh wave propagation, V s is the shear wave velocity; The objective function formula is as follows: Among them, E(m j ) is the jth generation model m j The corresponding objective function, N is the number of frequency points, V the (i) is the theoretical V at the ith frequency point s Speed, V obs (i) is the observed V at the ith frequency point s speed.

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