Method and system for finely detecting disaster source in front of roadway along with excavation sound waves

Abstract

The invention belongs to the technical field of deep mineral resource development, and provides a roadway front disaster source excavation-following sound wave fine detection method and system, multiple groups of detection data in the excavation process are obtained, and the detection data comprise seismic source vibration data and excavation-following sound wave data; the method comprises the following steps: preprocessing detection data to obtain a single-channel seismic record, carrying out delay calculation and beam forming directional superposition, optimizing a reflection coefficient sequence, and carrying out multiple times of beam forming directional superposition through combination; and establishing a geologic model based on the superposed multi-component wave field data, calculating wave velocity distribution by using a tomography algorithm, and carrying out disaster source imaging by using the calculated wave velocity distribution as an initial model and using a decoupling reverse time migration method. According to the invention, effective suppression of interference signals is realized, and spatial positioning and imaging of disaster sources are realized by using a decoupling elastic reverse time migration imaging method.

Description

Technical Field

[0001] This invention belongs to the field of deep mineral resource development technology, specifically relating to a method and system for fine acoustic detection of disaster sources in front of tunnels during excavation, and more specifically relating to a method and system for fine acoustic detection of disaster sources in front of tunnels during fluidized mining of deep solid resources. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Tunnel excavation is one of the most important and dangerous upstream production processes in underground mining, and it is easily affected by geological disasters. Therefore, more precise and faster detection of disaster sources ahead of the mining tunnels is required.

[0004] Deep tunnels differ significantly from traditional tunnels (such as mountain tunnels) in their geological conditions, posing unique challenges to hazard detection. In mining tunnels, fault fracture zones are often affected by goaf disturbances, exhibiting disordered distribution of fractured bodies and poor cementation. Excavation disturbances easily trigger block slippage and fracture expansion within these fracture zones, leading to a chain reaction of rock instability. Therefore, the dynamic instability of loose bodies in fault fracture zones and the highly developed adverse geological conditions present severe safety challenges and hazard risks to rock tunnel excavation.

[0005] Mineral resource extraction is developing towards deeper and more efficient methods. Traditional active source seismic wave methods in tunnels are difficult to accurately identify small and medium-sized unfavorable geological bodies such as small fracture zones, boulders, and karst due to limited underground space and interference from mining-induced fractures. At the same time, the detection process relies on shutdown operations, which affects tunneling efficiency and cannot meet the needs of tunnel construction for fluidized mining of deep solid resources. Summary of the Invention

[0006] To address the aforementioned problems, this invention proposes a method and system for fine-grained acoustic detection of hazards ahead of tunnels. This invention utilizes an integrated detection host mounted on the mining equipment to control an acoustic sensor that collects acoustic signal data during excavation, while a source characteristic leader sensor collects source signal data from the mining equipment. The collected data is then preprocessed, undergoing wavefield recovery and signal extraction, noise removal, and spatial localization and imaging of the hazards. This invention enables automated detection and long-distance discovery of hazards such as faults and folds ahead of the mining face, dynamically acquiring information such as the location and scale of the hazards, and solving the problem of timely detection of adverse geological conditions in in-situ fluidized bed mining of solid resources.

[0007] According to some embodiments, the present invention adopts the following technical solution: A method for precise acoustic detection of disaster sources ahead of tunnels during excavation includes the following steps: Multiple sets of detection data were acquired during the tunneling process, including seismic source vibration data and acoustic wave data during tunneling. The detection data is preprocessed to obtain single-channel seismic records for delay calculation and beamforming directional stacking. The reflection coefficient sequence is optimized, and multiple beamforming directional stackings are performed jointly. Based on the superimposed multi-component wavefield data, a geological model is established, and the wave velocity distribution is calculated using a tomographic imaging algorithm. Using the calculated wave velocity distribution as the initial model, the decoupled reverse time migration method is used to image the disaster source.

[0008] As an alternative implementation method, the preprocessing of the probe data includes mean removal, detrending, bandpass filtering, and normalization.

[0009] As an alternative implementation method, the beamforming directional superposition process includes determining the forward movement distance of the tunnel face based on the single detection data acquisition time and the normal tunneling speed of the mining equipment; obtaining the co-seismic source point seismic record of the tunnel face through seismic interference processing of the lead sensor signal and the signals of each sidewall; and using beamforming to superimpose the seismic traces with time delay to obtain a single-trace seismic record.

[0010] As an alternative implementation method, the process of performing multiple beamforming directional stackings in combination includes: obtaining n seismic records through n tunnel boring machine rock-breaking source detections; calculating and analyzing the arrival time and delay calculation of reflected waves from the same interface within the seismic traces; stacking the delay results of each trace containing random noise; selecting the optimal trace as the reference trace; calculating the correlation between the common reflection groups of each delayed seismic trace record and the reference trace seismic record; setting beamforming weights based on the correlation characteristics; and performing joint stacking to improve the contribution of high-quality seismic traces to the stacking results, reduce the impact of bad traces on the stacking results, and improve the signal-to-noise ratio.

[0011] As a further step, the process of calculating the correlation between the common reflection groups of each delayed seismic trace and the reference trace seismic trace includes: the correlation coefficient between two discrete seismic traces is calculated using the following formula:

[0012] In the formula, for and covariance, for variance When the random noise level is high and the signal-to-noise ratio is low, the correlation coefficient... R The correlation coefficient is relatively small; when the random noise value is small and the signal-to-noise ratio is high, the correlation coefficient is relatively small. R Larger; Based on the correlation coefficient values, each seismic trace is first weighted and then superimposed to achieve effective superposition of high signal-to-noise ratio data.

[0013] As an alternative implementation method, the process of imaging disaster sources includes: based on the finite-difference time-domain forward modeling method, assuming known detection data... Source waveform function and wave velocity distribution in the detection area Using the location of the earthquake source leader sensor as the earthquake source, and using the waveform function... A forward modeling simulation is performed on the source function to obtain the source wave field, denoted as . Using the location of the acoustic wave detector as the seismic source, and using the detected data... Performing a reverse-time forward modeling simulation on the source function yields the detector wavefield, denoted as . In the formula This indicates the grid position along the horizontal survey line. The grid position in the depth direction. Imaging is performed at different moments in the forward modeling process based on the source wavefield and the detector wavefield.

[0014] Furthermore, the process of imaging based on the source wavefield and the detector wavefield includes: imaging based on the source wavefield and detector wavefield obtained from forward modeling, using imaging conditions. .

[0015] A precision acoustic detection system for potential hazards ahead in tunnels includes: The detection data acquisition module is used to acquire multiple sets of detection data during the tunneling process, including source vibration data and tunneling acoustic wave data. The data preprocessing module is used to preprocess the detection data, obtain single-channel seismic records, perform time delay calculations and beamforming directional stacking, optimize the reflection coefficient sequence, and perform multiple beamforming directional stackings in combination. The fine imaging module is used to establish a geological model of the tunnel geological conditions based on the superimposed multi-component wavefield data, calculate the wave velocity distribution using tomography algorithm, use the calculated wave velocity distribution as the initial model, and perform disaster source imaging using the decoupled reverse time migration method.

[0016] As an alternative implementation, the detection data acquisition module includes a seismic source leader sensor installed at the front of the mining equipment, and multiple tunneling acoustic detection sensors installed behind the roadway at a certain distance from the leader sensor. The seismic source leader sensor is used to acquire seismic source vibration data, and the tunneling acoustic detection sensors are used to acquire tunneling acoustic data.

[0017] As a further defined implementation method, tunnel observation employs linear observation, array observation, or spatial three-dimensional observation.

[0018] As a further defined implementation method, linear observation is adopted in the tunnel, and multiple acoustic wave detection sensors are arranged on both sides of the tunnel, with a detector spacing of 2~5 m.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention addresses the spatial constraints of traditional active-source seismic wave methods in deep resource mining tunnels by designing an effective integrated solution for carrying out acoustic detection with mining equipment. It focuses on overcoming challenges related to detector spatial positioning and real-time data transmission for forecasting. The invention also studies the spatiotemporal characteristics of wavefields acquired using different observation methods, establishing an array observation method suitable for the confined spaces of tunnels.

[0020] This invention designs a multi-detector joint denoising method based on beamforming for tunneling acoustic data, which effectively suppresses interference signals. It also utilizes a decoupled elastic reverse time migration imaging method to achieve spatial positioning and imaging of disaster sources.

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0022] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0023] Figure 1 A flowchart illustrating a method for precise acoustic detection of disaster sources ahead of a fluidized mining roadway for deep solid resources, according to one embodiment; Figure 2 This is a design drawing of a tunneling acoustic detection device mounting scheme according to one embodiment; Figure 3 A schematic diagram of the deployment scheme of an underground acoustic monitoring system according to one embodiment; Figure 4 This is a schematic diagram illustrating the principle of multiple detection data beamforming in one embodiment; Figure 5 This is a result of correlation-weighted beamforming based on one embodiment.

[0024] In the diagram: 1. Detection host; 2. Tunneling acoustic detection sensor; 3. Mining equipment; 4. Seismic source leader sensor; 5. Adverse geological formation. Detailed Implementation

[0025] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0026] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0027] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0028] Where there is no conflict, the embodiments and features described in this application may be combined with each other.

[0029] Example 1 A method for precise acoustic detection of disaster sources ahead of tunnels, such as... Figure 1 As shown, it includes the following steps: (1) Deployment of equipment equipped with data observation system A seismic source pilot sensor is installed at the head of the mining equipment (in this embodiment, a tunneling machine is used as an example). A tunneling acoustic detection sensor is installed behind the tunnel within a certain range of the pilot sensor. An integrated detection host is installed near the operating room of the mining equipment. The mounted equipment has functions such as ultra-wideband high-speed wireless sensing and multi-channel high-throughput long-term acquisition, which can realize wireless interconnection and time synchronization control of tunnel environment sensors.

[0030] Based on the detection equipment mounted on the mining equipment, a hazard source observation system is constructed in front of the mining roadway using acoustic wave observation. The observation system includes one seismic source leader sensor for collecting seismic source vibration data and several acoustic wave detection sensors for collecting acoustic wave data. The deployment form includes, but is not limited to, linear observation, array observation, and spatial three-dimensional observation. The specific deployment form and number are determined according to the actual situation on site (observation space, data quality, detection distance).

[0031] (2) Processing and noise removal of acoustic wave data during tunneling To improve the signal-to-noise ratio of seismic records, forward modeling was used to reveal the arrival time characteristics of common reflection groups under different seismic-detection observations. An adaptive delay calculation method based on correlation analysis was proposed, and a correlation-weighted beamforming method was established.

[0032] As the tunneling machine advances, multiple noise source detection operations can be performed in non-uniform media, such as... Figure 4As shown, for probes at similar mileage (where the face movement is less than the probe depth), the seismic wave propagation path includes a shared probe area. Therefore, the seismic record captures the interface wave field response from the area covering the probe. Since the seismic wave advance probe lines, the tunneling machine's source, and the target area are approximately on a straight line, the reflected waves generated within the shared probe area exhibit the same apparent velocity direction in the seismic trace concentration. In this case, if the relative arrival times of the reflected waves from the shared probe interval to each geophone are consistent, then based on the consistency analysis of the relative travel times of the shared reflection group, delay calculations between adjacent traces and beamforming can be performed to enhance the forward probe energy.

[0033] Multiple seismic records obtained through data processing are time-delayed and then directionally superimposed using beamforming. The reflection coefficient sequence is optimized. By performing two separate beamforming directional superpositions, comprehensive multi-data analysis is achieved, improving the signal-to-noise ratio of the seismic records. (3) Multi-field localization imaging of disaster sources Based on the processed multi-component wavefield data, a wavefield decoupled reverse time migration imaging method is used for spatial location and imaging of disaster sources. This method first establishes a geological model that closely approximates the actual geological conditions; secondly, it uses a tomographic algorithm to calculate the wave velocity distribution; and finally, using this as the initial model, the decoupled reverse time migration method is employed to achieve disaster source imaging.

[0034] A more detailed explanation follows.

[0035] In this embodiment, the tunnel observation adopts a linear array arrangement, with 20 tunneling acoustic detection sensors arranged on each side wall of the tunnel, the detector spacing is 2~5 m, the total length of the measuring line is about 40~100 m, and one tunneling acoustic detector is installed at the head of the mining machine. The sampling frequency is 1000 Hz, and the sampling time is about 1~2 h.

[0036] The process of processing and removing noise from the acoustic wave data during excavation is as follows: The data processing flow includes mean removal, trend removal, bandpass filtering, normalization, segmented cross-correlation superposition, and denoising through multiple probe data combined with beamforming.

[0037] The noise reduction method is as follows: Beamforming of seismic source data from a single tunnel boring machine (TBM) rock-breaking operation. Given that the data acquisition time for a single detection is approximately 0.5–1 hour, and the TBM is operating normally (at a speed of approximately 0.9 m / h), with the tunnel face moving forward about 1 m, seismic records at resonance source points (with the tunnel face as a "virtual source") are obtained through seismic interferometry processing of signals from the leader sensor and signals from each sidewall. Beamforming is then used to time-delay and superimpose these seismic traces to obtain a single-track seismic record.

[0038] Beamforming of seismic source data from multiple tunnel boring machines breaking rock. n The rock-breaking seismic source detection of the secondary tunneling machine can obtain... n By analyzing and calculating the arrival time and delay of reflected waves from the same interface within the seismic trace, the seismic trace records with high signal-to-noise ratio are obtained. This allows for the relative arrival delay of common reflection groups observed in seismic trace records from different source locations and different detector locations. A second beamforming is then performed on this data.

[0039] Given the delay results containing random noise, as shown below:

[0040] Where u(t) is the seismic record, n(t) is the noise, and τ is the time difference between the arrival of the effective signal at different detectors.

[0041] Conventional beamforming directly superimposes this data as follows:

[0042] As shown above, conventional beamforming can enhance the relative intensity of the effective signal. To further eliminate the influence of bad traces (or low signal-to-noise ratio seismic traces) on the beamforming results, the optimal trace is selected as the reference trace, and the correlation of the common reflection groups between each delayed seismic trace record and the reference trace seismic record is calculated. Beamforming weights are set based on the correlation characteristics to increase the contribution of high-quality seismic traces to the stacking results, weaken the influence of bad traces on the stacking results, and further improve the signal-to-noise ratio, such as... Figure 5 As shown. For two discrete seismic records, their correlation coefficient can be calculated using the following formula:

[0043] In the formula, for and covariance, for variance When the random noise level is high and the signal-to-noise ratio is low, the correlation coefficient... R The correlation coefficient is relatively small; when the random noise value is small and the signal-to-noise ratio is high, the correlation coefficient is relatively small. R Larger.

[0044] By first weighting and then superimposing the seismic traces based on their correlation coefficient values, effective superposition of high signal-to-noise ratio data can be achieved. The weighting calculation for a specific seismic record can be expressed by the following formula:

[0045] In the formula, In order to be in k The instantaneous amplitude of the earthquake record after correlation weighting; For the first iIn the results of this detection k The instantaneous amplitude of the earthquake record was pointed out; For the first i The results of the second detection are in k The weighting function of the instantaneous amplitude at a point.

[0046] In the process of multi-field localization imaging of disaster sources, the finite-difference time-domain forward modeling method is used, assuming that the detection data is known. Source waveform function and wave velocity distribution in the detection area Using the location of the earthquake source leader sensor as the earthquake source, and using the waveform function... By performing a forward modeling simulation on the source function, the source wave field can be obtained, denoted as . Using the location of the acoustic wave detector as the seismic source, and using the detected data... By performing a reverse-time forward modeling of the source function, the detector wavefield can be obtained, denoted as . In the formula This indicates the grid position along the horizontal survey line. The grid position in the depth direction. These represent different moments in the forward modeling iteration process. Based on the source wavefield and detector wavefield obtained from the forward modeling simulation, imaging is performed using imaging conditions.

[0047] .

[0048] Example 2 A precision acoustic detection system for potential hazards ahead in tunnels includes: The detection data acquisition module is used to acquire multiple sets of detection data during the tunneling process, including source vibration data and tunneling acoustic wave data. The data preprocessing module is used to preprocess the detection data, obtain single-channel seismic records, perform time delay calculations and beamforming directional stacking, optimize the reflection coefficient sequence, and perform multiple beamforming directional stackings in combination. The fine imaging module is used to establish a geological model of the tunnel geological conditions based on the superimposed multi-component wavefield data, calculate the wave velocity distribution using tomography algorithm, use the calculated wave velocity distribution as the initial model, and perform disaster source imaging using the decoupled reverse time migration method.

[0049] In this embodiment, as Figure 2 and Figure 3 As shown, the detection data acquisition module includes a seismic source leader sensor 4 installed at the front of the mining equipment 3, and multiple tunneling acoustic detection sensors 2 installed behind the roadway at a certain distance from the seismic source leader sensor 4. The seismic source leader sensor 4 is used to acquire seismic source vibration data, and the tunneling acoustic detection sensors 2 are used to acquire tunneling acoustic data.

[0050] The detection host 1 mounted on the mining equipment 3 controls the tunneling acoustic wave detection sensor 2 to collect tunneling acoustic wave signal data, and the seismic source leader sensor 4 to collect seismic source signal data of the mining equipment. Then, the detection host 1 performs preprocessing, wave field recovery and signal extraction, noise interference removal and disaster source spatial positioning and imaging on the collected data.

[0051] The detection host 1 executes the corresponding steps in the method provided in Embodiment 1, which will not be repeated here.

[0052] This embodiment enables automated detection and long-distance discovery of disaster sources such as faults and folds in front of the mining face, dynamically acquires information such as the location and scale of disaster sources, and solves the problem of timely detection of adverse geological conditions in in-situ fluidized mining of solid resources.

[0053] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of one or more computer-usable storage media (including, but not limited to, disk storage, etc.) containing computer-usable program code. CD - ROM It takes the form of a computer program product implemented on (such as optical memory, etc.).

[0054] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0055] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0056] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0057] The above description is merely a preferred embodiment of the present invention and is not intended to limit the 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 by those skilled in the art without creative effort 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 for fine detection of a disaster source in front of a roadway by using a sound wave, characterized in that, The method comprises the following steps: obtaining a plurality of sets of detection data in the tunneling process, the detection data comprising seismic source vibration data and tunneling sound wave data; preprocessing the detection data to obtain single-channel seismic records for delay calculation and beamforming directional stacking, optimizing the reflection coefficient sequence, and jointly performing multiple beamforming directional stacking; based on the stacked multi-component wave field data, establishing a geological model, calculating the wave velocity distribution using the tomographic imaging algorithm, taking the calculated wave velocity distribution as the initial model, and performing disaster source imaging using the decoupling reverse time migration method.

2. The method for fine acoustic detection of disaster sources ahead of a roadway as described in claim 1, characterized in that, The preprocessing process of the detection data includes mean removal, trend removal, band-pass filtering and normalization processing.

3. The method for fine acoustic detection of disaster sources ahead of a roadway as described in claim 1, characterized in that, The beamforming directional stacking process includes determining the forward movement distance of the tunnel face according to the single detection data acquisition time and the normal tunneling speed of the mining equipment, obtaining the common source point seismic record of the tunnel face through seismic interference processing of the pilot sensor signal and each sidewall signal, and using beamforming to delay stack each seismic channel to obtain a single-channel seismic record.

4. The method according to claim 1, wherein the disaster source in front of the roadway is finely detected by the sound wave. The process of jointly performing multiple beamforming directional stacking includes: obtaining n-channel seismic records through n times of rock breaking seismic source detection of the tunneling machine, calculating the arrival time of the reflection wave of the same interface in the seismic channel for analysis and delay calculation, stacking the delay results of each channel containing random noise, selecting the optimal channel as the reference channel, and calculating the correlation of the common reflection group of each delay seismic channel record and the reference channel seismic record, setting the beamforming weight value according to the correlation characteristics, and jointly performing to improve the contribution of high-quality seismic channels to the stacking result, weaken the influence of bad channels on the stacking result, and improve the signal-to-noise ratio. ​ 5. The method according to claim 4, wherein the disaster source in front of the roadway is finely detected by the sound wave. The process of calculating the correlation of the common reflection group of each delay seismic channel record and the reference channel seismic record includes: the correlation coefficient of two discrete seismic records is calculated by the following formula: ​ In the formula, is with covariance, is variance, When the random noise value is large, the signal-to-noise ratio is low, and the correlation coefficient R is small; when the random noise value is small, the signal-to-noise ratio is high, and the correlation coefficient R is large; Based on the correlation coefficient value, each seismic channel is weighted and then stacked to effectively stack high signal-to-noise ratio data.

6. The method of claim 1, wherein the disaster source in front of the roadway is precisely detected by the sound wave as the tunnel is excavated, and the method is characterized by, The process of imaging disaster source comprises the following steps of: based on the time domain finite difference forward simulation method, assuming known detection data , source waveform function and wave velocity distribution of detection area , taking the source pilot sensor position as the source, taking the waveform function as the source function to perform forward forward simulation, obtaining the source wave field, denoted as ; taking the source wave detection sensor position as the source, taking the detection data as the source function to perform inverse time forward simulation, obtaining the detector wave field, denoted as , wherein is the grid position in the horizontal line direction, is the grid position in the depth direction, is the different time in the forward iteration process, and imaging is performed according to the source wave field and the detector wave field.

7. The method according to claim 6, wherein the disaster source in front of the roadway is finely detected by the sound wave. The imaging process based on the source wave field and the receiver wave field includes: ​ 。 8. A fine detection system of disaster source in front of a roadway by sound wave with tunneling, characterized in that, The detection data acquisition module is configured to obtain a plurality of sets of detection data in the tunneling process, the detection data comprising seismic source vibration data and tunneling sound wave data. The data preprocessing module is configured to preprocess the detection data to obtain single-channel seismic records for delay calculation and beamforming directional stacking, optimize the reflection coefficient sequence, and jointly perform multiple beamforming directional stacking. The fine imaging module is configured to establish a geological model of the roadway geological conditions based on the stacked multi-component wave field data, calculate the wave velocity distribution using the tomographic imaging algorithm, take the calculated wave velocity distribution as the initial model, and perform disaster source imaging using the decoupling reverse time migration method. The detection data acquisition module comprises a seismic source pilot sensor installed at the front of the mining equipment and a plurality of tunneling sound wave detection sensors installed behind the roadway within a certain range from the pilot sensor, and the seismic source vibration data is obtained using the seismic source pilot sensor and the tunneling sound wave data is obtained using the tunneling sound wave detection sensors.

9. The system as claimed in claim 8, wherein the system is characterized by: The internal observation adopts linear observation, array observation or spatial three-dimensional observation. ​ 10. The system as claimed in claim 9, wherein the system is characterized by: Linear observation is used in the roadway, and multiple sensors are arranged on the sidewalls of the roadway to detect the sound wave.

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