Method and system for dynamically detecting inner structure on side surface of roadway by using passive seismic wave signal of coal breaking in coal roadway driving

Abstract

The invention discloses a method and a system for dynamically detecting an internal structure on the side surface of a roadway by using a coal roadway tunneling coal breaking passive seismic wave signal. The method comprises the following steps of: S1, acquiring the coal roadway tunneling coal breaking passive seismic wave signal in real time and performing pulse processing on the coal roadway tunneling coal breaking passive seismic wave signal; s2, constructing an initial imaging model, and performing local optimal self-adaptive trigger correction on the signal obtained after pulse processing to obtain a signal after trigger correction; s3, utilizing the constructed initial imaging model and a signal obtained after triggering correction to carry out tunnel lateral inner structure imaging; advanced exploration of the abnormal geological structure in the coal face is synchronously achieved in the roadway construction process through tunneling coal breaking passive seismic wave signals, construction noise interference is avoided, limitation and danger of traditional hammering or explosive sources are avoided, tunneling efficiency is improved, safety risks are reduced, and the method is suitable for large-scale popularization and application. And the time-space domain stack of the real-time dynamic detection result significantly improves the detection precision.

Description

Technical Field

[0001] This invention relates to the field of mine geophysical exploration technology, and more specifically to a method and system for dynamic detection of the internal structure of the roadway side using passive seismic wave signals from coal roadway excavation. Background Technology

[0002] Coal will remain the primary energy source for a considerable period of time. To better fulfill its fundamental and safety-guarantee role, the intelligent transformation of coal mines is an inevitable path. However, the complex geological conditions and varied geological structures of coal resources lead to frequent disasters such as gas, water, and rock bursts, which seriously affect the safe and efficient production of coal mines and greatly hinder the progress of intelligent development. Therefore, how to achieve efficient and accurate exploration of hidden structures is a major challenge to ensure the safe production of coal mines and support the intelligent transformation.

[0003] Coal mining is mainly carried out underground. After the phased mining area is divided, the mining area is constructed by going up and down the slope, the working face is constructed by cutting the face, and the coal mining face production system is constructed. In this process, in order to avoid geological disasters during roadway excavation and working face mining, geophysical advance exploration work is required.

[0004] However, due to current limitations in detection technology, the detection work for roadway excavation and face mining is separate. That is, after the roadway excavation is completed using seismic wave detection technologies such as MSP, it is necessary to use radio wave imaging, seismic wave CT, and other technologies to investigate abnormal geological structures within the coal mining face. This mode actually results in duplication of detection work. This is because the seismic wave detection technologies mainly used in the two application scenarios are in principle the same. Theoretically, a single detection can complete the advance prediction of the internal structure in front of and beside the roadway. Therefore, the existing geophysical detection mode affects the progress of coal mining and leads to an excessive increase in mining costs.

[0005] Therefore, how to utilize the passive seismic wave signals from coal face breaking during coal roadway excavation to dynamically detect the internal structure of the roadway side and achieve advanced exploration of abnormal geological structures within the coal mining face during roadway excavation is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides a method and system for dynamic detection of the internal structure of the roadway side using passive seismic wave signals from coal roadway excavation to solve some of the technical problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A method for dynamic detection of lateral internal structures in coal roadways using passive seismic wave signals from coal breaking during roadway excavation includes the following steps:

[0009] S1. Real-time acquisition of passive seismic wave signals from coal roadway excavation and coal breaking, and pulse processing;

[0010] S2. Construct an initial imaging model, and perform local optimal adaptive trigger correction on the signal obtained after pulsed processing to obtain the signal after trigger correction;

[0011] S3. Using the constructed initial imaging model and the signal obtained after trigger correction, perform side-plane internal structure imaging of the tunnel.

[0012] Preferably, in step S1, seismic detectors are installed on the side of the coal mining face transport roadway and return air roadway near the working face to form a dual-roadway observation system. The dual-roadway observation system follows the advance of each roadway and collects the passive seismic wave signals of coal roadway excavation and coal breaking in real time.

[0013] Preferably, in step S1, the pulsed processing specifically includes:

[0014]

[0015] in, The signal is pulsed, where u and v are the reference signal and the received signal selected for pulsed processing, respectively. n represents the current time step of calculation, k is the time offset of signals u and v, and ω is the time offset of signals u and v. u ω v are the filtering factors for eliminating the coal-breaking seismic source characteristics of signals u and v, respectively, and * denotes the discrete convolution operation.

[0016] Preferably, the specific content of step S2 is as follows:

[0017] S21. Conduct in-situ tests of physical property parameters and analyze the borehole logging data of the coal mining face to obtain the physical property parameters of the coal seam and construct an initial imaging model;

[0018] S22. Based on the spatial location of each geophone and the virtual location of the coal breaking source, the trigger correction reference signal under the initial imaging model is obtained by theoretical seismic wavelet simulation.

[0019] S23. Identify the time of maximum amplitude of the near-source seismic trace in the signal obtained after pulsed processing, and select the transition data segment to trigger correction;

[0020] S24. Select the near-source seismic trace of the reference signal as the reference seismic trace, convert the reference seismic trace and the transition data segment from the time domain to the frequency domain, perform discrete convolution calculation, and convert the discrete convolution calculation result from the frequency domain to the time domain to obtain the time domain convolution result;

[0021] S25. Normalize the seismic data of each trace in the time-domain convolution result and the reference signal, identify the time of the maximum amplitude of each seismic data, and calculate the difference function of each seismic data.

[0022] S26. Identify the mode of the minimum point of the difference function of the seismic data in the difference function, and select the effective data segment after trigger correction;

[0023] S27. Identify all maximum point times in each seismic data trace of the temporal convolution result, and iteratively judge the amplitude values ​​of the maximum point times in each seismic trace in the effective data segment after triggering correction until the value is equal to zero.

[0024] S28. Reduce all amplitude values ​​before the maximum point in each seismic data in the effective data segment by a predetermined factor to obtain the signal after trigger correction.

[0025] Preferably, step S27, the specific content of the loop judgment is: if If it is greater than zero, then And iterate through the checks until... Equal to zero; if If less than zero, then And iterate through the checks until... It equals zero; This indicates the j-th seismic trace and the j-th seismic trace in the valid data segment after the correction is triggered. The amplitude value at time , The temporal convolution result represents the recognition. The times of all maximum points in each seismic data stream.

[0026] Preferably, the specific content of step S3, which involves performing side-plane internal structural imaging of the tunnel, is as follows:

[0027] S31. Using the constructed initial imaging model, set the imaging parameters according to the on-site data acquisition method;

[0028] S32. The signal after trigger correction is filtered by a low-pass filter in different frequency bands, and imaging is performed from the low frequency band to the high frequency band. Specifically, nonlinear optimization and wavelet inversion algorithms are used to solve the objective function and obtain the imaging results.

[0029] Preferably, the objective function is:

[0030]

[0031] in, Forward modeling and actual seismic data, g represents the g-th trace, m represents the imaging medium model, and x... s With x rThis represents the virtual source and receiver locations of the coal-breaking seismic source, ns and nr are the number of source and receiver locations, T is the seismic data recording time, ||m|| TV Here, ε represents the regularization constraint term, and ε is the weight coefficient of the regularization term. Indicates model correction;

[0032] The model calibration method is as follows: Define the in-plane direction of the roadway side as the x-direction, the roadway excavation direction as the y-direction, and the imaging area as a horizontal profile. Use the x-direction grid of the imaging model as the calibration unit; that is, in each round of model calibration, according to... The sequence is repeated in a loop.

[0033] A dynamic detection system for the internal structure of a roadway side using passive seismic wave signals from coal roadway excavation and coal breaking, based on the aforementioned method for dynamic detection of the internal structure of a roadway side using passive seismic wave signals from coal roadway excavation and coal breaking, includes: a data acquisition device, a preprocessing module, a trigger correction module, and an imaging module.

[0034] The data acquisition device is used to collect passive seismic wave signals of coal breaking during coal roadway excavation in real time.

[0035] The preprocessing module is used to pulse the passive seismic wave signal;

[0036] The trigger correction module is used to construct an initial imaging model and perform local optimal adaptive trigger correction on the signal obtained after pulsed processing to obtain the signal after trigger correction.

[0037] The imaging module is used to perform side-plane internal structure imaging of the tunnel using the constructed initial imaging model and the signal obtained after trigger correction.

[0038] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method for dynamic detection of the internal structure of a roadway side using passive seismic wave signals from coal roadway excavation.

[0039] A processing terminal includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the method for dynamic detection of the internal structure of the roadway side using passive seismic wave signals from coal roadway excavation.

[0040] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method and system for dynamic detection of internal geological structures on the side of a coal face using passive seismic wave signals from coal roadway excavation. By utilizing the passive seismic wave signals from coal roadway excavation, the abnormal geological structures within the coal mining face can be detected simultaneously during roadway construction. This not only avoids affecting normal coal production but also avoids repetitive detection work, improving coal mining efficiency and reducing coal mining costs. The present invention is not affected by construction noise and does not require the use of explosives or blasting, making it highly applicable. Compared with traditional radio wave imaging and seismic CT techniques, which only perform a single static detection of abnormal geological structures within the coal mining face, the present invention utilizes the passive seismic wave signals generated daily during roadway excavation to achieve real-time dynamic detection of abnormal geological structures within the face. The spatiotemporal superposition of multiple detection results can significantly improve detection accuracy. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0042] Figure 1 A schematic diagram of a method for dynamic detection of the internal structure of a roadway side using passive seismic wave signals from coal breaking during roadway excavation, provided by the present invention;

[0043] Figure 2 This is a schematic diagram of the dual-lane observation system provided by the present invention;

[0044] Figure 3 This is a schematic diagram of the passive seismic wave signal for coal breaking during tunneling provided by the present invention;

[0045] Figure 4 This is a schematic diagram of the pulsed processing result of the passive seismic wave signal for coal breaking in tunneling provided by the present invention;

[0046] Figure 5 This is a schematic diagram of the initial imaging model provided by the present invention;

[0047] Figure 6 This is a schematic diagram illustrating the triggering correction of the pulsed processing result of the passive seismic wave signal for coal breaking during tunneling, provided by the present invention.

[0048] Figure 7 This is a schematic diagram of the results of the side-face internal structure detection of the roadway using the passive seismic wave signal of coal breaking during coal roadway excavation, provided by the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] This invention discloses a method for dynamic detection of the internal structure of a roadway side using passive seismic wave signals from coal breaking during roadway excavation, comprising the following steps:

[0051] S1. Real-time acquisition of passive seismic wave signals from coal roadway excavation and coal breaking, and pulse processing;

[0052] S2. Construct an initial imaging model, and perform local optimal adaptive trigger correction on the signal obtained after pulsed processing to obtain the signal after trigger correction;

[0053] S3. Using the constructed initial imaging model and the signal obtained after trigger correction, perform side-plane internal structure imaging of the tunnel.

[0054] To further implement the above technical solution, in step S1, as follows: Figure 2 Seismic detectors are installed on the side of the coal face transport roadway and return air roadway closest to the working face, forming a dual-roadway observation system. This system tracks the advance of each roadway, collecting passive seismic wave signals from coal face excavation in real time. Figure 3 .

[0055] In this embodiment, the seismic geophones are installed in the middle of the sidewall of the roadway, with a measuring point spacing of 3-5m. A special anchor bolt is used to couple the seismic geophones, with a hole depth of 0.8m. The inner section of the hole is filled with 0.6m of yellow mud, and the outer section is filled with one ring of anchoring agent. The seismic geophones are magnetically attached to the top of the anchor bolt. X / Z component geophones are selected, with 24-36 measuring points in each of the two roadways. The dual-roadway observation system follows the advance of each roadway. Every 30m of advance, the 6 geophones at the end of the measuring line are disassembled and installed on 6 special anchor bolts near the face, collecting X and Z component signals and recording the spatial position of each geophone.

[0056] To further implement the above technical solution, the specific content of the pulsed processing in step S1 is as follows:

[0057]

[0058] in, The signal is pulsed, where u and v are the reference signal and the received signal selected for pulsed processing, respectively. n represents the current time step of calculation, k is the time offset of signals u and v, and ω is the time offset of signals u and v. u ω vThese are the filtering factors for eliminating the coal-breaking seismic source characteristics of signals u and v, respectively. * denotes the discrete convolution operation. The processing result is as follows: Figure 4 As shown.

[0059] In this embodiment, both the acquired X and Z component signals are pulsed and subjected to local optimal adaptive triggering correction.

[0060] To further implement the above technical solution, the specific content of step S2 is as follows:

[0061] S21. Conduct in-situ tests of physical property parameters and analyze the borehole logging data from the coal mining face to obtain the physical property parameters of the coal seam and construct an initial imaging model, such as... Figure 5 ;

[0062] The physical properties of the coal seam in the coal mining face include the longitudinal wave velocity V. p transverse wave velocity V s and density D;

[0063] S22. Based on the spatial location of each geophone on site and the virtual location of the coal-breaking seismic source, the trigger correction reference signal under the initial imaging model is obtained using theoretical seismic wavelet simulation.

[0064] S23. Identify the signal obtained after pulse processing. The maximum amplitude point T1 of the near-source end seismic trace is selected, and the transition data segment that triggers the correction is chosen.

[0065] In this embodiment, the transition data segment for triggering correction is selected from the 500 sampling points before the maximum value time T1 in the pulsed signal to the 4500 sampling points after the maximum value time T1.

[0066] S24. Select the reference signal The near-source seismic trace is the reference seismic trace. Reference earthquake trace With transition data segment The process involves transforming the data from the time domain to the frequency domain and performing discrete convolution calculations. and the discrete convolution calculation results Transform from the frequency domain to the time domain to obtain the temporal convolution result.

[0067] S25. Results of temporal convolution With reference signal Each seismic data point in the dataset undergoes single-channel normalization, and the time of the maximum amplitude value for each seismic data point is identified. Calculate and The difference function for each seismic data point in the dataset;

[0068] In this embodiment, the difference function is calculated as follows:

[0069]

[0070] Where i represents the location of the sampling point, and j represents the location of the seismic trace;

[0071] S26. Identify the mode T3 at the time of the minimum point of the difference function of the seismic data in the difference function, and select the effective data segment after trigger correction;

[0072] In this embodiment, the data segment from time T3 to the last sampling point in the pulsed signal is selected as the valid data segment after triggering correction.

[0073] S27. Identify temporal convolution results Time T4 of all maximum points in each seismic data trace j The amplitude values ​​at the maximum points of each seismic trace in the effective data segment after triggering correction are iteratively judged until the value is equal to zero.

[0074] S28. Transfer valid data segments The time of the maximum value point in each seismic data track All previous amplitude values ​​are reduced by a predetermined factor to obtain the signal after trigger correction. The results are as follows Figure 6 As shown, the predetermined multiple in practical applications is 10. 30 times.

[0075] To further implement the above technical solution, step S27, the specific content of the loop judgment, is as follows: if... If it is greater than zero, then And iterate through the checks until... Equal to zero; if If less than zero, then And iterate through the checks until... It equals zero; This indicates the j-th seismic trace and the j-th seismic trace in the valid data segment after the correction is triggered. The amplitude value at time , The temporal convolution result represents the recognition. The times of all maximum points in each seismic data stream.

[0076] To further implement the above technical solution, the specific content of step S3, which involves performing side-plane internal structural imaging of the tunnel, is as follows:

[0077] S31. Using the constructed initial imaging model, set the imaging parameters according to the on-site data acquisition method;

[0078] S32. Use a low-pass filter to correct the signal after triggering. Filtering is performed across different frequency bands, gradually moving from low to high frequencies for imaging. Specifically, nonlinear optimization and wavelet-inversion-independent algorithms are used to solve the objective function, yielding the imaging results, such as... Figure 7 .

[0079] To further implement the above technical solution, the objective function is:

[0080]

[0081] in, Forward modeling and actual seismic data, g represents the g-th trace, m represents the imaging medium model, and x... s With x r This represents the virtual source and receiver locations of the coal-breaking seismic source, ns and nr are the number of source and receiver locations, T is the seismic data recording time, ||m|| TV Here, ε represents the regularization constraint term, and ε is the weight coefficient of the regularization term. Indicates model correction;

[0082] In this embodiment, the actual detected seismic data, i.e., the signal after triggering correction, includes two components: X and Z. obs =(U obs X U obs Y Imaging is performed based on two-component signals.

[0083] The model calibration method is as follows: Define the in-plane direction of the roadway side as the x-direction, the roadway excavation direction as the y-direction, and the imaging area as a horizontal profile. Use the x-direction grid of the imaging model as the calibration unit; that is, in each round of model calibration, according to... The sequence is repeated in a loop.

[0084] In this embodiment, the specific content of model correction is as follows:

[0085] (1) Define m0 as a certain grid parameter value in the initial imaging model, m1 as a certain grid parameter value in the processed imaging result, and Δm = m1 - m0 as the model update amount. Calculate Δm and make a judgment. If Δm < λm0, λ = 0.2 ~ 0.5, then correct Δm = 0.

[0086] (2) For a certain y-row, update the model at the l-th grid in the x-direction by Δm. l Model update amount Δm at the (l+1)th grid l+1 By comparison, Δm was selected. l >0&Δm l+1 >0, or Δml <0&Δm l+1 <0, or Δm l >0&Δm l +1 =0, or Δm l <0&Δm l+1 =0, but does not satisfy Δm l >0&Δm l+1 =0&Δm l-1 a grid positions at position l that satisfy the condition when = 0;

[0087] (3) For a of the l that satisfy the condition e Compare the model update amounts at the grid positions -1, e = 1…a, and if the following conditions are met... or And satisfy δ(l) e+2 -l e+1 )>(l e -l e-1 +l e+4 -l e+3 ),δ=2~5, andδ(l e+2 -l e+1 )>(l e+1 -l e ), and δ(l) e+2 -l e+1 )>(l e+3 -l e+2 Then correct It is 1 / 6 of the original value.

[0088] A dynamic detection system for the internal structure of a roadway side using passive seismic wave signals from coal roadway excavation and coal breaking, based on a method for dynamic detection of the internal structure of a roadway side using passive seismic wave signals from coal roadway excavation and coal breaking, includes: a data acquisition device, a preprocessing module, a trigger correction module, and an imaging module.

[0089] The data acquisition device is used to collect passive seismic wave signals of coal breaking during coal roadway excavation in real time.

[0090] The preprocessing module is used to pulse the passive seismic wave signal;

[0091] The trigger correction module is used to construct an initial imaging model and perform local optimal adaptive trigger correction on the signal obtained after pulsed processing to obtain the signal after trigger correction.

[0092] The imaging module is used to perform side-plane internal structure imaging of the tunnel using the constructed initial imaging model and the signal obtained after trigger correction.

[0093] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for dynamic detection of the internal structure of the roadway side using passive seismic wave signals from coal roadway excavation.

[0094] A processing terminal includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements a method for dynamic detection of the internal structure of the roadway side using passive seismic wave signals from coal roadway excavation.

[0095] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0096] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for dynamic detection of the internal structure of a roadway side using passive seismic wave signals from coal breaking during roadway excavation, characterized in that, Includes the following steps: S1. Real-time acquisition of passive seismic wave signals from coal roadway excavation and coal breaking, and pulse processing; S2. Construct an initial imaging model, and perform local optimal adaptive trigger correction on the signal obtained after pulsed processing to obtain the signal after trigger correction; S3. Using the constructed initial imaging model and the signal obtained after trigger correction, perform side-plane internal structure imaging of the tunnel.

2. The method for dynamic detection of the internal structure of a roadway side using passive seismic wave signals from coal roadway excavation as described in claim 1, characterized in that, In step S1, seismic detectors are installed on the side of the coal mining face transport roadway and return air roadway near the working face to form a dual-roadway observation system. The dual-roadway observation system follows the advance of each roadway and collects the passive seismic wave signals of coal roadway excavation and coal breaking in real time.

3. The method for dynamic detection of the internal structure of a roadway side using passive seismic wave signals from coal roadway excavation as described in claim 1, characterized in that, In step S1, the pulsed processing specifically includes: in, The signal is pulsed, where u and v are the reference signal and the received signal selected for pulsed processing, respectively. n represents the current time step of calculation, k is the time offset of signals u and v, and ω is the time offset of signals u and v. u ω v are the filtering factors for eliminating the coal-breaking seismic source characteristics of signals u and v, respectively, and * denotes the discrete convolution operation.

4. A method for dynamic detection of the internal structure of a roadway side using passive seismic wave signals from coal roadway excavation as described in claim 1, characterized in that, The specific content of step S2 is as follows: S21. Conduct in-situ tests of physical property parameters and analyze the borehole logging data of the coal mining face to obtain the physical property parameters of the coal seam and construct an initial imaging model; S22. Based on the spatial location of each geophone and the virtual location of the coal breaking source, the trigger correction reference signal under the initial imaging model is obtained by theoretical seismic wavelet simulation. S23. Identify the time of maximum amplitude of the near-source seismic trace in the signal obtained after pulsed processing, and select the transition data segment to trigger correction; S24. Select the near-source seismic trace of the reference signal as the reference seismic trace, convert the reference seismic trace and the transition data segment from the time domain to the frequency domain, perform discrete convolution calculation, and convert the discrete convolution calculation result from the frequency domain to the time domain to obtain the time domain convolution result; S25. Normalize the seismic data of each trace in the time-domain convolution result and the reference signal, identify the time of the maximum amplitude of each seismic data, and calculate the difference function of each seismic data. S26. Identify the mode of the minimum point of the difference function of the seismic data in the difference function, and select the effective data segment after trigger correction; S27. Identify all maximum point times in each seismic data trace of the temporal convolution result, and iteratively judge the amplitude values ​​of the maximum point times in each seismic trace in the effective data segment after triggering correction until the value is equal to zero. S28. Reduce all amplitude values ​​before the maximum point in each seismic data in the effective data segment by a predetermined factor to obtain the signal after trigger correction.

5. A method for dynamic detection of lateral internal structures in a coal roadway using passive seismic wave signals from coal breaking during roadway excavation, as described in claim 1, is characterized in that... Step S27, the specific content of the loop judgment is: if If it is greater than zero, then T4 j =T4 j +1, and loop through the results until... Equal to zero; if If it is less than zero, then T4 j =T4 j -1, and loop through the results until... It equals zero; This indicates the j-th seismic trace and the T4-th seismic trace in the valid data segment after the correction is triggered. j Amplitude value at time T4 j The temporal convolution result represents the recognition. The times of all maximum points in each seismic data stream.

6. A method for dynamic detection of the internal structure of a roadway side using passive seismic wave signals from coal roadway excavation as described in claim 1, characterized in that, The specific content of step S3, which involves performing side-plane internal structural imaging of the tunnel, is as follows: S31. Using the constructed initial imaging model, set the imaging parameters according to the on-site data acquisition method; S32. The signal after trigger correction is filtered by a low-pass filter in different frequency bands, and imaging is performed from the low frequency band to the high frequency band. Specifically, nonlinear optimization and wavelet inversion algorithms are used to solve the objective function and obtain the imaging results.

7. A method for dynamic detection of the internal structure of a roadway side using passive seismic wave signals from coal roadway excavation as described in claim 6, characterized in that, The objective function is: Among them, u cal and Forward modeling and actual seismic data, g represents the g-th trace, m represents the imaging medium model, and x... s With x r This represents the virtual source and receiver locations of the coal-breaking seismic source, ns and nr are the number of source and receiver locations, T is the seismic data recording time, ||m|| TV Here, ε represents the regularization constraint term, and ε is the weight coefficient of the regularization term. Indicates model correction; The model calibration method is as follows: Define the in-plane direction of the roadway side as the x-direction, the roadway excavation direction as the y-direction, and the imaging area as a horizontal profile. Use the x-direction grid of the imaging model as the calibration unit; that is, in each round of model calibration, according to... The sequence is repeated in a loop.

8. A dynamic detection system for the internal structure of a roadway side using passive seismic wave signals from coal breaking during roadway excavation, characterized in that, A method for dynamic detection of the internal structure of a roadway side using passive seismic wave signals from coal roadway excavation, based on any one of claims 1-7, includes: a data acquisition device, a preprocessing module, a triggering correction module, and an imaging module. The data acquisition device is used to collect passive seismic wave signals of coal breaking during coal roadway excavation in real time. The preprocessing module is used to pulse the passive seismic wave signal; The trigger correction module is used to construct an initial imaging model and perform local optimal adaptive trigger correction on the signal obtained after pulsed processing to obtain the signal after trigger correction. The imaging module is used to perform side-plane internal structure imaging of the tunnel using the constructed initial imaging model and the signal obtained after trigger correction.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for dynamic detection of the internal structure of the roadway side using passive seismic wave signals from coal roadway excavation as described in any one of claims 1-7.

10. A processing terminal, comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements a method for dynamic detection of the internal structure of the roadway side using passive seismic wave signals from coal roadway excavation as described in any one of claims 1-7.

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