Method for removing multiple wave in dense array acoustic wave detection in TBM tunneling disturbance area

Abstract

This disclosure provides a method for suppressing and removing multiples in dense array acoustic detection of TBM disturbance zones, relating to the field of tunnel advanced geological prediction data processing technology. The method includes: acquiring array acoustic detection data in TBM disturbance zones and performing denoising preprocessing to obtain clean, effective signals; selecting multiple reflection areas using a pre-windowing strategy; projecting multiple and primary reflections onto a feature space using generalized singular value decomposition; reconstructing the primary reflection component through low-rank component reconstruction to obtain reconstructed data; further employing a local correlation method to calculate the local similarity between the reconstructed data and the effective signal; and extracting strongly similar data to achieve effective separation of primary and multiple reflections. This disclosure effectively achieves primary wave extraction and multiple wave suppression under conditions of limited data volume in array acoustic detection of TBM disturbance zones.

Description

Technical Field

[0001] This disclosure relates to the field of tunnel advanced geological prediction data processing technology, specifically to a method for suppressing and removing multiple waves from dense array acoustic wave detection in the disturbance zone of TBM tunneling. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] Because the rock mass inside the fault fracture zone is fractured and structural planes are developed, multiple waves are generated. Due to the more complex propagation path of these waves, they are difficult to be effectively offset and repositioned during imaging, which can easily lead to artifacts. Therefore, they need to be removed during data processing.

[0004] However, unlike TBM tunnel construction noise, multiple reflection interference is a coherent noise, with frequency bands and phase axis slopes similar to effective reflected waves, making suppression of multiple reflection interference difficult. Current advanced geological prediction data processing methods lack multiple suppression techniques. In the field of surface oil and gas exploration, methods such as Surface Correlated Multiple Suppression (SRME), Sparse Inversion (EPSI), Inverse Scattering Series, and Virtual Phase Axis Method exist. These methods require observation data with high sampling density, large offset, and high coverage. However, array acoustic detection methods in TBM-disturbed areas are limited by small observation apertures, allowing only a small number of measurement points to be deployed. Small offsets and insufficient data volume result in indistinct differences between reflected and multiple wave characteristics. Therefore, it is urgent to research multiple suppression methods suitable for array acoustic detection in TBM-disturbed areas. Summary of the Invention

[0005] To address the aforementioned issues, this disclosure proposes a method for suppressing and removing multiple reflections in dense array acoustic wave detection of TBM tunneling disturbance zones. Based on expert experience, the deep multiple reflection development area is selected from the data. Then, using the generalized singular value decomposition method, multiple reflections and primary reflections are projected onto the feature space. By extracting the low-rank component corresponding to the primary reflection, primary reflection data in the acoustic wave data profile is reconstructed. Furthermore, a local correlation method is employed to calculate the local similarity between the reconstructed data and the acoustic wave data profile. By setting a similarity threshold, effective extraction of primary reflections and effective suppression of multiple reflections are achieved.

[0006] According to some embodiments, the present disclosure adopts the following technical solutions: Methods for suppressing and removing multiple waves from the dense array acoustic wave detection in the TBM tunneling disturbance zone include: Acquire acoustic wave detection data of the TBM disturbance region array and perform noise reduction preprocessing on it; The development region of multiple reflections in the acoustic data is selected by pre-windowing strategy. The multiple reflections and the first reflection are projected into the feature space by generalized singular value decomposition. Then, the first reflection component of the acoustic data is reconstructed by low-rank component reconstruction to obtain the reconstructed data. By employing the local correlation method, the local similarity between the reconstructed data and the effective signal is calculated. A similarity threshold is set, and a binary mask for the primary reflection wave data is constructed and applied to the reconstructed data. This effectively suppresses reconstruction noise and residual multiple reflection waves, thereby achieving effective separation of the primary reflection wave and multiple reflection waves.

[0007] According to some embodiments, the present disclosure adopts the following technical solutions: The TBM tunneling disturbance zone dense array acoustic detection multiple wave suppression and removal system includes: The signal acquisition module is used to acquire acoustic wave detection data of the TBM disturbance zone array and perform noise reduction preprocessing on it; The primary reflection wave reconstruction module is used to select the multiple wave development area in the acoustic wave data through a pre-windowing strategy, project the multiple reflection waves and primary reflection waves into the feature space using the generalized singular value decomposition method, and then reconstruct the primary reflection wave component of the acoustic wave data through low-rank component reconstruction to obtain the reconstructed data. The multiple reflection suppression module is used to calculate the local similarity between the reconstructed data and the effective signal using the local correlation method, set a similarity threshold, construct a binary mask for the primary reflection data, and apply it to the reconstructed data. This effectively suppresses reconstruction noise and residual multiple reflections, thereby achieving effective separation of primary and multiple reflections.

[0008] According to some embodiments, the present disclosure adopts the following technical solutions: A computer program product includes a computer program that, when executed by a processor, implements the TBM tunneling disturbance zone dense array acoustic wave detection multiple wave suppression and removal method.

[0009] According to some embodiments, the present disclosure adopts the following technical solutions: A non-transitory computer-readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement the TBM tunneling disturbance zone dense array acoustic wave detection multiple wave suppression and removal method.

[0010] According to some embodiments, the present disclosure adopts the following technical solutions: An electronic device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to perform the TBM tunneling disturbance zone dense array acoustic wave detection multiple wave suppression and removal method.

[0011] Compared with the prior art, the beneficial effects of this disclosure are as follows: This disclosed method for suppressing and removing multiples in a dense array acoustic detection system for TBM tunneling disturbance zones first selects the multiple development area based on the characteristic that multiples arrive late. Then, it uses the generalized singular value decomposition method to project multiple and primary reflections onto a feature space. Using a low-rank approximation, it reconstructs the primary reflection data in the acoustic data profile. However, since reconstruction inevitably introduces reconstruction noise and leaves a small number of multiples, a local correlation method is further employed. By calculating the local similarity between the reconstructed data and the original acoustic data profile, and then setting a similarity threshold, a binary mask for the primary reflection data is constructed to achieve effective extraction of primary reflections and effective suppression of multiple reflections.

[0012] This disclosed method for suppressing and removing multiples in the dense array acoustic detection zone of a TBM tunneling disturbance area employs a pre-windowing strategy to select the multiple development region. Furthermore, it utilizes generalized singular value decomposition (SSD) to solve the low-rank reconstruction problem constrained by multiple components, effectively addressing the reconstruction of primary reflections. The advantage of this method lies in fully leveraging the late arrival time of multiple reflections. Based on the differences in energy and complexity between primary and multiple reflections, SSD projects both into a feature space. By using multiple development data as constraints, this method effectively distinguishes primary and multiple reflections in the feature space, ensuring primary reflections dominate in the low-rank space and multiple reflections dominate in the high-rank space. This allows for easier selection of appropriate singular values ​​for reconstruction, resulting in a cleaner reconstruction of primary reflections and laying a solid foundation for subsequent multiple reflection suppression.

[0013] This disclosure discloses a method for suppressing and removing multiple reflections in the dense array acoustic detection zone of a TBM (Tunnel Boring Machine) tunneling disturbance area. A local similarity method is used to suppress reconstruction noise and residual multiple reflections. This method effectively measures the similarity of local features between the reconstructed data and the original data. Since the primary reflection data has a stable shape, it usually has a high similarity to the original data. Reconstructed noise and residual noise, due to their weak energy, have weak similarity. Therefore, by setting an appropriate threshold, a binary mask matrix is ​​constructed and applied to the reconstructed data, thereby enabling the effective extraction of primary reflections and the removal of multiple reflections. Attached Figure Description

[0014] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0015] Figure 1 This is a flowchart of the TBM tunneling disturbance zone dense array acoustic wave detection multiple wave suppression and removal method according to an embodiment of the present disclosure; Figure 2 This is a feature of multiple wave data from the acoustic detection of a tunnel array according to an embodiment of the present disclosure; wherein, Figure 2 (a) in the figure represents acoustic wave data; Figure 2 (b) in the figure represents the first wave data; Figure 2 (c) in the figure represents multiple wave data; Figure 3 This refers to the raw data in the simulation data multiple suppression results of the embodiments of this disclosure; Figure 4 This refers to the reconstructed primary reflection wave data from the simulation data multiple wave suppression results of this embodiment of the present disclosure; Figure 5 This refers to the local similarity in the simulation data multiple suppression results of the embodiments of this disclosure; Figure 6 This refers to the spatial information of the primary reflected wave in the simulation data of multiple wave suppression results in this embodiment of the present disclosure; Figure 7 This is the result of extracting a single reflected wave from the simulation data multiple wave suppression results of this embodiment of the disclosure; Figure 8 This refers to the multiple wave removal result in the simulation data multiple wave suppression result of this embodiment of the disclosure; Figure 9 The images are the original data before and after multiple wave removal in this embodiment of the present disclosure. Figure 10 The images show the results of a single reflected wave before and after multiple wave removal in this embodiment of the present disclosure. Figure 11 The residual of the imaging results before and after multiple wave removal in the embodiments of this disclosure is shown. Detailed Implementation

[0016] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0017] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this disclosure. 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 disclosure pertains.

[0018] 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 exemplary embodiments according to this disclosure. 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.

[0019] Example 1 One embodiment of this disclosure provides a method for suppressing and removing multiple waves from dense array acoustic wave detection in the disturbance zone of a TBM tunneling machine. The method includes the following steps: Step 1: Acquire acoustic wave detection data of the TBM disturbance zone array and perform noise reduction preprocessing on it; Step 2: Select the multiple reflection regions in the acoustic data using a pre-windowing strategy, project the multiple reflections and the primary reflections into the feature space using the generalized singular value decomposition method, and then reconstruct the primary reflection component of the acoustic data through low-rank component reconstruction to obtain the reconstructed data. Step 3: Using the local correlation method, calculate the local similarity between the reconstructed data and the effective signal, set a similarity threshold, construct a binary mask for the primary reflection wave data, and apply it to the reconstructed data. This can effectively suppress reconstruction noise and residual multiple reflection waves, thereby achieving effective separation of primary reflection waves and multiple waves.

[0020] As one embodiment, this disclosure discovers that multiple reflections in the primary reflection domain of tunnel array acoustic data have different characteristics in terms of energy and coherence. Primary reflections have high energy and simple ray paths, thus exhibiting high correlation; multiple reflections have weak energy and, due to their complex ray paths, have even weaker correlation. Therefore, this disclosure proposes a method for suppressing and removing multiple reflections in the dense array acoustic detection of TBM tunneling disturbance zones. This method reconstructs primary reflections using the generalized singular value decomposition (GSVD) method under the constraint of multiple reflection characteristics, and further calculates local similarity with the original data to obtain the spatial location information of the primary reflections. This enables the effective extraction of primary reflections and the removal of multiple reflections. The specific implementation process includes: Step 1: Acquire acoustic wave detection data of the TBM disturbance zone array and perform noise reduction preprocessing on it; like Figure 2 As shown, this disclosure illustrates multiple waves generated by a model of a dipping fault fracture zone. From Figure 2Figures (a) to (c) respectively show the acoustic data profiles, primary wave data profiles, and multiple wave data profiles obtained through acoustic forward modeling. It can be clearly observed from the figures that the phase axis of the primary reflection wave data is clear, and energy dominates the data profile. The multiple waves, due to their more complex ray paths, exhibit inconsistent curvature of their phase axis and lack a clear pattern, which is consistent with the above analysis. Therefore, multiple waves are mainly concentrated below the phase axis of the primary reflection wave, with weaker multiple waves developing between the primary reflection waves.

[0021] Therefore, the denoised acoustic data obtained by performing denoising preprocessing on the array acoustic data in this disclosure takes into account the interference of multiple reflected waves generated by the tunnel wall and fault fracture zone. The acoustic data includes not only single reflected waves... p It also includes multiple reflected waves. m Then it can also be written as: (1) Step 2: Select the multiple reflection regions in the acoustic data using a pre-windowing strategy, project the multiple reflections and the primary reflections into the feature space using the generalized singular value decomposition method, and then reconstruct the primary reflection component of the acoustic data through low-rank component reconstruction to obtain the reconstructed data. Specifically, the multiple wave removal task can be viewed as extracting data from acoustic wave data. s Direct estimation of multiple waves m The data is then matched and subtracted. However, this disclosure considers that the primary reflected wave in the tunnel array acoustic data has strong energy characteristics and correlation; therefore, it first uses the acoustic data... s Reconstructing the first reflected wave p Then, multiple waves m Suppress the reflected wave. Use the low-rank approximation method to suppress the reflected wave. p Reconstruction can be represented as: (2) in, Indicates by selecting before k The acoustic data obtained by reconstructing singular values, under ideal conditions Containing only one wave, rank(·) represents the rank of the matrix. The rank should be less than that of the acoustic data. s The rank of the primary wave. As can be observed from Figure 2, in the acoustic data profile, based on experience, the main development areas of the primary and secondary waves can be preliminarily identified. For example, the secondary waves mainly develop below the primary reflected wave, and their energy differs significantly from that of the effective reflected wave. This feature can be used to outline the secondary reflected wave area in advance, and this prior information can be used to constrain formula (2), then: (3) At this point, it is necessary to process both the data fitting term and the regularization term simultaneously. This disclosure employs the Generalized Singular Value Decomposition (GSVD) method to solve this problem. GSVD is a matrix decomposition of matrix pairs, and its principle formula is as follows: (4) in, C , S This is a diagonal matrix, and its diagonal elements are respectively A , B The singular values ​​of have the following relationship: C T C + S T S = I , I It is an identity matrix. U , V It is an orthogonal matrix composed of singular vectors. According to formula (4), GSVD projects two datasets with certain correlations but different features onto the same feature space by simultaneously identifying the similarity and differences between two columns-matching but row-independent matrices. H Above. And A , B Matrix projection to H The features have the same characteristics as the eigenvalues λ The relative sizes are related, when λ c > λ s hour, H medium matrix A The characteristic components are dominant, while when λ s > λ c hour, H medium matrix B The characteristic components are dominant, when λ s = λ c At that time, matrix A , B Characteristic components in H The components account for a considerable proportion.

[0022] This disclosure introduces the aforementioned GSVD into primary reflection wave reconstruction. Firstly, based on the characteristics of multiple waves arriving later and having weaker energy, multiple waves are widely developed in the deep regions of acoustic data. This characteristic can be used to initially extract the effective signal. s By selecting a bounding box within the multiple reflection data, the effective signal is divided into overlapping regions of primary and multiple reflection waves. s 1. Multiple reflected wavess The data matrices of the two regions are then decomposed using the GSVD method to obtain the feature vector matrices for each region. U , V , H and diagonal matrix C , S , can be represented as: (5) At this point, by comparing the diagonal matrix C , S The magnitude of the singular values ​​in the equation, when λ c > λ s When the reflected wave component is dominant, the energy of the multiple reflected waves is weak. λ c < λ s At that time, the multiple wave component is dominant, and the energy of the multiple reflected waves is weak. By selecting the previous wave... k Reconstructing the wave using singular vectors can better suppress multiple wave components and obtain purer primary reflection wave components. The reconstructed primary reflection wave data is as follows: (6) in, u and h Orthogonal matrices U , H eigenvectors, λ diagonal matrix C eigenvalues.

[0023] Step 3: Using the local correlation method, calculate the local similarity between the reconstructed data and the effective signal. By setting a similarity threshold, construct a binary mask for the primary reflection wave data and apply it to the reconstructed data to effectively suppress reconstruction noise and residual multiple reflection waves, thereby achieving effective separation of primary reflection waves and multiple waves. Specifically, after obtaining the reconstructed data, this disclosure utilizes the stable morphology of primary reflection wave data and the randomness of reconstruction noise to propose an interference removal method based on local similarity. Since primary reflection wave data has a stable morphology, it typically exhibits high similarity to the original data. Conversely, reconstruction noise and residual noise, due to their weak energy, exhibit weak similarity. Therefore, by setting an appropriate threshold, a binary mask matrix is ​​constructed and applied to the reconstructed data, thereby enabling the effective extraction of primary reflection waves and the removal of multiple reflection waves. Specific implementation details include: Two sound wave signals a ( t ), b ( tThe global correlation coefficient of ) can be expressed as: (7) Where <·> denotes the inner product of two vectors, and taking the square of the related sparsity, we have: (8) Where a and b are signals a ( t ), b ( t The vector form of ). Further assume there are two matrices, A and B, let a = diag(A), b = diag(B), where diag(·) represents taking the elements of the main diagonal of the matrix. Then the vectors a and b in formula (8) can be replaced by matrices A and B, and the solution can be obtained by the shaping regularization method: (9) Where I is the identity matrix, λ Let S be the relative weight of the data fitting term and the smoothing term, and let S be the integer regularization method. By approximating formula (9) using iterative methods such as conjugate gradients, c1 and c2 can be approximately solved, thus obtaining the final local similarity index. Γ for: (10) in, Represents the Hadamard product. Local similarity index. Γ Within the range [0,1], the closer to 1, the higher the similarity between the two matrices at this point. Utilizing the characteristics of high correlation in primary reflections and low coherence between reconstruction noise and residual multiple reflections, a similarity threshold is set. γ thred Then a mask matrix can be constructed. , represented as: (11) in, α The attenuation coefficient controls the cutoff effect; a smaller value indicates better performance. α It can achieve relatively smooth results, but there will be residual reflections from multiple waves. Mask matrix. This represents the spatial location information of a reflected wave; 1 indicates the presence of a reflected wave, and 0 indicates its absence. A similarity threshold is then used. γ thred The reconstruction noise and spatial location information of residual multiple reflections have been removed; therefore, the mask matrix... If the data contains only the spatial location information of a single reflected wave, then this information can be used to extract the single reflected wave and remove multiple reflected waves, which can be represented as: (12) in, This represents the extracted primary reflection wave data. The above process enables the effective extraction of primary reflection waves and the removal of multiple reflection waves. The local similarity calculation method is crucial, as the obtained local similarity matrix determines the extraction effectiveness of the primary reflection wave.

[0024] Simulation test To verify the effectiveness of the proposed method for suppressing and removing multiple waves using dense array acoustic wave detection in the TBM tunneling disturbance zone, a typical fault fracture zone model was established. Acoustic data obtained through acoustic forward modeling are shown below. Figure 3 As shown, the first four in-phase axes represent primary reflections, while the others represent secondary reflections. This data reveals strong superposition of secondary reflections among the primary reflections, increasing the difficulty of reflection separation and extraction. Furthermore, this data clearly shows that the tunnel's secondary reflections do not possess stable in-phase axis curvature and arrival time difference; their in-phase axis curvature changes with tunnel arrival time. Based on the development location of the primary reflections, this disclosure divides the acoustic data into two parts, denoted as follows: s 1 and s 2. The results of the single-wave reflection reconstruction, single-wave reflection extraction, and multiple-wave reflection removal are as follows: Figure 4 , Figure 7 as well as Figure 8 As shown.

[0025] First, the data is decomposed and reconstructed using the GSVD method, and the reconstructed result of the first reflection wave is as follows. Figure 4 As shown, there is obvious high-frequency noise interference in the reconstruction results; at the same time, multiple reflected waves with similar slopes to the first reflected wave are also reconstructed below, but their energy is relatively weak and their phase axis continuity is worse than that of the first reflected wave. Figure 5 The local similarity results between the original data and the reconstructed data are presented. The similarity at the location of a single reflection event is close to 1. At the location where single and multiple reflections overlap, single and multiple reflections can also be distinguished relatively well. The reconstructed multiple reflections, due to their lower energy and poorer continuity, have a much lower similarity than the single reflections. This provides an effective basis for the subsequent extraction of spatial information of the single reflection. Furthermore, this disclosure calculates the spatial information of the single reflection according to formula (11), and the results are as follows: Figure 6 As shown, the spatial distribution of the primary reflection wave is highlighted, while the spatial location information of multiple reflection waves and noise is significantly suppressed. Under this spatial constraint, the primary reflection wave data is extracted using formula (12), and the results are shown in [the table / example]. Figure 7The extracted primary reflection wave is highly consistent with the primary reflection event in the original data in terms of time location and spatial morphology. The energy of the complex multiple reflections in the lower part is significantly weakened. However, at the superposition location of the primary and multiple reflection waves, there is leakage of the primary reflection wave and a small amount of residual multiple reflection waves, but their impact on the imaging results is negligible. Finally, the extracted primary reflection wave data is subtracted from the original data to obtain the multiple reflection removal result, as shown below. Figure 8 As shown, multiple reflected waves are well eliminated, and multiple waves between the phase axes of the primary reflected wave are also accurately removed. However, when the curvature of the primary reflected wave and the multiple reflected waves are similar and the distance is close, there are a small amount of primary reflected wave slope and multiple reflected wave residues, but they have little impact on the imaging results.

[0026] This disclosure further demonstrates the effectiveness of the proposed method through imaging results. Using the reverse time migration method, the original data and the extracted primary reflection wave data are imaged, and the difference between the two imaging results is calculated to obtain the following result: Figure 9 , Figure 10 as well as Figure 11 As shown, the gray curves represent the edges of tunnels, fault fracture zones, and large rock blocks. Figure 9 The image results from the original data show that multiple reflected waves interfere with the deep imaging, resulting in strong artifacts that easily lead to misjudgments. After removing the multiple reflected waves and performing imaging again, the results are as follows... Figure 10 As shown, false anomalies caused by multiple deep reflections have been suppressed, and the imaging results of the fault fracture zone interface are clearer. Figure 11 The imaging residuals shown show that the residuals are mainly concentrated in the deep and complex regions, and the corresponding multiple wave imaging artifacts are effectively separated. The residual energy of the main reflection event at the structural boundary is relatively weak, indicating that the leakage of the aforementioned primary reflection wave did not have a significant impact on the imaging results.

[0027] This disclosed method reconstructs a primary reflected wave under multiple wave characteristic constraints using the GSVD method, and further solves for local similarity with the original data to obtain the spatial location information of the primary reflected wave. This enables the effective extraction of the primary reflected wave and the removal of multiple reflected waves. The effectiveness of the proposed method is verified through simulation data testing.

[0028] Example 2 One embodiment of this disclosure provides a dense array acoustic wave detection multiple wave suppression and removal system for TBM tunneling disturbance zones, comprising: The signal acquisition module is used to acquire acoustic wave detection data of the TBM disturbance zone array and perform noise reduction preprocessing on it; The primary reflection wave reconstruction module is used to select the multiple wave development area in the acoustic wave data through a pre-windowing strategy, project the multiple reflection waves and primary reflection waves into the feature space using the generalized singular value decomposition method, and then reconstruct the primary reflection wave component of the acoustic wave data through low-rank component reconstruction to obtain the reconstructed data. The multiple reflection suppression module is used to calculate the local similarity between the reconstructed data and the effective signal using the local correlation method, set a similarity threshold, construct a binary mask for the primary reflection data, and apply it to the reconstructed data. This effectively suppresses reconstruction noise and residual multiple reflections, thereby achieving effective separation of primary and multiple reflections.

[0029] Example 3 One embodiment of this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the TBM tunneling disturbance zone dense array acoustic wave detection multiple wave suppression and removal method.

[0030] Example 4 One embodiment of this disclosure provides a non-transitory computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, they implement the TBM tunneling disturbance zone dense array acoustic wave detection multiple wave suppression and removal method.

[0031] Example 5 One embodiment of this disclosure provides an electronic device, including a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to perform the TBM tunneling disturbance zone dense array acoustic wave detection multiple wave suppression and removal method.

[0032] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. 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, create a machine 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.

[0033] 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.

[0034] While the specific embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.

Claims

1. A method for suppressing and removing multiple waves from a dense array acoustic wave detection system in the disturbance zone of a TBM tunneling machine, characterized in that... include: Acquire acoustic wave detection data of the TBM disturbance region array and perform noise reduction preprocessing on it; The development region of multiple reflections in the acoustic data is selected by pre-windowing strategy. The multiple reflections and the first reflection are projected into the feature space by generalized singular value decomposition. Then, the first reflection component of the acoustic data is reconstructed by low-rank component reconstruction to obtain the reconstructed data. By employing the local correlation method, the local similarity between the reconstructed data and the effective signal is calculated. A similarity threshold is set, and a binary mask for the primary reflection wave data is constructed and applied to the reconstructed data. This effectively suppresses reconstruction noise and residual multiple reflection waves, thereby achieving effective separation of the primary reflection wave and multiple reflection waves.

2. The method for suppressing and removing multiple waves from dense array acoustic wave detection in the TBM tunneling disturbance zone as described in claim 1, characterized in that, The array acoustic wave detection data includes valid signal and noise data, and the valid signal includes single-reflection waves and multiple-reflection waves.

3. The method for suppressing and removing multiple waves from dense array acoustic wave detection in the TBM tunneling disturbance zone as described in claim 1, characterized in that, The process involves pre-windowing to select regions of multiple reflections in the acoustic data, projecting multiple and primary reflections into a feature space using generalized singular value decomposition, and then reconstructing the primary reflection component of the acoustic data through low-rank component reconstruction to obtain reconstructed data, including: First, the multiple wave data in the effective signal are selected by box selection, and the effective signal is divided into a region where the primary and multiple reflection waves overlap and a region where multiple reflection waves overlap, and data matrices for the two regions are constructed. The two data matrices are decomposed using the generalized singular value decomposition method to obtain the eigenvector matrix and the diagonal matrix, respectively. By comparing the magnitudes of the singular values ​​in the diagonal matrix, the dominant component of the primary reflected wave is obtained based on the comparison results, and the reconstructed data is obtained.

4. The method for suppressing and removing multiple waves from dense array acoustic wave detection in the TBM tunneling disturbance zone as described in claim 1, characterized in that, The characteristics of single-wave and multiple-wave data are as follows: the reconstructed data of single-wave data has a stable shape and a high similarity to the original effective signal; the reconstruction noise is newly introduced noise during the reconstruction process and is not correlated; the residual multiple-wave signal has a low similarity to the original effective signal due to its incomplete energy or structure.

5. The method for suppressing and removing multiple waves from dense array acoustic wave detection in the TBM tunneling disturbance zone as described in claim 1, characterized in that, The local similarity index is set within the range [0,1], with the closer to 1 indicating higher similarity between the two matrices at that point. Utilizing the high correlation of primary reflections and the low coherence of reconstruction noise and residual multiple reflections, a mask matrix is ​​constructed by setting a similarity threshold. in, α Γ is the attenuation coefficient, Γ is the local similarity index, and the mask matrix represents the spatial location information of the first reflected wave, i.e., 1 indicates the existence of a first reflected wave and 0 indicates the absence of a first reflected wave. By using the similarity threshold, the spatial location information of the reconstructed noise and residual multiple reflected waves is removed. Therefore, the mask matrix only contains the spatial location information of the first reflected wave. This information is then used to extract the first reflected wave and remove the multiple reflected waves.

6. The method for suppressing and removing multiple waves from dense array acoustic wave detection in the TBM tunneling disturbance zone as described in claim 1, characterized in that, The method of calculating the local similarity between the reconstructed data and the effective signal using local correlation includes: Construct the global correlation coefficient of the signal; Using the vector matrix of global correlation coefficients, an approximate solution is obtained through integer regularization and iterative methods. Finally, a local similarity index is constructed using the approximate solution values.

7. The method for suppressing and removing multiple waves from dense array acoustic wave detection in the TBM tunneling disturbance zone as described in claim 6, characterized in that, The local similarity index is finally obtained by approximating the solution using the conjugate gradient iteration method. Γ for: in, Represents the Hadamard product, a local similarity index. Γ The values ​​located in the range [0,1] and the closer they are to 1, the higher the similarity between the two matrices in this range.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the TBM tunneling disturbance zone dense array acoustic wave detection multiple wave suppression and removal method as described in any one of claims 1-7.

9. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, which, when executed by a processor, implement the TBM tunneling disturbance zone dense array acoustic wave detection multiple wave suppression and removal method as described in any one of claims 1-7.

10. An electronic device, characterized in that, include: The device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to perform the TBM tunneling disturbance zone dense array acoustic wave detection multiple wave suppression and removal method as described in any one of claims 1-7.

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