A DAS-VSP data electric signal noise suppression method based on dual-tree complex wavelet transform

Abstract

The application discloses a DAS-VSP data electric signal noise suppression method based on a dual-tree complex wavelet transform, which comprises the following steps: decomposing signal data of each record by using the dual-tree complex wavelet transform to obtain decomposition coefficients of signal real parts and imaginary parts; setting a comparison threshold according to a signal-to-noise ratio of seismic data; selecting to-be-adjusted decomposition coefficients from the decomposition coefficients of the signal real parts and imaginary parts; comparing the to-be-adjusted decomposition coefficients with the comparison threshold; adjusting the to-be-adjusted decomposition coefficients according to a comparison result; reconstructing signal data after decomposition coefficient adjustment by using the dual-tree complex wavelet inverse transform to obtain signal data after noise suppression.

Description

Technical Field

[0001] This invention relates to the field of seismic data processing technology, and in particular to a method for suppressing electrical signal noise in DAS-VSP data based on dual-tree complex wavelet transform. Background Technology

[0002] Compared to traditional seismic detectors, DAS technology acquires VSP data with a lower signal-to-noise ratio, and the data contains various types of noise, such as random noise, fading noise, checkerboard noise, and low-frequency noise. These noises severely obscure the effective signal, posing significant challenges to the imaging and interpretation of seismic exploration data. Several methods have been researched and attempted to address the DAS-VSP noise problem. One method, based on a multi-stage progressive denoising network, constructs a denoising network with multiple stages. Each stage extracts feature information at different levels of detail and performs feature fusion and complementary feature processing to achieve progressive noise suppression and signal reconstruction of DAS-VSP data. This method performs well in handling complex noise and recovering signal details. Another method, based on a multi-scale parallel attention network, combines a multi-scale structure and an attention mechanism. By extracting feature information from the data at different scales and resolutions and focusing on the effective signal using an attention mechanism, it achieves suppression of various background noises and recovery of weak effective signals. The DAS noise suppression method based on morphological component analysis (MCA) and frequency-wavenumber (FK) transform uses the MCA algorithm to separate coupled noise from the effective signal, and then uses the FK transform to further extract the residual effective signal from the coupled noise, thus achieving more effective noise suppression and signal recovery. In practical applications, it is necessary to select an appropriate method or combine multiple methods for comprehensive processing based on the specific data characteristics and noise type. Due to the complexity and diversity of DAS-VSP noise problems, there is currently no completely universal method that can solve all types of noise problems. Summary of the Invention

[0003] To address the aforementioned technical problems, at least one embodiment of the present invention provides a method for suppressing noise in DAS-VSP data electrical signals based on dual-tree complex wavelet transform.

[0004] In some alternative embodiments, the method includes performing the following steps on each record in the seismic data:

[0005] The signal data of each record is decomposed using dual-tree complex wavelet transform to obtain the decomposition coefficients of the real and imaginary parts of the signal.

[0006] Set a comparison threshold based on the signal-to-noise ratio of the seismic data;

[0007] Select the decomposition coefficient to be adjusted from the decomposition coefficients of the real and imaginary parts of the signal, compare the decomposition coefficient to be adjusted with the comparison threshold, and adjust the decomposition coefficient to be adjusted according to the comparison result;

[0008] The signal data after the decomposition coefficients are adjusted is reconstructed using the dual-tree complex wavelet inverse transform to obtain the noise-suppressed signal data.

[0009] In some optional embodiments, the decomposition coefficients of the real and imaginary parts of the signal include real part low-frequency coefficients and real part high-frequency coefficients, and imaginary part low-frequency coefficients and imaginary part high-frequency coefficients.

[0010] In some optional embodiments, setting a threshold based on the signal-to-noise ratio of seismic data includes:

[0011] The higher the data signal-to-noise ratio, the lower the threshold should be set; the lower the data signal-to-noise ratio, the higher the threshold should be set.

[0012] In some optional embodiments, the decomposition coefficients to be adjusted include high-frequency coefficients of the real part and high-frequency coefficients of the imaginary part.

[0013] In some optional embodiments, comparing the decomposition coefficients to be adjusted with the comparison threshold and adjusting the decomposition coefficients to be adjusted based on the comparison result includes:

[0014] Set the decomposition coefficients to be adjusted that are less than the comparison threshold to zero.

[0015] In some optional embodiments, comparing the decomposition coefficients to be adjusted with the comparison threshold and adjusting the decomposition coefficients to be adjusted based on the comparison result includes:

[0016] The decomposition coefficients to be adjusted that are greater than the comparison threshold are reduced.

[0017] In some optional embodiments, the method further includes:

[0018] Keep the low-frequency coefficients of the real part and the low-frequency coefficients of the imaginary part unchanged.

[0019] At least one embodiment of the present invention also provides a DAS-VSP data electrical signal noise suppression device based on dual-tree complex wavelet transform, characterized in that it includes:

[0020] The decomposition module is used to decompose the signal data of each record using dual-tree complex wavelet transform to obtain the decomposition coefficients of the real and imaginary parts of the signal.

[0021] The settings module is used to set a comparison threshold based on the signal-to-noise ratio of the seismic data.

[0022] An adjustment module is used to select a decomposition coefficient to be adjusted from the decomposition coefficients of the real and imaginary parts of the signal, compare the decomposition coefficient to be adjusted with the comparison threshold, and adjust the decomposition coefficient to be adjusted according to the comparison result.

[0023] The suppression module is used to reconstruct the signal data after the decomposition coefficients are adjusted using the dual-tree complex wavelet inverse transform, so as to obtain the signal data after noise suppression.

[0024] At least one embodiment of the present invention also provides an electronic device, characterized in that it comprises:

[0025] At least one processor; and,

[0026] A memory communicatively connected to the at least one processor; wherein,

[0027] The memory stores instructions that can be executed by the at least one processor, which enable the at least one processor to perform the DAS-VSP data electrical signal noise suppression method based on dual-tree complex wavelet transform as described above.

[0028] At least one embodiment of the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the DAS-VSP data electrical signal noise suppression method based on dual-tree complex wavelet transform as described above.

[0029] At least one embodiment of the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the DAS-VSP data electrical signal noise suppression method based on dual-tree complex wavelet transform as described above.

[0030] Compared with existing technologies, the embodiments of the present invention provide a method for suppressing electrical signal noise in DAS-VSP data based on dual-tree complex wavelet transform. This method decomposes the data using dual-tree complex wavelet transform to obtain decomposition coefficients. Then, a suitable threshold is set, and the decomposition coefficients are thresholded. Coefficients smaller than the threshold are set to zero (i.e., considered noise), while coefficients larger than the threshold are contracted (i.e., their amplitude is reduced). Finally, an inverse dual-tree complex wavelet transform is performed to obtain the reconstructed signal, resulting in a noise-suppressed signal. This method achieves ideal implementation results, demonstrating a significant suppression effect on electrical signal noise in DAS-VSP data, and possesses high application value. Attached Figure Description

[0031] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0032] Figure 1 This is a flowchart of the steps of the DAS-VSP data electrical signal noise suppression method based on dual-tree complex wavelet transform used in Embodiment 1 of the present invention.

[0033] Figure 2 This is a schematic diagram of the original DAS-VSP with biased common shot gather containing electrical signal noise before and after denoising in Embodiment 2 of the present invention.

[0034] Figure 3 This is a schematic diagram of the original 1098-meter common depth point gather of DAS-VSP containing electrical signal noise before and after denoising in Embodiment 2 of the present invention;

[0035] Figure 4 This is a schematic diagram of the original 2334-meter common depth point gather of DAS-VSP containing electrical signal noise before and after denoising in Embodiment 2 of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details are presented in the embodiments of the present invention to facilitate a better understanding of the invention. However, the technical solutions claimed in the present invention can be implemented even without these technical details and various variations and modifications based on the following embodiments. The division of the following embodiments is for ease of description and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined with and referenced by each other without contradiction.

[0037] As mentioned earlier, during DAS-VSP data acquisition, electrical signal interference caused by factors such as electronic equipment, transmission lines, or the external environment can reduce the signal-to-noise ratio and resolution of seismic data, affecting subsequent data processing and interpretation. To address this, this invention proposes a method for suppressing electrical signal noise in DAS-VSP data based on dual-tree complex wavelet transform. This method utilizes dual-tree complex wavelet transform to obtain the signal decomposition coefficients, and then performs threshold processing on these coefficients to achieve signal denoising.

[0038] The implementation details of the above method are described in detail below through examples. The following content is only for the convenience of understanding the implementation details and is not necessary for implementing this solution.

[0039] Example 1:

[0040] This embodiment provides a method for suppressing electrical signal noise in DAS-VSP data based on dual-tree complex wavelet transform. The basic idea is as follows: For background electrical signal noise in DAS-VSP data, the data is decomposed using dual-tree complex wavelet transform to obtain decomposition coefficients. Then, an appropriate threshold is set, and the decomposition coefficients are thresholded. Decomposition coefficients smaller than the threshold are set to zero (i.e., considered as noise), while decomposition coefficients larger than the threshold are contracted (i.e., their amplitude is reduced). Finally, dual-tree complex wavelet inverse transform is performed to obtain the reconstructed signal, thus obtaining the noise-suppressed signal.

[0041] like Figure 1 As shown, the method mainly includes the following steps:

[0042] Step 1: Perform a dual-tree complex wavelet transform on the i-th recorded signal s(t) from the seismic data to obtain the decomposition coefficients cA, cH, cV, and cD. Here, cA represents the real part low-frequency coefficients, cH represents the real part high-frequency coefficients, cV represents the imaginary part low-frequency coefficients, and cD represents the imaginary part high-frequency coefficients.

[0043] Step 2: Set a threshold based on the signal-to-noise ratio (SNR) of the seismic data. If the SNR is high, set the threshold lower (e.g., set the threshold less than 5); if the SNR is low, set the threshold higher (e.g., set the threshold greater than 5).

[0044] Step 3: Keep the low-frequency coefficients in the decomposition coefficients unchanged, and perform thresholding on the high-frequency coefficients of the real and imaginary parts. Set the high-frequency coefficients below the threshold to zero (i.e., consider them noise), and shrink the high-frequency coefficients above the threshold (multiply by a percentage factor to reduce their amplitude).

[0045] Step 4: Perform inverse complex tree wavelet transform on the decomposed coefficients processed in Step 3 to obtain the reconstructed signal. At this point, the noise in the signal is filtered out.

[0046] Step 5: Repeat steps 1 to 4 to traverse all trace signals in the seismic data to obtain DAS-VSP data after electrical signal noise suppression.

[0047] Example 2

[0048] The following example will be used to verify the effectiveness of the noise suppression method for DAS-VSP data based on dual-tree complex wavelet transform. The method is verified by processing actual DAS-VSP seismic data.

[0049] Figure 2 This is a schematic diagram comparing the electrical signals of the common shot recorder in Walkaway DAS-VSP data from a certain region before and after denoising. Figure 2 (a) Before denoising of the common shot record electrical signal Figure 2 (b) The denoised electrical signal of the common shot record.

[0050] Figure 3 This is a schematic diagram comparing the 2D DAS-VSP before and after denoising in a 1098-meter common depth point gather. Figure 3 (a) Before denoising of the 1098-meter common depth point gather. Figure 3 (b) Noise removed from the 1098-meter common depth point gather.

[0051] Figure 4 This is a comparative illustration of the 2D DAS-VSP before and after denoising in a common depth point gather at 2334 meters. Figure 4 (a) Before denoising of the 2334-meter common depth point gather. Figure 4 (b) Noise removal of the 2334-meter common depth point gather.

[0052] from Figures 2 to 4 The data before denoising shows that the electrical signal noise energy from DAS acquisition is very strong. Especially as propagation time increases, and the effective signal energy weakens due to absorption and attenuation by the strata, the strong electrical signal noise almost overwhelms the seismic signal. After noise suppression using the DAS-VSP data electrical signal noise suppression method based on dual-tree complex wavelet transform provided in this invention, the electrical signal noise energy intensity is significantly reduced, and the relative energy of the effective signal is significantly improved. The signal-to-noise ratio of the DAS-VSP data after electrical signal noise suppression is greatly improved, which provides better basic data for subsequent processing.

[0053] Example 3

[0054] Another embodiment of the present invention relates to a DAS-VSP data electrical signal noise suppression device based on dual-tree complex wavelet transform, comprising:

[0055] The decomposition module is used to decompose the signal data of each record using dual-tree complex wavelet transform to obtain the decomposition coefficients of the real and imaginary parts of the signal.

[0056] The settings module is used to set a comparison threshold based on the signal-to-noise ratio of the seismic data.

[0057] An adjustment module is used to select a decomposition coefficient to be adjusted from the decomposition coefficients of the real and imaginary parts of the signal, compare the decomposition coefficient to be adjusted with the comparison threshold, and adjust the decomposition coefficient to be adjusted according to the comparison result.

[0058] The suppression module is used to reconstruct the signal data after the decomposition coefficients are adjusted using the dual-tree complex wavelet inverse transform, so as to obtain the signal data after noise suppression.

[0059] Example 4:

[0060] Another embodiment of the present invention relates to an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the DAS-VSP data electrical signal noise suppression method based on dual-tree complex wavelet transform in the above embodiments.

[0061] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0062] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0063] Example 5:

[0064] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the DAS-VSP data electrical signal noise suppression method based on dual-tree complex wavelet transform described in the above embodiments.

[0065] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0066] Example 6

[0067] Another embodiment of the present invention relates to a computer program product, including a computer program that, when executed by a processor, implements the steps of the DAS-VSP data electrical signal noise suppression method based on dual-tree complex wavelet transform described in the above embodiments.

[0068] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing the present invention, and in practical applications, various changes in form and detail may be made without departing from the spirit and scope of the present invention.

Claims

1. A method for suppressing noise in DAS-VSP data electrical signals based on dual-tree complex wavelet transform, characterized in that, Perform the following steps for each record in the seismic data: The signal data of each record is decomposed using dual-tree complex wavelet transform to obtain the decomposition coefficients of the real and imaginary parts of the signal. Set a comparison threshold based on the signal-to-noise ratio of the seismic data; Select the decomposition coefficient to be adjusted from the decomposition coefficients of the real and imaginary parts of the signal, compare the decomposition coefficient to be adjusted with the comparison threshold, and adjust the decomposition coefficient to be adjusted according to the comparison result; The signal data after the decomposition coefficients are adjusted is reconstructed using the dual-tree complex wavelet inverse transform to obtain the noise-suppressed signal data.

2. The method for suppressing noise in DAS-VSP data electrical signals based on dual-tree complex wavelet transform according to claim 1, characterized in that, The decomposition coefficients of the real and imaginary parts of the signal include low-frequency coefficients and high-frequency coefficients of the real part, and low-frequency coefficients and high-frequency coefficients of the imaginary part.

3. The DAS-VSP data electrical signal noise suppression method based on dual-tree complex wavelet transform according to claim 1, characterized in that, The step of setting a threshold based on the signal-to-noise ratio of seismic data includes: The higher the data signal-to-noise ratio, the lower the threshold should be set; the lower the data signal-to-noise ratio, the higher the threshold should be set.

4. The method for suppressing noise in DAS-VSP data electrical signals based on dual-tree complex wavelet transform according to claim 1, characterized in that, The decomposition coefficients to be adjusted include high-frequency coefficients of the real part and high-frequency coefficients of the imaginary part.

5. The DAS-VSP data electrical signal noise suppression method based on dual-tree complex wavelet transform according to claim 4, characterized in that, The step of comparing the decomposition coefficient to be adjusted with the comparison threshold and adjusting the decomposition coefficient to be adjusted according to the comparison result includes: Set the decomposition coefficients to be adjusted that are less than the comparison threshold to zero.

6. The method for suppressing noise in DAS-VSP data electrical signals based on dual-tree complex wavelet transform according to claim 5, characterized in that, The step of comparing the decomposition coefficient to be adjusted with the comparison threshold and adjusting the decomposition coefficient to be adjusted according to the comparison result includes: The decomposition coefficients to be adjusted that are greater than the comparison threshold are reduced.

7. The DAS-VSP data electrical signal noise suppression method based on dual-tree complex wavelet transform according to claim 4, characterized in that, The method further includes: Keep the low-frequency coefficients of the real part and the low-frequency coefficients of the imaginary part unchanged.

8. A DAS-VSP data electrical signal noise suppression device based on dual-tree complex wavelet transform, characterized in that, include: The decomposition module is used to decompose the signal data of each record using dual-tree complex wavelet transform to obtain the decomposition coefficients of the real and imaginary parts of the signal. The settings module is used to set a comparison threshold based on the signal-to-noise ratio of the seismic data. An adjustment module is used to select a decomposition coefficient to be adjusted from the decomposition coefficients of the real and imaginary parts of the signal, compare the decomposition coefficient to be adjusted with the comparison threshold, and adjust the decomposition coefficient to be adjusted according to the comparison result. The suppression module is used to reconstruct the signal data after the decomposition coefficients are adjusted using the dual-tree complex wavelet inverse transform, so as to obtain the signal data after noise suppression.

9. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the DAS-VSP data electrical signal noise suppression method based on dual-tree complex wavelet transform as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the DAS-VSP data electrical signal noise suppression method based on dual-tree complex wavelet transform as described in any one of claims 1 to 7.

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