Earthquake signal processing method, medium and device

Abstract

The present invention discloses a seismic signal processing method, medium and device. The method includes: decomposing original seismic data according to a plurality of different sliding time windows; for each sliding time window, representing the original seismic data within the time window as seismic data under mathematical morphology, removing the noise signals of the seismic data under mathematical morphology, and obtaining a seismic data gather of the time window under mathematical morphology; calculating the segmented scale coefficient of each sliding time window; and obtaining the reconstructed seismic data based on the segmented scale coefficient of each sliding time window and the seismic data gather under mathematical morphology. The present invention utilizes the advantages of mathematical morphology, reconstructs signals based on sliding time windows, achieves the purpose of multi-scale signal reconstruction, abandons the fixed coefficients and specific scales of the inherent denoising methods, achieves a better denoising effect, fully protects the effective signals, and has a good effect on noise removal and effective signal protection in seismic monitoring.

Description

Technical Field

[0001] The present invention belongs to the technical field of seismic data processing, and particularly relates to a seismic signal processing method, medium and electronic device. Background Art

[0002] Under the current domestic geological exploration situation, the use of hydraulic fracturing is becoming increasingly common in the exploration and development process. Microseismic monitoring is an essential method for signal monitoring during the hydraulic fracturing process. Since microseismic signals are very small, it is difficult to monitor them, so noise suppression and signal detection have become the primary tasks of microseismic monitoring. Therefore, improving the signal-to-noise ratio of microseismic signals has always been the top priority of research. Currently, the commonly used methods mainly include frequency filtering, but this method is not applicable when the noise and signal frequency bands overlap; there is also a method of using ensemble empirical mode decomposition and adaptive threshold to suppress noise in the f-x domain. The current relatively advanced ensemble empirical mode decomposition method is based on the mathematical morphology method, which mainly decomposes seismic data in mathematical morphology to obtain the scale coefficients used in the decomposition, and reconstructs based on the scale coefficients and the decomposed seismic data. The advantage of this method is that it can retain signal energy and hardly damage the spectrum.

[0003] However, when the current mathematical morphology-based method performs signal reconstruction, it can only use fixed scale coefficients. Using fixed scale coefficients will cause partial loss of effective signals and at the same time, the noise cannot be removed cleanly.

[0004] Therefore, a method that can fully protect effective signals and has a good denoising effect is needed. Summary of the Invention

[0005] The object of the present invention is to propose a method that can fully protect effective signals and has a good denoising effect.

[0006] In a first aspect, the present invention provides a seismic signal processing method, including: decomposing the original seismic data according to multiple different sliding time windows; for each sliding time window, representing the original seismic data within the time window as seismic data under mathematical morphology, removing the noise signals of the seismic data under mathematical morphology to obtain a seismic data gather under mathematical morphology of the time window; calculating the segmented scale coefficients of each sliding time window; and obtaining the reconstructed seismic data based on the segmented scale coefficients of each sliding time window and the seismic data gather under mathematical morphology.

[0007] Optionally, the seismic data trace set in mathematical morphology of the time window is obtained through the following steps: the original seismic data within the time window is decomposed at multiple scales to obtain the data represented by the original data at each scale in mathematical morphology; from the data represented by the original data at all scales within the time window in mathematical morphology, the data represented by the original data at the scale where the noise signal is located is removed; the data after removing the noise signal is used as the seismic data trace set in mathematical morphology of the time window.

[0008] Optionally, the seismic data representation in mathematical morphology of the time window is:

[0009]

[0010] where f is the original seismic data within the sliding time window, λ represents the λ-th scale, s is the structuring element, and y λ represents the data after the original data at the λ-th scale is acted upon by the structuring element at the λ-th scale, and F λ represents the data represented by the original data at the λ-th scale in mathematical morphology, and n represents the number of scales.

[0011] Optionally, the segmented scale coefficient of each sliding time window is calculated through the following steps: for each sliding time window, obtain the initial segmented scale coefficient expression corresponding to the time window, record the L2 norm constraint in the initial segmented scale coefficient expression to obtain the segmented scale coefficient expression after adding the norm constraint, and calculate the segmented scale coefficient expression after adding the norm constraint to obtain the segmented scale coefficient.

[0012] Optionally, the initial segmented scale coefficient expression is:

[0013]

[0014] where α i is the segmented scale coefficient of the i-th sliding time window, R i is the operator of the i-th sliding time window, f is the original seismic data of the i-th sliding time window, and F i is the seismic data trace set in mathematical morphology of the i-th sliding time window.

[0015] Optionally, the segmented scale coefficient expression after adding the norm constraint is:

[0016]

[0017] where α i is the segmented scale coefficient of the i-th sliding time window, R i is the operator of the i-th sliding time window, f is the original seismic data of the i-th sliding time window, and F iis the seismic data trace gather under mathematical morphology for the i-th sliding time window, and λ is the constraint coefficient.

[0018] Optionally, the segmented scale coefficient of the calculated sliding time window is:

[0019]

[0020] where α i is the segmented scale coefficient of the i-th sliding time window, R i is the operator of the i-th sliding time window, is the transpose of the operator of the i-th sliding time window, f is the original seismic data of the i-th sliding time window, F i is the seismic data trace gather under mathematical morphology for the i-th sliding time window, is the seismic data trace gather under mathematical morphology for the i-th sliding time window, λ is the constraint coefficient, and I is the identity matrix.

[0021] Optionally, the reconstructed seismic data is obtained by using the following formula:

[0022]

[0023] where is the reconstructed seismic data, R i is the operator of the i-th sliding time window, is the transpose of the operator of the i-th sliding time window, F i is the seismic data trace gather under mathematical morphology for the i-th sliding time window, and α i is the segmented scale coefficient of the i-th sliding time window.

[0024] In a second aspect, the present invention also provides an electronic device, which includes: a memory storing executable instructions; a processor, and the processor runs the executable instructions in the memory to implement the above seismic signal processing method.

[0025] In a third aspect, the present invention also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the above seismic signal processing method is implemented.

[0026] The beneficial effects of the present invention are as follows: The seismic signal processing method of the present invention utilizes the advantages of mathematical morphology (MMD), reconstructs signals based on sliding time windows, achieves the purpose of multi-scale signal reconstruction, abandons the fixed coefficients and specific scales of the inherent denoising methods, achieves a better denoising effect, fully protects the effective signals, and has a good effect on noise removal and effective signal protection in seismic monitoring.

[0027] The present invention has other characteristics and advantages, which will be apparent from the accompanying drawings incorporated herein and the following specific embodiments, or will be described in detail in the accompanying drawings incorporated herein and the following specific embodiments. These accompanying drawings and specific embodiments are used together to explain the specific principles of the present invention. Description of the Drawings

[0028] By describing the exemplary embodiments of the present invention in more detail in conjunction with the accompanying drawings, the above and other objects, features, and advantages of the present invention will become more apparent. In the exemplary embodiments of the present invention, the same reference numerals generally represent the same components.

[0029] Figure 1 A flowchart showing a method for processing seismic signals according to an embodiment of the present invention is shown.

[0030] Figure 2 A schematic diagram showing the original signal and mathematical morphological operators of a method for processing seismic signals according to an embodiment of the present invention is shown.

[0031] Figure 3 An example diagram of multi-scale morphological decomposition of a method for processing seismic signals according to an embodiment of the present invention is shown.

[0032] Figure 4 A schematic diagram showing the application of a method for synthesizing signals of a method for processing seismic signals according to an embodiment of the present invention is shown. Detailed Description of the Embodiments

[0033] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0034] The present invention provides a method for processing seismic signals, including: decomposing the original seismic data according to a plurality of different sliding time windows; for each sliding time window, representing the original seismic data within the time window as seismic data under mathematical morphology, removing the noise signals of the seismic data under mathematical morphology, and obtaining a seismic data trace gather under mathematical morphology of the time window; calculating the segmented scale coefficients of each sliding time window; and obtaining the reconstructed seismic data based on the segmented scale coefficients of each sliding time window and the seismic data trace gather under mathematical morphology.

[0035] Scale reconstruction is performed using an unfixed coefficient. Data at different scales are divided according to a sliding time window. For each sliding time window, the segmented scale coefficient is calculated, and then the segmented scale coefficient is added with an L2 norm constraint to obtain the final segmented scale coefficient. Based on the final segmented scale coefficient and the seismic data within its corresponding sliding time window, seismic data reconstruction is carried out to obtain the reconstructed seismic data. Different from the fixed scale in the traditional mathematical morphology method, in this application (SWMMR), the signal is divided into different scales through overlapping sliding time windows. After removing the scales containing a large amount of noise, for each time window, the reconstruction coefficients of other scales are calculated by the least squares method with an l2-norm constraint. By combining the reconstructed parts, better denoising and less loss of effective signals can be obtained.

[0036] According to an exemplary embodiment, the seismic signal processing method utilizes the advantages of mathematical morphology (MMD), reconstructs the signal based on a sliding time window to achieve the purpose of multi-scale signal reconstruction, abandons the fixed coefficient and specific scale of the inherent denoising method, achieves a better denoising effect, fully protects the effective signal, and has a good effect on noise removal and effective signal protection in seismic monitoring.

[0037] As an alternative solution, the seismic data trace gather under mathematical morphology of the time window is obtained through the following steps: the original seismic data within the time window is decomposed at multiple scales to obtain the data represented by the original data at each scale under mathematical morphology; from the data represented by the original data at all scales within the time window under mathematical morphology, the data represented by the original data at the scale where the noise signal is located is removed; the data after removing the noise signal is used as the seismic data trace gather under mathematical morphology of the time window.

[0038] Specifically, mathematical morphology is an effective graphic processing tool based on lattice and topology, used to describe the quantization structure of an image. The basic use of mathematical morphology is to select the shape of the structural element according to the characteristics of the signal, and the basic operations are erosion and dilation, and each operation is represented by the following formulas respectively:

[0039]

[0040]

[0041] Among them, f(z) represents the value at z, s(x) is the structural element, and the opening and closing operations are composed of erosion and dilation, as shown in the following formulas (3) and (4):

[0042]

[0043]

[0044] The selection of structural elements also affects the calculation results. Shape, amplitude, and length are the three key components of structural elements.

[0045] The original seismic data is represented by mathematical morphology, expressed as the sum of corresponding scales under the action of structural elements. Different scales are set using multi-scale morphological decomposition, dividing the structural elements into multiple scales, thus obtaining multi-scale seismic signals.

[0046] As an alternative, the seismic data under the mathematical morphology of the time window is expressed as:

[0047]

[0048] where f is the original seismic data within the sliding time window, λ represents the λth scale, s is the structural element, and y λ represents the data after the original data of the λth scale is affected by the structural element of the λth scale, and F λ represents the data of the original data of the λth scale represented under mathematical morphology, and n represents the number of scales.

[0049] Specifically, the data of the original data of each scale represented under mathematical morphology is obtained using the above formula.

[0050] As an alternative, the segmented scale coefficient of each sliding time window is calculated through the following steps: For each sliding time window, obtain the initial segmented scale coefficient expression corresponding to the time window, record the L2 norm constraint in the initial segmented scale coefficient expression, obtain the segmented scale coefficient expression after adding the norm constraint, calculate the segmented scale coefficient expression after adding the norm constraint, and obtain the segmented scale coefficient.

[0051] As an alternative, the initial segmented scale coefficient expression is:

[0052]

[0053] where α i is the segmented scale coefficient of the i-th sliding time window, R i is the operator of the i-th sliding time window, f is the original seismic data of the i-th sliding time window, and F i is the seismic data trace set of the i-th sliding time window under mathematical morphology.

[0054] Specifically, different sliding time windows are set according to different decomposition scales, a sliding time window with an appropriate length is selected, and then it is slid downwards to decompose the seismic data of mathematical morphology into the seismic data within multiple sliding time windows. For the seismic data within the same time window, the initial segmented scale coefficient corresponding to the time window is calculated.

[0055] As an alternative, the expression of the segmented scale coefficient after adding the norm constraint is:

[0056]

[0057] where α i is the segmented scale coefficient of the i-th sliding time window, R i is the operator of the i-th sliding time window, f is the original seismic data of the i-th sliding time window, F i is the seismic data gather under mathematical morphology of the i-th sliding time window, and λ is the constraint coefficient.

[0058] Specifically, based on the initial segmented scale coefficient, the L2-norm is added for constraint.

[0059] As an alternative, the calculated segmented scale coefficient of the sliding time window is:

[0060]

[0061] where α i is the segmented scale coefficient of the i-th sliding time window, R i is the operator of the i-th sliding time window, is the transpose of the operator of the i-th sliding time window, f is the original seismic data of the i-th sliding time window, F i is the seismic data gather under mathematical morphology of the i-th sliding time window, is the seismic data gather under mathematical morphology of the i-th sliding time window, λ is the constraint coefficient, and I is the identity matrix.

[0062] Specifically, the expression of the segmented scale coefficient after adding the L2-norm constraint is calculated and sorted out to obtain the segmented scale coefficient.

[0063] As an alternative, the following formula is used to obtain the reconstructed seismic data:

[0064]

[0065] where is the reconstructed seismic data, R i is the operator of the i-th sliding time window, is the transpose of the operator of the i-th sliding time window, F i is the seismic data gather under mathematical morphology of the i-th sliding time window, and α i is the segmented scale coefficient of the i-th sliding time window.

[0066] Specifically, after obtaining the segmented scale coefficient of each sliding time window and the seismic data gather under mathematical morphology, they are substituted into the reconstruction formula to obtain the reconstructed seismic data.

[0067] It should be noted that the length of the sliding time window should be equal to or greater than the number of scales; otherwise, underfitting will occur.

[0068] In a second aspect, the present invention further provides an electronic device, which includes: a memory storing executable instructions; and a processor that runs the executable instructions in the memory to implement the above-mentioned seismic signal processing method.

[0069] In a third aspect, the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned seismic signal processing method.

[0070] Embodiment 1

[0071] Figure 1 FIG. shows a flowchart of a seismic signal processing method according to an embodiment of the present invention. Figure 2 FIG. shows a schematic diagram of an original signal and a mathematical morphology operator of a seismic signal processing method according to an embodiment of the present invention. Figure 3 FIG. shows an example diagram of multi-scale morphological decomposition of a seismic signal processing method according to an embodiment of the present invention. Figure 4 FIG. shows a schematic diagram of the application of a synthetic signal method of a seismic signal processing method according to an embodiment of the present invention.

[0072] Combined with Figure 1 、 Figure 2 、 Figure 3 and Figure 4 as shown, the seismic signal processing method includes:

[0073] Step 1: Decompose the original seismic data according to multiple different sliding time windows;

[0074] Step 2: For each sliding time window, represent the original seismic data within the time window as seismic data under mathematical morphology, remove the noise signal of the seismic data under mathematical morphology, and obtain a seismic data trace gather under mathematical morphology of the time window;

[0075] Step 3: Calculate the segmented scale coefficients of each sliding time window;

[0076] Step 4: Based on the segmented scale coefficients of each sliding time window and the seismic data trace gather under mathematical morphology, obtain the reconstructed seismic data.

[0077] Among them, the seismic data trace gather under the mathematical morphology of the time window is obtained through the following steps: The original seismic data within the time window is decomposed at multiple scales to obtain the data represented by the original data at each scale under the mathematical morphology; From the data represented by the original data at all scales within the time window under the mathematical morphology, the data represented by the original data at the scale where the noise signal is located is removed; The data after removing the noise signal is used as the seismic data trace gather under the mathematical morphology of the time window.

[0078] Among them, the seismic data representation under the mathematical morphology of the time window is:

[0079]

[0080] Among them, f is the original seismic data within the sliding time window, λ represents the λ-th scale, s is the structuring element, and y λ represents the data after the original data at the λ-th scale acts on the structuring element at the λ-th scale, and F λ represents the data represented by the original data at the λ-th scale under the mathematical morphology, and n represents the number of scales.

[0081] Among them, the segmented scale coefficient of each sliding time window is calculated through the following steps: For each sliding time window, the initial segmented scale coefficient expression corresponding to the time window is obtained, the L2 norm constraint is recorded in the initial segmented scale coefficient expression, the segmented scale coefficient expression after adding the norm constraint is obtained, and the segmented scale coefficient expression after adding the norm constraint is calculated to obtain the segmented scale coefficient.

[0082] Among them, the initial segmented scale coefficient expression is:

[0083]

[0084] Among them, α i is the segmented scale coefficient of the i-th sliding time window, R i is the operator of the i-th sliding time window, f is the original seismic data of the i-th sliding time window, and F i is the seismic data trace gather under the mathematical morphology of the i-th sliding time window.

[0085] Among them, the segmented scale coefficient expression after adding the norm constraint is:

[0086]

[0087] Among them, α i is the segmented scale coefficient of the i-th sliding time window, R i is the operator of the i-th sliding time window, f is the original seismic data of the i-th sliding time window, and F i is the seismic data trace gather under the mathematical morphology of the i-th sliding time window, and λ is the constraint coefficient.

[0088] Among them, the segmented scale coefficient of the calculated sliding time window is as follows:

[0089]

[0090] Among them, α i is the segmented scale coefficient of the i-th sliding time window, R i is the operator of the i-th sliding time window, is the transpose of the operator of the i-th sliding time window, f is the original seismic data of the i-th sliding time window, F i is the seismic data gather under mathematical morphology of the i-th sliding time window, is the seismic data gather under mathematical morphology of the i-th sliding time window, λ is the constraint coefficient, and I is the identity matrix.

[0091] Among them, the reconstructed seismic data is obtained by using the following formula:

[0092]

[0093] Among them, is the reconstructed seismic data, R i is the operator of the i-th sliding time window, is the transpose of the operator of the i-th sliding time window, F i is the seismic data gather under mathematical morphology of the i-th sliding time window, α i is the segmented scale coefficient of the i-th sliding time window.

[0094] As Figure 2 represents four operations in the original signal and mathematical morphology. The black line represents the signal, the black and blue dotted lines represent erosion and dilation, and the red and yellow dotted lines represent opening and closing. All values of the erosion result are smaller than the original signal, while dilation is exactly the opposite. The opening operation will reduce the peak value, and closing will fill the trough.

[0095] Figure 3 shows examples of multi-scale decomposition. The first result is the Ricker wavelet, the second result is random noise, the third result is low-frequency noise, the fourth is the received original data, and the 5th to 11th results are the results of multi-scale decomposition. Scale 1 contains most of the random noise, scales 2-6 are the decomposed parts of the signal, and scale 7 is the extracted low-frequency energy.

[0096] Figure 4It is the application of the synthetic signal. The first trace represents the Ricker wavelet with a frequency domain of 100 Hz, the second represents random noise, the third represents noise data with a signal-to-noise ratio of -11.3121 dB, the 4th - 10th represent the components of the multi-scale morphological decomposition, the 11th and 12th lines respectively represent the results of this method and the traditional method, and the 13th and 14th lines are the corresponding reconstruction errors. The multi-scale morphological decomposition can extract a large amount of useless information, such as small-scale random noise (such as the 4th and 5th traces) and a large amount of low-frequency noise (the 10th trace), and then use the 6th - 9th lines to obtain the final denoising result.

[0097] Embodiment 2

[0098] The present disclosure provides an electronic device including: a memory storing executable instructions; a processor that runs the executable instructions in the memory to implement the above seismic signal processing method.

[0099] The electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0100] The memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0101] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In an embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory.

[0102] Those skilled in the art should understand that, in order to solve the technical problem of how to obtain a good user experience effect, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included in the protection scope of the present disclosure.

[0103] For the detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, and details will not be repeated here.

[0104] Embodiment 3

[0105] The present disclosure provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the above seismic signal processing method is implemented.

[0106] A computer-readable storage medium according to an embodiment of the present disclosure stores non-transitory computer-readable instructions. When the non-transitory computer-readable instructions are run by a processor, all or part of the steps of the methods of the various embodiments of the present disclosure described above are executed.

[0107] The above computer-readable storage medium includes but is not limited to: optical storage media (e.g., CD-ROMs and DVDs), magneto-optical storage media (e.g., MOs), magnetic storage media (e.g., magnetic tapes or external hard drives), media with built-in rewritable non-volatile memories (e.g., memory cards), and media with built-in ROMs (e.g., ROM cartridges).

[0108] The various embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is also not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments.

Claims

1. A method for processing seismic signals, characterized in that, Including: Decompose the original seismic data according to multiple different sliding time windows; For each sliding time window, represent the original seismic data within the time window as seismic data under mathematical morphology, remove the noise signal of the seismic data under mathematical morphology, and obtain the seismic data gather under mathematical morphology of the time window; Calculate the segmented scale coefficient of each sliding time window; Based on the segmented scale coefficient of each calculated sliding time window and the seismic data gather under mathematical morphology, obtain the reconstructed seismic data; Wherein, the segmented scale coefficient of each sliding time window is calculated through the following steps: For each sliding time window, obtain the initial segmented scale coefficient expression corresponding to the time window, record the L2 norm constraint in the initial segmented scale coefficient expression, obtain the segmented scale coefficient expression after adding the norm constraint, calculate the segmented scale coefficient expression after adding the norm constraint, and obtain the segmented scale coefficient; Wherein, the initial segmented scale coefficient expression is: Among them, α i is the segmentation scale coefficient of the i-th sliding time window, R i is the operator of the i-th sliding time window, f is the original seismic data of the i-th sliding time window, F i is the seismic data gather under mathematical morphology of the i-th sliding time window; Wherein, the segmented scale coefficient expression after adding the norm constraint is: Wherein, λ is the constraint coefficient; Wherein, the segmented scale coefficient of the calculated sliding time window is: Among them, is the transpose of the operator of the i-th sliding time window, and f is the original seismic data of the i-th sliding time window. is the seismic data gather under mathematical morphology of the i-th sliding time window, and I is the identity matrix.

2. The seismic signal processing method according to claim 1, wherein Obtain the seismic data gather under mathematical morphology of the time window through the following steps: Decompose the original seismic data within the time window according to multiple scales, and obtain the data represented by the original data at each scale under mathematical morphology; Remove the data represented by the original data at the scale where the noise signal is located from the data represented by the original data at all scales within the time window under mathematical morphology; Use the data after removing the noise signal as the seismic data gather under mathematical morphology of the time window.

3. The seismic signal processing method according to claim 2, wherein, The seismic data under mathematical morphology of the time window is expressed as: Among them, f is the original seismic data within the sliding time window, ε represents the ε-th scale, s is the structural element, and y ε represents the data after the action of the structural element of the ε-th scale on the original data of the ε-th scale, and F ε represents the data represented by the original data of the ε-th scale under mathematical morphology, and n represents the number of scales.

4. The seismic signal processing method according to claim 1, wherein Use the following formula to obtain the reconstructed seismic data: Among them, is the reconstructed seismic data.

5. An electronic device, characterized in that, The electronic device includes: A memory storing executable instructions; A processor that runs the executable instructions in the memory to implement the seismic signal processing method according to any one of claims 1-4.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the seismic signal processing method according to any one of claims 1-4.

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