Seismic signal denoising method and device and storage medium

Abstract

The invention discloses a seismic signal denoising method and device and a storage medium, and relates to the technical field of signal processing, and the method is characterized in that the method comprises the steps: obtaining an originally collected seismic signal; performing wavelet transform threshold denoising on the seismic signal to obtain a first signal sequence without white noise; detecting the first signal sequence, determining the position of each impulse noise in the first signal sequence, and marking as an impulse noise point; and performing median filtering processing on each pulse noise point in the first signal sequence to obtain a second signal sequence with pulse noise removed as a denoised seismic signal sequence. Through the denoising method, white noise can be efficiently removed, pulse noise is specifically removed, the original information of the signal is reserved to the maximum extent, and the preprocessing quality of the seismic signal is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a seismic signal denoising method, device and storage medium. Background Art

[0002] This section is intended to provide a background or context to the embodiments recited in the claims. No admission is made that the description herein is prior art by inclusion in this section.

[0003] In the field of signal acquisition, such as during seismic acquisition, due to the influence of the natural and human environment and the weak instantaneous excitation energy of the controllable vibrator, the original seismic data collected by the seismic node instrument is mixed with irregular noise such as white noise and impulse noise. In order to remove the influence of white noise and impulse noise, the traditional method usually adopts the median filtering algorithm, which mainly performs median filtering on each original signal sample in the time domain or frequency domain, which can remove white noise and impulse noise well. However, at the same time, a single median filtering will also lead to excessive denoising, resulting in many levels of the signal, that is, the same value, and the signal is seriously distorted due to excessive denoising. Summary of the invention

[0004] In response to the above technical problems, the present invention proposes a seismic signal denoising method, device and storage medium, which can efficiently remove white noise and specifically remove pulse noise. Compared with the traditional median filtering algorithm that simultaneously removes white noise and pulse noise from the original signal, it can retain more original signal information and improve the preprocessing quality of the seismic signal.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention includes four aspects.

[0006] In a first aspect, a method for denoising a seismic signal is provided, comprising the following steps:

[0007] Obtaining the original acquired seismic signals;

[0008] Perform wavelet transform threshold denoising on the seismic signal to obtain a first signal sequence without white noise;

[0009] Detecting the first signal sequence, determining the position of each impulse noise in the first signal sequence, and marking it as an impulse noise point;

[0010] Each impulse noise point in the first signal sequence is subjected to median filtering to obtain a second signal sequence without impulse noise as a denoised seismic signal sequence.

[0011] In some embodiments, performing wavelet transform threshold denoising on the seismic signal to obtain a first signal sequence after removing white noise includes:

[0012] Performing time-frequency transformation on the seismic signal by using Morlet wavelet transform to obtain a time-frequency spectrum of the seismic signal;

[0013] Adaptively determine the denoising threshold according to the characteristics of the seismic signal in the time and frequency domain;

[0014] Remove white noise from the time-frequency spectrum according to the denoising threshold to obtain a wavelet coefficient matrix after threshold processing;

[0015] The wavelet coefficient matrix is ​​reconstructed by performing inverse wavelet transform to obtain a first signal sequence with white noise removed.

[0016] In some embodiments, the step of adaptively determining the denoising threshold according to the characteristics of the seismic signal in the time-frequency domain includes:

[0017] Adaptively obtaining the noise standard deviation according to the characteristics of the seismic signal in the time and frequency domains;

[0018] Determine a denoising threshold according to the noise standard deviation;

[0019] The noise standard deviation is denoted as σ, and the formula of the noise standard deviation is: σ=M / 0.6745;

[0020] Where M is the median of the modulus of the wavelet transform coefficients of the signal at the minimum scale;

[0021] The denoising threshold is recorded as T, and the formula of the denoising threshold is: T = σ (√2ln (n));

[0022] Where ln(n) represents the natural logarithm of the signal length n.

[0023] In some embodiments, removing white noise from the time-frequency spectrum according to the denoising threshold to obtain a wavelet coefficient matrix after threshold processing includes:

[0024] Determine the wavelet coefficient matrix to be processed according to the time-frequency spectrum;

[0025] Based on the scale factor and the time factor, the wavelet coefficient matrix to be processed is traversed, and the wavelet coefficient whose modulus value is less than the denoising threshold is determined as the wavelet coefficient corresponding to the white noise;

[0026] The wavelet coefficients corresponding to white noise are set to zero to obtain the wavelet coefficient matrix after threshold processing.

[0027] In some embodiments, detecting the first signal sequence, determining the position of each impulse noise in the first signal sequence, and marking it as an impulse noise point includes:

[0028] Determine the detection window length;

[0029] Setting a number of points to be detected according to the first signal sequence;

[0030] Analyze and process each to-be-detected point in turn according to the detection window length, and determine the to-be-detected point that satisfies a preset discriminant as an impulse noise position point;

[0031] Each impulse noise position point is marked as an impulse noise point.

[0032] In some embodiments, analyzing and processing each to-be-detected point in turn according to the detection window length, and determining the to-be-detected point satisfying a preset discriminant as an impulse noise position point, includes:

[0033] Perform sliding processing on each point to be detected according to the following steps in sequence until all impulse noise points are marked:

[0034] Determine a number of neighbor signal sample points of the point to be detected within the detection window length according to the detection window length and the position of the point to be detected, and group them as neighbor points;

[0035] Analyze and process the difference between the point to be detected and each neighbor signal sample point, and determine the number of difference values ​​greater than a preset difference threshold among the plurality of difference values;

[0036] Determine whether the ratio of the number of difference values ​​to the number of neighbor signal samples is greater than a preset percentage threshold;

[0037] If so, when it is determined that the point to be detected is an impulse noise position point, it is marked as an impulse noise point.

[0038] In some embodiments, the discriminant formula for determining the impulse noise position point is as follows:

[0039]

[0040] In the formula, y(t i ) is the point to be detected; y(t) is the neighbor signal sample point; N(t i ) is the neighbor point of the point to be detected within the window length; Tv is the difference threshold between the point to be detected and the neighbor signal sample points; Tn is the number threshold for judging the large difference between the point to be detected and the neighbor sample points.

[0041] In some embodiments, performing median filtering on the impulse noise points in the first signal sequence to obtain the second signal sequence from which the impulse noise is removed includes:

[0042] Take the first signal segment with the window length win, centering on the location of the impulse noise point;

[0043] Sort the first segment of the signal, and take out the median and the value that is adjacent to the median and smaller than the median, add and average them to obtain the two-point mean;

[0044] Replace the impulse noise points in the original acquired signal with the mean of two points;

[0045] The above steps are repeated for each impulse noise point of the first signal sequence in turn and sliding window processing is performed to obtain a second signal sequence with the impulse noise removed.

[0046] In a second aspect, a seismic signal denoising device is provided, comprising:

[0047] A signal acquisition module, used to obtain the original acquired seismic signal;

[0048] A white noise processing module, used for performing wavelet transform threshold denoising on the seismic signal to obtain a first signal sequence without white noise;

[0049] A signal processing module, used to detect the first signal sequence, determine the position of each impulse noise in the first signal sequence, and mark it as an impulse noise point;

[0050] The impulse noise processing module is used to perform median filtering on each impulse noise point in the first signal sequence to obtain a second signal sequence with the impulse noise removed as a denoised seismic signal sequence.

[0051] In a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the aforementioned denoising method when executing the computer program.

[0052] In a fourth aspect, a computer-readable storage medium is provided, and when the computer program is executed by a processor, the steps of the denoising method as described above are implemented.

[0053] Compared with the prior art, one or more embodiments of the above scheme may have the following advantages or beneficial effects:

[0054] The present application provides a method, device and storage medium for denoising a seismic signal. The denoising method comprises the following steps: obtaining the seismic signal collected originally; performing wavelet transform threshold denoising on the seismic signal to obtain a first signal sequence with white noise removed; detecting the first signal sequence to determine the position of each pulse noise in the first signal sequence and marking it as a pulse noise point; performing median filtering on each pulse noise point in the first signal sequence to obtain a second signal sequence with pulse noise removed as a denoised seismic signal sequence. Through the denoising method, the white noise can be well removed by using the wavelet transform threshold denoising technology, and then the median filtering is improved based on the position where the pulse noise point appears, so as to remove the pulse noise. Not only the white noise is removed efficiently, but also the pulse noise is removed in a targeted manner. Compared with the traditional median filtering algorithm that removes the white noise and pulse noise of the original signal at the same time, more original signal information can be retained, and the preprocessing quality of the seismic signal is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Hereinafter, the present application will be described in more detail based on embodiments and with reference to the accompanying drawings;

[0056] Figure 1 It is a waveform diagram showing the result of removing white noise and impulse noise by median filtering in the prior art;

[0057] Figure 2 is an exemplary flow chart of the seismic signal denoising method in Embodiment 1 of the present invention;

[0058] Figure 3 In the embodiment of the present invention, Figure 2 An exemplary flow chart of step S2 shown in;

[0059] Figure 4 In the embodiment of the present invention, Figure 3 An exemplary flow chart of step S22 shown in FIG.

[0060] Figure 5 In the embodiment of the present invention, Figure 3 An exemplary flow chart of step S23 shown in FIG.

[0061] Figure 6 In the embodiment of the present invention, Figure 2 An exemplary flow chart of step S3 shown in FIG.

[0062] Figure 7 In the embodiment of the present invention, Figure 6 An exemplary flow chart of step S33 shown in FIG.

[0063] Figure 8 In the embodiment of the present invention, Figure 2An exemplary flow chart of step S4 shown in;

[0064] Fig. 9 A waveform diagram of the removal result of white noise and impulse noise by the denoising method according to an embodiment of the present invention;

[0065] Fig.10 A schematic block diagram of a seismic signal denoising device provided in Embodiment 2 of the present invention;

[0066] Fig.11 A schematic block diagram of an electronic device provided in Embodiment 3 of the present invention;

[0067] Fig.12 This is a schematic diagram of a computer-readable storage medium provided in Embodiment 4 of the present invention.

[0068] In the drawings, the same reference numerals are used for the same components and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0069] The present disclosure is further described below in conjunction with the embodiments shown in the accompanying drawings.

[0070] Embodiment 1:

[0071] In the field of signal acquisition, the collected original signal is usually mixed with irregular noises such as white noise and impulse noise. In order to remove the influence of white noise and impulse noise, the existing methods usually adopt the median filtering algorithm to perform median filtering on each original signal sample in the time domain or frequency domain, thereby removing white noise and impulse noise at the same time. However, a single median filtering will also lead to excessive denoising, making the signal appear at many levels, that is, the same value, and the signal will be severely distorted due to excessive denoising, such as Figure 1 As shown, the result of removing white noise and impulse noise by median filtering in the prior art removes too much original signal, affecting the signal quality.

[0072] The present application embodiment discloses a method for denoising a seismic signal. Figure 2As shown, the method includes the following steps: obtaining the original collected seismic signal; performing wavelet transform threshold denoising on the seismic signal to obtain a first signal sequence with white noise removed; detecting the first signal sequence to determine the position of each pulse noise in the first signal sequence and marking it as a pulse noise point; performing median filtering on each pulse noise point in the first signal sequence to obtain a second signal sequence with pulse noise removed as a denoised seismic signal sequence. This method can well remove white noise using wavelet transform threshold denoising technology, and then perform median filtering based on the position where the pulse noise point appears, thereby removing pulse noise. Not only can white noise be removed efficiently, but also pulse noise can be removed in a targeted manner. Compared with the traditional median filtering algorithm that removes white noise and pulse noise from the original signal at the same time, more original signal information can be retained, thereby improving the preprocessing quality of the seismic signal.

[0073] At least one embodiment of the present disclosure provides a seismic signal denoising method, which is not limited to denoising seismic signals, but can be used to denoise other signals that require white noise and impulse noise removal. The seismic signal denoising method can be implemented in the form of software, hardware, firmware or any combination thereof, and loaded and executed by a processor in a device such as a mobile phone, a tablet computer, a laptop computer, a desktop computer, a network server, etc., so as to achieve efficient removal of white noise and targeted removal of impulse noise, retain more original signal information, and improve the preprocessing quality of seismic signals.

[0074] Reference below Figure 2 As shown, a seismic signal denoising method provided by at least one embodiment of the present disclosure is described, and the denoising method includes steps S1 to S4.

[0075] S1. Obtain the original acquired seismic signal.

[0076] In some embodiments, seismic node instruments can be used to collect seismic signals in the target area, and then transmitted via the network to the implementation carrier of the denoising method for analysis and processing.

[0077] S2. Perform wavelet transform threshold denoising on the seismic signal to obtain a first signal sequence without white noise.

[0078] In some embodiments, step S2, such as Figure 3 As shown, steps S21 to S24 are included:

[0079] S21. Performing time-frequency transformation on the seismic signal by using Morlet wavelet transform to obtain a time-frequency spectrum of the seismic signal.

[0080] In some embodiments, a complex Morlet wavelet basis is used as a wavelet basis function, and the wavelet basis function is expressed as:

[0081]

[0082] Where, I is the false reporting unit, ω0 is the wavelet central angular frequency, and t is the sampling time;

[0083] The scale factor of the wavelet transform is a i =2 i / Nv , the corresponding sequence is recorded as a={a i},i=1,2,...,J,J=L*Nv, where Nv is the scale and can be 32, n is the signal length, then the wavelet transform expression in the time domain is:

[0084]

[0085] In the formula, x(t) represents the seismic signal sequence; “—” represents the conjugate function; a i is the scale factor; b j is the time shift factor; t is the sampling time.

[0086] Furthermore, W(a i ,b j ) is abbreviated as W i,j , represents the wavelet coefficient at the jth time point under the i-th scale factor, so as to facilitate the subsequent white noise processing.

[0087] Through the above expression, the calculation principle of wavelet transform in time domain is the convolution of signal and wavelet function under each scale factor. Therefore, the calculation principle of transforming to frequency domain is the conjugate direct product of Fourier transform result of signal and wavelet transform frequency domain expression with scale factor. The calculation efficiency of transforming in frequency domain will be higher than that of convolution in time domain. The specific operation is as follows:

[0088] The Fourier transform of the signal x(t) is X(ω), and the specific formula is as follows:

[0089] X(ω)=fx(t)e -Iωt dt;

[0090] Where ω represents the angular frequency, I is the imaginary unit, and t is the sampling time;

[0091] The inner product of the functions f(τ) and g(τ) is:

[0092]

[0093] In the formula, “<>” represents the inner product of two functions, and “—” represents the conjugate of the function;

[0094] The convolution of function f(τ) and g(τ) is:

[0095] f(t)*g(t)=ff(t)g(tt)dt;

[0096] In the formula, “*” represents the convolution symbol of the function;

[0097] According to the definition of inner product and convolution of the above functions, the wavelet transform in the time domain can be equivalent to the following second formula:

[0098]

[0099] The wavelet basis function is denoted as The Fourier transform of According to the theory of function inversion and conjugation in Fourier transform:

[0100]

[0101] The Fourier transform of is:

[0102]

[0103] This formula calculates the discrete value of ω for each scale factor in [0,2π], and obtains a matrix A(i,j) of a wavelet function with respect to the scale sequence a. According to the Fourier transform theory, the time domain convolution of two functions is equal to the product of the corresponding functions in the frequency domain. The calculation of the expression on the left side of the second formula that is equivalent to the frequency domain expression is equivalent to the dot multiplication of each row of the matrix A(i,j) and the matrix X(ω), that is, the wavelet coefficient matrix W(a i ,b j ) and form the time-frequency spectrum of the seismic signal, so as to remove white noise through the wavelet coefficient matrix and the time-frequency spectrum, and improve the efficiency of removing white noise alone.

[0104] S22. Adaptively determine a denoising threshold according to the characteristics of the seismic signal in the time-frequency domain.

[0105] In some embodiments, step S22, such as Figure 4 As shown, including:

[0106] S221, adaptively obtaining the noise standard deviation according to the characteristics of the seismic signal in the time-frequency domain;

[0107] S222: Determine a denoising threshold according to the noise standard deviation.

[0108] Wherein, the noise standard deviation is recorded as σ, and the formula of the noise standard deviation is: σ=M / 0.6745;

[0109] Where M is the median of the modulus of the wavelet transform coefficients of the signal at the minimum scale;

[0110] The denoising threshold is recorded as T, and the formula of the denoising threshold is:

[0111] Where ln(n) represents the natural logarithm of the signal length n.

[0112] In some embodiments, the denoising threshold T is obtained by calculating the median value based on the wavelet coefficient matrix using the wavelet coefficient modulus corresponding to the minimum scale.

[0113] S23, removing white noise from the time-frequency spectrum according to the denoising threshold to obtain a wavelet coefficient matrix after threshold processing.

[0114] In some embodiments, step S23, such as Figure 5 As shown, including:

[0115] S231, determining a wavelet coefficient matrix to be processed according to the time-frequency spectrum;

[0116] S232, traversing the wavelet coefficient matrix to be processed based on the scale factor and the time factor, and determining the wavelet coefficient whose modulus value is less than the denoising threshold as the wavelet coefficient corresponding to the white noise;

[0117] S233, setting the wavelet coefficients corresponding to the white noise to zero, and obtaining a wavelet coefficient matrix after threshold processing.

[0118] S24, performing inverse wavelet transform reconstruction on the wavelet coefficient matrix to obtain a first signal sequence from which white noise is removed.

[0119] In some embodiments, the first signal sequence is recorded as y(i), and the wavelet coefficient matrix is ​​reconstructed by inverse wavelet transform to obtain the first signal sequence with white noise removed, as follows:

[0120]

[0121] Wherein, the function real() represents taking the real part of the complex number; n is the signal length, i represents the index value corresponding to the i-th scale; j represents the j-th time shift factor; W ij A represents the wavelet coefficient at the jth time point under the i-th scale factor, that is, the i-th row and j-th column element of the matrix W; ij represents the element in the i-th row and j-th column of matrix A; a i is the i-th scale factor.

[0122] S3. Detect the first signal sequence, determine the position of each impulse noise in the first signal sequence, and mark it as an impulse noise point.

[0123] In some embodiments, the pulse noise in the first signal sequence is detected using a salt and pepper noise detection method in the image field, which can accurately mark each pulse noise point in the first signal sequence, so as to accurately remove each pulse noise while retaining the original signal information, thereby improving the signal processing quality.

[0124] In some embodiments, step S3, such as Figure 6 As shown, steps S31-S33 are included:

[0125] S31. Determine the detection window length.

[0126] In some embodiments, the detection window length is denoted as win, and a length of 5, 7, 9, etc. is generally selected; in this embodiment, the detection window length range is set to 7, and the first signal sequence is divided by the detection window length, so that each pulse noise point can be marked by sliding window processing.

[0127] S32. Set a number of points to be detected according to the first signal sequence.

[0128] S33, analyzing and processing each to-be-detected point in turn according to the detection window length, determining the to-be-detected points that meet the preset discriminant equation as impulse noise position points and marking them as impulse noise points.

[0129] In some embodiments, step S33, such as Figure 7 As shown, it includes: sliding processing is performed on each point to be detected in turn according to the following steps until all impulse noise points are marked:

[0130] S331, determining a number of neighbor signal sample points of the point to be detected within the detection window length according to the detection window length and the position of the point to be detected, and grouping them as neighbor points;

[0131] S332, analyzing and processing the difference between the point to be detected and each neighbor signal sample point, and determining the number of differences greater than a preset difference threshold among the plurality of differences;

[0132] S333, determining whether the ratio of the number of difference values ​​to the number of neighbor signal samples is greater than a preset percentage threshold;

[0133] S334: If yes, when determining that the point to be detected is an impulse noise position point, mark it as an impulse noise point.

[0134] In some embodiments, the preset difference threshold is recorded as Tv. In this embodiment, the threshold Tv=30 is used to determine whether the difference between the point to be detected and the neighboring signal sample point is large enough. The percentage threshold is recorded as Tn. In this embodiment, It is used to determine whether the number of points to be detected that are significantly different from their neighboring points is large enough.

[0135] In some embodiments, the discriminant for determining the impulse noise location point is as follows:

[0136]

[0137] In the formula, y(t i ) is the point to be detected; y(t) is the neighbor signal sample point; N(t i ) is the neighbor point of the point to be detected within the window length; Tv is the difference threshold between the point to be detected and the neighbor signal sample points; Tn is the number threshold for judging the large difference between the point to be detected and the neighbor sample points; #[] is the number of sample points that satisfy [].

[0138] The discriminant can quickly determine the pulse noise points within the set window length range, and the various divided detection window lengths are analyzed and processed through sliding window processing, so that the positions of the pulse noise points in the first signal sequence can be quickly and accurately detected, thereby facilitating the removal of only the pulse noise points by median filtering, thereby maintaining the original information of the signal to the greatest extent.

[0139] S4. Perform median filtering on each pulse noise point in the first signal sequence to obtain a second signal sequence from which the pulse noise is removed as a denoised seismic signal sequence.

[0140] In some embodiments, step S4, such as Figure 8 As shown, including:

[0141] S41, taking the position of the impulse noise point as the center and taking out the first segment of the signal with a window length of win;

[0142] S42, sorting the first segment of signals, taking out the median and the value that is adjacent to the median and smaller than the median, adding and averaging them, and obtaining a two-point mean;

[0143] S43, replacing the impulse noise point in the original collected signal with the mean of two points;

[0144] S44, repeat the above steps for each impulse noise point of the first signal sequence in turn and perform sliding window processing to obtain a second signal sequence with the impulse noise removed.

[0145] In some embodiments, the detected impulse noise point is recorded as y(i), and the sequence

[0146] {y(i-3),...,y(i),...y(i+3)} are sorted in ascending order to obtain a new sequence {P1,P2,P3,P4,P5,P6,P7}, then y(i) is replaced by (P3+P4) / 2. Mean processing of the median can ensure the smoothness of the signal after processing, thereby improving the original information of the signal to the greatest extent after the impulse noise is removed, and ensuring the smoothness of the signal after processing, thereby improving the preprocessing quality of the seismic signal.

[0147] like Fig. 9 As shown, the results of removing white noise and impulse noise by the seismic signal denoising method provided above show that the original information of the signal is maintained to the greatest extent, the smoothness of the signal after processing is guaranteed, and the preprocessing quality of the seismic signal is improved.

[0148] The seismic signal denoising method provided by the embodiments of the present disclosure can use wavelet transform to remove high-frequency white noise, while temporarily retaining pulse noise, and further perform pulse noise monitoring on the signal sequence from which the white noise has been removed, accurately mark the position of each pulse noise point in the signal sequence, and then improve each pulse noise point by removing it through median filtering, thereby accurately and specifically removing the pulse noise, thereby not only efficiently removing the white noise, but also specifically removing the pulse noise, maximally retaining more original signal information, improving the preprocessing quality of seismic signal denoising, and providing high-quality data for subsequent high-precision seismic data processing.

[0149] Embodiment 2:

[0150] At least one embodiment of the present disclosure further provides a seismic signal denoising device, wherein each module in the device corresponds to the steps in the above method, and the effects achieved and the problems solved have been described above and will not be repeated here. Fig.10 As shown, including:

[0151] A signal acquisition module 11 is used to obtain the original acquired seismic signal;

[0152] A white noise processing module 12 is used to perform wavelet transform threshold denoising on the seismic signal to obtain a first signal sequence without white noise;

[0153] A signal processing module 13, configured to detect the first signal sequence, determine the position of each impulse noise in the first signal sequence, and mark it as an impulse noise point;

[0154] The impulse noise processing module 14 is used to perform median filtering on each impulse noise point in the first signal sequence to obtain a second signal sequence with the impulse noise removed as a denoised seismic signal sequence.

[0155] In some embodiments, the output end of the signal acquisition module 11 is connected to the input end of the white noise processing module 12 , the output end of the white noise processing module 12 is connected to the input end of the signal processing module 13 ; the output end of the signal processing module 13 is connected to the input end of the pulse noise processing module 14 .

[0156] The output end of the impulse noise processing module 14 may be connected to a display device (such as a display) to present the denoising result to the staff through the screen.

[0157] In some embodiments, the device may be a part of the software, or may be implemented in combination with corresponding hardware, which will not be described in detail here.

[0158] Embodiment three:

[0159] At least some embodiments of the present disclosure also provide an electronic device, such as Fig.11 As shown, the electronic device includes a memory 21 and a processor 22, wherein the memory 21 stores a computer program, and when the computer program is executed by the processor, the steps of the denoising method provided in any embodiment of the present disclosure are performed.

[0160] In some embodiments, the processor 22 is used to execute all or part of the steps in the denoising method of any embodiment of the present disclosure. The memory 21 is used to store various types of data, which may include instructions of any application or method in the electronic device, and data related to the application.

[0161] The processor 22 can be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and is used to execute the denoising method in the above-mentioned embodiment 1.

[0162] The memory 21 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0163] Embodiment 4:

[0164] At least some embodiments of the present disclosure also provide a computer-readable storage medium, such as Fig.12 As shown, a computer program 31 is stored on the readable storage medium, and when the computer program 31 is executed by a processor, the steps of the denoising method provided by any embodiment of the present disclosure are implemented.

[0165] In some embodiments, the storage medium can be a tangible medium that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0166] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0167] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0168] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0169] Embodiment five:

[0170] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the denoising method provided in any embodiment of the present disclosure.

[0171] In summary, the present application provides a seismic signal denoising method, device and storage medium, which can use wavelet transform to remove high-frequency white noise, while temporarily retaining pulse noise, and further perform pulse noise monitoring on the signal sequence from which the white noise has been removed, accurately mark the position of each pulse noise point in the signal sequence, and then improve each pulse noise point by median filtering to remove it, thereby accurately and specifically removing the pulse noise, thereby not only efficiently removing the white noise, but also specifically removing the pulse noise, maximally retaining more original signal information, improving the preprocessing quality of seismic signal denoising, and providing high-quality data for subsequent high-precision seismic data processing.

[0172] The various embodiments in the present disclosure are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0173] The protection scope of the present disclosure is not limited to the above-mentioned embodiments. Obviously, those skilled in the art can make various changes and modifications to the present disclosure without departing from the scope and spirit of the present disclosure. If these changes and modifications fall within the scope of the claims of the present disclosure and their equivalents, the intention of the present disclosure also includes these changes and modifications.

Claims

1. A seismic signal denoising method, characterized in that: The following steps are involved: Obtaining the original acquired seismic signals; Perform wavelet transform threshold denoising on the seismic signal to obtain a first signal sequence without white noise; Detecting the first signal sequence, determining the position of each impulse noise in the first signal sequence, and marking it as an impulse noise point; Each impulse noise point in the first signal sequence is subjected to median filtering to obtain a second signal sequence without impulse noise as a denoised seismic signal sequence.

2. A seismic signal denoising method according to claim 1, characterized in that: The step of performing wavelet transform threshold denoising on the seismic signal to obtain a first signal sequence without white noise comprises: Performing time-frequency transformation on the seismic signal by using Morlet wavelet transform to obtain a time-frequency spectrum of the seismic signal; Adaptively determine the denoising threshold according to the characteristics of the seismic signal in the time and frequency domain; Remove white noise from the time-frequency spectrum according to the denoising threshold to obtain a wavelet coefficient matrix after threshold processing; The wavelet coefficient matrix is ​​reconstructed by performing inverse wavelet transform to obtain a first signal sequence with white noise removed.

3. A seismic signal denoising method according to claim 2, characterized in that: The step of adaptively determining the denoising threshold according to the characteristics of the seismic signal in the time-frequency domain comprises: Adaptively obtaining the noise standard deviation according to the characteristics of the seismic signal in the time and frequency domains; Determine a denoising threshold according to the noise standard deviation; The noise standard deviation is denoted as σ, and the formula of the noise standard deviation is: σ=M / 0.6745; Where M is the median of the modulus of the wavelet transform coefficients of the signal at the minimum scale; The denoising threshold is recorded as T, and the formula of the denoising threshold is: Where ln(n) represents the natural logarithm of the signal length n.

4. A seismic signal denoising method according to claim 2, characterized in that: The step of removing white noise from the time-frequency spectrum according to the denoising threshold to obtain a wavelet coefficient matrix after threshold processing includes: Determine the wavelet coefficient matrix to be processed according to the time-frequency spectrum; Based on the scale factor and the time factor, the wavelet coefficient matrix to be processed is traversed, and the wavelet coefficient whose modulus value is less than the denoising threshold is determined as the wavelet coefficient corresponding to the white noise; The wavelet coefficients corresponding to white noise are set to zero to obtain the wavelet coefficient matrix after threshold processing.

5. A seismic signal denoising method according to claim 1, characterized in that: The detecting the first signal sequence, determining the position of each impulse noise in the first signal sequence, and marking it as an impulse noise point, includes: Determine the detection window length; Setting a number of points to be detected according to the first signal sequence; Analyze and process each to-be-detected point in turn according to the detection window length, and determine the to-be-detected point that satisfies a preset discriminant as an impulse noise position point; Each impulse noise position point is marked as an impulse noise point.

6. A seismic signal denoising method according to claim 5, characterized in that: The analyzing and processing each to-be-detected point in turn according to the detection window length, and determining the to-be-detected point that satisfies a preset discriminant as an impulse noise position point, comprises: Perform sliding processing on each point to be detected according to the following steps in sequence until all impulse noise points are marked: Determine a number of neighbor signal sample points of the point to be detected within the detection window length according to the detection window length and the position of the point to be detected, and group them as neighbor points; Analyze and process the difference between the point to be detected and each neighbor signal sample point, and determine the number of difference values ​​greater than a preset difference threshold among the plurality of difference values; Determine whether the ratio of the number of difference values ​​to the number of neighbor signal samples is greater than a preset percentage threshold; If so, when it is determined that the point to be detected is an impulse noise position point, it is marked as an impulse noise point.

7. A seismic signal denoising method according to claim 6, characterized in that: The discriminant formula for determining the impulse noise position point is as follows: In the formula, y(t i ) is the point to be detected; y(t) is the neighbor signal sample point; N(t i ) is the neighbor point of the point to be detected within the window length; Tv is the difference threshold between the point to be detected and the neighboring signal sample points; Tn is the number threshold for judging the large difference between the point to be detected and the neighboring sample points.

8. A seismic signal denoising method according to claim 1, characterized in that: The step of performing median filtering on the impulse noise points in the first signal sequence to obtain a second signal sequence without the impulse noise comprises: Take the first signal segment with the window length win, centering on the location of the impulse noise point; Sort the first segment of the signal, and take out the median and the value that is adjacent to the median and smaller than the median, add and average them to obtain the two-point mean; Replace the impulse noise points in the original acquired signal with the mean of two points; The above steps are repeated for each impulse noise point of the first signal sequence in turn and sliding window processing is performed to obtain a second signal sequence with the impulse noise removed.

9. A seismic signal denoising device, characterized in that: include: A signal acquisition module, used to obtain the original acquired seismic signal; A white noise processing module, used for performing wavelet transform threshold denoising on the seismic signal to obtain a first signal sequence without white noise; A signal processing module, used to detect the first signal sequence, determine the position of each impulse noise in the first signal sequence, and mark it as an impulse noise point; The impulse noise processing module is used to perform median filtering on each impulse noise point in the first signal sequence to obtain a second signal sequence with the impulse noise removed as a denoised seismic signal sequence.

10. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the denoising method according to any one of claims 1 to 8 when executing the computer program.

11. A computer-readable storage medium, characterized in that: When the computer program is executed by a processor, the steps of the denoising method according to any one of claims 1 to 8 are implemented.

Images