Seismic signal denoising method and device, medium and equipment

Abstract

The invention relates to the field of data processing, in particular to a seismic signal denoising method and device, a medium and equipment. Target decomposition signals are obtained by decomposing target signals, and the target confusion degree of each target decomposition signal is quantized; preliminarily classifying the target decomposition signal into a noise signal, a target seismic signal or an intermediate signal by combining preset first and second confusion degree thresholds, and further quantifying the target cross-correlation degree of the intermediate signal and the initial seismic signal; and subdividing the intermediate signal into a noise signal, a target seismic signal or a mixed signal by combining preset first and second cross-correlation degree thresholds, further taking the mixed signal as a target signal again to perform signal decomposition, analysis and classification, continuously iteratively processing the mixed signal, separating noise from the mixed signal as much as possible, and obtaining a target seismic signal. According to the invention, the denoising accuracy and reliability of the initial seismic signal are improved, and high-quality data support is provided for subsequent seismic research, monitoring and other work.

Description

Technical Field

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

[0002] Denoising of seismic data is crucial for accurate analysis of earthquake activities, underground geological structures, etc. In the task of seismic signal denoising, the existing technology decomposes the initial seismic signal, classifies the decomposed signal according to the degree of confusion and steepness of the decomposed signal, and obtains the denoised seismic signal to achieve the denoising effect of the initial seismic signal.

[0003] However, when the above method processes complex seismic data such as teleseismic data, the seismic waves propagate over long distances, pass through various geological media, and produce multiple reflections, refractions and scatterings, resulting in the seismic data containing multiple wave components, and the waveform and frequency components are extremely complex; and the signal and noise are highly mixed in the frequency domain and time domain. Various interference factors make the signal amplitude distribution complex, and the peak coefficient mainly measures the steepness of the signal amplitude distribution. It cannot effectively cope with the complex characteristics of teleseismic data, and it is difficult to accurately distinguish between noise modes and signal modes, resulting in poor denoising effect and difficulty in meeting the needs of high-precision seismic signal processing.

[0004] Therefore, when faced with complex seismic data, how to improve the denoising effect of seismic signals becomes an urgent problem to be solved. Summary of the invention

[0005] In view of the above technical problems, the technical solution adopted by the present invention is a seismic signal denoising method, which comprises the following steps: S100, taking the acquired initial seismic signal as the target signal.

[0006] S200, performing signal decomposition on the target signal to obtain K target decomposition signals, where K is an integer greater than 1.

[0007] S300, respectively obtaining the target confusion degree corresponding to each target decomposition signal.

[0008] S400, classify all target decomposition signals according to the target confusion degree corresponding to each target decomposition signal, a preset first confusion degree threshold and a preset second confusion degree threshold, and obtain the first signal type corresponding to each target decomposition signal, wherein the first signal type includes a noise signal, a target seismic signal and an intermediate signal.

[0009] S500, respectively obtaining the target cross-correlation degree between each intermediate signal and the initial seismic signal.

[0010] S600, classify all intermediate signals according to the target mutual correlation degree between each intermediate signal and the initial seismic signal, the preset first mutual correlation degree threshold and the preset second mutual correlation degree threshold, and obtain the second signal type corresponding to each intermediate signal, wherein the second signal type includes a noise signal, a target seismic signal and a mixed signal.

[0011] S700, for any mixed signal, determine the current mixed signal as the target signal, return to execute step S200, until the preset stop condition is met, and obtain all target seismic signals corresponding to the initial seismic signal.

[0012] S800, acquiring a denoised seismic signal corresponding to the initial seismic signal according to all target seismic signals corresponding to the initial seismic signal.

[0013] The present invention also provides a seismic signal denoising device, the seismic signal denoising device comprising: The signal acquisition module is used to use the acquired initial seismic signal as the target signal.

[0014] The signal decomposition module is used to decompose the target signal to obtain K target decomposition signals, where K is an integer greater than 1.

[0015] The chaos degree acquisition module is used to respectively acquire the target chaos degree corresponding to each target decomposition signal.

[0016] The first signal classification module is used to classify all target decomposition signals according to the target confusion degree corresponding to each target decomposition signal, a preset first confusion degree threshold and a preset second confusion degree threshold, and obtain the first signal type corresponding to each target decomposition signal, wherein the first signal type includes noise signals, target seismic signals and intermediate signals.

[0017] The cross-correlation degree acquisition module is used to respectively acquire the target cross-correlation degree between each intermediate signal and the initial seismic signal.

[0018] The second signal classification module is used to classify all intermediate signals according to the target cross-correlation degree between each intermediate signal and the initial seismic signal, a preset first cross-correlation degree threshold and a preset second cross-correlation degree threshold, and obtain the second signal type corresponding to each intermediate signal, wherein the second signal type includes noise signals, target seismic signals and mixed signals.

[0019] The iterative processing module is used to determine the current mixed signal as the target signal for any mixed signal, return to the execution signal decomposition module until the preset stop condition is met, and obtain all the target seismic signals corresponding to the initial seismic signal.

[0020] The signal denoising module is used to obtain a denoised seismic signal corresponding to the initial seismic signal based on all target seismic signals corresponding to the initial seismic signal.

[0021] The present invention also provides a non-transitory computer-readable storage medium, in which at least one instruction or at least one program is stored, and the at least one instruction or at least one program is loaded and executed by a processor to implement the above-mentioned seismic signal denoising method.

[0022] The present invention also provides an electronic device, comprising a processor and the above-mentioned non-transitory computer-readable storage medium.

[0023] The present invention has at least the following beneficial effects: by setting the acquired initial seismic signal as the target signal, the object is determined for subsequent operations; by decomposing the target signal to obtain K target decomposition signals, and quantifying the target confusion degree of each target decomposition signal, the target decomposition signal is preliminarily classified into a noise signal, a target seismic signal or an intermediate signal in combination with the preset first and second confusion degree thresholds; the target mutual correlation degree between the intermediate signal and the initial seismic signal is further quantified, and the intermediate signal is further subdivided into a noise signal, a target seismic signal or a mixed signal in combination with the preset first and second mutual correlation degree thresholds; and then the mixed signal is used as the target signal again, and the signal is decomposed, analyzed and classified again, and iterated until the preset stop condition is met, and the noise is separated from the mixed signal as much as possible by continuously iteratively processing the mixed signal, so as to obtain a pure target seismic signal, further improve the accuracy and reliability of denoising, and provide high-quality data support for subsequent seismic research, monitoring and the like. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 A flowchart of a seismic signal denoising method provided in Embodiment 1 of the present invention; Figure 2 A schematic diagram of the structure of a seismic signal denoising device provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It is understood that, where appropriate, the above-mentioned terms used to distinguish similar objects can be interchanged so that the present invention can also implement other embodiments other than the above-mentioned illustrated embodiments or described embodiments. In addition, the terms "including" and "having" and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Embodiment 1 This embodiment provides a method for denoising a seismic signal. Figure 1 As shown, the seismic signal denoising method includes the following steps: S100, taking the acquired initial seismic signal as the target signal.

[0029] S200, performing signal decomposition on the target signal to obtain K target decomposition signals, where K is an integer greater than 1.

[0030] S300, respectively obtaining the target confusion degree corresponding to each target decomposition signal.

[0031] S400, classify all target decomposition signals according to the target confusion degree corresponding to each target decomposition signal, a preset first confusion degree threshold and a preset second confusion degree threshold, and obtain the first signal type corresponding to each target decomposition signal, wherein the first signal type includes a noise signal, a target seismic signal and an intermediate signal.

[0032] S500, respectively obtaining the target cross-correlation degree between each intermediate signal and the initial seismic signal.

[0033] S600, classify all intermediate signals according to the target mutual correlation degree between each intermediate signal and the initial seismic signal, the preset first mutual correlation degree threshold and the preset second mutual correlation degree threshold, and obtain the second signal type corresponding to each intermediate signal, wherein the second signal type includes a noise signal, a target seismic signal and a mixed signal.

[0034] S700, for any mixed signal, determine the current mixed signal as the target signal, return to execute step S200, until the preset stop condition is met, and obtain all target seismic signals corresponding to the initial seismic signal.

[0035] S800, acquiring a denoised seismic signal corresponding to the initial seismic signal according to all target seismic signals corresponding to the initial seismic signal.

[0036] Among them, the initial seismic signal refers to the seismic signal that has noise and needs to be denoised. The initial seismic signal is first used as the target signal to provide a signal basis for subsequent signal decomposition, analysis and processing.

[0037] Through signal decomposition, the complex initial seismic signal is decomposed into K relatively simple target decomposition signals, so as to more carefully analyze and process signal components with different characteristics. Among them, variational mode decomposition is an adaptive, completely non-recursive signal processing method. This embodiment can decompose the target signal based on variational mode decomposition. Specifically, by assuming that the target signal can be decomposed into several decomposition signals with center frequencies, and the sum of the estimated bandwidths of these decomposition signals is minimized, while satisfying the constraint that the sum of all decomposition signals is equal to the target signal, the target signal can be effectively decomposed. When performing variational mode decomposition on the target signal, it is necessary to give a target decomposition number K to decompose the target signal into K signals as the basis for achieving effective decomposition of the target signal.

[0038] The degree of chaos of each decomposed signal is quantified as a data basis for judging the orderliness or randomness of the signal. Generally, noise signals usually have a higher degree of chaos, while target seismic signals are relatively more regular and have a lower degree of chaos. Therefore, this embodiment classifies each target decomposed signal into a noise signal, a target seismic signal, or an intermediate signal by evaluating the degree of chaos of each target decomposed signal and a preset first chaos degree threshold and a preset second chaos degree threshold.

[0039] It is difficult to accurately classify the intermediate signal by the degree of confusion, so this embodiment further quantifies the degree of mutual correlation between the intermediate signal and the initial seismic signal as a data basis for judging the correlation between the intermediate signal and the original seismic signal. In general, the higher the degree of mutual correlation between the intermediate signal and the initial seismic signal, the stronger the correlation between the intermediate signal and the original seismic signal, and the more likely it is to be part of the target seismic signal. On the contrary, the lower the degree of mutual correlation, the more inclined to be a noise signal. Therefore, this embodiment evaluates the degree of mutual correlation and the preset first mutual correlation degree threshold and the preset second mutual correlation degree threshold, and further classifies each central signal into a noise signal, a target seismic signal or a mixed signal.

[0040] The mixed signal can be regarded as a mixture of noise signal and seismic signal. Therefore, each mixed signal is re-decomposed and classified to separate the noise from the mixed signal as much as possible through continuous iterative processing of the mixed signal, obtain the pure target seismic signal, and further improve the denoising accuracy.

[0041] The preset stop condition may be that the amount of mixed signals is zero, or that the signal change is no longer significant after a certain number of iterations.

[0042] Among them, those skilled in the art know that any method for calculating the degree of disorder and the degree of cross-correlation in the prior art fall within the protection scope of the present invention, and will not be described in detail here. For example, the method for calculating the degree of disorder may be a permutation entropy or sample entropy calculation method. The method for calculating the degree of cross-correlation may be a discrete cross-correlation or a fast Fourier transform combined with a cross-power spectral density calculation method.

[0043] In a specific implementation, the seismic signal denoising method further includes the following steps: S11, obtaining a number of seismic signal samples.

[0044] S12, for any seismic signal sample, performing signal decomposition on the current seismic signal sample to obtain a plurality of decomposed signal samples corresponding to the current seismic signal sample.

[0045] S13, performing waveform analysis and spectrum feature analysis on each decomposed signal sample to obtain a reference signal type corresponding to each decomposed signal sample, wherein the reference signal type includes a reference noise signal, a reference seismic signal and other signals.

[0046] S14, for any reference seismic signal, performing signal decomposition on the current reference seismic signal to obtain a plurality of reference decomposition signals corresponding to the current reference seismic signal.

[0047] S15, performing waveform analysis and spectrum feature analysis on each reference decomposition signal to obtain a predicted signal type corresponding to each reference decomposition signal, wherein the predicted signal type includes a predicted noise signal, a predicted seismic signal and other signals.

[0048] S16, respectively obtaining a reference confusion degree corresponding to each reference decomposition signal and a reference cross-correlation degree between each reference decomposition signal and a corresponding reference seismic signal.

[0049] S17, according to the predicted signal type, reference confusion degree and reference cross-correlation degree corresponding to each reference decomposition signal, obtain the first confusion degree subset and the first cross-correlation degree subset of the predicted noise signal relative to the current seismic signal sample, as well as the second confusion degree subset and the second cross-correlation degree subset of the predicted seismic signal relative to the current seismic signal sample.

[0050] S18, traverse all seismic signal samples to obtain a first confusion degree set and a first mutual correlation degree set corresponding to the predicted noise signal, and a second confusion degree set and a second mutual correlation degree set corresponding to the predicted seismic signal.

[0051] S19, according to the first confusion level set and the first mutual correlation level set corresponding to the predicted noise signal, obtain the preset first confusion level threshold and the preset first mutual correlation level threshold, according to the second confusion level set and the second mutual correlation level set corresponding to the predicted seismic signal, obtain the preset second confusion level threshold and the preset second mutual correlation level threshold.

[0052] Among them, firstly, a sufficient number of seismic signal samples are collected to cover the characteristics of seismic signals in different situations, which helps to more accurately reflect the characteristics of actual seismic signals, thereby making the determined threshold more universal and effective in practical applications.

[0053] The waveform and spectrum characteristics can reflect the different essential characteristics of the signal. By performing waveform analysis and spectrum characteristic analysis on the decomposed signal samples, the decomposed signal samples can be classified into reference noise signals, reference seismic signals or other signals, and the possible noise and seismic signal components can be preliminarily screened out to facilitate subsequent targeted processing.

[0054] The part judged as the reference seismic signal is decomposed again to have a deeper understanding of the internal composition structure of the reference seismic signal, so as to provide conditions for the subsequent accurate judgment of whether there is noise or other mixed components inside it. Therefore, by performing waveform analysis and spectrum feature analysis on the reference decomposition signal, the reference decomposition signal is classified into predicted noise signal, predicted seismic signal or other signal, so as to more accurately judge the signal component, and quantify the reference confusion degree of each reference decomposition signal and the reference cross-correlation degree with the original reference seismic signal, so as to classify and summarize the reference confusion degree and reference cross-correlation degree according to the predicted signal type corresponding to the reference decomposition signal, and obtain the subsets of confusion degree and cross-correlation degree corresponding to the predicted noise signal and the predicted seismic signal, so as to clearly see the distribution of confusion degree and cross-correlation degree of different predicted signal types, and provide an intuitive data distribution reference for determining the threshold.

[0055] By traversing all seismic signals, the chaos and cross-correlation data of all predicted noise signals and predicted seismic signals can be summarized to form a complete data set to more comprehensively reflect the characteristic distribution of predicted noise signals and predicted seismic signals in different samples, providing a sufficient data basis for determining accurate and universal thresholds.

[0056] Based on the first confusion degree set and the first mutual correlation degree set, a preset first confusion degree threshold and a first mutual correlation degree threshold are determined by a preset statistical method, which are used to classify the noise signal in the actual seismic signal denoising process. Based on the second confusion degree set and the second mutual correlation degree set, a preset second confusion degree threshold and a second mutual correlation degree threshold are determined by a preset statistical method, which are used to classify the target seismic signal in the actual seismic signal denoising process. The preset statistical method can be set by the implementer according to the actual situation. For example, the preset statistical method can be to use the mean, median, quantile and other values ​​in each set as the corresponding threshold.

[0057] In the above, by performing signal decomposition on each seismic signal sample, a plurality of decomposed signal samples are obtained, and the decomposed signal samples are preliminarily divided into reference noise signals, reference seismic signals and other signals through waveform and spectrum feature analysis, and the reference seismic signal is decomposed again, and waveform and spectrum analysis is performed on the reference decomposition signal to obtain predicted noise signals, predicted seismic signals and other signals, and then the reference confusion degree of each reference decomposition signal and the reference cross-correlation degree with the corresponding reference seismic signal are quantified, and according to the predicted signal type, the quantitative indicators are respectively classified into relevant subsets of predicted noise signals and predicted seismic signals, and the complete set of confusion degree and cross-correlation degree of predicted noise signals and predicted seismic signals is summarized, and finally, appropriate statistical methods are used to determine By determining the preset first and second confusion degree thresholds and the first and second cross-correlation degree thresholds, it can be seen that rich and diverse samples help to more accurately reflect the characteristics of actual seismic signals, thereby making the determined thresholds more universal and effective in practical applications, and reflecting the different essential characteristics of the signal through waveform and spectral characteristics, and more comprehensively reflecting the characteristic distribution of predicted noise signals and predicted seismic signals in different samples through data set aggregation, thereby improving the accuracy of threshold acquisition, providing key parameters for accurately classifying signals in the subsequent seismic signal denoising process, so that when performing the denoising task of the initial seismic signal, there is no need to perform a series of waveform and spectral analyses on each signal, which greatly improves the denoising efficiency of the initial seismic signal while ensuring the denoising accuracy.

[0058] In a specific implementation, S13 includes the following steps: S131, performing waveform analysis on each decomposed signal sample to obtain waveform data corresponding to each decomposed signal sample, wherein the waveform data includes amplitude data, shape data and phase data.

[0059] S132, performing spectrum transformation on each decomposed signal sample to obtain a decomposed signal spectrum corresponding to each decomposed signal sample.

[0060] S133, acquiring spectrum data corresponding to each decomposed signal sample according to the decomposed signal spectrum corresponding to each decomposed signal sample, wherein the spectrum data includes a frequency range, a frequency peak, and a resolution.

[0061] S134, obtaining a reference signal type corresponding to each decomposed signal sample according to the waveform data and the spectrum data corresponding to each decomposed signal sample.

[0062] Among them, the amplitude data reflects the intensity of the decomposed signal samples at different times; the shape data involves the overall outline of the waveform of the decomposed signal samples, such as whether it is a sine wave shape, a square wave shape or an irregular shape, etc., which is of great significance for judging the regularity and characteristics of the signal; the phase data reflects the relative position information of the decomposed signal samples on the time axis. In the analysis of some complex signals, the phase relationship is crucial for understanding the superposition and interaction of signals. By obtaining the waveform data in detail, we can intuitively understand the basic characteristics of the decomposed signal samples from the time domain perspective, which can serve as the basis for preliminary judgment of the signal type.

[0063] The frequency range determines the span of the frequency components contained in the decomposed signal sample; the frequency peak reflects the frequency point where the energy in the decomposed signal sample is more concentrated; the resolution reflects the degree of fineness with which the spectrum analysis can distinguish different frequency components. The appropriate resolution is crucial for accurately identifying the frequency characteristics of the signal. Among them, high resolution can clearly distinguish the main frequency peaks in the signal spectrum and the frequency details around them. By understanding the frequency range, we can intuitively understand the basic characteristics of the decomposed signal sample from the frequency domain perspective, which can serve as a basis for preliminary judgment of the signal type.

[0064] Among them, those skilled in the art know that any spectrum transformation method in the prior art falls within the protection scope of the present invention, and will not be described in detail here. For example, the spectrum transformation method can be a Fourier transformation method or a wavelet transformation method.

[0065] In a specific implementation, S134 includes the following steps: S1341, obtaining a first waveform judgment rule and a first spectrum judgment rule corresponding to the reference noise signal, and a second waveform judgment rule and a second spectrum judgment rule corresponding to the reference seismic signal.

[0066] S1342, for any decomposed signal sample, if the waveform data corresponding to the current decomposed signal sample satisfies the first waveform judgment rule, and the spectrum data corresponding to the current decomposed signal sample satisfies the first spectrum judgment rule, then determine that the reference signal type corresponding to the current decomposed signal sample is a reference noise signal.

[0067] S1343: If the waveform data corresponding to the current decomposed signal sample satisfies the second waveform judgment rule, and the spectrum data corresponding to the current decomposed signal sample satisfies the second spectrum judgment rule, then determine that the reference signal type corresponding to the current decomposed signal sample is a reference seismic signal.

[0068] S1344: If the reference signal type corresponding to the current decomposed signal sample is not a reference noise signal or a reference seismic signal, determine that the reference signal type corresponding to the current decomposed signal sample is other signals.

[0069] Among them, the specific rule contents of the first waveform judgment rule and the first spectrum judgment rule corresponding to the reference noise signal, and the second waveform judgment rule and the second spectrum judgment rule corresponding to the reference seismic signal can be set by the implementer according to actual conditions.

[0070] For example, based on the waveform data including amplitude data, shape data and phase data, combined with the waveform characteristics of noise signals and seismic signals, the first waveform judgment rule can be set as follows: the amplitude change is highly random, with a large fluctuation range and no obvious regularity; the waveform shape is disorderly, without obvious periodicity or regular geometric shape; the phase change is discontinuous and has no fixed pattern. The second waveform judgment rule can be set as follows: the amplitude change fluctuates relatively stably within a certain range; it presents a waveform that is approximately periodic; the phase change has continuity and a certain logic.

[0071] Based on the spectrum data including frequency range, frequency peak and resolution, combined with the spectrum characteristics of noise signals and seismic signals, the first spectrum judgment rule can be set as follows: the frequency distribution range covers a wide frequency band from low frequency to high frequency; there is no obvious frequency peak; the spectrum graph shows a relatively uniform distribution state, without obvious frequency intervals or regular frequency distribution patterns. The second spectrum judgment rule can be set as follows: the frequency is concentrated in a specific range; there is an obvious frequency peak; the resolution is high enough.

[0072] As mentioned above, classifying the decomposed signal samples from the two dimensions of waveform and spectrum helps to more accurately identify different types of signals in seismic signal processing and provides a clear signal classification basis for subsequent processing and analysis.

[0073] In a specific implementation, S200 includes the following steps: S210, initialize the initial decomposition number M=2.

[0074] S220, decomposing the target signal into M initial decomposed signals.

[0075] S230, respectively acquiring a first correlation degree between the target signal and each initial decomposition signal, and a second correlation degree between any two initial decomposition signals.

[0076] S240: Obtain decomposition overlap degrees corresponding to the M initial decomposition signals according to all the first correlation degrees and all the second correlation degrees.

[0077] S250: If the decomposition overlap degree does not satisfy the preset decomposition condition, update M=M+1, and return to execute step S220 until the decomposition overlap degree satisfies the preset decomposition condition.

[0078] S260, obtaining a target decomposition number K=M-1 according to an initial decomposition number M corresponding to when the decomposition overlap degree satisfies a preset decomposition condition.

[0079] If the target decomposition number K is set to be less than the number of useful components in the target signal, that is, the target signal is under-decomposed, which will lead to modal aliasing. If the target decomposition number K is set to be greater than the number of useful components in the target signal, that is, the target signal is over-decomposed, which will lead to the generation of some useless false components.

[0080] This embodiment first sets the initial decomposition number M=2, decomposes the target signal into two initial decomposition signals, and compares the first correlation degree between the target signal and each of the decomposed initial decomposition signals at the decomposition level of M=2, which is used to represent the correlation between the target signal and the initial decomposition signal, and the second correlation degree between the two initial decomposition signals, which is used to represent the correlation between the initial decomposition signals.

[0081] Since the initial decomposition signal is separated from the target signal, the first correlation degree between the target signal and the initial decomposition signal should be greater than the second correlation degree between the initial decomposition signals. If the initial decomposition signal at the decomposition level of M=2 meets this condition, M=M+1 is updated, and the decomposition step of the target signal is returned to obtain M initial decomposition signals with higher decomposition levels, until the first correlation degree between the target signal and the initial decomposition signal is less than or equal to the second correlation degree between the initial decomposition signals, indicating modal decomposition overlap.

[0082] Therefore, this embodiment determines the target decomposition number K=M-1, which means that at the decomposition level K, the target signal can be most thoroughly decomposed without modal decomposition overlap, so that the target seismic signal and noise signal in the target signal can be extracted to the maximum extent, so as to improve the denoising effect of the initial seismic signal.

[0083] In the above, the decomposition overlap degree of the target signal at the decomposition level M is measured according to the first correlation degree between the target signal and each decomposed initial decomposition signal, and the second correlation degree between the two initial decomposition signals. The decomposition degree of the target signal is improved by continuously updating the size of M until the decomposition overlap degree meets the preset decomposition condition, indicating that the target signal is over-decomposed. Therefore, the target decomposition number K is obtained according to M, so that the target signal can be most thoroughly decomposed without modal decomposition overlap, so that the target seismic signal and the noise signal in the target signal can be extracted to the maximum extent, thereby improving the denoising effect of the initial seismic signal.

[0084] In a specific implementation, S240 includes the following steps: S241: Determine an average value of all first correlation levels as a first correlation level mean.

[0085] S242: Determine an average value of all second correlation levels as a second correlation level mean.

[0086] S243: Determine the difference between the second correlation degree mean and the first correlation degree mean as the decomposition overlap degree corresponding to the M initial decomposition signals.

[0087] In a specific implementation, the preset decomposition condition is that the decomposition overlap degree is greater than zero.

[0088] Among them, if the decomposition overlap degree is greater than zero, that is, the first correlation degree between the target signal and the initial decomposition signal is less than or equal to the second correlation degree between the initial decomposition signals, it indicates modal decomposition overlap. At this time, stop updating M, obtain the maximum M value, and determine M-1 as the target decomposition number. Under the premise that no modal decomposition overlap occurs, the target signal can be decomposed most thoroughly.

[0089] In a specific implementation, S400 includes the following steps: S410: For any target decomposition signal, if the target confusion degree corresponding to the current target decomposition signal is greater than a preset first confusion degree threshold, determine that the first signal type corresponding to the current target decomposition signal is a noise signal.

[0090] S420: If the target confusion degree corresponding to the current target decomposition signal is less than a preset second confusion degree threshold, determine that the first signal type corresponding to the current target decomposition signal is a target seismic signal.

[0091] S430: If the target confusion degree corresponding to the current target decomposition signal is greater than or equal to a preset second confusion degree threshold and less than or equal to a preset first confusion degree threshold, determine that the first signal type corresponding to the current target decomposition signal is an intermediate signal.

[0092] In a specific implementation, S500 includes the following steps: S510: For any intermediate signal, if the target mutual correlation degree corresponding to the current intermediate signal is less than a preset first mutual correlation degree threshold, determine that the second signal type corresponding to the current intermediate signal is a noise signal.

[0093] S520: If the target mutual correlation degree corresponding to the current intermediate signal is greater than or equal to a preset second mutual correlation degree threshold, determine that the second signal type corresponding to the current intermediate signal is a target seismic signal.

[0094] S530: If the target mutual correlation degree corresponding to the current intermediate signal is greater than or equal to a preset first mutual correlation degree threshold and less than or equal to a preset second mutual correlation degree threshold, determine that the second signal type corresponding to the current intermediate signal is a mixed signal.

[0095] Among them, the degree of chaos is an indicator to measure the randomness and disorder of the signal. Noise is caused by various random factors, and its performance in the time domain and frequency domain is relatively disordered, with a high degree of chaos. The target seismic signal is usually generated by the seismic wave source, which has a certain regularity and predictability, and the degree of chaos is relatively low.

[0096] The degree of cross-correlation measures the similarity or correlation between the intermediate signal and the initial seismic signal. The noise signal is usually weakly correlated with the initial seismic signal due to its randomness and independence from the source of the initial seismic signal. The target seismic signal originates from the initial seismic signal and has a strong correlation with the initial seismic signal.

[0097] In actual signal processing, some intermediate signals neither fully behave as typical noise signals nor fully conform to the characteristics of the target seismic signals. They may contain both some noise components and some seismic signal components. Therefore, they are classified as mixed signals to provide an intermediate classification for subsequent more precise judgment and processing, avoiding misjudgment caused by direct judgment, making the entire signal classification process more detailed and complete, and improving the accuracy and reliability of signal classification.

[0098] In the above, the acquired initial seismic signal is set as the target signal to determine the object for subsequent operations; K target decomposition signals are obtained by decomposing the target signal, and the target confusion degree of each target decomposition signal is quantified, and the target decomposition signal is preliminarily classified into a noise signal, a target seismic signal or an intermediate signal in combination with the preset first and second confusion degree thresholds; the target cross-correlation degree between the intermediate signal and the initial seismic signal is further quantified, and the intermediate signal is subdivided into a noise signal, a target seismic signal or a mixed signal in combination with the preset first and second cross-correlation degree thresholds; and the mixed signal is then used as the target signal again, and the signal is decomposed, analyzed and classified again, and it is iterated until the preset stop condition is met. By continuously iteratively processing the mixed signal, the noise is separated from the mixed signal as much as possible, and a pure target seismic signal is obtained, thereby further improving the accuracy and reliability of denoising, and providing high-quality data support for subsequent seismic research, monitoring and other work.

[0099] Embodiment 2 This embodiment 2 provides a seismic signal denoising device, such as Figure 2 As shown, the seismic signal denoising device comprises: The signal acquisition module 21 is used to use the acquired initial seismic signal as the target signal.

[0100] The signal decomposition module 22 is used to decompose the target signal to obtain K target decomposition signals, where K is an integer greater than 1.

[0101] The chaos degree acquisition module 23 is used to respectively acquire the target chaos degree corresponding to each target decomposition signal.

[0102] The first signal classification module 24 is used to classify all target decomposition signals according to the target confusion degree corresponding to each target decomposition signal, a preset first confusion degree threshold and a preset second confusion degree threshold, and obtain the first signal type corresponding to each target decomposition signal, wherein the first signal type includes noise signals, target seismic signals and intermediate signals.

[0103] The cross-correlation degree acquisition module 25 is used to respectively acquire the target cross-correlation degree between each intermediate signal and the initial seismic signal.

[0104] The second signal classification module 26 is used to classify all intermediate signals according to the target mutual correlation degree between each intermediate signal and the initial seismic signal, the preset first mutual correlation degree threshold and the preset second mutual correlation degree threshold, and obtain the second signal type corresponding to each intermediate signal, wherein the second signal type includes noise signals, target seismic signals and mixed signals.

[0105] The iterative processing module 27 is used to determine the current mixed signal as the target signal for any mixed signal, return to the execution signal decomposition module until the preset stop condition is met, and obtain all the target seismic signals corresponding to the initial seismic signal.

[0106] The signal denoising module 28 is used to obtain a denoised seismic signal corresponding to the initial seismic signal based on all target seismic signals corresponding to the initial seismic signal.

[0107] In a specific embodiment, the seismic signal denoising device further includes: The signal sample acquisition module is used to acquire a number of seismic signal samples.

[0108] The sample signal decomposition module is used to perform signal decomposition on the current seismic signal sample for any seismic signal sample, and obtain a number of decomposed signal samples corresponding to the current seismic signal sample.

[0109] The third signal classification module is used to perform waveform analysis and spectrum feature analysis on each decomposed signal sample to obtain a reference signal type corresponding to each decomposed signal sample, wherein the reference signal type includes a reference noise signal, a reference seismic signal and other signals.

[0110] The reference signal decomposition module is used to perform signal decomposition on the current reference seismic signal for any reference seismic signal, and obtain a number of reference decomposition signals corresponding to the current reference seismic signal.

[0111] The fourth signal classification module is used to perform waveform analysis and spectrum feature analysis on each reference decomposition signal to obtain the predicted signal type corresponding to each reference decomposition signal, wherein the predicted signal type includes predicted noise signal, predicted seismic signal and other signals.

[0112] The degree analysis module is used to obtain the reference confusion degree corresponding to each reference decomposition signal and the reference cross-correlation degree between each reference decomposition signal and the corresponding reference seismic signal.

[0113] A subset acquisition module is used to acquire a first confusion degree subset and a first mutual correlation degree subset of the predicted noise signal relative to the current seismic signal sample, as well as a second confusion degree subset and a second mutual correlation degree subset of the predicted seismic signal relative to the current seismic signal sample, based on the predicted signal type, reference confusion degree and reference mutual correlation degree corresponding to each reference decomposition signal.

[0114] The set acquisition module is used to traverse all seismic signal samples to obtain the first confusion degree set and the first mutual correlation degree set corresponding to the predicted noise signal, and the second confusion degree set and the second mutual correlation degree set corresponding to the predicted seismic signal.

[0115] A threshold acquisition module is used to obtain a preset first confusion level threshold and a preset first mutual correlation level threshold based on a first confusion level set and a first mutual correlation level set corresponding to the predicted noise signal, and to obtain a preset second confusion level threshold and a preset second mutual correlation level threshold based on a second confusion level set and a second mutual correlation level set corresponding to the predicted seismic signal.

[0116] In a specific implementation, the third signal classification module includes: The waveform analysis submodule is used to perform waveform analysis on each decomposed signal sample to obtain waveform data corresponding to each decomposed signal sample, wherein the waveform data includes amplitude data, shape data and phase data.

[0117] The spectrum conversion submodule is used to perform spectrum conversion on each decomposed signal sample to obtain the decomposed signal spectrum corresponding to each decomposed signal sample.

[0118] The spectrum analysis submodule is used to obtain spectrum data corresponding to each decomposed signal sample according to the decomposed signal spectrum corresponding to each decomposed signal sample, wherein the spectrum data includes frequency range, frequency peak and resolution.

[0119] The third signal classification submodule is used to obtain the reference signal type corresponding to each decomposed signal sample according to the waveform data and spectrum data corresponding to each decomposed signal sample.

[0120] In a specific implementation, the third signal classification submodule includes: The rule acquisition unit is used to acquire the first waveform judgment rule and the first spectrum judgment rule corresponding to the reference noise signal, and the second waveform judgment rule and the second spectrum judgment rule corresponding to the reference seismic signal.

[0121] The first judgment unit is used to determine, for any decomposed signal sample, that the reference signal type corresponding to the current decomposed signal sample is a reference noise signal if the waveform data corresponding to the current decomposed signal sample satisfies the first waveform judgment rule and the spectrum data corresponding to the current decomposed signal sample satisfies the first spectrum judgment rule.

[0122] The second judgment unit is used to determine that the reference signal type corresponding to the current decomposed signal sample is a reference seismic signal if the waveform data corresponding to the current decomposed signal sample meets the second waveform judgment rule and the spectrum data corresponding to the current decomposed signal sample meets the second spectrum judgment rule.

[0123] The third judgment unit is used to determine that the reference signal type corresponding to the current decomposed signal sample is other signals if the reference signal type corresponding to the current decomposed signal sample is not a reference noise signal or a reference seismic signal.

[0124] In a specific implementation, the signal decomposition module 22 includes: The quantity initialization submodule is used to initialize the initial decomposition quantity M=2.

[0125] The signal decomposition submodule is used to decompose the target signal into M initial decomposition signals.

[0126] The correlation degree acquisition submodule is used to respectively acquire a first correlation degree between the target signal and each initial decomposition signal, and a second correlation degree between any two initial decomposition signals.

[0127] The decomposition overlap degree acquisition submodule is used to acquire the decomposition overlap degrees corresponding to the M initial decomposition signals according to all the first correlation degrees and all the second correlation degrees.

[0128] The iterative processing submodule is used to update M=M+1 if the decomposition overlap degree does not meet the preset decomposition condition, and return to the execution signal decomposition submodule until the decomposition overlap degree meets the preset decomposition condition.

[0129] The target decomposition quantity acquisition submodule is used to obtain the target decomposition quantity K=M-1 according to the initial decomposition quantity M corresponding to the decomposition overlap degree that meets the preset decomposition condition.

[0130] In a specific implementation, the first signal classification module 24 includes: The first signal classification submodule is used to determine, for any target decomposition signal, if the target confusion degree corresponding to the current target decomposition signal is greater than a preset first confusion degree threshold, that the first signal type corresponding to the current target decomposition signal is a noise signal.

[0131] The second signal classification submodule is used to determine that the first signal type corresponding to the current target decomposition signal is a target seismic signal if the target confusion degree corresponding to the current target decomposition signal is less than a preset second confusion degree threshold.

[0132] The third signal classification submodule is used to determine that the first signal type corresponding to the current target decomposition signal is an intermediate signal if the target confusion level corresponding to the current target decomposition signal is greater than or equal to a preset second confusion level threshold and less than or equal to a preset first confusion level threshold.

[0133] In a specific implementation, the mutual correlation degree acquisition module 25 includes: The fourth signal classification submodule is used to determine, for any intermediate signal, that the second signal type corresponding to the current intermediate signal is a noise signal if the target mutual correlation degree corresponding to the current intermediate signal is less than a preset first mutual correlation degree threshold.

[0134] The fifth signal classification submodule is used to determine that the second signal type corresponding to the current intermediate signal is a target seismic signal if the target mutual correlation degree corresponding to the current intermediate signal is greater than or equal to a preset second mutual correlation degree threshold.

[0135] The sixth signal classification submodule is used to determine that the second signal type corresponding to the current intermediate signal is a mixed signal if the target mutual correlation degree corresponding to the current intermediate signal is greater than or equal to a preset first mutual correlation degree threshold and less than or equal to a preset second mutual correlation degree threshold.

[0136] It should be noted that the information interaction, execution process and other contents between the above modules are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0137] Embodiment 3 Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, wherein at least one instruction or at least one program is stored in the non-transitory computer-readable storage medium, and the at least one instruction or at least one program is loaded and executed by a processor to implement the steps: S100, taking the acquired initial seismic signal as the target signal.

[0138] S200, performing signal decomposition on the target signal to obtain K target decomposition signals, where K is an integer greater than 1.

[0139] S300, respectively obtaining the target confusion degree corresponding to each target decomposition signal.

[0140] S400, classify all target decomposition signals according to the target confusion degree corresponding to each target decomposition signal, a preset first confusion degree threshold and a preset second confusion degree threshold, and obtain the first signal type corresponding to each target decomposition signal, wherein the first signal type includes a noise signal, a target seismic signal and an intermediate signal.

[0141] S500, respectively obtaining the target cross-correlation degree between each intermediate signal and the initial seismic signal.

[0142] S600, classify all intermediate signals according to the target mutual correlation degree between each intermediate signal and the initial seismic signal, the preset first mutual correlation degree threshold and the preset second mutual correlation degree threshold, and obtain the second signal type corresponding to each intermediate signal, wherein the second signal type includes a noise signal, a target seismic signal and a mixed signal.

[0143] S700, for any mixed signal, determine the current mixed signal as the target signal, return to execute step S200, until the preset stop condition is met, and obtain all target seismic signals corresponding to the initial seismic signal.

[0144] S800, acquiring a denoised seismic signal corresponding to the initial seismic signal according to all target seismic signals corresponding to the initial seismic signal.

[0145] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronization link DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0146] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0147] Embodiment 4 A fourth embodiment of the present invention provides an electronic device, which includes a processor and the non-transitory computer-readable storage medium in the third embodiment of the present invention.

[0148] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A seismic signal denoising method, characterized in that: The seismic signal denoising method comprises the following steps: S100, taking the acquired initial seismic signal as a target signal; S200, performing signal decomposition on the target signal to obtain K target decomposition signals, where K is an integer greater than 1; S300, respectively obtaining the target confusion degree corresponding to each target decomposition signal; S400, classifying all target decomposition signals according to the target confusion degree corresponding to each target decomposition signal, a preset first confusion degree threshold, and a preset second confusion degree threshold, and obtaining a first signal type corresponding to each target decomposition signal, wherein the first signal type includes a noise signal, a target seismic signal, and an intermediate signal; S500, respectively obtaining a target cross-correlation degree between each intermediate signal and the initial seismic signal; S600, classifying all intermediate signals according to the target mutual correlation degree between each intermediate signal and the initial seismic signal, a preset first mutual correlation degree threshold, and a preset second mutual correlation degree threshold, and obtaining a second signal type corresponding to each intermediate signal, wherein the second signal type includes a noise signal, a target seismic signal, and a mixed signal; S700, for any mixed signal, determining the current mixed signal as a target signal, returning to step S200, until a preset stop condition is met, and obtaining all target seismic signals corresponding to the initial seismic signal; S800, acquiring a denoised seismic signal corresponding to the initial seismic signal according to all target seismic signals corresponding to the initial seismic signal.

2. The seismic signal denoising method according to claim 1, characterized in that: The seismic signal denoising method further comprises the following steps: S11, obtaining a number of seismic signal samples; S12, for any seismic signal sample, performing signal decomposition on the current seismic signal sample to obtain a number of decomposed signal samples corresponding to the current seismic signal sample; S13, performing waveform analysis and spectrum feature analysis on each decomposed signal sample to obtain a reference signal type corresponding to each decomposed signal sample, wherein the reference signal type includes a reference noise signal, a reference seismic signal and other signals; S14, for any reference seismic signal, performing signal decomposition on the current reference seismic signal to obtain a plurality of reference decomposition signals corresponding to the current reference seismic signal; S15, performing waveform analysis and spectrum feature analysis on each reference decomposition signal to obtain a prediction signal type corresponding to each reference decomposition signal, wherein the prediction signal type includes a prediction noise signal, a prediction seismic signal and other signals; S16, respectively obtaining a reference confusion degree corresponding to each reference decomposition signal, and a reference cross-correlation degree between each reference decomposition signal and a corresponding reference seismic signal; S17, according to the predicted signal type, reference confusion degree and reference mutual correlation degree corresponding to each reference decomposition signal, obtain a first confusion degree subset and a first mutual correlation degree subset of the predicted noise signal relative to the current seismic signal sample, and a second confusion degree subset and a second mutual correlation degree subset of the predicted seismic signal relative to the current seismic signal sample; S18, traversing all seismic signal samples, obtaining a first confusion degree set and a first mutual correlation degree set corresponding to the predicted noise signal, and a second confusion degree set and a second mutual correlation degree set corresponding to the predicted seismic signal; S19, according to the first confusion level set and the first mutual correlation level set corresponding to the predicted noise signal, obtain the preset first confusion level threshold and the preset first mutual correlation level threshold, according to the second confusion level set and the second mutual correlation level set corresponding to the predicted seismic signal, obtain the preset second confusion level threshold and the preset second mutual correlation level threshold.

3. The seismic signal denoising method according to claim 2, characterized in that: S13 includes the following steps: S131, performing waveform analysis on each decomposed signal sample to obtain waveform data corresponding to each decomposed signal sample, wherein the waveform data includes amplitude data, shape data and phase data; S132, performing spectrum transformation on each decomposed signal sample to obtain a decomposed signal spectrum corresponding to each decomposed signal sample; S133, acquiring spectrum data corresponding to each decomposed signal sample according to the decomposed signal spectrum corresponding to each decomposed signal sample, wherein the spectrum data includes a frequency range, a frequency peak, and a resolution; S134, obtaining a reference signal type corresponding to each decomposed signal sample according to the waveform data and the spectrum data corresponding to each decomposed signal sample.

4. The seismic signal denoising method according to claim 3, characterized in that: S134 includes the following steps: S1341, obtaining a first waveform judgment rule and a first spectrum judgment rule corresponding to the reference noise signal, and a second waveform judgment rule and a second spectrum judgment rule corresponding to the reference seismic signal; S1342, for any decomposed signal sample, if the waveform data corresponding to the current decomposed signal sample satisfies the first waveform judgment rule, and the spectrum data corresponding to the current decomposed signal sample satisfies the first spectrum judgment rule, then determining that the reference signal type corresponding to the current decomposed signal sample is a reference noise signal; S1343, if the waveform data corresponding to the current decomposed signal sample satisfies the second waveform judgment rule, and the spectrum data corresponding to the current decomposed signal sample satisfies the second spectrum judgment rule, then determining that the reference signal type corresponding to the current decomposed signal sample is a reference seismic signal; S1344: If the reference signal type corresponding to the current decomposed signal sample is not a reference noise signal or a reference seismic signal, determine that the reference signal type corresponding to the current decomposed signal sample is other signals.

5. The seismic signal denoising method according to claim 1, characterized in that: S200 includes the following steps: S210, initializing the initial decomposition number M=2; S220, decomposing the target signal into M initial decomposed signals; S230, respectively acquiring a first correlation degree between the target signal and each initial decomposition signal, and a second correlation degree between any two initial decomposition signals; S240, acquiring decomposition overlap degrees corresponding to the M initial decomposition signals according to all the first correlation degrees and all the second correlation degrees; S250, if the decomposition overlap degree does not meet the preset decomposition condition, update M=M+1, and return to execute step S220 until the decomposition overlap degree meets the preset decomposition condition; S260, obtaining a target decomposition number K=M-1 according to an initial decomposition number M corresponding to when the decomposition overlap degree satisfies a preset decomposition condition.

6. The seismic signal denoising method according to claim 1, characterized in that: S400 includes the following steps: S410, for any target decomposition signal, if the target confusion degree corresponding to the current target decomposition signal is greater than the preset first confusion degree threshold, determining that the first signal type corresponding to the current target decomposition signal is a noise signal; S420, if the target confusion degree corresponding to the current target decomposition signal is less than the preset second confusion degree threshold, determining that the first signal type corresponding to the current target decomposition signal is a target seismic signal; S430: If the target confusion degree corresponding to the current target decomposition signal is greater than or equal to the preset second confusion degree threshold and less than or equal to the preset first confusion degree threshold, determine that the first signal type corresponding to the current target decomposition signal is an intermediate signal.

7. The seismic signal denoising method according to claim 1, characterized in that: S500 includes the following steps: S510, for any intermediate signal, if the target mutual correlation degree corresponding to the current intermediate signal is less than the preset first mutual correlation degree threshold, determining that the second signal type corresponding to the current intermediate signal is a noise signal; S520, if the target mutual correlation degree corresponding to the current intermediate signal is greater than or equal to the preset second mutual correlation degree threshold, determining that the second signal type corresponding to the current intermediate signal is a target seismic signal; S530: If the target mutual correlation degree corresponding to the current intermediate signal is greater than or equal to the preset first mutual correlation degree threshold and less than or equal to the preset second mutual correlation degree threshold, determine that the second signal type corresponding to the current intermediate signal is a mixed signal.

8. A seismic signal denoising device, characterized in that: The seismic signal denoising device comprises: A signal acquisition module, used to use the acquired initial seismic signal as a target signal; A signal decomposition module, used to decompose the target signal to obtain K target decomposition signals, where K is an integer greater than 1; A chaos degree acquisition module is used to respectively acquire the target chaos degree corresponding to each target decomposition signal; A first signal classification module, used to classify all target decomposition signals according to the target confusion degree corresponding to each target decomposition signal, a preset first confusion degree threshold and a preset second confusion degree threshold, and obtain a first signal type corresponding to each target decomposition signal, wherein the first signal type includes a noise signal, a target seismic signal and an intermediate signal; A cross-correlation degree acquisition module, used to respectively acquire a target cross-correlation degree between each intermediate signal and the initial seismic signal; A second signal classification module is used to classify all intermediate signals according to the target mutual correlation degree between each intermediate signal and the initial seismic signal, a preset first mutual correlation degree threshold and a preset second mutual correlation degree threshold, and obtain a second signal type corresponding to each intermediate signal, wherein the second signal type includes a noise signal, a target seismic signal and a mixed signal; An iterative processing module, for determining the current mixed signal as a target signal for any mixed signal, returning to execute the signal decomposition module until a preset stop condition is met, and obtaining all target seismic signals corresponding to the initial seismic signal; The signal denoising module is used to obtain a denoised seismic signal corresponding to the initial seismic signal based on all target seismic signals corresponding to the initial seismic signal.

9. A non-transitory computer-readable storage medium, wherein at least one instruction or at least one program is stored in the non-transitory computer-readable storage medium, characterized in that: The at least one instruction or the at least one program is loaded and executed by the processor to implement the seismic signal denoising method as described in any one of claims 1-7.

10. An electronic device, characterized in that: The invention comprises a processor and the non-transitory computer-readable storage medium as claimed in claim 9.

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