A seismic data denoising method, device, equipment, medium and program

Abstract

This invention relates to the field of natural seismology and proposes a method, apparatus, device, medium, and program for seismic data denoising. The method includes: acquiring seismic data; performing deconvolution processing on the seismic data to obtain spatiotemporal domain data; reconstructing the spatiotemporal domain data using a preset linear transform to obtain intercept slowness domain data; calculating the position of the spatiotemporal domain data according to a preset geological structure direction to obtain position data; filtering the intercept slowness domain data using the position data to obtain scattered Rayleigh waves; performing an inverse transform calculation on the scattered Rayleigh waves to obtain spatiotemporal domain scattered Rayleigh waves; and performing data separation on the spatiotemporal domain data based on the spatiotemporal domain scattered Rayleigh waves to obtain denoised data. By achieving seismic denoising through data separation, the denoising efficiency can be improved, avoiding signal loss or noise residue problems that may occur in traditional denoising methods, thereby enhancing denoising efficiency.

Description

Technical Field

[0001] This disclosure relates to the field of natural seismology, and in particular to a method, apparatus, device, medium, and program for denoising seismic data. Background Technology

[0002] An earthquake is a violent event caused by the fracturing, displacement, or volcanic eruption of the Earth's internal rock layers. It propagates in the form of seismic waves, causing ground vibrations. Earthquake data is the information of seismic waves recorded by seismographs, which includes the time, location, intensity, and waveform of the earthquake. It is an important basis for studying the earthquake mechanism, geological structure, internal structure of the Earth, and earthquake prediction and early warning.

[0003] Short-period dense arrays have been widely used in crustal structure detection in natural seismology. They are characterized by short observation periods, low construction costs, environmental friendliness, and high resolution, making them an effective means of detecting regional fault structures and deep processes, and studying the structure of small-scale sedimentary basins and their adjacent areas. On the one hand, densely deployed arrays allow hundreds of stations to acquire a large amount of three-component natural seismic waveform data in a short time, offering advantages such as short periods and low costs. On the other hand, the smaller station spacing results in a high spatial sampling rate for the seismic wavefield, good waveform coherence between adjacent stations, and the use of higher frequency bands and wavefield-based data processing and migration imaging techniques. However, in areas with complex surface conditions, dramatic topographic relief and strong near-surface velocity variations can affect the results of deep structure detection: the short wavelengths corresponding to the high-frequency signals used in short-period data may be comparable to the topographic scale, and the scattering effects of complex surface conditions (such as basin-mountain transition zones and steep, undulating terrain) can generate strong interference waves.

[0004] In the field of exploration seismology, data preprocessing under complex surface conditions has developed relatively mature methods, techniques, and processes. However, in the field of natural seismology, especially in the short-period dense array observation method that has emerged in the last 10 years, most of the methods used are conventional methods from traditional broadband stations, and no matching and mature reliable data processing methods and techniques have been developed. Therefore, this invention proposes to quantify the impact of complex surface conditions (such as basin-mountain transition zones, steep and undulating terrain, etc.) on the teleseismic receiver function and design an adaptive denoising scheme to improve the signal-to-noise ratio of the teleseismic receiver function. Summary of the Invention

[0005] To address the aforementioned problems, embodiments of the present invention provide a method, apparatus, device, medium, and program for denoising seismic data.

[0006] In a first aspect, embodiments of the present invention provide a method for denoising seismic data, comprising:

[0007] Seismic data is collected and deconvolution is performed on the seismic data to obtain spatiotemporal domain data;

[0008] The spatiotemporal domain data is reconstructed using a preset linear transformation to obtain intercept slow-degree domain data;

[0009] The location data is obtained by calculating the location of the spatiotemporal data according to the preset geological structure direction;

[0010] The location data is used to filter the slow-intercept domain data to obtain the scattered Rayleigh wave;

[0011] The scattered Rayleigh wave is inversely transformed to obtain the spatiotemporal scattered Rayleigh wave;

[0012] Based on the scattered Rayleigh waves in the spatiotemporal domain, the spatiotemporal domain data is separated to obtain denoised data.

[0013] According to an embodiment of the present invention, the step of performing deconvolution processing on the seismic data to obtain spatiotemporal domain data includes:

[0014] The seismic data is corrected according to preset parameters to obtain corrected data;

[0015] The correction data is converted according to a preset format to obtain data in a unified format.

[0016] The unified format data is deconvolved using a preset deconvolution algorithm to obtain spatiotemporal domain data.

[0017] According to an embodiment of the present invention, the reconstructing of the spatiotemporal domain data using a preset linear transformation to obtain intercept slow-degree domain data includes:

[0018] The inverse transform operator and the forward transform operator are calculated using the regularization factor, the model weight matrix, and the data weight matrix combined with the conjugate gradient algorithm.

[0019] Intercept slowness domain data are calculated based on the inverse transform operator and the spatiotemporal domain data.

[0020] According to an embodiment of the present invention, the step of calculating the location of the spatiotemporal data based on a preset geological structural direction to obtain location data includes:

[0021] Calculate the angle between the preset geological structure direction and the preset reference wave based on the preset geological structure direction and the preset reference wave;

[0022] The apparent propagation velocity of the preset reference wave is calculated based on the included angle;

[0023] The slope value of the preset reference wave is calculated based on the apparent propagation velocity;

[0024] The position data of the preset reference wave are calculated based on the preset reference wave slope value.

[0025] According to an embodiment of the present invention, filtering the intercept slow-degree domain data using the location data to obtain a scattered Rayleigh wave includes:

[0026] The coordinates of the location data are matched using a preset filter to obtain coordinate information;

[0027] By using a preset filter to retain signals that are the same as the coordinate information and to filter out signals that are different from the coordinate information, scattered Rayleigh waves are obtained.

[0028] According to an embodiment of the present invention, the step of performing an inverse transform calculation on the scattered Rayleigh wave to obtain a spatiotemporally scattered Rayleigh wave includes:

[0029] The scattered Rayleigh wave is calculated by performing an inverse transform on the scattered Rayleigh wave based on the intercept slowness domain data and the positive transform operator, wherein the positive transform operator and the inverse transform operator are conjugate.

[0030] Secondly, embodiments of the present invention provide a seismic data noise reduction device, comprising:

[0031] The acquisition and processing module is used to acquire seismic data and perform deconvolution processing on the seismic data to obtain spatiotemporal domain data.

[0032] The data reconstruction module is used to reconstruct the spatiotemporal domain data using a preset linear transformation to obtain intercept slow domain data.

[0033] The location calculation module is used to perform location calculation on the spatiotemporal domain data according to the preset geological structure direction to obtain location data;

[0034] The filtering module is used to filter the intercept slow-degree domain data using the position data to obtain scattered Rayleigh waves;

[0035] The inverse transform calculation module is used to perform inverse transform calculations on the scattered Rayleigh waves to obtain the spatiotemporal scattered Rayleigh waves;

[0036] The data separation module is used to separate the spatiotemporal domain data based on the spatiotemporal scattered Rayleigh waves to obtain noise-reduced data.

[0037] Thirdly, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the seismic data noise reduction method described above.

[0038] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the seismic data denoising method described above.

[0039] Fifthly, embodiments of the present invention provide a computer program product, including a computer program that, when executed by a processor, implements the steps of the seismic data denoising method.

[0040] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial effects:

[0041] Acquiring seismic data and performing deconvolution processing can effectively remove waveform variations caused by the complexity of the subsurface medium, wavelet morphology, and propagation path, thereby restoring the seismic response characteristics generated by the subsurface interface and obtaining clearer and more accurate spatiotemporal domain data. Reconstructing the spatiotemporal domain data into intercept slowness domain data allows for more effective extraction of target seismic waves, such as scattered Rayleigh waves. Intercept slowness domain data better reflects the propagation direction and velocity information of seismic waves, which is beneficial for subsequent filtering and analysis. Calculating the location of the spatiotemporal domain data based on the preset geological structure direction can more accurately determine the specific location of each point on the seismic wave propagation path, which helps to extract scattered Rayleigh wave data more effectively and improve the accuracy of seismic analysis. Filtering the intercept slowness domain data using location data can more effectively extract scattered Rayleigh waves and remove irrelevant noise signals, thereby improving the noise reduction effect of seismic data. Reducing the influence of geological structures on seismic wave propagation, location data can be used to more accurately locate scattered Rayleigh waves. By analyzing the propagation path of Rayleigh waves, noise signals from other directions can be filtered out more effectively, resulting in purer scattered Rayleigh wave data. Inverse transformation of the scattered Rayleigh waves yields spatiotemporal scattered Rayleigh waves, allowing the filtered data to be restored to the original spatiotemporal domain for more intuitive observation and analysis. Comparison with the original data further enhances the denoising effect. Seismic denoising through data separation improves efficiency by separating the original spatiotemporal data from noise signals, avoiding signal loss or noise residue issues common in traditional denoising methods. This also maintains signal integrity, preventing signal distortion or loss of signal characteristics during filtering. Furthermore, it effectively adapts to complex environments, accurately identifying noise signals even in the presence of various types of noise, thus improving the method's applicability. Finally, it enhances seismic analysis accuracy, more accurately reflecting geological structural information and providing more reliable data for geological exploration. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 A flowchart illustrating the seismic data denoising method according to Embodiment 1 of the present invention is shown.

[0044] Figure 2 The image shows a scattered Rayleigh wave plot in the synthetic wavefield of a ridge model of the seismic data denoising method of Embodiment 1 of the present invention;

[0045] Figure 3 The image shows a scattered Rayleigh wave plot in the synthetic wavefield of a basin model of the seismic data denoising method of Embodiment 1 of the present invention;

[0046] Figure 4 The diagram shows the application of the seismic data denoising method of Embodiment 1 of the present invention to a denoising model of a combination of steep terrain and inclined interface.

[0047] Figure 5 This image shows a comparison of the receiver function of a dense array in a complex surface area on the northern edge of the Ordos region before and after denoising, based on the seismic data denoising method of Embodiment 1 of the present invention.

[0048] Figure 6 The diagram shows the functional block diagram of the seismic data noise reduction device according to Embodiment 2 of the present invention;

[0049] Figure 7 The diagram shows the composition of an electronic device for implementing the seismic data noise reduction method according to Embodiment 4 of the present invention. Detailed Implementation

[0050] The present disclosure will be further described below with reference to the embodiments shown in the accompanying drawings.

[0051] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0052] This invention enables comprehensive damage identification of bridge structures within a short period of time, achieving all-round identification with high identification efficiency.

[0053] Example 1

[0054] like Figure 1 As shown in the figure, a seismic data denoising method provided in this disclosure includes the following steps:

[0055] S1. Collect seismic data and perform deconvolution processing on the seismic data to obtain spatiotemporal domain data.

[0056] In this embodiment of the invention, the acquisition of seismic data refers to using seismic instruments to deploy sensors on the Earth's surface to receive seismic waves generated by artificial explosions or natural earthquakes, and recording these waveforms to form raw seismic data. This data contains information on the structure and properties of the subsurface medium, which is the basis for subsequent seismic data processing and analysis. The deconvolution processing is a signal processing operation performed on the acquired seismic data, aiming to eliminate waveform changes caused by the complexity of the subsurface medium, wavelet morphology, and propagation path during the propagation of the seismic waveform, thereby restoring the seismic response characteristics generated by the subsurface interface. It is like "denoising" the seismic data, making the subsurface reflection signal clearer and providing more accurate information for subsequent analysis. The principle of deconvolution processing is that seismic waves interact with different media when propagating underground, causing changes in the waveform. This change can be regarded as a "convolution" operation on the original seismic signal, and deconvolution processing is the inverse operation of this "convolution" operation, thereby restoring the original signal.

[0057] In this embodiment of the invention, the step of performing deconvolution processing on the seismic data to obtain spatiotemporal domain data includes:

[0058] The seismic data is corrected according to preset parameters to obtain corrected data;

[0059] The correction data is converted according to a preset format to obtain the reflected wave waveform;

[0060] The reflected wave waveform is extracted using a preset receiving function to obtain spatiotemporal domain data.

[0061] In this embodiment of the invention, preset parameters refer to a set of parameter values ​​pre-set during the deconvolution process to eliminate the influence of factors such as time delay and instrument response during seismic data acquisition, and to unify the data format. These parameters include time correction parameters, format conversion parameters, and deconvolution algorithm parameters. For example, time correction parameters can be used to compensate for time differences between different seismic receivers, format conversion parameters can be used to convert seismic data of different formats into a unified format, and deconvolution algorithm parameters can be used to control the intensity and accuracy of deconvolution. The selection of these preset parameters can be set according to the specific characteristics of the seismic data, the acquisition environment, and the processing objectives to ensure that the deconvolution process can effectively eliminate noise and distortion and obtain high-quality spatiotemporal domain data. The preset format refers to uniformly converting the corrected seismic data into a standard format to facilitate subsequent deconvolution processing and data analysis. Different seismic data acquisition systems or software can use different data formats, while deconvolution processing usually requires a unified data format. Yes, it can be performed. The formulation of the preset format needs to consider factors such as data storage efficiency, data processing efficiency, and data compatibility. For example, commonly used seismic data formats include SEG-Y format and SEGY format. The preset deconvolution algorithm refers to the algorithm used to eliminate waveform changes in seismic data caused by the complexity of the subsurface medium, wavelet morphology, and propagation path, thereby restoring the seismic response characteristics caused by the subsurface interface. Common deconvolution algorithms include least squares deconvolution, prediction error filtering, and spectral whitening deconvolution. These algorithms, based on different principles and application scenarios, use different mathematical operations to remove noise and interference from seismic waveforms, ultimately obtaining clearer and more accurate reflected wave waveforms. The preset receiver function refers to a mathematical model used to extract specific types of seismic waves from the reflected wave waveform obtained by deconvolution processing. The receiver function can be designed according to specific seismic wave propagation characteristics and subsurface medium models, and can effectively separate the target seismic wave from other noise and interference, thereby obtaining purer scattered Rayleigh wave data.

[0062] In this embodiment of the invention, the correction refers to a series of modifications to the acquired seismic data to eliminate errors and biases introduced during data acquisition, making the data more accurately reflect the true condition of the subsurface medium. These errors mainly originate from factors such as time delay, instrument response, and topographic relief. The correction process mainly includes time correction, instrument correction, and geometric correction, using preset parameters to perform these correction operations. For example, time correction parameters are used to compensate for time differences between different seismic receivers, instrument correction parameters are used to eliminate the response bias of the seismic instrument itself, and geometric correction parameters are used to correct the influence of topographic relief on seismic wave propagation. The deconvolution processing refers to using the preset deconvolution algorithm to process the uniformly formatted seismic data to eliminate the distortion of the seismic waveform caused by the complexity of the subsurface medium during propagation, thereby restoring the true waveform of the seismic reflection wave. This process is similar to "denoising" the seismic data, making the subsurface reflection signal clearer and providing more accurate information for subsequent analysis and interpretation. The data extraction refers to using the preset receiving function to extract specific types of reflection wave data from the reflection wave waveform obtained by deconvolution processing and converting it into spatiotemporal domain data for further research and analysis.

[0063] In detail, the seismic data undergoes deconvolution processing. By correcting the original data using preset parameters, errors such as time delays and instrument response during data acquisition can be eliminated. The corrected data is then converted into a unified format for easier subsequent processing. Next, a preset deconvolution algorithm is used to remove distortions in the seismic waveform during propagation, restoring the seismic response characteristics caused by subsurface interfaces. Finally, a preset receiving function is used to extract specific types of reflected wave data and convert them into spatiotemporal domain data, providing more accurate and clearer information for subsequent seismic analysis.

[0064] In this embodiment of the invention, the acquisition of seismic data and the deconvolution process can effectively remove waveform changes in seismic data caused by the complexity of the subsurface medium, wavelet morphology and propagation path, thereby restoring the seismic response characteristics caused by the subsurface interface and obtaining clearer and more accurate spatiotemporal domain data.

[0065] S2. The spatiotemporal domain data is reconstructed using a preset linear transformation to obtain intercept slow-degree domain data.

[0066] In this embodiment of the invention, the preset linear transformation refers to a mathematical method that transforms spatiotemporal domain data from a time and space coordinate system to an intercept slowness domain. Slowness is the reciprocal of the propagation speed of seismic waves. Data in the intercept slowness domain can better reflect the propagation direction and speed information of seismic waves. The reconstruction refers to transforming spatiotemporal domain data from a time and space coordinate system to an intercept slowness domain, which can more clearly understand the propagation characteristics of seismic waves and extract target seismic waves more effectively.

[0067] In this embodiment of the invention, the reconstructing of the spatiotemporal domain data using a preset linear transformation to obtain intercept slow-degree domain data includes:

[0068] The inverse transform operator and the forward transform operator are calculated using the regularization factor, the model weight matrix, and the data weight matrix combined with the conjugate gradient algorithm.

[0069] Intercept slowness domain data are calculated based on the inverse transform operator and the spatiotemporal domain data.

[0070] In this embodiment of the invention, the inverse transform operator and the forward transform operator can be calculated using the following formulas, based on the regularization factor, the model weight matrix, and the data weight matrix, combined with the conjugate gradient algorithm:

[0071]

[0072] Where λ is the regularization factor, W m W is the model weight matrix. d This is the data weight matrix;

[0073] The model weight matrix can be obtained using the following formula:

[0074] diag(W m ) i =|m i | 1 / 2

[0075] Where, m i The intercept slowness domain data is used for iteration, and i is the iteration number;

[0076] The data weight matrix can be obtained using the following formula:

[0077] diag(W d ) i =|r i | -1 / 2

[0078] Where, r i Let be the standard deviation of the data, and i be the number of iterations;

[0079] The intercept slowness domain data can be calculated using the following formula based on the inverse transform operator and the spatiotemporal domain data;

[0080] m = L T d

[0081] Where m represents the intercept slowness domain data, and L... T Here, d is the inverse transform operator, and d represents the spatiotemporal domain data.

[0082] Furthermore, the following formula can be used to perform inverse transform calculations on spatiotemporal domain data:

[0083] d=Lm

[0084] Where m represents the intercept slow domain data, and L represents the positive transform operator;

[0085] The spatiotemporal domain data matrix d and the intercept slowness domain data matrix m are not square matrices; the forward transform operator L is not a unitary operator; the inverse transform operator L... T This is not an exact calculation of the inverse transform operator; L is a forward transform operator, and the forward transform operator L itself is not invertible. In this embodiment, an iterative method is used to solve for an approximate inverse transform operator L when obtaining the inverse transform operator corresponding to the forward transform operator L. T In this process, the forward transform operator L and its conjugate operator are continuously adjusted so that the conjugate operator of the forward transform operator L approaches the inverse transform operator L infinitely. T When the product of the positive transform operator L and its conjugate operator approaches 1 infinitely, the conjugate operator of the positive transform operator L is considered as the inverse transform operator L. T This method in this step guarantees the forward transform operator L and the inverse transform operator L. T (Conjugacy), and the product of the two approaches 1 infinitely, thus making the inverse transform operator L T The conjugate operator of the alternative positive transform operator L; the m i For the intercept slow domain data of the iterative process, the embodiments of the present invention use multiple iterations to make the product of the positive transform operator L and the conjugate operator of the positive transform operator L approach 1 as much as possible. As the iteration proceeds, the energy cluster of the intercept slow domain data becomes more concentrated and the accuracy becomes higher and higher.

[0086] In detail, in this embodiment of the invention, a preset linear transformation is used to reconstruct spatiotemporal domain data into intercept-slow-degree domain data. First, based on the regularization factor, model weight matrix, and data weight matrix, the inverse transformation operator and the forward transformation operator are calculated using the conjugate gradient algorithm. Then, the calculated inverse transformation operator is used to transform the spatiotemporal domain data into intercept-slow-degree domain data, thereby realizing the reconstruction of spatiotemporal domain data into intercept-slow-degree domain data. This method can effectively improve the efficiency of earthquake analysis and provide a more reliable basis for earthquake research and disaster prevention and mitigation work.

[0087] In this embodiment of the invention, the spatiotemporal domain data is reconstructed into intercept slowness domain data, which can more effectively extract target seismic waves, such as scattered Rayleigh waves; the intercept slowness domain data can better reflect the propagation direction and velocity information of seismic waves, which is beneficial for subsequent filtering and analysis.

[0088] S3. Calculate the location of the spatiotemporal data according to the preset geological structure direction to obtain the location data.

[0089] In this embodiment of the invention, the preset geological structure direction refers to a direction pre-defined based on existing geological exploration information or experience, representing the orientation of the underground geological structure. This direction information can be used for location calculation to more accurately locate the propagation path of seismic waves, thereby extracting scattered Rayleigh wave data more effectively. The preset geological structure direction can be the azimuth of the seismic source, the orientation of the seismic array, and the orientation of the terrain. The location calculation refers to determining the specific location information of each point on the propagation path of seismic waves based on the preset geological structure direction and using the time and space information in the spatiotemporal domain data.

[0090] In this embodiment of the invention, the step of calculating the location of the spatiotemporal data according to a preset geological structural direction to obtain location data includes:

[0091] Calculate the angle between the preset geological structure direction and the preset reference wave based on the preset geological structure direction and the preset reference wave;

[0092] The apparent propagation velocity of the preset reference wave is calculated based on the included angle;

[0093] The slope value of the preset reference wave is calculated based on the apparent propagation velocity;

[0094] The position data of the preset reference wave are calculated based on the preset reference wave slope value.

[0095] In this embodiment of the invention, the calculation of the angle between the preset geological structure direction and the preset reference wave can be achieved by representing the preset geological structure direction and the preset reference wave as vectors, then using the vector dot product formula to calculate the cosine value of the angle based on the dot product value of the two vectors, and finally obtaining the degree of the angle through the inverse cosine function.

[0096] In this embodiment of the invention, the apparent propagation speed of the preset reference wave can be calculated based on the included angle using the following formula;

[0097]

[0098] Where θ is the angle between the preset geological structure direction and the preset reference wave;

[0099] The slope value of the preset reference wave can be calculated based on the apparent propagation velocity using the following formula;

[0100]

[0101] in, To determine the velocity of scattered Rayleigh waves with the same propagation direction as the preset reference wave, To determine the velocity of scattered Rayleigh waves that are opposite to the propagation direction of the preset reference wave, The apparent velocity of the preset reference wave along the dense array;

[0102] The position data of the preset reference wave can be calculated based on the preset reference wave slope value using the following formula:

[0103]

[0104] Where k is the slope value of the preset reference wave;

[0105] The preset reference wave is a scattered Rayleigh wave, and the position data refers to the coordinate information of the scattered Rayleigh wave in the intercept and slowness domain, where the two coordinate axes are the intercept and slowness, respectively.

[0106] In detail, this step calculates the location of the scattered Rayleigh wave based on the seismic source azimuth, the orientation of the seismic array, and the topographic orientation using spatiotemporal data. First, the angle between the orientation of the seismic array and the scattered Rayleigh wave is calculated. Then, the apparent propagation velocity of the scattered Rayleigh wave is calculated based on the angle, thus obtaining the slope value of the scattered Rayleigh wave. Finally, based on the slope value of the scattered Rayleigh wave and the coordinate information of the spatiotemporal data, the location data of the scattered Rayleigh wave is calculated. This step can effectively obtain the location data of the scattered Rayleigh wave, providing more accurate information for subsequent seismic analysis.

[0107] In this embodiment of the invention, the location calculation of spatiotemporal data based on the preset geological structure direction can more accurately determine the specific location of each point on the seismic wave propagation path. This helps to extract scattered Rayleigh wave data more effectively and improve the accuracy of earthquake analysis.

[0108] S4. Filter the intercept slow domain data using the location data to obtain the scattered Rayleigh wave.

[0109] In this embodiment of the invention, the filtering refers to processing the intercept slow domain data using location data to remove signals unrelated to the scattered Rayleigh waves, thereby extracting the scattered Rayleigh wave data; this process can obtain the target seismic wave more clearly.

[0110] In this embodiment of the invention, the step of filtering the intercept slow-degree domain data using the location data to obtain the scattered Rayleigh wave includes:

[0111] The coordinates of the location data are matched using a preset filter to obtain coordinate information;

[0112] By using a preset filter to retain signals that are the same as the coordinate information and to filter out signals that are different from the coordinate information, scattered Rayleigh waves are obtained.

[0113] In this embodiment of the invention, the preset filter is a mathematical tool designed according to specific conditions. Essentially, it is a function that can filter signals based on their characteristics, retaining signals that meet specific conditions and filtering out other signals that do not. In this embodiment, the preset filter is used to extract scattered Rayleigh waves from intercept slow-range domain data, and its working principle is as follows:

[0114] First, the preset filter establishes a matching filtering condition based on the coordinate information in the location data. This condition can be spatial location, time information, frequency range, amplitude, etc., depending on the characteristics of the target signal. Second, the preset filter compares each data point in the intercept slow domain data with the filtering condition. If the data point meets the filtering condition, it is retained; if the data point does not meet the filtering condition, it is discarded. Through this filtering process, the preset filter ultimately retains signals that meet specific conditions, namely scattered Rayleigh waves, and filters out other signals that do not meet the conditions. In this embodiment of the invention, the preset filter can be a bandpass filter.

[0115] In detail, firstly, a preset filter is used to construct a filtering condition based on the coordinate information of the location data; this filtering condition can be spatial location. According to the preset filtering condition, each data point in the intercept slow domain data is judged; if a data point meets the filtering condition, it is retained; if it does not meet the filtering condition, it is filtered out. Through this filtering process, the preset filter can effectively remove signals from the intercept slow domain data that are inconsistent with the propagation path of the scattered Rayleigh waves, retaining signals that match the coordinate information of the location data. The ultimately retained signal is the scattered Rayleigh wave. This location data-based filtering method can improve the accuracy of scattered Rayleigh wave extraction and effectively remove noise interference, ultimately obtaining purer scattered Rayleigh wave data, providing a more reliable basis for subsequent seismic analysis.

[0116] In this embodiment of the invention, filtering the intercept slow domain data using location data can more effectively extract scattered Rayleigh waves and remove irrelevant noise signals, thereby improving the noise reduction effect of seismic data; it also reduces the influence of geological structures on seismic wave propagation. By using location data, the propagation path of scattered Rayleigh waves can be located more accurately, thereby more effectively filtering out noise signals from other directions and obtaining purer scattered Rayleigh wave data.

[0117] S5. Perform inverse transform calculation on the scattered Rayleigh wave to obtain the spatiotemporal scattered Rayleigh wave.

[0118] In this embodiment of the invention, the inverse transform calculation refers to converting the scattered Rayleigh wave data in the slow intercept domain back to the spatiotemporal domain so that it can be compared and analyzed with the original spatiotemporal domain data. Because the filtering operation is performed in the slow intercept domain, and we ultimately need to obtain the denoised spatiotemporal domain data, it is necessary to perform an inverse transform calculation to convert the filtered scattered Rayleigh waves back to the spatiotemporal domain.

[0119] In this embodiment of the invention, the step of performing an inverse transform calculation on the scattered Rayleigh wave to obtain the spatiotemporal scattered Rayleigh wave includes:

[0120] The scattered Rayleigh wave is calculated by performing an inverse transform on the scattered Rayleigh wave based on the intercept slowness domain data and the positive transform operator, wherein the positive transform operator and the inverse transform operator are conjugate.

[0121] In this embodiment of the invention, the following formula can be used to calculate the spatiotemporal scattered Rayleigh wave by performing an inverse transform on the scattered Rayleigh wave based on the intercept slowness domain data and the positive transform operator:

[0122] d=Lm

[0123] Where L is the positive transform operator and m is the intercept slow domain data;

[0124] In multiple iterations of step S2, the forward transform operator L and the inverse transform operator L T The product of these factors approaches 1 infinitely, thus making the inverse transform operator L... T The conjugate operator of the subtransform operator L is replaced, so the subtransform operator L and the inverse transform operator L are now the same. T As a conjugate relationship, it is possible to perform inverse transform calculations of spatiotemporally scattered Rayleigh waves through scattered Rayleigh waves.

[0125] In detail, in order to convert the scattered Rayleigh wave data in the slow intercept domain back to the spatiotemporal domain, this embodiment of the invention employs an inverse transformation calculation method. This embodiment uses a pre-calculated forward transformation operator to perform an inverse transformation on the scattered Rayleigh wave data in the slow intercept domain, and finally obtains the spatiotemporal domain scattered Rayleigh wave data. The spatiotemporal domain scattered Rayleigh wave data obtained by the inverse transformation calculation can be compared and analyzed with the original spatiotemporal domain data to more intuitively observe the noise reduction effect.

[0126] In this embodiment of the invention, the scattered Rayleigh wave is inversely transformed to obtain the spatiotemporal scattered Rayleigh wave, which can restore the filtered data to the spatiotemporal domain of the original data for more intuitive observation and analysis, and compare it with the original data to more effectively evaluate the noise reduction effect.

[0127] S6. Based on the scattered Rayleigh waves in the spatiotemporal domain, perform data separation on the spatiotemporal domain data to obtain noise-reduced data.

[0128] In this embodiment of the invention, the data separation refers to processing the original spatiotemporal domain data using spatiotemporal domain scattered Rayleigh wave data to separate the scattered Rayleigh wave signal from the noise signal in the original data. Specifically, data separation involves identifying the scattered Rayleigh wave signal from the original data based on its characteristics, such as propagation path, amplitude, and frequency, and separating it from other noise signals. This separation can be achieved using various methods, such as morphological filtering or machine learning-based methods. Morphological filtering methods typically utilize structuring elements of specific shapes to process the data, for example, using structuring elements similar in shape to the scattered Rayleigh wave. The process involves extracting the scattered Rayleigh wave signal and using structuring elements with shapes similar to noise signals to remove the noise. Machine learning methods can build a model based on the characteristics of the scattered Rayleigh waves to classify the data into two categories: scattered Rayleigh waves and noise. For example, support vector machines or neural network models can be used to classify the data based on the characteristics of the scattered Rayleigh wave signal. Ultimately, the data separation process decomposes the original spatiotemporal domain data into two parts: one containing the scattered Rayleigh wave signal and the other containing the noise signal. After data separation, the scattered Rayleigh wave signal is separated, thereby removing the noise signal and obtaining the denoised seismic data.

[0129] like Figure 2 , Figure 3 , Figure 4 and Figure 5 As shown in the embodiments of the present invention, seismic denoising is achieved through data separation, which can improve denoising efficiency. By separating the original spatiotemporal domain data from the noise signal, the signal loss or noise residue problems that may occur in traditional denoising methods are avoided, thereby improving denoising efficiency. At the same time, signal integrity is maintained, avoiding signal distortion or loss of signal features that may be caused by the filtering process. It can effectively adapt to complex environments, and can accurately identify noise signals even in the presence of multiple types of noise, improving the applicability of the method. Furthermore, it improves the accuracy of seismic analysis, can more accurately reflect geological structural information, thereby improving the accuracy of seismic analysis and providing a more reliable basis for geological exploration, etc.

[0130] Example 2

[0131] like Figure 6 As shown in the figure, this embodiment also provides a functional block diagram of a seismic data noise reduction device.

[0132] The seismic data denoising device 100 described in this embodiment can be installed in an electronic device. Depending on the functions implemented, the seismic data denoising device 100 may include an acquisition and processing module 101, a data reconstruction module 102, a location calculation module 103, a filtering module 104, an inverse transform calculation module 105, and a data separation module 106. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0133] In this embodiment, the functions of each module / unit are as follows:

[0134] The acquisition and processing module 101 is used to acquire seismic data and perform deconvolution processing on the seismic data to obtain spatiotemporal domain data.

[0135] The data reconstruction module 102 is used to reconstruct the spatiotemporal domain data using a preset linear transformation to obtain intercept slow domain data.

[0136] The location calculation module 103 is used to perform location calculation on the spatiotemporal domain data according to the preset geological structure direction to obtain location data;

[0137] The filtering module 104 is used to filter the intercept slow domain data using the position data to obtain scattered Rayleigh waves;

[0138] The inverse transformation calculation module 105 is used to perform inverse transformation calculation on the scattered Rayleigh wave to obtain the spatiotemporal scattered Rayleigh wave.

[0139] The data separation module 106 is used to separate the spatiotemporal data based on the spatiotemporal scattered Rayleigh waves to obtain noise-reduced data.

[0140] In detail, each module in the seismic data denoising device 100 described in this embodiment of the invention uses the same technical means as the seismic data denoising method described in Embodiment 1, and can produce the same technical effect, which will not be repeated here.

[0141] Example 3

[0142] like Figure 7 As shown, this embodiment also provides a computer electronic device, which may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as an earthquake data noise reduction program.

[0143] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., executing seismic data noise reduction programs) and calls data stored in the memory 11 to perform various functions of the electronic device and process data.

[0144] The memory 11 includes at least one type of readable storage medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device, such as a plug-in portable hard drive, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. Furthermore, the memory 11 can include both internal and external storage units of the electronic device. The memory 11 can be used not only to store application software and various types of data installed on the electronic device, such as the code for earthquake data noise reduction programs, but also to temporarily store data that has been output or will be output.

[0145] The communication bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0146] The communication interface 13 is used for communication between the aforementioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, Bluetooth interface, etc.), typically used to establish communication connections between the electronic device and other electronic devices. The user interface may be a display, an input unit (such as a keyboard), or, optionally, a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device and to display a visual user interface.

[0147] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0148] For example, although not shown, the electronic device may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0149] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0150] The seismic data noise reduction program stored in the memory 11 of the electronic device is a combination of multiple instructions. When run in the processor 10, it can achieve the following:

[0151] Seismic data is collected and deconvolution is performed on the seismic data to obtain spatiotemporal domain data;

[0152] The spatiotemporal domain data is reconstructed using a preset linear transformation to obtain intercept slow-degree domain data;

[0153] The location data is obtained by calculating the location of the spatiotemporal data according to the preset geological structure direction;

[0154] The location data is used to filter the slow-intercept domain data to obtain the scattered Rayleigh wave;

[0155] The scattered Rayleigh wave is inversely transformed to obtain the spatiotemporal scattered Rayleigh wave;

[0156] Based on the scattered Rayleigh waves in the spatiotemporal domain, the spatiotemporal domain data is separated to obtain denoised data.

[0157] Specifically, the specific implementation method of the processor 10 for the above instructions can be referred to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, and will not be repeated here.

[0158] Furthermore, if the modules / units integrated into the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0159] Example 4

[0160] This embodiment provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the seismic data noise reduction method described above.

[0161] This program code can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 Steps of a specified function in one or more processes.

[0162] Storage media include permanent and non-permanent, removable and non-removable media, and can be used to store information by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by computing devices.

[0163] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0164] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0165] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0166] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0167] Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects. The scope of the invention is not limited to the foregoing description, and all variations within the meaning and scope of equivalents falling within the protection scope are intended to be included in the invention.

[0168] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0169] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0170] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for denoising seismic data, characterized in that, The method includes: Seismic data is acquired, and the seismic data is deconvolved to obtain spatiotemporal domain data; The spatiotemporal domain data is reconstructed using a preset linear transformation to obtain intercept slow-degree domain data; The location data is obtained by calculating the location of the spatiotemporal data according to the preset geological structure direction; The location data is used to filter the slow-intercept domain data to obtain the scattered Rayleigh wave; The scattered Rayleigh wave is inversely transformed to obtain the spatial-temporal scattered Rayleigh wave; Based on the spatiotemporal scattered Rayleigh waves, the spatiotemporal data is separated to obtain denoised data.

2. The seismic data denoising method as described in claim 1, characterized in that, The process of deconvolution on the seismic data to obtain spatiotemporal domain data includes: The seismic data is corrected according to preset parameters to obtain corrected data; The correction data is converted according to a preset format to obtain data in a unified format. The unified format data is deconvolved using a preset deconvolution algorithm to obtain spatiotemporal domain data.

3. The seismic data denoising method as described in claim 1, characterized in that, The process of reconstructing the spatiotemporal domain data using a preset linear transformation to obtain intercept slow-degree domain data includes: The inverse transform operator and the forward transform operator are calculated using the regularization factor, the model weight matrix, and the data weight matrix combined with the conjugate gradient algorithm. Intercept slowness domain data are calculated based on the inverse transform operator and the spatiotemporal domain data.

4. The seismic data denoising method as described in claim 1, characterized in that, The step of calculating the location of the spatiotemporal data according to the preset geological structure direction to obtain location data includes: Calculate the angle between the preset geological structure direction and the preset reference wave based on the preset geological structure direction and the preset reference wave; The apparent propagation velocity of the preset reference wave is calculated based on the included angle; The slope value of the preset reference wave is calculated based on the apparent propagation velocity; The position data of the preset reference wave are calculated based on the preset reference wave slope value.

5. The seismic data denoising method as described in claim 4, characterized in that, The step of filtering the intercept slow-domain data using the location data to obtain the scattered Rayleigh wave includes: The coordinates of the location data are matched using a preset filter to obtain coordinate information; By using a preset filter to retain signals that are the same as the coordinate information and to filter out signals that are different from the coordinate information, scattered Rayleigh waves are obtained.

6. The seismic data denoising method as described in claim 1, characterized in that, The inverse transform calculation of the scattered Rayleigh wave to obtain the spatiotemporal scattered Rayleigh wave includes: The scattered Rayleigh wave is calculated by performing an inverse transform on the scattered Rayleigh wave based on the intercept slowness domain data and the positive transform operator, wherein the positive transform operator and the inverse transform operator are conjugate.

7. A seismic data noise reduction device, characterized in that, The device includes: The acquisition and processing module is used to acquire seismic data and perform deconvolution processing on the seismic data to obtain spatiotemporal domain data. The data reconstruction module is used to reconstruct the spatiotemporal domain data using a preset linear transformation to obtain intercept slow domain data. The location calculation module is used to perform location calculation on the spatiotemporal domain data according to the preset geological structure direction to obtain location data; The filtering module is used to filter the intercept slow-degree domain data using the position data to obtain scattered Rayleigh waves; The inverse transform calculation module is used to perform inverse transform calculations on the scattered Rayleigh waves to obtain the spatiotemporal scattered Rayleigh waves; The data separation module is used to separate the spatiotemporal domain data based on the spatiotemporal scattered Rayleigh waves to obtain noise-reduced data.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the seismic data noise reduction method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the seismic data denoising method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the seismic data denoising method according to any one of claims 1 to 6.

Images