Method, device, equipment and storage medium for first arrival data denoising

Abstract

The present application belongs to the technical field of petroleum geophysical exploration data processing, and discloses a method, device and equipment for first arrival wave data denoising and a storage medium, wherein the method comprises the following steps: exciting and recording seismic single shot data; designing a first arrival wave time window by using a regional technology, and determining the range of each first arrival wave time window; obtaining a noise time window of each first arrival wave time window, and calculating the mean value of the root mean square amplitude ratio of the data in the first arrival wave time window and the noise time window; performing curvelet transform on the data in each first arrival wave time window, and designing an iterative threshold; correcting the iterative threshold to obtain a curvelet coefficient iterative threshold; iteratively correcting the curvelet coefficient according to the curvelet coefficient iterative threshold, and obtaining the final curvelet coefficient after the iteration is completed; and inversely transforming the final curvelet coefficient to obtain shot gather data after the data in the first arrival wave time window are denoised. The present application is used for improving the signal-to-noise ratio of first arrival wave data, can accurately suppress noise, and provides basic data for near-surface velocity modeling and the like.

Description

Technical Field

[0001] This invention belongs to the field of petroleum geophysical exploration data processing technology, and relates to a method, apparatus, equipment and storage medium for denoising first arrival data. Background Technology

[0002] In seismic exploration, first-arrival picking is a fundamental preliminary step for static correction and near-surface velocity modeling. The first-arrival picking rate, to a certain extent, limits the accuracy of static correction and near-surface velocity modeling. With the development of seismic exploration technology, high-density, wide-azimuth observation methods have been widely used, resulting in a significant increase in acquired data. Coupled with the influence of field noise, first arrivals are often overwhelmed by noise, leading to low signal-to-noise ratios (SNR) data and making first-arrival picking increasingly arduous. For low SNR data, current methods mainly employ fx filtering, non-stationary predictive filtering, bandpass filtering, median filtering, and wavelet shaping to improve data quality. FX filtering can attenuate interference from direct waves, refracted waves, and regular noise, but its effect on suppressing random noise is poor. Non-stationary predictive filtering, while effectively suppressing random noise, is ineffective at suppressing coherent noise. Bandpass filtering, while removing noise, loses much important information. Median filtering replaces the value of each point in the seismic data with the median of all values ​​in its neighborhood, leading to a loss of seismic signal details. Wavelet shaping may cause time shifts in the first-arrival wavelet. Existing low signal-to-noise ratio (SNR) data preprocessing techniques all have certain limitations. Therefore, it is of great significance to adopt a new processing method to reduce noise, highlight initial arrival energy, and improve the automatic initial arrival acquisition effect of low SNR data under existing conditions. Summary of the Invention

[0003] The purpose of this invention is to provide a method for denoising first-arrival wave data, which uses curvelet transform to accurately suppress noise, thereby providing basic data for static correction and near-surface velocity modeling.

[0004] Another object of the present invention is to provide an apparatus, device and storage medium for denoising first arrival data.

[0005] To achieve the above objectives, the technical solution adopted by this invention is as follows:

[0006] A method for denoising first-arrival data includes the following steps:

[0007] Excite and collect / record seismic single-shot data;

[0008] The first arrival wave time window is designed using a regional technology to determine the range of the first arrival wave time window for each shot in each area of ​​the target work zone.

[0009] Based on the range of the first arrival time window for each single shot in each region, the noise time window for each first arrival time window is obtained. The root mean square amplitude ratio of the data within the first arrival time window and the data within the noise time window for all channels is calculated, and the mean value of the root mean square amplitude ratio is obtained.

[0010] Based on the data within the time window of each first arrival wave in each region, perform curvelet transform to obtain curvelet coefficients, and design an iterative threshold based on the curvelet coefficients;

[0011] The iteration threshold is corrected based on the mean of the root mean square amplitude ratio to obtain the curve wave coefficient iteration threshold;

[0012] The curve wave coefficients are iteratively corrected according to the curve wave coefficient iteration threshold. After the number of iterations is completed, the final curve wave coefficients are obtained. The final curve wave coefficients are then inversely transformed to obtain the shot gather data after denoising the data within the first arrival time window.

[0013] As a constraint, a regional design technique is used to design the first arrival wave time window, determining the range of the first arrival wave time window for each shot in each area of ​​the target work zone, including:

[0014] Based on surface conditions and data signal-to-noise ratio, seed shots are selected in the target work area, and the first arrival time windows of the seed shot seismic traces are designed in different regions.

[0015] Based on the first arrival wave time window of each area of ​​the seed gun, the spatial interpolation method is used to determine the range of the first arrival wave time window of each shot in each area of ​​the target work area.

[0016] As a further clarification, based on surface conditions and data signal-to-noise ratio, seed shots are selected in the target work area, and the first arrival time windows of the seed shot seismic traces are designed regionally, including:

[0017] Based on surface conditions and signal-to-noise ratio, a seed shot is selected in the target area. Using the firing point of the seed shot as the center point, the shot line as the vertical axis, and the seismic receiver line as the horizontal axis, the seismic gather data from a single shot is divided into Z=2 segments in a rectangular coordinate system. k A set of regions, 0≤k≤4, sort the seismic traces in each region in ascending order of shot-receiver distance, and design the first arrival wave time window for each region.

[0018] As a further refinement, the seismic gather data from a single shot are divided into Z=2 segments in a rectangular coordinate system. k When collecting sets of regions, it is done according to intervals. Divide the rectangular coordinate system into regions;

[0019] The first arrival time window length is 400–1000 ms.

[0020] As a second limitation, based on the range of the first arrival time window for each shot in each region, the noise time window for each first arrival time window is obtained. The root mean square amplitude ratio of the data within the first arrival time window to the data within the noise time window is calculated for all channels, and the mean value of the root mean square amplitude ratio is obtained, including:

[0021] Based on the range of the first arrival time window of each shot in each region, noise data with a length equal to the length of the first arrival time window is taken upwards from the top of each first arrival time window as the noise time window;

[0022] The root mean square amplitude ratio of the first arrival wave data within the time window to the noise data within the time window is calculated for each region, and the mean value of the root mean square amplitude ratio is obtained.

[0023] As a third constraint, a curvelet transform is performed on the data within the time window of each first arrival wave for each region to obtain curvelet coefficients. An iterative threshold is then designed based on these curvelet coefficients, including:

[0024] Based on the data of each first arrival wave within the time window for each region, the curvelet transform is performed to obtain the curvelet coefficients for each scale and each orientation. The obtained curvelet coefficients are sorted from smallest to largest, and the smallest curvelet coefficient and the largest curvelet coefficient of q% are removed respectively, where 0.1≤q≤0.5. The average value of the remaining curvelet coefficients is calculated.

[0025] Based on the average value of the curve coefficients, design the maximum and minimum values ​​of the iteration threshold;

[0026] Design the iteration threshold based on the maximum and minimum values ​​of the iteration threshold.

[0027] As a further limitation, the formula for calculating the maximum value of the iteration threshold is:

[0028]

[0029] The formula for calculating the minimum value of the iteration threshold is:

[0030]

[0031] Where γ1 and γ2 represent adjustment parameters, 10≤γ1≤100; 0.1≤γ2≤10; This represents the average value of the curvature coefficients;

[0032] The formula for calculating the iteration threshold is:

[0033] σ[z]=C max [z]-(d-1)·(C max [z]-C min [z]) / (D-1)

[0034] Where σ[z] represents the iteration threshold of the original curved wave coefficients obtained from the data within the first arrival time window of the z-th region, and d represents the d-th iteration; 1≤d≤D, where D represents the total number of iterations.

[0035] As a fourth constraint, the formula for calculating the iteration threshold of the curve wave coefficient is:

[0036] σ x [z]=σ[z]·(p[z]) r

[0037] Where, σ x [z] represents the iteration threshold of the curve coefficients obtained in the z-th region after curve transformation; r represents the power exponent; p[z] represents the mean of the root mean square amplitude ratio; σ[z] represents the iteration threshold of the original curve coefficients obtained from the data within the first arrival time window of the z-th region.

[0038] As a fifth qualifier, the curvelet coefficients are iteratively corrected based on the curvelet coefficient iteration threshold. After the number of iterations is completed, the final curvelet coefficients are obtained. The final curvelet coefficients are then inversely transformed to obtain the shot gather data after denoising the data within the first arrival time window, including:

[0039] Based on the curvelet coefficient iteration threshold, coefficients smaller than the curvelet coefficient iteration threshold are set to zero to obtain new curvelet coefficients. The new curvelet coefficients are then subjected to inverse transform to obtain shot gather data. Curvelet transform is then performed on the shot gather data to obtain iterative curvelet coefficients. The above iterative process is repeated on the iterative curvelet coefficients. After the number of iterations is completed, the final curvelet coefficients are obtained. The final curvelet coefficients are then subjected to inverse transform to obtain the shot gather data after denoising the data within the first arrival time window.

[0040] The present invention also provides an application of the above-described method for denoising first-arrival wave data in static correction and near-surface velocity modeling.

[0041] The present invention also provides an apparatus for denoising first-arrival data, for implementing the above-described method for denoising first-arrival data, comprising:

[0042] The data acquisition module is used to excite and collect and record seismic single-shot data;

[0043] The first arrival wave time window range determination module is used to design the first arrival wave time window using regional technology, and to determine the range of the first arrival wave time window for each shot in each area of ​​the target work area.

[0044] The root mean square amplitude ratio calculation module is used to obtain the noise window of each first arrival time window based on the range of the first arrival time window of each shot in each region, calculate the root mean square amplitude ratio of the data in the first arrival time window and the data in the noise time window of all channels, and obtain the mean value of the root mean square amplitude ratio.

[0045] The iterative threshold design module is used to perform curvelet transformation on the data within the time window of each first arrival wave for each region to obtain curvelet coefficients, and to design iterative thresholds based on the curvelet coefficients.

[0046] The curve wave coefficient iteration threshold design module corrects the iteration threshold based on the mean value of the root mean square amplitude ratio to obtain the curve wave coefficient iteration threshold.

[0047] The shot gather denoising module is used to iteratively correct the curve wave coefficients according to the curve wave coefficient iteration threshold. After the number of iterations is completed, the final curve wave coefficients are obtained. The final curve wave coefficients are then inversely transformed to obtain the shot gather data after denoising the data within the first arrival time window.

[0048] The present invention also provides a computer device, the computer device including a processor and a memory, the memory being used to store at least one computer program, the at least one computer program being loaded by the processor and executed as described above for the method of denoising first arrival wave data.

[0049] The present invention also provides a storage medium for storing at least one computer program for executing the above-described method for denoising first arrival data.

[0050] The present invention, by adopting the above-described technical solution, achieves the following technical advancements compared to existing technologies:

[0051] (1) After exciting and recording seismic single-shot data, the present invention uses regional technology to design the first arrival time window, then calculates the root mean square amplitude ratio of the data in the first arrival time window and the corresponding noise time window, and obtains the mean value of the root mean square amplitude ratio; performs curvelet transform on the data in the first arrival time window, and designs a curvelet coefficient iteration threshold, and iterates and corrects the curvelet coefficient according to the curvelet coefficient iteration threshold to obtain the shot gather data after denoising the data in the first arrival time window;

[0052] (2) The present invention can highlight the first arrival energy, improve the first arrival automatic picking effect of low signal-to-noise ratio data, reduce manual input, and improve the first arrival picking efficiency.

[0053] In summary, this invention is applicable to improving the signal-to-noise ratio of first-arrival wave data, accurately suppressing noise, improving the quality of low signal-to-noise ratio first-arrival wave data, and providing basic data for near-surface velocity modeling, etc. Attached Figure Description

[0054] Figure 1 The diagram shown is an overall flowchart of the method for denoising first-arrival data in Embodiment 1 of the present invention.

[0055] Figure 2 The diagram shown is a schematic diagram of the division of regions on the observation system in Embodiment 1 of the present invention;

[0056] Figure 3 The figure shown is a schematic diagram of the first arrival wave time window designed in Embodiment 1 of the present invention;

[0057] Figure 4 The figure shown is a schematic diagram of the first arrival picking result before denoising processing of the first arrival wave data within the time window in Embodiment 1 of the present invention;

[0058] Figure 5 The image shows the shot gather data after denoising the data within the first arrival time window in Embodiment 1 of the present invention;

[0059] Figure 6 The figure shown is a schematic diagram of the first arrival picking result after denoising processing of the first arrival wave data within the time window in Embodiment 1 of the present invention;

[0060] Figure 7 The diagram shown is a structural block diagram of the device for denoising first arrival wave data in Embodiment 2 of the present invention;

[0061] Figure 8 The diagram shown is a structural schematic of the computer device in Embodiment 2 of the present invention;

[0062] Figure 9 The diagram shown is a schematic diagram of the structure of the computer storage medium in Embodiment 2 of the present invention. Detailed Implementation

[0063] To better explain and facilitate understanding of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0064] Example 1

[0065] like Figure 1 As shown, this embodiment is a method for denoising first-arrival wave data, including the following steps:

[0066] S1. In the field, based on the designed observation system, excite and collect and record seismic single-shot data;

[0067] The observation system parameters were set as follows: receiver line spacing of 160 meters, channel spacing of 40 meters, 225 channels per receiver line, 600 sampling points per channel, sampling interval of 4ms, and 42 receiver lines.

[0068] S2. The first arrival wave time window is designed using regional technology to determine the range of the first arrival wave time window for each shot in each area of ​​the target work zone.

[0069] In practical implementation, there are various methods for designing the first arrival wave time window using regional technology and determining the range of the first arrival wave time window for each shot in each area of ​​the target work zone. The specific methods given in this embodiment include:

[0070] Based on surface conditions and data signal-to-noise ratio, seed shots are selected in the target work area, and the first arrival time windows of the seed shot seismic traces are designed in different regions.

[0071] Based on the first arrival wave time window of each area of ​​the seed gun, the spatial interpolation method is used to determine the range of the first arrival wave time window of each shot in each area of ​​the target work area.

[0072] Furthermore, based on surface conditions and data signal-to-noise ratio, seed shots are selected in the target work area, and the first arrival time windows of the seed shot seismic traces are designed regionally, including:

[0073] Based on surface conditions and signal-to-noise ratio, a seed shot is selected in the target area. Using the firing point of the seed shot as the center point, the shot line as the vertical axis, and the seismic receiver line as the horizontal axis, the seismic gather data from a single shot is divided into Z=2 segments in a rectangular coordinate system. k A set of regions, 0≤k≤4, sort the seismic traces in each region according to the shot-receiver distance from smallest to largest, and design the first arrival time window for each region, with the first arrival time window length being 400~1000ms.

[0074] Among them, the seismic gather data of a single shot are divided into Z=2 in a rectangular coordinate system. k When collecting sets of regions, it is done according to intervals. Divide the rectangular coordinate system into regions. The more complex the surface conditions, the larger the value of k should be.

[0075] like Figure 2 The diagram shown is a schematic of the region division in this embodiment. The seismic gather data of a single shot is divided into 4 regions at 90° intervals in a rectangular coordinate system. A first arrival time window is designed for each region, and the length of the first arrival time window is 1000ms.

[0076] S3. Based on the range of the first arrival time window of each single shot in each region, obtain the noise time window of each first arrival time window, calculate the root mean square amplitude ratio of the data in the first arrival time window and the data in the noise time window of all channels, and obtain the mean value of the root mean square amplitude ratio.

[0077] The method described in this embodiment specifically includes:

[0078] Based on the range of the first arrival wave time window for each single shot in each region, noise data with a length equal to the length of the first arrival wave time window is taken upwards from the top of each first arrival wave time window as the noise time window; where the lengths of the first arrival wave time window and the noise time window are equal, the noise time window is denoted as WN[z][s][m], and the first arrival wave time window is denoted as WS[z][s][m], where z represents the z-th region, 1≤z≤Z; s represents the s-th receiving line, 1≤s≤S, where S represents the total number of receiving lines; m represents the m-th receiving point on the s-th receiving line, 1≤m≤M, where M represents the total number of receiving points on the s-th receiving line;

[0079] The root mean square amplitude ratio of the first arrival wave data within the time window to the noise data within the time window is calculated for each region, and the mean value of the root mean square amplitude ratio is obtained.

[0080] like Figure 3 The diagram shows the first-arrival time window designed in this embodiment. Due to the large surface undulations in this area, static elevation correction is required before preprocessing to obtain the noise time window. The lengths of the first-arrival time window and the noise time window are both 1000ms. By calculating the root mean square amplitude ratio of the data within the first-arrival time window to the data within the noise time window, the first-arrival signal-to-noise ratio is found to be 0.92. Figure 4 The diagram shows the results of first arrival picking before denoising the data within the first arrival time window. It can be seen that the first arrival is barely visible before preprocessing, and the number of effective first arrivals that can be picked is small.

[0081] S4. Perform curvelet transform on the data within the time window of each first arrival wave for each region to obtain curvelet coefficients, and design the iteration threshold based on the curvelet coefficients.

[0082] In practice, curvelet transform is performed on the data within the time window of each first arrival wave for each region to obtain curvelet coefficients. There are various methods for designing iterative thresholds based on curvelet coefficients. The method given in this embodiment includes:

[0083] Based on the data of each first arrival wave within the time window for each region, the curvelet transform is performed to obtain the curvelet coefficients for each scale and each orientation. The obtained curvelet coefficients are sorted from smallest to largest, and the smallest curvelet coefficient and the largest curvelet coefficient of q% are removed respectively, where 0.1≤q≤0.5. The average value of the remaining curvelet coefficients is calculated.

[0084] Based on the average value of the curve coefficients, design the maximum and minimum values ​​of the iteration threshold;

[0085] The formula for calculating the maximum value of the iteration threshold is:

[0086]

[0087] The formula for calculating the minimum value of the iteration threshold is:

[0088]

[0089] Where γ1 and γ2 represent adjustment parameters, 10≤γ1≤100; 0.1≤γ2≤10; This represents the average value of the curvature coefficients;

[0090] Design the iteration threshold based on the maximum and minimum values ​​of the iteration threshold;

[0091] The formula for calculating the iteration threshold is:

[0092] σ[z]=C max [z]-(d-1)·(C max [z]-C min [z]) / (D-1)

[0093] Where σ[z] represents the iteration threshold of the original curved wave coefficients obtained from the data within the first arrival time window of the z-th region, and d represents the d-th iteration; 1≤d≤D, where D represents the total number of iterations.

[0094] In this embodiment, the minimum and maximum curvature coefficients of q% are removed, mainly to eliminate abnormal curvature coefficients and make the average value of the obtained curvature coefficients more reasonable. In this embodiment, q is set to 0.2, and the average value of the obtained curvature coefficients is...

[0095] The maximum and minimum values ​​of the iteration threshold, γ1 and γ2, are set based on the data signal-to-noise ratio (SNR). The higher the SNR, the larger the values ​​of γ1 and γ2, and vice versa. In this embodiment, γ1 = 15 and γ2 = 1.0, resulting in a maximum iteration threshold C. max [z] = 27.3 and the minimum value C min [z] = 1.82.

[0096] The total number of iterations D in the iteration threshold is also set according to the data signal-to-noise ratio. When the data signal-to-noise ratio is high, the value of D is smaller, and when the data signal-to-noise ratio is low, the value of D is larger. In this embodiment, D = 25.

[0097] S5. Correct the iteration threshold based on the mean value of the root mean square amplitude ratio to obtain the curve wave coefficient iteration threshold;

[0098] The formula for calculating the iteration threshold of the curve coefficient is:

[0099] σ x [z]=σ[z]·(p[z]) r

[0100] Where, σ x [z] represents the iteration threshold of the curve coefficients obtained in the z-th region after curve transformation; r represents the power exponent; p[z] represents the mean of the root mean square amplitude ratio; σ[z] represents the iteration threshold of the original curve coefficients obtained from the data within the first arrival time window of the z-th region.

[0101] In this embodiment, the exponent r is set according to the data signal-to-noise ratio. The higher the data signal-to-noise ratio, the larger the value of r, and vice versa. In this embodiment, r = -1.2.

[0102] S6. The curve wave coefficient is iteratively corrected according to the curve wave coefficient iteration threshold. After the number of iterations is completed, the final curve wave coefficient is obtained. The final curve wave coefficient is inversely transformed to obtain the shot gather data after denoising the data within the first arrival time window.

[0103] The method described in this embodiment specifically includes:

[0104] Based on the curvelet coefficient iteration threshold, the coefficients in the curvelet coefficients that are less than the curvelet coefficient iteration threshold are set to zero to obtain new curvelet coefficients. The new curvelet coefficients are inversely transformed to obtain shot gather data. The shot gather data is then subjected to curvelet transformation to obtain iterative curvelet coefficients. The above iterative process is repeated for the iterative curvelet coefficients. After the number of iterations is completed, the final curvelet coefficients are obtained. The final curvelet coefficients are then inversely transformed to obtain the shot gather data after denoising the data within the first arrival time window.

[0105] Specifically, during the first iteration, based on the curvelet coefficient iteration threshold obtained when d is 1, the coefficients of the curvelet coefficients that are smaller than the iteration coefficients are set to zero to obtain new curvelet coefficients. The new curvelet coefficients are then inversely transformed to obtain the shot gather data. The shot gather data is then subjected to curvelet transformation to obtain the iterative curvelet coefficients.

[0106] During the second iteration, based on the curvelet coefficient iteration threshold obtained when d is 2, the coefficients of the curvelet coefficients obtained above that are less than the curvelet coefficient iteration threshold obtained when d is 2 are set to zero, and new curvelet coefficients are obtained again. The new curvelet coefficients are then inversely transformed to obtain the shot gather data. The shot gather data is then subjected to curvelet transformation to obtain the iterative curvelet coefficients again.

[0107] Repeat the above iterative process for the iterative curve coefficients until the total number of iterations is reached. After the number of iterations is reached, the final curve coefficients are obtained. Perform an inverse transform on the final curve coefficients to obtain the shot gather data after denoising the data within the first arrival time window.

[0108] like Figure 5 The image shows the shot gather data after denoising the data within the first arrival time window obtained in this embodiment, and... Figure 3 In comparison, after iterative processing, a large amount of random noise is suppressed, the first arrival characteristics are enhanced, and the first arrivals of adjacent channels are relatively smooth and continuous.

[0109] Figure 6 The figure shows the result of first arrival picking after denoising the data within the first arrival time window obtained in this embodiment. As can be seen from the figure, the number of pickings has been significantly improved.

[0110] Example 2

[0111] This embodiment describes a device for denoising first-arrival wave data, such as... Figure 7The diagram shown is a block diagram of an apparatus for denoising first-arrival data according to the method of Embodiment 1. The apparatus includes:

[0112] The data acquisition module is used to excite and collect and record seismic single-shot data;

[0113] The first arrival wave time window range determination module is used to design the first arrival wave time window using regional technology, and to determine the range of the first arrival wave time window for each shot in each area of ​​the target work area.

[0114] The root mean square amplitude ratio calculation module is used to obtain the noise window of each first arrival time window based on the range of the first arrival time window of each shot in each region, calculate the root mean square amplitude ratio of the data in the first arrival time window and the data in the noise time window of all channels, and obtain the mean value of the root mean square amplitude ratio.

[0115] The iterative threshold design module is used to perform curvelet transformation on the data within the time window of each first arrival wave for each region to obtain curvelet coefficients, and to design iterative thresholds based on the curvelet coefficients.

[0116] The curve wave coefficient iteration threshold design module corrects the iteration threshold based on the mean value of the root mean square amplitude ratio to obtain the curve wave coefficient iteration threshold.

[0117] The shot gather denoising module is used to iteratively correct the curve wave coefficients according to the curve wave coefficient iteration threshold. After the number of iterations is completed, the final curve wave coefficients are obtained. The final curve wave coefficients are then inversely transformed to obtain the shot gather data after denoising the data within the first arrival time window.

[0118] In this embodiment, the device for denoising first-arrival data is illustrated by the above-mentioned division of functional modules when performing data processing. In actual applications, the above functions can be assigned to different functional modules as needed.

[0119] This embodiment also provides an application of the method for denoising first-arrival wave data based on Embodiment 1 in static correction and near-surface velocity modeling.

[0120] Based on the same inventive concept, such as Figure 8 As shown, this embodiment also provides a computer device, including: at least a processor and a memory, the memory being used to store at least one computer program, the at least one computer program being loaded by the processor and executed as the method for denoising first arrival data of Embodiment 1.

[0121] Based on the same inventive concept, such as Figure 9 As shown, this embodiment also provides a computer-readable storage medium for storing at least one computer program for executing the method for denoising first-arrival data of Embodiment 1.

[0122] Finally, it should be noted that those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods.

[0123] Furthermore, it should be understood that the computer-readable storage medium (e.g., memory) described herein may be volatile memory or non-volatile memory, or may include both volatile memory and non-volatile memory.

[0124] Those skilled in the art will also understand that the various exemplary logic blocks, modules, circuits, and algorithm steps described in conjunction with the disclosure herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the functionality of various illustrative components, blocks, modules, circuits, and steps has been generally described. Whether this functionality is implemented as software or as hardware depends on the specific application and the design constraints imposed on the system as a whole. Those skilled in the art can implement the functionality in various ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the embodiments disclosed herein.

[0125] It should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can still modify the technical solutions described in the above embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for denoising first-arrival wave data, characterized in that, Includes the following steps: Excite and collect / record seismic single-shot data; The first arrival wave time window is designed using a regional technology to determine the range of the first arrival wave time window for each shot in each area of ​​the target work zone. Based on the range of the first arrival time window for each single shot in each region, the noise time window for each first arrival time window is obtained. The root mean square amplitude ratio of the data within the first arrival time window and the data within the noise time window for all channels is calculated, and the mean value of the root mean square amplitude ratio is obtained. Based on the data within the time window of each first arrival wave in each region, perform curvelet transform to obtain curvelet coefficients, and design an iteration threshold based on the curvelet coefficients; The iteration threshold is corrected based on the mean of the root mean square amplitude ratio to obtain the curve wave coefficient iteration threshold; The curve wave coefficients are iteratively corrected according to the curve wave coefficient iteration threshold. After the number of iterations is completed, the final curve wave coefficients are obtained. The final curve wave coefficients are then inversely transformed to obtain the shot gather data after denoising the data within the first arrival time window.

2. The method for denoising first-arrival wave data according to claim 1, characterized in that, The first arrival time window is designed using a regional technique, determining the range of the first arrival time window for each shot in each area of ​​the target work zone, including: Based on surface conditions and data signal-to-noise ratio, seed shots are selected in the target work area, and the first arrival time windows of the seed shot seismic traces are designed in different regions. Based on the first arrival wave time window of each area of ​​the seed gun, the spatial interpolation method is used to determine the range of the first arrival wave time window of each shot in each area of ​​the target work area.

3. The method for denoising first-arrival wave data according to claim 2, characterized in that, Based on surface conditions and data signal-to-noise ratio, seed seismic sources are selected in the target area, and the first arrival time windows of the seed seismic traces are designed regionally, including: Based on surface conditions and signal-to-noise ratio, a seed shot is selected in the target area. Using the firing point of the seed shot as the center point, the shot line as the vertical axis, and the seismic receiver line as the horizontal axis, the seismic gather data from a single shot is divided into Z=2 segments in a rectangular coordinate system. k A set of regions, 0≤k≤4, sort the seismic traces in each region in ascending order of shot-receiver distance, and design the first arrival wave time window for each region.

4. The method for denoising first-arrival wave data according to claim 3, characterized in that, The seismic gather data from a single shot are divided into Z=2 segments in a rectangular coordinate system. k When collecting sets of regions, it is done according to intervals. Divide the rectangular coordinate system into regions; The first arrival time window length is 400–1000 ms.

5. The method for denoising first-arrival wave data according to claim 1, characterized in that, Based on the range of the first arrival time window for each channel in each region, the noise time window for each channel's first arrival time window is obtained. The root mean square amplitude ratio of the data within the first arrival time window to the data within the noise time window for all channels is calculated, and the mean of the root mean square amplitude ratio is obtained, including: Based on the range of the first arrival time window of each shot in each region, noise data with a length equal to the length of the first arrival time window is taken upwards from the top of each first arrival time window as the noise time window; The root mean square amplitude ratio of the first arrival wave data within the time window to the noise data within the time window is calculated for each region, and the mean value of the root mean square amplitude ratio is obtained.

6. The method for denoising first-arrival wave data according to claim 1, characterized in that, Based on the data within the time window of each first arrival wave in each region, a curvelet transform is performed to obtain curvelet coefficients. An iterative threshold is then designed based on these curvelet coefficients, including: Based on the data of each first arrival wave within the time window for each region, the curvelet transform is performed to obtain the curvelet coefficients for each scale and each orientation. The obtained curvelet coefficients are sorted from smallest to largest, and the smallest curvelet coefficient and the largest curvelet coefficient of q% are removed respectively, where 0.1≤q≤0.

5. The average value of the remaining curvelet coefficients is calculated. Based on the average value of the curve coefficients, design the maximum and minimum values ​​of the iteration threshold; Design the iteration threshold based on the maximum and minimum values ​​of the iteration threshold.

7. The method for denoising first-arrival wave data according to claim 6, characterized in that, The formula for calculating the maximum value of the iteration threshold is: The formula for calculating the minimum value of the iteration threshold is: Where γ1 and γ2 represent adjustment parameters, 10≤γ1≤100; 0.1≤γ2≤10; This represents the average value of the curvature coefficients; The formula for calculating the iteration threshold is: σ[z]=Cmax[z]-(d-1)·(Cmax[z]-Cmin[z]) / (D-1) Where σ[z] represents the iteration threshold of the original curved wave coefficients obtained from the data within the first arrival time window of the z-th region, and d represents the d-th iteration; 1≤d≤D, where D represents the total number of iterations.

8. The method for denoising first-arrival wave data according to claim 1, characterized in that, The formula for calculating the iteration threshold of the curve coefficient is: σ x [z]=σ[z]·(p[z]) r Where, σ x [z] represents the iteration threshold of the curve coefficients obtained in the z-th region after curve transformation; r represents the power exponent; p[z] represents the mean of the root mean square amplitude ratio; σ[z] represents the iteration threshold of the original curve coefficients obtained from the data within the first arrival time window of the z-th region.

9. The method for denoising first-arrival wave data according to claim 1, characterized in that, The curvelet coefficients are iteratively corrected based on the curvelet coefficient iteration threshold. After the iteration count is completed, the final curvelet coefficients are obtained. The final curvelet coefficients are then inversely transformed to obtain the shot gather data after denoising the data within the first arrival time window, including: Based on the curvelet coefficient iteration threshold, coefficients smaller than the curvelet coefficient iteration threshold are set to zero to obtain new curvelet coefficients. The new curvelet coefficients are then subjected to inverse transform to obtain shot gather data. Curvelet transform is then performed on the shot gather data to obtain iterative curvelet coefficients. The above iterative process is repeated on the iterative curvelet coefficients. After the number of iterations is completed, the final curvelet coefficients are obtained. The final curvelet coefficients are then subjected to inverse transform to obtain the shot gather data after denoising the data within the first arrival time window.

10. The application of a method for denoising first-arrival wave data based on any one of claims 1 to 9 in static correction and near-surface velocity modeling.

11. An apparatus for denoising first-arrival data, used to implement the method for denoising first-arrival data as described in any one of claims 1 to 9, characterized in that, include: The data acquisition module is used to excite and collect and record seismic single-shot data; The first arrival wave time window range determination module is used to design the first arrival wave time window using regional technology, and to determine the range of the first arrival wave time window for each shot in each area of ​​the target work area. The root mean square amplitude ratio calculation module is used to obtain the noise window of each first arrival time window based on the range of the first arrival time window of each shot in each region, calculate the root mean square amplitude ratio of the data in the first arrival time window and the data in the noise time window of all channels, and obtain the mean value of the root mean square amplitude ratio. The iterative threshold design module is used to perform curvelet transformation on the data within the time window of each first arrival wave for each region to obtain curvelet coefficients, and to design iterative thresholds based on the curvelet coefficients. The curve wave coefficient iteration threshold design module corrects the iteration threshold based on the mean value of the root mean square amplitude ratio to obtain the curve wave coefficient iteration threshold. The shot gather denoising module is used to iteratively correct the curve wave coefficients according to the curve wave coefficient iteration threshold. After the number of iterations is completed, the final curve wave coefficients are obtained. The final curve wave coefficients are then inversely transformed to obtain the shot gather data after denoising the data within the first arrival time window.

12. A computer device, characterized in that, The computer device includes a processor and a memory, the memory being used to store at least one computer program, the at least one computer program being loaded by the processor and executed as described in any one of claims 1 to 9 for the method of denoising first arrival data.

13. A storage medium, characterized in that, The storage medium is used to store at least one computer program, which is used to execute the method for denoising first-arrival data as described in any one of claims 1 to 9.

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