Abnormal first arrival detection and removal method and device, storage medium and electronic equipment

Abstract

The present application relates to the field of earth science, and in particular to an abnormal first arrival detection and removal method, device, storage medium and electronic equipment, the method comprising: collecting seismic data and first arrival data of a target work area; obtaining a two-dimensional array according to the first arrival data; converting the two-dimensional array into a two-dimensional gray image according to the seismic data; performing binaryzation processing on the two-dimensional gray image to obtain a target gray image; traversing the target gray image to obtain the number of connected domains and the number of first arrivals in each connected domain; detecting abnormal first arrivals according to the number of connected domains and the number of first arrivals in each connected domain, and removing the detected abnormal first arrivals. The workload of technical personnel is effectively reduced, the efficiency and accuracy of first arrival data processing are improved, and the quality control effect of first arrival data is improved.

Description

Technical Field

[0001] This invention belongs to the field of Earth Science and relates to petroleum geophysical exploration technology. Specifically, it relates to a method, device, storage medium, and electronic equipment for anomaly first arrival detection and removal based on connected component algorithms applied in the process of seismic data processing. Background Technology

[0002] In the field of petroleum geophysical exploration, first arrival picking is a fundamental and crucial operational step in seismic data processing, playing a vital role in applications such as static correction of refracted waves, shallow refracted wave exploration, and tomographic imaging. First arrival picking constitutes a large proportion of seismic processing work and is also the most tedious step, especially for seismic data from areas with complex near-surface conditions and low signal-to-noise ratios. The difficulty and time cost of manual picking increase exponentially, and the accuracy and efficiency are difficult to guarantee. Therefore, automated first arrival picking has become one of the important issues for seismic exploration work under complex near-surface conditions. To date, although various techniques for automated first arrival picking exist in this field, such as correlation methods, fractal dimension methods, and neural network methods, their application results are often not ideal, especially for first arrival picking of seismic data in complex areas, where there is still significant room for improvement in accuracy and stability. At the same time, the explosive growth in exploration data volume brought about by advancements in acquisition technology has also made processing efficiency for massive seismic datasets a new challenge.

[0003] First-arrival acquisition is the most time-consuming and labor-intensive step in seismic data processing. With the surge in acquired data volume and the widespread application of controlled-source seismic data, the processing time for first-arrival acquisition is increasing, severely impacting the project's cycle time and processing quality. Most existing first-arrival acquisition methods focus on automatic acquisition, with little attention paid to automatic quality control of the data. While commercial software offers some quality control methods, these often rely on manual intervention, such as identifying and removing or correcting abnormal first-arrivals. This process is not only labor-intensive but also inefficient.

[0004] In recent years, with the surge in seismic data, how to quickly control the quality of first arrival picking results has become one of the most pressing technical issues for relevant personnel. Most existing commercial applications rely on manual interaction for first arrival quality control. While various tools are provided for quality control, they haven't effectively reduced the workload of technical personnel, nor have they significantly improved the accuracy and effectiveness of quality management. Faced with massive amounts of seismic data, manual processing greatly increases the workload for technical personnel, and it's also difficult to guarantee the accuracy and validity of the final results, hindering subsequent work. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides a method, apparatus, storage medium, and electronic device for detecting and removing abnormal first arrivals. These devices can automatically detect and remove anomalies in automatically acquired first arrival results, effectively reducing the workload of technical personnel, improving the efficiency and accuracy of seismic data processing, and enhancing the quality control of first arrival data.

[0006] In a first aspect, the present invention provides a method for detecting and removing abnormal arrivals, the method comprising:

[0007] Collect seismic data and first arrival data for the target work area;

[0008] Based on the initial arrival data, a two-dimensional array is obtained;

[0009] Based on the earthquake data, the two-dimensional array is converted into a two-dimensional grayscale image;

[0010] The two-dimensional grayscale image is binarized to obtain the target grayscale image;

[0011] Traverse the target grayscale image to obtain the number of connected components and the number of initial arrivals in each connected component;

[0012] Based on the number of connected components and the number of first arrivals within each connected component, abnormal first arrivals are detected and removed.

[0013] In some embodiments, the two-dimensional array stores the initial arrival offset and the initial arrival time.

[0014] In some embodiments, converting the two-dimensional array into a two-dimensional grayscale image based on the seismic data includes:

[0015] The time interval is obtained based on the track spacing and surface velocity, wherein the seismic data includes the track spacing and surface velocity of the target work area;

[0016] The two-dimensional array is converted into a two-dimensional grayscale image based on the channel spacing and the time interval.

[0017] In some embodiments, before converting the two-dimensional array into a two-dimensional grayscale image based on the seismic data, the method further includes:

[0018] Traverse the two-dimensional array to obtain the initial maximum offset distance off. max Minimum offset distance min Maximum initial arrival time fa max Minimum arrival time fa min .

[0019] In some embodiments, the two-dimensional grayscale image includes:

[0020] With the offset distance as the horizontal axis, the number of pixels in the two-dimensional grayscale image along the offset distance direction is: (off) max -off min ) / Δd+1, where Δd is the track spacing;

[0021] With the arrival time as the vertical axis, the number of pixels in the two-dimensional grayscale image along the arrival time direction is: (fa max -fa min ) / Δt+1, where Δt is the time interval.

[0022] In some embodiments, binarizing the two-dimensional grayscale image to obtain the target grayscale image includes:

[0023] Traverse the two-dimensional grayscale image to obtain the initial number of arrivals in each pixel and the maximum and minimum initial number of arrivals in a single pixel;

[0024] The grayscale threshold value is obtained based on the earthquake data and the maximum and minimum number of first arrivals within a single pixel.

[0025] When the number of initial arrivals within a pixel is greater than the grayscale threshold value, it is marked as the first grayscale value;

[0026] When the number of initial values ​​within a pixel is less than or equal to the grayscale threshold value, it is marked as the second grayscale value.

[0027] In some embodiments, detecting abnormal first arrivals based on the number of connected components and the number of first arrivals within each connected component, and removing the detected abnormal first arrivals, includes:

[0028] Abnormal connected components are obtained based on the number of connected components and the number of initial arrivals within each connected component;

[0029] Obtain all abnormal pixels in the abnormal connected component;

[0030] Obtain the initial arrival of all abnormalities within all the aforementioned abnormal pixels;

[0031] Reset all exceptions to invalid values.

[0032] In a second aspect, the present invention provides an apparatus comprising:

[0033] The acquisition module is used to acquire seismic data and first arrival data for the target work area;

[0034] The first acquisition module is used to obtain a two-dimensional array based on the initial arrival data;

[0035] A conversion module is used to convert the two-dimensional array into a two-dimensional grayscale image based on the seismic data;

[0036] The processing module is used to perform binarization processing on the two-dimensional grayscale image to obtain the target grayscale image;

[0037] The second acquisition module is used to traverse the target grayscale image and acquire the number of connected components and the number of initial arrivals in each connected component;

[0038] The removal module is used to detect abnormal first arrivals based on the number of connected components and the number of first arrivals in each connected component, and to remove the detected abnormal first arrivals.

[0039] In a third aspect, the present invention provides a storage medium storing a computer program that, when executed by a processor, implements the anomaly initial arrival detection and removal method described above.

[0040] In a fourth aspect, the present invention provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that, when executed by the processor, implements the abnormal arrival detection and removal method described above.

[0041] Compared with the prior art, the technical solution of the present invention has the following advantages or beneficial effects:

[0042] Automatic anomaly detection and removal are performed on the automatically picked initial arrival results, effectively reducing the workload of technicians; the number of connected components in the binary grayscale image is obtained based on the connected component algorithm, and the number of invalid connected components and the number of all abnormal initial arrivals in the invalid connected components are identified, which improves the efficiency and accuracy of initial arrival data processing; abnormal initial arrivals in the original initial arrival data are removed, improving the quality control effect of the initial arrival data. Attached Figure Description

[0043] 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 embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0044] Figure 1 A flowchart of the anomaly initial arrival removal method provided in the embodiments of this application;

[0045] Figure 2 The effect diagram of the automatic picking initial arrival results provided in the embodiments of this application is displayed in the time offset intersection diagram;

[0046] Figure 3 The effect diagram of automatically picking the first arrival results provided in the embodiment of this application is displayed in the shot collection record;

[0047] Figure 4 The results of removing anomalies provided in the embodiments of this application are shown in the time offset intersection diagram.

[0048] Figure 5 The results of removing abnormal initial arrivals provided in the embodiments of this application are shown in the shot collection record.

[0049] Figure 6 This is a schematic diagram of the structure of a device provided in an embodiment of this application;

[0050] Figure 7 This is a connection block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0051] The embodiments of this application will be described in detail below with reference to the accompanying drawings and examples, so that the implementation process of how the present invention uses technical means to solve technical problems and achieve corresponding technical effects can be fully understood and implemented accordingly. The embodiments of this application and the various features in the embodiments can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this application.

[0052] As the background technology indicates, in the field of petroleum geophysical exploration, first arrival picking is a fundamental and crucial operational step in seismic data processing, playing a vital role in applications such as static correction of refracted waves, shallow refracted wave exploration, and tomographic imaging exploration. First arrival picking constitutes a large proportion of seismic processing work and is also the most tedious step, especially for seismic data from areas with complex near-surface conditions and low signal-to-noise ratios. The difficulty and time cost of manual picking increase exponentially, and the accuracy and efficiency of picking are difficult to guarantee. Therefore, automated first arrival picking has become one of the important issues for seismic exploration work under complex near-surface conditions. To date, although various techniques for automated picking exist in this field, such as correlation methods, fractal dimension methods, and neural network methods, their application results are often not ideal, especially for first arrival picking of seismic data in complex areas, where there is still considerable room for improvement in terms of accuracy and stability. At the same time, the explosive growth in exploration data volume brought about by advancements in acquisition technology has also made processing efficiency for massive seismic datasets a new challenge.

[0053] First-arrival acquisition is the most time-consuming and labor-intensive step in seismic data processing. With the surge in acquired data volume and the widespread application of controlled-source seismic data, the processing time for first-arrival acquisition is increasing, severely impacting the project's cycle time and processing quality. Most existing first-arrival acquisition methods focus on automatic acquisition, with little attention paid to automatic quality control of the data. While commercial software offers some quality control methods, these often rely on manual intervention, such as identifying and removing or correcting abnormal first-arrivals. This process is not only labor-intensive but also inefficient.

[0054] In recent years, with the surge in seismic data, how to quickly control the quality of first arrival picking results has become one of the most pressing technical issues for relevant personnel. Most existing commercial applications rely on manual interaction for first arrival quality control. While various tools are provided for quality control, they haven't effectively reduced the workload of technical personnel, nor have they significantly improved the accuracy and effectiveness of quality management. Faced with massive amounts of seismic data, manual processing greatly increases the workload for technical personnel, and it's also difficult to guarantee the accuracy and validity of the final results, hindering subsequent work.

[0055] In view of this, the present invention proposes an abnormal first arrival detection and removal method based on connected component algorithm, which can automatically detect and remove anomalies in automatically picked first arrival results, effectively reduce the workload of technicians, improve the efficiency and accuracy of seismic data processing, and enhance the quality control effect of first arrival data.

[0056] Example 1

[0057] This embodiment provides a method for detecting and removing initial arrivals of anomalies. Figure 1 A flowchart illustrating the anomaly initial arrival removal method provided in this application embodiment. For example... Figure 1 As shown, the method in this embodiment may include the following steps:

[0058] S100: Collect seismic data and first arrival data for the target work area.

[0059] In the process of seismic exploration, the first step is to collect seismic data of the target area and save the collected seismic data. In addition, the first arrival data is automatically picked up for the target area.

[0060] It should be noted that the collected initial arrival data can be saved in a file. This file can store the initial arrival data for multiple tracks or the initial arrival data for the entire target work area. It can be saved according to actual needs, and there is no limitation here.

[0061] It should be further noted that the track spacing and surface velocity are related to the actual geological conditions of the target work area and can be obtained from the seismic data of the target work area. The surface velocity is related to the geology of the target work area, and different geological conditions result in different surface velocities. Track spacing includes the distance between seismic tracks.

[0062] S200: Obtain a two-dimensional array based on the initial arrival data.

[0063] In some embodiments, the two-dimensional array stores the initial arrival offset and initial arrival time.

[0064] For example, let the two-dimensional array be array. The offset of the first arrival can be stored in array[0][0], the arrival time of the first arrival can be stored in array[0][1], the offset of the second arrival can be stored in array[1][0], the arrival time of the second arrival can be stored in array[1][1], and so on. These will not be listed one by one here. Of course, other methods can also be used to store the offset, which is not limited here.

[0065] In some embodiments, each arrival time in the file can be traversed to generate a table with a two-dimensional array as the key and a single arrival time record as the value. The maximum / minimum offset can then be obtained using the offset and arrival time data stored in this table. max off min ) and maximum / minimum arrival time (fa max fa min ), where off maxMaximum offset, off min Minimum offset distance, fa max Maximum arrival time, fa min Minimum arrival time.

[0066] S300: Converts a two-dimensional array into a two-dimensional grayscale image based on seismic data.

[0067] In some embodiments, converting a two-dimensional array into a two-dimensional grayscale image based on seismic data includes:

[0068] The time interval is obtained based on the track spacing and surface velocity, where the seismic data includes the track spacing and surface velocity of the target work area;

[0069] The two-dimensional array is converted into a two-dimensional grayscale image based on the channel spacing and time interval.

[0070] In some embodiments, before converting the two-dimensional array into a two-dimensional grayscale image based on seismic data, the method further includes:

[0071] Traverse the two-dimensional array to obtain the maximum offset. max Minimum offset distance min and maximum initial arrival time fa max Minimum arrival time fa min .

[0072] In some embodiments, a two-dimensional grayscale image includes:

[0073] With the offset distance as the horizontal axis, the number of pixels in the 2D grayscale image along the offset distance direction is: (off) max -off min ) / Δd+1, where Δd is the track spacing;

[0074] With the first arrival time as the vertical axis, the number of pixels in the two-dimensional grayscale image along the first arrival time direction is: (fa max -fa min ) / Δt+1, where Δt is the time interval.

[0075] Specifically, the time interval Δt can be obtained by the following formula:

[0076] Δt=Δd / v*1000

[0077] The unit is milliseconds (ms), where Δd is the track spacing and v is the ground velocity in the target work area.

[0078] It should be noted that in a two-dimensional grayscale image, the grayscale value of each pixel is related to the number of initial arrivals falling within that pixel.

[0079] For any pixel (off)i ,fa j ), count the number of initial arrivals (faN) within this pixel. ij For the initial arrival of the kth path, when At the same time At that time, the k-th channel first arrives at pixel (off) i fa i On, where offset k fa represents the offset of the first arrival at the k-th track. k This represents the arrival time of the kth channel, in milliseconds (ms).

[0080] S400: Performs binarization processing on a two-dimensional grayscale image to obtain the target grayscale image.

[0081] In some embodiments, binarizing a two-dimensional grayscale image to obtain a target grayscale image includes:

[0082] Traverse the 2D grayscale image to obtain the initial number of arrivals in each pixel and the maximum and minimum initial number of arrivals in a single pixel;

[0083] The grayscale threshold value is obtained based on earthquake data and the maximum and minimum number of first arrivals within a single pixel.

[0084] When the number of initial values ​​in a pixel is greater than the grayscale threshold, it is marked as the first grayscale value;

[0085] When the number of initial values ​​within a pixel is less than or equal to the grayscale threshold, it is marked as the second grayscale value.

[0086] Specifically, the target grayscale image is the image obtained after binarizing the two-dimensional grayscale image. Iterate through all the pixels in the target grayscale image to obtain the initial arrival of each pixel, as well as the maximum and minimum number of initial arrivals in each pixel, and save the obtained data into the pixel initial arrival table.

[0087] It should be noted that the first grayscale value can be set to 255, and the second grayscale value can be set to 0. In actual application, these values ​​can be set according to actual needs.

[0088] S500: Traverse the target grayscale image to obtain the number of connected components and the number of initial arrivals in each connected component.

[0089] Specifically, iterate through all pixels in the target grayscale image to obtain the number of connected components and the number of initial nodes in each connected component.

[0090] In some embodiments, all pixels in the target grayscale image are traversed, and the number of connected components and the number of initial arrivals in each connected component are obtained based on the connected component algorithm.

[0091] S600: Based on the number of connected components and the number of first arrivals in each connected component, detect abnormal first arrivals and remove the detected abnormal first arrivals.

[0092] In some embodiments, abnormal first arrivals are detected based on the number of connected components and the number of first arrivals within each connected component, and the detected abnormal first arrivals are removed, including:

[0093] Identify abnormal connected components based on the number of connected components and the number of initial arrivals within each connected component;

[0094] Obtain all abnormal pixels in the abnormal connected components;

[0095] Obtain all initial arrivals of all abnormal pixels;

[0096] Reset all exception initial values ​​to invalid values.

[0097] The target grayscale image is labeled and stored using a connected component algorithm to obtain the initial number of elements in each connected component, denoted as cDSet:

[0098] (connectedDomainNum1, connectedDomainNum2,...connectedDomainNum n ),

[0099] Where n represents the number of connected domains, and for any connected domain i, the number of initial arrivals in its connected domain is denoted as connectedDomainNum. i .

[0100] Sort cDSet in ascending order from smallest to largest, and calculate the values ​​in sequence. The value of , where k∈{n-1,…,1}, if when s k <threshold ab When *S, all connected components with indices {k, ..., 1} are designated as invalid connected components (i.e., abnormal connected components). The first arrivals in invalid connected components are considered abnormal first arrivals, and all first arrivals in invalid connected components are set to invalid values. Here, threshold... ab ∈(0,1) represents the threshold value, and S represents the total number of initial arrivals involved in the calculation.

[0101] After all connected regions with indices {k,…,1} are marked as invalid connected regions, the pixels corresponding to all invalid connected regions with indices {k,…,1} are saved. Then, the initial arrival of each pixel is obtained from the saved pixel initial arrival table. For any initial arrival, the corresponding initial arrival is found according to its corresponding field record number and channel number, and the value of the initial arrival is set to invalid.

[0102] In this embodiment, the input data is the initial arrival result automatically picked from a file, and the output data is the result after optimizing the input data and removing abnormal initial arrivals. Abnormal initial arrivals are removed from the input initial arrival data. The entire processing is completed automatically without any extra manual operation, which not only has high convenience but also a high degree of intelligence.

[0103] This embodiment acquires seismic data and first arrival data for the target work area; obtains a two-dimensional array based on the first arrival data; converts the two-dimensional array into a two-dimensional grayscale image based on the seismic data; performs binarization processing on the two-dimensional grayscale image to obtain the target grayscale image; traverses the target grayscale image to obtain the number of connected components and the number of first arrivals within each connected component; detects abnormal first arrivals based on the number of connected components and the number of first arrivals within each connected component, and removes the detected abnormal first arrivals. This method can automatically detect and remove anomalies in automatically acquired first arrival results, effectively reducing the workload of technical personnel, improving the efficiency and accuracy of seismic data processing, and enhancing the quality control effect of first arrival data.

[0104] Example 2

[0105] This embodiment is a specific example provided by the present invention. In this embodiment, abnormal first arrivals in the original first arrival data are effectively removed, verifying the rationality, efficiency and accuracy of the abnormal first arrival detection and removal method provided by the present invention.

[0106] In this embodiment, a target work area with low signal-to-noise ratio characteristics in seismic data is selected for verification.

[0107] In the seismic data used in this embodiment, the file storing first arrival data contains 452 shots and a total of 1,375,506 traces of first arrival data. First, 1,375,506 tables are formed using (offset, first arrival time) as the key and the first arrival information of each trace as the value. The maximum / minimum offset (offmax, offmin) and the maximum / minimum first arrival time (famax, famin) in the tables are then calculated. Where, off... max Maximum offset, off min Minimum offset distance, fa max Maximum arrival time, fa min Minimum arrival time.

[0108] With offset distance as the horizontal axis, the number of pixels in the 2D grayscale image along the offset distance direction is: (offmax - offmin) / Δd + 1, where Δd is the trace spacing. With first arrival time as the vertical axis, the number of pixels in the 2D grayscale image along the first arrival time direction is: (famax - famin) / Δt + 1, where Δt is the time interval. Trace spacing and surface velocity are obtained from the seismic data of the work area. Based on trace spacing Δd = 20 and time interval Δt = 10, all first arrival data (offset distance, first arrival time) are converted into a 2D grayscale image.

[0109] In some embodiments, the time interval Δt can be obtained by the following formula:

[0110] Δt=Δd / v*1000

[0111] The unit is milliseconds (ms), where Δd is the track spacing and v is the ground velocity in the target work area.

[0112] The number of pixels in the offset direction is 903, and the number of pixels in the initial arrival direction is 266. Then, the initial arrival count in each pixel is counted, and the two-dimensional grayscale image is binarized to obtain the target grayscale image. Specifically, when the initial arrival count in a pixel is greater than the grayscale threshold value, it is marked as the first grayscale value, and when the initial arrival count in a pixel is less than or equal to the grayscale threshold value, it is marked as the second grayscale value. The first grayscale value is set to 255, and the second grayscale value is set to 0.

[0113] In some embodiments, the grayscale threshold value is obtained from seismic data and the maximum and minimum first arrivals within each pixel.

[0114] Traverse the target grayscale image to obtain the number of connected components and the number of initial arrivals in each connected component. Based on the target grayscale image, the number of connected components can be obtained as 473. Abnormal connected components are obtained based on the number of connected components and the number of initial arrivals in each connected component. And, obtain all abnormal pixels corresponding to the abnormal connected components. Figure 2 The effect diagram of the automatic picking initial arrival results provided in the embodiments of this application is displayed in the time offset intersection diagram, such as... Figure 2 As shown, 471 connected components are identified as small connected components, which are considered abnormal connected components. The first arrivals in these abnormal connected components are considered abnormal first arrivals, totaling 2759 abnormal first arrival points, accounting for 0.2% of the total data.

[0115] It should be noted that in a two-dimensional grayscale image, the grayscale value of each pixel is related to the number of initial arrivals falling within that pixel.

[0116] For any pixel (off) i ,fa j), count the number of initial arrivals (faN) within this pixel. ij For the initial arrival of the kth path, when At the same time When the k-th channel first arrives at pixel (off) i fa i On, where offset k fa represents the offset of the first arrival at the k-th track. k This represents the arrival time of the kth channel, in milliseconds (ms).

[0117] The target grayscale image is labeled and stored using a connected component algorithm to obtain the initial number of elements in each connected component, denoted as cDSet:

[0118] (connectedDomainNum1, connectedDomainNum2,...connectedDomainNum n ), where n represents the number of connected domains. For any connected domain i, the number of initial arrivals in its connected domain is denoted as connectedDomainNum. i .

[0119] Sort cDSet in ascending order from smallest to largest, and calculate the values ​​in sequence. The value of , where k∈{n-1,…,1}, if when s k <threshold ab When *S, all connected components with indices {k, ..., 1} are designated as invalid connected components (i.e., abnormal connected components). The first arrivals in invalid connected components are considered abnormal first arrivals, and all first arrivals in invalid connected components are set to invalid values. Here, threshold... ab ∈(0,1) represents the threshold value, and S represents the total number of initial arrivals involved in the calculation.

[0120] After all connected regions with indices {k,…,1} are marked as invalid connected regions, the pixels corresponding to all invalid connected regions with indices {k,…,1} are saved. Then, the initial arrival of each pixel is obtained from the saved pixel initial arrival table. For any initial arrival, the corresponding initial arrival is found according to its corresponding field record number and channel number, and the value of the initial arrival is set to invalid.

[0121] Figure 3 The effect diagram of automatically picking the first arrival results provided in the embodiment of this application is displayed in the shot gathering record, such as... Figure 3 As shown, there is an incorrect initial arrival position in the middle, which is an abnormal initial arrival.

[0122] Figure 4The results of removing anomalies provided in the embodiments of this application are shown in the time offset intersection diagram, as follows: Figure 4 As shown, when the initial arrivals in the 471 small connected components are set to invalid values, the remaining initial arrivals exhibit a relatively reasonable distribution.

[0123] Figure 5 The effect diagram of removing abnormal first arrival results provided in the embodiments of this application is shown in the shot collection record, such as... Figure 5 As shown, this is the effect exhibited by the remaining initial terminations after the initial terminations in the 471 small connected components are set to invalid values.

[0124] In this embodiment, the input data is the initial arrival result automatically picked from a file, and the output data is the result after optimizing the input data and removing abnormal initial arrivals. Abnormal initial arrivals are removed from the input initial arrival data. The entire process is completed automatically without any extra manual operation. It is not only highly convenient but also highly intelligent, effectively verifying the convenience, effectiveness, and quality control effect of the abnormal initial arrival detection and removal method provided by this invention.

[0125] Example 3

[0126] Another aspect of the present invention provides an apparatus, Figure 6 This is a schematic diagram of the structure of a device provided in an embodiment of this application. Figure 6 As shown, the apparatus of this embodiment may include:

[0127] The acquisition module 201 is used to acquire seismic data and first arrival data of the target work area.

[0128] In the process of seismic exploration, the first step is to collect seismic data of the target area and save the collected seismic data. In addition, the first arrival data is automatically picked up for the target area.

[0129] It should be noted that the collected initial arrival data can be saved in a file. This file can store initial arrival data for multiple tracks or for a specific work area. The file can be saved according to actual needs, and there are no restrictions here.

[0130] It should be further noted that the track spacing and surface velocity are related to the actual geological conditions of the target work area and can be obtained from the seismic data of the target work area. The surface velocity is related to the geology of the target work area, and different geological conditions result in different surface velocities. Track spacing includes the distance between seismic tracks.

[0131] The first acquisition module 202 is used to obtain a two-dimensional array based on the initial arrival data.

[0132] In some embodiments, the two-dimensional array stores the initial arrival offset and initial arrival time.

[0133] In some embodiments, each arrival time in the file can be traversed to generate a table with a two-dimensional array as the key and a single arrival time record as the value. The maximum / minimum offset can then be obtained using the offset and arrival time data stored in this table. max off min ) and maximum / minimum arrival time (fa max fa min ), where off max Maximum offset, off min Minimum offset distance, fa max Maximum arrival time, fa min Minimum arrival time.

[0134] In some embodiments, the two-dimensional array obtained by the first acquisition module 202 stores the initial arrival offset and the initial arrival time.

[0135] The conversion module 203 is used to convert a two-dimensional array into a two-dimensional grayscale image based on seismic data.

[0136] In some embodiments, the conversion module 203 converts a two-dimensional array into a two-dimensional grayscale image based on seismic data, including:

[0137] The time interval is obtained based on the track spacing and surface velocity, where the seismic data includes the track spacing and surface velocity of the target work area;

[0138] The two-dimensional array is converted into a two-dimensional grayscale image based on the channel spacing and time interval.

[0139] In some embodiments, before converting the two-dimensional array into a two-dimensional grayscale image based on seismic data, the method further includes:

[0140] Traverse the two-dimensional array to obtain the initial maximum offset distance off. max Minimum offset distance min Maximum initial arrival time fa max Minimum arrival time fa min .

[0141] In some embodiments, a two-dimensional grayscale image includes:

[0142] With the offset distance as the horizontal axis, the number of pixels in the 2D grayscale image along the offset distance direction is: (off) max -off min ) / Δd+1, where Δd is the track spacing;

[0143] With the first arrival time as the vertical axis, the number of pixels in the two-dimensional grayscale image along the first arrival time direction is: (fa max -fa min ) / Δt+1, where Δt is the time interval.

[0144] Specifically, the time interval Δt can be obtained by the following formula:

[0145] Δt=Δd / v*1000

[0146] The unit is milliseconds (ms), where Δd is the track spacing and v is the ground velocity in the target work area.

[0147] It should be noted that in a two-dimensional grayscale image, the grayscale value of each pixel is related to the number of initial arrivals falling within that pixel.

[0148] For any pixel (off) i ,fa j ), count the number of initial arrivals (faN) within this pixel. ij For the initial arrival of the kth path, when At the same time At that time, the k-th channel first arrives at pixel (off) i fa i On, where offset k fa represents the offset of the first arrival at the k-th track. k This represents the arrival time of the kth channel, in milliseconds (ms).

[0149] The processing module 204 is used to perform binarization processing on the two-dimensional grayscale image to obtain the target grayscale image.

[0150] In some embodiments, the processing module 204 performs binarization processing on the two-dimensional grayscale image to obtain a target grayscale image, including:

[0151] Traverse the 2D grayscale image to obtain the initial number of arrivals in each pixel and the maximum and minimum initial number of arrivals in a single pixel;

[0152] The grayscale threshold value is obtained based on earthquake data and the maximum and minimum number of initial arrivals within a single pixel.

[0153] When the number of initial values ​​in a pixel is greater than the grayscale threshold, it is marked as the first grayscale value;

[0154] When the number of initial values ​​within a pixel is less than or equal to the grayscale threshold, it is marked as the second grayscale value.

[0155] Specifically, the target grayscale image is the image obtained after binarizing the two-dimensional grayscale image. Iterate through all the pixels in the target grayscale image to obtain the initial arrival count of each pixel, as well as the maximum and minimum initial arrival counts of each pixel, and save the obtained data into the pixel initial arrival table.

[0156] It should be noted that the first grayscale value can be set to 255, and the second grayscale value can be set to 0. In actual application, these values ​​can be set according to actual needs.

[0157] The second acquisition module 205 is used to traverse the target grayscale image and obtain the number of connected components and the number of initial arrivals in each connected component.

[0158] Specifically, the second acquisition module 205 is used to traverse all pixels in the target grayscale image to obtain the number of connected components and the number of initial nodes in each connected component.

[0159] In some embodiments, the second acquisition module 205 is used to traverse all pixels in the target grayscale image and obtain the number of connected components and the number of initial arrivals in each connected component based on the connected component algorithm.

[0160] The removal module 206 is used to detect abnormal first arrivals based on the number of connected components and the number of first arrivals in each connected component, and to remove the detected abnormal first arrivals.

[0161] In this embodiment, the removal module 206 can detect abnormal first arrivals in the connected domain and remove the detected abnormal first arrivals.

[0162] In some embodiments, the apparatus further includes: a connected component acquisition submodule, a pixel acquisition submodule, and an assignment submodule.

[0163] Specifically, the connected component acquisition submodule is used to acquire abnormal connected components based on the number of connected components and the number of initial arrivals within each connected component;

[0164] The pixel acquisition submodule is used to obtain all abnormal pixels corresponding to the abnormal connected components;

[0165] The assignment submodule is used to obtain the initial values ​​of all abnormal pixels and reset the initial values ​​to invalid values.

[0166] The target grayscale image is labeled and stored using a connected component algorithm to obtain the initial number of elements in each connected component, denoted as cDSet:

[0167] (connectedDomainNum1, connectedDomainNum2,...connectedDomainNum n ),

[0168] Where n represents the number of connected domains, and for any connected domain i, the number of initial arrivals in its connected domain is denoted as connectedDomainNum. i .

[0169] Sort cDSet in ascending order from smallest to largest, and calculate the values ​​in sequence. The value of , where k∈{n-1,…,1}, if when s k <threshold ab When *S, all connected components with indices {k, ..., 1} are designated as invalid connected components (abnormal connected components). The first arrivals in invalid connected components are considered abnormal first arrivals, and the first arrivals in all invalid connected components are set to invalid values. Here, threshold... ab ∈(0,1) represents the threshold value, and S represents the total number of initial arrivals involved in the calculation.

[0170] After all connected components with indices {k,…,1} are marked as invalid connected components (abnormal connected components), the pixels corresponding to all invalid connected components with indices {k,…,1} are saved. Then, the initial arrival of each pixel is obtained from the saved pixel initial arrival table. For any initial arrival, the corresponding initial arrival is found according to its corresponding field record number and channel number, and the value of the initial arrival is set to invalid.

[0171] In this embodiment, the input data is the initial arrival result automatically picked from a file, and the output data is the result after optimizing the input data and removing abnormal initial arrivals. Abnormal initial arrivals are removed from the input initial arrival data. The entire processing is completed automatically without any extra manual operation, which not only has high convenience but also a high degree of intelligence.

[0172] This embodiment employs an acquisition module to collect seismic data and first arrival data for the target work area; a first acquisition module to obtain a two-dimensional array based on the first arrival data; a conversion module to convert the two-dimensional array into a two-dimensional grayscale image based on the seismic data; a processing module to binarize the two-dimensional grayscale image to obtain a target grayscale image; a second acquisition module to traverse the target grayscale image to obtain the number of connected components and the number of first arrivals within each connected component; and a removal module to detect and remove abnormal first arrivals based on the number of connected components and the number of first arrivals within each connected component. This allows for automatic anomaly detection and removal of automatically acquired first arrival results, effectively reducing the workload of technical personnel, improving the efficiency and accuracy of seismic data processing, and enhancing the quality control effect of first arrival data.

[0173] Example 4

[0174] Another aspect of the present invention provides a storage medium storing a computer program, which, when executed by a processor, implements the above-described method for detecting and removing anomalies upon arrival.

[0175] The process involves collecting seismic and first-arrival data for the target work area; obtaining a two-dimensional array based on the first-arrival data; converting the two-dimensional array into a two-dimensional grayscale image based on the seismic data; binarizing the two-dimensional grayscale image to obtain the target grayscale image; traversing the target grayscale image to obtain the number of connected components and the number of first-arrivals within each connected component; detecting abnormal first-arrivals based on the number of connected components and the number of first-arrivals within each connected component, and removing the detected abnormal first-arrivals.

[0176] In some embodiments, the two-dimensional array stores the initial arrival offset and initial arrival time.

[0177] In some embodiments, converting a two-dimensional array into a two-dimensional grayscale image based on seismic data includes:

[0178] The time interval is obtained based on the track spacing and surface velocity, where the seismic data includes the track spacing and surface velocity of the target work area;

[0179] The two-dimensional array is converted into a two-dimensional grayscale image based on the channel spacing and time interval.

[0180] In some embodiments, before converting the two-dimensional array into a two-dimensional grayscale image based on seismic data, the method further includes:

[0181] Traverse the two-dimensional array to obtain the initial maximum offset distance off. max Minimum offset distance min Maximum initial arrival time fa max Minimum arrival time fa min .

[0182] In some embodiments, a two-dimensional grayscale image includes:

[0183] With the offset distance as the horizontal axis, the number of pixels in the 2D grayscale image along the offset distance direction is: (off) max -off min ) / Δd+1, where Δd is the track spacing;

[0184] With the first arrival time as the vertical axis, the number of pixels in the two-dimensional grayscale image along the first arrival time direction is: (fa max -fa min ) / Δt+1, where Δt is the time interval.

[0185] In some embodiments, binarizing a two-dimensional grayscale image to obtain a target grayscale image includes:

[0186] Traverse the 2D grayscale image to obtain the initial number of arrivals in each pixel and the maximum and minimum initial number of arrivals in a single pixel;

[0187] The grayscale threshold value is obtained based on earthquake data and the maximum and minimum number of first arrivals within a single pixel.

[0188] When the number of initial values ​​in a pixel is greater than the grayscale threshold, it is marked as the first grayscale value;

[0189] When the number of initial values ​​within a pixel is less than or equal to the grayscale threshold, it is marked as the second grayscale value.

[0190] In some embodiments, abnormal first arrivals are detected based on the number of connected components and the number of first arrivals within each connected component, and the detected abnormal first arrivals are removed, including:

[0191] Identify abnormal connected components based on the number of connected components and the number of initial arrivals within each connected component;

[0192] Obtain all abnormal pixels in the abnormal connected components;

[0193] Obtain all initial arrivals of all abnormal pixels;

[0194] Reset all exception initial values ​​to invalid values.

[0195] The target grayscale image is labeled and stored using a connected component algorithm to obtain the initial number of elements in each connected component, denoted as cDSet:

[0196] (connectedDomainNum1, connectedDomainNum2,...connectedDomainNum n ),

[0197] Where n represents the number of connected domains, and for any connected domain i, the number of initial arrivals in its connected domain is denoted as connectedDomainNum. i .

[0198] Sort cDSet in ascending order from smallest to largest, and calculate the values ​​in sequence. The value of , where k∈{n-1,…,1}, if when s k <threshold ab When *S, all connected components with indices {k, ..., 1} are designated as invalid connected components (abnormal connected components). The first arrivals in invalid connected components are considered abnormal first arrivals, and the first arrivals in all invalid connected components are set to invalid values. Here, threshold... ab ∈(0,1) represents the threshold value, and S represents the total number of initial arrivals involved in the calculation.

[0199] After all connected components with indices {k,…,1} are marked as invalid connected components (abnormal connected components), the pixels corresponding to all invalid connected components with indices {k,…,1} are saved. Then, the initial arrival of each pixel is obtained from the saved pixel initial arrival table. For any initial arrival, the corresponding initial arrival is found according to its corresponding field record number and channel number, and the value of the initial arrival is set to invalid.

[0200] Example 5

[0201] Another aspect of the present invention provides an electronic device, Figure 7 A connection block diagram of an electronic device provided in an embodiment of this application, such as... Figure 7 As shown, the electronic device 500 may include a processor 501 and a memory 502, wherein the memory 502 stores a computer program, and when the computer program is executed by the processor, it implements the anomaly detection and removal method described above:

[0202] The process involves collecting seismic and first-arrival data for the target work area; obtaining a two-dimensional array based on the first-arrival data; converting the two-dimensional array into a two-dimensional grayscale image based on the seismic data; binarizing the two-dimensional grayscale image to obtain the target grayscale image; traversing the target grayscale image to obtain the number of connected components and the number of first-arrivals within each connected component; detecting abnormal first-arrivals based on the number of connected components and the number of first-arrivals within each connected component, and removing the detected abnormal first-arrivals.

[0203] In some embodiments, the two-dimensional array stores the initial arrival offset and initial arrival time.

[0204] In some embodiments, converting a two-dimensional array into a two-dimensional grayscale image based on seismic data includes:

[0205] The time interval is obtained based on the track spacing and surface velocity, where the seismic data includes the track spacing and surface velocity of the target work area;

[0206] The two-dimensional array is converted into a two-dimensional grayscale image based on the channel spacing and time interval.

[0207] In some embodiments, before converting the two-dimensional array into a two-dimensional grayscale image based on seismic data, the method further includes:

[0208] Traverse the two-dimensional array to obtain the initial maximum offset distance off. max Minimum offset distance min Maximum initial arrival time fa max Minimum arrival time fa min .

[0209] In some embodiments, a two-dimensional grayscale image includes:

[0210] With the offset distance as the horizontal axis, the number of pixels in the 2D grayscale image along the offset distance direction is: (off) max -off min ) / Δd+1, where Δd is the track spacing;

[0211] With the first arrival time as the vertical axis, the number of pixels in the two-dimensional grayscale image along the first arrival time direction is: (fa max -fa min ) / Δt+1, where Δt is the time interval.

[0212] In some embodiments, binarizing a two-dimensional grayscale image to obtain a target grayscale image includes:

[0213] Traverse the 2D grayscale image to obtain the initial number of arrivals in each pixel and the maximum and minimum initial number of arrivals in a single pixel;

[0214] The grayscale threshold value is obtained based on earthquake data and the maximum and minimum number of first arrivals within a single pixel.

[0215] When the number of initial values ​​in a pixel is greater than the grayscale threshold, it is marked as the first grayscale value;

[0216] When the number of initial values ​​within a pixel is less than or equal to the grayscale threshold, it is marked as the second grayscale value.

[0217] In some embodiments, abnormal first arrivals are detected based on the number of connected components and the number of first arrivals within each connected component, and the detected abnormal first arrivals are removed, including:

[0218] Identify abnormal connected components based on the number of connected components and the number of initial arrivals within each connected component;

[0219] Obtain all abnormal pixels in the abnormal connected components;

[0220] Obtain all initial arrivals of all abnormal pixels;

[0221] Reset all exception initial values ​​to invalid values.

[0222] The target grayscale image is labeled and stored using a connected component algorithm to obtain the initial number of elements in each connected component, denoted as cDSet:

[0223] (connectedDomainNum1, connectedDomainNum2,...connectedDomainNum n ),

[0224] Where n represents the number of connected domains, and for any connected domain i, the number of initial arrivals in its connected domain is denoted as connectedDomainNum. i .

[0225] Sort cDSet in ascending order from smallest to largest, and calculate the values ​​in sequence. The value of , where k∈{n-1,…,1}, if when s k <threshold ab When *S, all connected components with indices {k, ..., 1} are designated as invalid connected components (abnormal connected components). The first arrivals in invalid connected components are considered abnormal first arrivals, and the first arrivals in all invalid connected components are set to invalid values. Here, threshold... ab ∈(0,1) represents the threshold value, and S represents the total number of initial arrivals involved in the calculation.

[0226] After all connected components with indices {k,…,1} are marked as invalid connected components (abnormal connected components), the pixels corresponding to all invalid connected components with indices {k,…,1} are saved. Then, the initial arrival of each pixel is obtained from the saved pixel initial arrival table. For any initial arrival, the corresponding initial arrival is found according to its corresponding field record number and channel number, and the value of the initial arrival is set to invalid.

[0227] It should be noted that the memory 502 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk, etc.

[0228] The processes, functions, methods, and / or software described above may be recorded, stored, or fixed in one or more computer-readable storage media, including computer programs that will be implemented by a computer to enable a processor to execute the program code. The storage media may also individually include computer programs, data files, data structures, etc., or a combination thereof. The storage media or computer programs may be specifically designed and understood by those skilled in the art of computer software, or the storage media or computer programs may be known and available to those skilled in the art of computer software. Examples of computer programs include machine code (e.g., code generated by a compiler) and files containing high-level code that can be executed by a computer using an interpreter. The described hardware devices may be configured to function as one or more software modules to perform the operations and methods described above, and vice versa. Furthermore, computer-readable storage media may be distributed across networked computer systems, allowing for the decentralized storage and execution of computer-readable code or computer programs.

[0229] It should be noted that the device may include one or more processors and memory (i.e., computer-readable storage media), which can be connected via a bus or other means. Memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The processor executes various functional applications and data processing of the device by running the non-volatile software programs, program code, and modules stored in the memory, thereby implementing the anomaly detection and removal method described above.

[0230] In summary, this application provides a method, apparatus, storage medium, and electronic device for detecting and removing abnormal first arrivals. The method includes: acquiring seismic data and first arrival data of a target work area; obtaining a two-dimensional array based on the first arrival data; converting the two-dimensional array into a two-dimensional grayscale image based on the seismic data; performing binarization processing on the two-dimensional grayscale image to obtain a target grayscale image; traversing the target grayscale image to obtain the number of connected components and the number of first arrivals in each connected component; detecting abnormal first arrivals based on the number of connected components and the number of first arrivals in each connected component, and removing the detected abnormal first arrivals.

[0231] In the several embodiments provided in this application, the abnormal arrival detection and removal methods described above can be used to perform the methods. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0232] Furthermore, it should be understood that the methods disclosed in the several embodiments provided in this application can also be implemented in other ways. The method embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functions, and operations of possible implementations of methods and apparatus according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, computer program segment, or part of a computer program, which includes one or more computer programs for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings, and may actually be executed substantially in parallel. They may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified functions or actions, or can be implemented using a combination of dedicated hardware and computer programs.

[0233] In this invention, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. In this invention, the use of terms such as "first," "second," etc., is for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly indicating the number or sequence of indicated technical features. In the description of this invention, unless otherwise expressly defined, terms such as "two-dimensional array," "offset distance," "grayscale," "connected region," and "abnormal first arrival" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of these terms in this invention in conjunction with the specific content of the technical solution. Furthermore, in the description of this application, unless otherwise stated, "a plurality of" or "more" means at least two.

[0234] Finally, it should be noted that in the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "a single example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0235] While the embodiments disclosed in this invention are as described above, the content is merely for the purpose of facilitating understanding of the invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope disclosed herein; however, the scope of protection of this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A method for detecting and removing abnormal initial arrivals, characterized in that, The method includes: Collect seismic data and first arrival data for the target work area; Based on the initial arrival data, a two-dimensional array is obtained; wherein, the two-dimensional array stores the initial arrival offset and the initial arrival time; Based on the seismic data, the two-dimensional array is converted into a two-dimensional grayscale image; this includes: obtaining a time interval based on the track spacing and surface velocity, wherein the seismic data includes the track spacing and surface velocity of the target work area; and converting the two-dimensional array into a two-dimensional grayscale image based on the track spacing and the time interval. The two-dimensional grayscale image is binarized to obtain a target grayscale image; this includes: traversing the two-dimensional grayscale image to obtain the number of initial arrivals in each pixel and the maximum and minimum number of initial arrivals in a single pixel; obtaining a grayscale threshold value based on the seismic data and the maximum and minimum number of initial arrivals in a single pixel; when the number of initial arrivals in a pixel is greater than the grayscale threshold value, it is marked as a first grayscale value; when the number of initial arrivals in a pixel is less than or equal to the grayscale threshold value, it is marked as a second grayscale value. Traverse the target grayscale image to obtain the number of connected components and the number of initial arrivals in each connected component; Based on the number of connected components and the number of first arrivals within each connected component, abnormal first arrivals are detected and removed.

2. The method according to claim 1, characterized in that, Before converting the two-dimensional array into a two-dimensional grayscale image based on the seismic data, the method further includes: Traverse the two-dimensional array to obtain the initial maximum offset distance off. max Minimum offset distance min Maximum initial arrival time fa max Minimum arrival time fa min .

3. The method according to claim 2, characterized in that, The two-dimensional grayscale image includes: With the offset distance as the horizontal axis, the number of pixels in the two-dimensional grayscale image along the offset distance direction is: (off) max -off min ) / + 1, where, For lane spacing; With the arrival time as the vertical axis, the number of pixels in the two-dimensional grayscale image along the arrival time direction is: (fa max -fa min ) / + 1, where, For time intervals.

4. The method according to claim 1, characterized in that, The step of detecting abnormal first arrivals based on the number of connected components and the number of first arrivals within each connected component, and removing the detected abnormal first arrivals, includes: Abnormal connected components are obtained based on the number of connected components and the number of initial arrivals within each connected component; Obtain all abnormal pixels in the abnormal connected component; Obtain the initial arrival of all abnormalities within all the aforementioned abnormal pixels; Reset all exceptions to invalid values.

5. An apparatus, characterized in that, include: The acquisition module is used to acquire seismic data and first arrival data for the target work area; The first acquisition module is used to obtain a two-dimensional array based on the initial arrival data; wherein the two-dimensional array stores the initial arrival offset and the initial arrival time; A conversion module is used to convert the two-dimensional array into a two-dimensional grayscale image based on the seismic data; including: obtaining a time interval based on the track spacing and surface velocity, wherein the seismic data includes the track spacing and surface velocity of the target work area; and converting the two-dimensional array into a two-dimensional grayscale image based on the track spacing and the time interval. The processing module is used to perform binarization processing on the two-dimensional grayscale image to obtain a target grayscale image; including: traversing the two-dimensional grayscale image to obtain the number of initial arrivals in each pixel and the maximum and minimum number of initial arrivals in a single pixel; obtaining a grayscale threshold value based on the seismic data and the maximum and minimum number of initial arrivals in a single pixel; when the number of initial arrivals in a pixel is greater than the grayscale threshold value, it is marked as a first grayscale value; when the number of initial arrivals in a pixel is less than or equal to the grayscale threshold value, it is marked as a second grayscale value. The second acquisition module is used to traverse the target grayscale image and acquire the number of connected components and the number of initial arrivals in each connected component; The removal module is used to detect abnormal first arrivals based on the number of connected components and the number of first arrivals in each connected component, and to remove the detected abnormal first arrivals.

6. A storage medium, characterized in that, The computer program stored in the storage medium can be executed by one or more processors and can be used to implement the anomaly initial arrival detection and removal method as described in any one of claims 1 to 4.

7. An electronic device, characterized in that, It includes a memory and a processor, wherein a computer program is stored in the memory, and the memory and the processor are interconnected. When the computer program is executed by the processor, it performs the abnormal arrival detection and removal method as described in any one of claims 1 to 4.

Images