First arrival picking method and device, electronic equipment and storage medium
By automatically picking and filtering waveform data in seismic data, restoring negative values and removing outliers, the problem of inaccurate first arrival picking caused by software misjudgment is solved, achieving efficient and accurate first arrival picking, and reducing the intensity of manual intervention and project costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA NAT PETROLEUM CORP
- Filing Date
- 2024-12-03
- Publication Date
- 2026-06-05
AI Technical Summary
Existing software has misjudgments during the initial arrival process, requiring a lot of manual intervention, resulting in low efficiency and inaccuracy.
By automatically picking waveform data from seismic data, recovering negative data and removing true outliers, and using a weighted standard deviation screening method, the accuracy of the data is improved.
It automates and improves the accuracy of initial pickup, reduces the intensity of human intervention, improves pickup efficiency and quality, and reduces project costs.
Smart Images
Figure CN122151202A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of digital information technology in geophysical exploration, and in particular to a first arrival acquisition method, electronic device, storage medium and application. Background Technology
[0002] In current seismic data processing projects, with increasing project scale (some large-scale projects include as many as 230,000 shots, each with 25,000 receivers), first arrival (IOR) acquisition has become a massive undertaking. Due to project requirements, seed shots are used for subsequent modeling processes, thus requiring high accuracy. However, existing software removes outliers during seed shot acquisition, but the software has significant errors in identifying outliers, leading to misjudgments and the deletion of data that shouldn't be deleted. This results in inaccurate IOR acquisition, necessitating manual quality control, which consumes significant manpower and resources, increasing project costs. Faced with a huge workload and limited acquisition time, it's difficult to complete IOR acquisition tasks in engineering projects. Therefore, there is an urgent need for an IOR acquisition method to address the problems of software misjudgment requiring manual identification, the potential for data misinterpretation during manual identification, and the inability to automatically identify and correct misjudgments. Summary of the Invention
[0003] This application provides a first-arrival picking method to solve the problems in related technologies where seed gun picking does not meet the requirements, there are a large number of outliers, software misjudgments can only be identified manually one by one, and manual identification may cause data misjudgment, making it impossible to automatically identify and correct misjudgments, and these problems further lead to low efficiency and insufficient accuracy.
[0004] Firstly, this application provides a first arrival picking method, which includes: acquiring receiver point information and waveform data of receiver points from seismic data, wherein the waveform data includes multiple raw first arrivals; wherein the raw first arrivals include normal waveform data and abnormal waveform data; automatically picking the waveform data to obtain a first dataset, wherein the first dataset includes multiple outliers; wherein the multiple outliers include misidentified outliers; processing the multiple outliers based on the waveform data; filtering the waveform data in the first dataset to remove true outliers; and using the waveform data of the first dataset after removing the true outliers as the first arrival picking result.
[0005] As an optional implementation, the plurality of outliers includes negative data, and the processing of the plurality of outliers based on the waveform data includes: restoring the negative data to positive data based on the waveform data; and saving the positive data to the first dataset.
[0006] As an optional implementation, the step of filtering the waveform data in the first dataset to remove true outliers includes: calculating the waveform data in the first dataset that meet a first condition; and determining the values that meet the first condition as true outliers.
[0007] As an optional implementation, the step of calculating the waveform data in the first dataset that meets the first condition includes: for each detector point, calculating the weighted standard deviation of the waveform data corresponding to the detector point using the following formula to obtain the waveform data that meets the first condition; wherein, the first condition is that the standard deviation exceeds a preset threshold;
[0008]
[0009] Where N represents the total number of detector points selected adjacent to the detector point, M represents the number of detector points whose waveform data has non-negative values before the anomaly processing, and w i x represents the first-arrival data weight vector of the adjacent detector points. i Represents the vector of initial arrival data values. This represents the weighted average of the first arrival data of the adjacent detector points.
[0010] As an optional implementation, restoring the negative data to positive data includes: converting the negative data into positive data while maintaining the absolute values of the positive data and the negative data being equal.
[0011] As an optional implementation, the method further includes: constructing a normal waveform dataset for storing the initial arrival picking results; and storing waveform data in the first dataset that do not meet the first condition into the normal waveform dataset.
[0012] As an optional implementation, the method further includes: retaining the misidentified outliers, wherein the misidentified outliers are waveform data that do not meet the first condition in the processed data of the plurality of outliers, and are stored in the normal waveform dataset.
[0013] Secondly, this application provides a first arrival picking device, comprising: a first module for acquiring receiver point information and waveform data of receiver points from seismic data, wherein the waveform data includes multiple raw first arrivals; wherein the raw first arrivals include normal waveform data and abnormal waveform data; a second module for automatically picking the waveform data to obtain a first dataset, wherein the first dataset includes multiple outliers; wherein the multiple outliers include misidentified outliers; a third module for processing the multiple outliers based on the waveform data; a fourth module for filtering the waveform data in the first dataset to remove true outliers; and a fifth module for using the waveform data of the first dataset after removing the true outliers as the first arrival picking result.
[0014] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the initial arrival picking method as described above.
[0015] Fourthly, the application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory is used to store computer programs; and the processor, when executing the program stored in the memory, implements the initial arrival pickup method as described above.
[0016] The technical solutions provided in this application have the following advantages compared with the prior art:
[0017] The initial arrival picking method provided in this application effectively solves the problems in related technologies where software misjudgments can only be identified manually one by one, and where manual identification may lead to data misjudgments, making automatic identification and correction of misjudgments impossible. The method employs the recovery of negative initial arrival values and the removal of outliers. The overall solution incorporates values with adjustment significance into the valid data area, replacing manual picking, and effectively removes values that cause inaccurate initial arrival picking. This replaces manual identification one by one, improving the quality control level of initial arrival picking, increasing accuracy, and achieving rapid correction of near-shot offset initial arrival picking errors. Simultaneously, it reduces the intensity of manual labor during human-computer interaction, lowers project costs, and improves labor protection levels to meet industry data confidentiality requirements. Furthermore, it reduces the possibility of data deletion due to human factors, increases the total amount of valid data, and is conducive to improving the quality of seismic data, improving picking efficiency and quality, and achieving high-efficiency quality control. This method has strong practical applicability in actual engineering, possesses significant advantages in initial arrival picking quality control, and can meet market demands. Attached Figure Description
[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0020] Figure 1 This schematically illustrates the system architecture of the initial arrival pickup method and apparatus applicable to embodiments of this application;
[0021] Figure 2 This is a flowchart illustrating an initial arrival picking method according to an embodiment of this application;
[0022] Figure 3 This is one of the waveform data schematic diagrams of a first arrival picking method according to an embodiment of this application;
[0023] Figure 4 This is a second schematic diagram of waveform data for a first-arrival picking method according to an embodiment of this application;
[0024] Figure 5 This is a schematic diagram of the initial arrival data for an initial arrival picking method according to another embodiment of this application;
[0025] Figure 6 This is a schematic diagram of the data before the filtering process in the initial arrival picking method according to another embodiment of this application;
[0026] Figure 7 This is a schematic diagram of the data after filtering processing according to another embodiment of the initial arrival picking method of this application;
[0027] Figure 8 This is one of the schematic diagrams illustrating the initial arrival data statistics of an initial arrival picking method according to another embodiment of this application;
[0028] Figure 9 This is a second schematic diagram of the initial arrival data statistics of the initial arrival picking method according to another embodiment of this application;
[0029] Figure 10 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation
[0030] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0031] Figure 1 The schematic illustration shows the system architecture of the initial arrival picking method and apparatus applicable to embodiments of this application.
[0032] Reference Figure 1 As shown, the system architecture 100 of the initial arrival pickup method and apparatus applicable to embodiments of this application includes: terminal devices 101, 102, and 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the terminal devices 101, 102, and 103 and the server 105. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.
[0033] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Terminal devices 101, 102, and 103 can have audio / video capture devices, image / audio / video playback applications, etc., installed. Other communication client applications can also be installed, such as web browser applications, search applications, image storage and processing applications, instant messaging tools, email clients, and social media platform software (for example only).
[0034] Terminal devices 101, 102, and 103 may be various electronic devices that can display screens and support software capable of implementing the initial arrival pickup method. The electronic devices may further include an initial arrival pickup generation device, such as electronic devices including but not limited to smartphones, tablets, laptops, desktop computers, etc.
[0035] Server 105 can be a server that provides various services, such as a back-end management server that provides support for data processing of work area data received and stored by users using terminal devices 101, 102, and 103 (this is just an example). The back-end management server can analyze and process the received data and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.
[0036] It should be noted that the initial arrival detection method provided in this application embodiment can generally be executed by server 105 or a terminal device with a certain computing power. Correspondingly, the initial arrival detection device provided in this application embodiment can generally be installed in server 105 or the aforementioned terminal device with a certain computing power. The initial arrival detection method provided in this application embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105. Correspondingly, the initial arrival detection device provided in this application embodiment can also be installed in a server or server cluster that is different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105.
[0037] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.
[0038] Figure 2 This is a flowchart illustrating the initial arrival picking method according to an embodiment of this application; as shown Figure 2 As shown in the figure, this application provides a method for initial arrival picking, which includes the following steps:
[0039] S101, acquire receiver point information and waveform data of receiver points from seismic data, wherein the waveform data includes multiple original first arrivals;
[0040] S102, the waveform data is automatically picked up to obtain a first dataset, the first dataset including multiple outliers;
[0041] S103, Based on the waveform data, process the multiple outlier values;
[0042] S104, Filter the waveform data in the first dataset to remove real outliers;
[0043] S105, the waveform data of the first dataset after removing the real outliers is used as the initial picking result.
[0044] Specifically, Figure 3 This is one of the waveform data schematic diagrams of an initial arrival picking method according to an embodiment of this application. Figure 4 This is a second schematic diagram of waveform data for a first-arrival picking method according to an embodiment of this application. The acquired seismic data is as follows: Figure 3As shown in the figure, the horizontal axis represents receiver information, and the vertical axis represents time. This seismic data includes waveform data from the receivers. Specifically, the data may include receiver information such as station number, and the corresponding time for each receiver, i.e., first arrival data. This data can be stored in file format, such as a .txt file, or it can be displayed in a graphical interface, such as... Figure 3 As shown.
[0045] In one possible implementation, for example, Figure 3 The red data points shown represent the initial arrival values, and the green lines represent pre-set time windows, such as 400ms or 600ms, before automatic data acquisition. For example, the red points in the graph that clearly exceed the time window represent multiple outliers requiring further processing and filtering. Figure 4 The waveform data shown represents the data after processing by S103.
[0046] In one possible implementation, Figure 5 This is a schematic diagram of the initial arrival data for an initial arrival picking method according to another embodiment of this application. The automatically picked data can also be exported as follows: Figure 5 The data file shown includes receiver information and first arrival data.
[0047] Specifically, in the initial arrival picking method, the original initial arrival includes normal waveform data and abnormal waveform data. Automatic picking of the waveform data can be implemented by using software (e.g., KLSeisII) to automatically pick the waveform data, obtaining a first dataset. This first dataset includes multiple outliers; wherein, the multiple outliers include misidentified outliers. The automatically picked data can be stored in file format.
[0048] Specifically, multiple outliers can include negative data. For example... Figure 3 and Figure 5 The negative values shown are common in practical engineering applications. Near-shot offset initial arrival picking errors often occur. Near-shot offset represents the receiver point that receives the seismic source's influence relatively early and is closer to the shot point. Picking errors frequently occur at these receiver points; that is, negative values can be generated at near-shot offset, for example, misidentifying valid data as invalid data. Figure 3 The data shown in the figure that clearly exceeds the time window range and does not conform to the characteristics of a normal waveform, and is represented as negative data, can be used to find different velocity curves at near and far offsets based on the picked-up first arrival time and the shot-receiver relationship of SPS auxiliary data (e.g., Figure 3 and 4 (From the waveform data), it can be seen that normal arrivals are usually evenly distributed near the curve, while abnormal arrivals are far away from the curve.
[0049] S103 can be specifically implemented by including the following operations: based on the waveform data, restoring the negative value data to positive value data; and saving the positive value data to the first dataset.
[0050] Based on the operation in S103, potentially misidentified first arrivals within the negative data are restored to positive values and included in the first dataset for subsequent filtering, thereby identifying true outliers. This operation prevents inaccurate picking where valid first arrivals are misidentified as outliers, achieves rapid recovery of negative values, maximizes the retention of valid values within the range, provides reasonable information density for subsequent data filtering, and ensures the multidimensionality and breadth of the data.
[0051] S104 can be specifically implemented by including the following operations: calculating waveform data in the first dataset that meet the first condition; and determining the values that meet the first condition as true outliers.
[0052] For each detector point, the weighted standard deviation of the waveform data corresponding to that detector point is calculated using the following formula.
[0053]
[0054] Where N represents the total number of detector points selected adjacent to the detector point, M represents the number of detector points whose waveform data has non-negative values before the anomaly processing, and w i x represents the first-arrival data weight vector of the adjacent detector points. i Represents the vector of initial arrival data values. This represents the weighted average of the first arrival data of the adjacent detector points.
[0055] For each receiver point, the weighted standard deviation of the waveform data corresponding to that receiver point is calculated to obtain waveform data that meets the first condition; where the first condition is that the weighted standard deviation corresponding to the receiver point exceeds a preset threshold. This preset threshold can be set according to actual engineering needs. Specifically, N can be set according to specific regional differences, or according to the degree of waveform undulation, taking into account the slope variation of the points. For example, for smaller undulations, a relatively larger number of points can be selected, such as setting N=20, while for relatively larger undulations, N=10 can be set. It should be noted that the preset threshold is also set according to specific regional differences. For example, different provinces have significantly different geographical conditions, so the preset threshold will also be different. For a certain work area, the preset threshold can be fixed, while for different work areas, the preset threshold can be different.
[0056] In one possible implementation, Figure 6 This is a schematic diagram of the data before the filtering process in the initial arrival picking method according to another embodiment of this application; Figure 7This is a schematic diagram of the data after filtering processing according to another embodiment of the initial arrival picking method of this application. Figure 6 As shown, there are multiple outliers. These outliers are true outliers and need to be removed using the method described above. After restoring negative values to positive values in S103 and including them in the first dataset, the data is further filtered in S104 as follows. Figure 7 As shown, true outliers have been removed.
[0057] In one possible implementation, restoring the negative data to positive data can be specifically implemented by: converting the negative data into positive data while maintaining the absolute values of the positive data and the negative data being equal. For example, Figure 5 The negative value -1396.70 shown in the figure becomes 1396.70 after being restored to a positive value.
[0058] In one possible implementation, the method may further include: constructing a normal waveform dataset to store the initial picking results; storing waveform data in the first dataset that do not meet the first condition in the normal waveform dataset. The method may also include: retaining the misidentified outliers, wherein the misidentified outliers are waveform data in the processed data of the plurality of outliers that do not meet the first condition, and these outliers are stored in the normal waveform dataset.
[0059] Specifically, the initial arrival data may include normal waveform data and abnormal waveform data. In the aforementioned operation, the automatically picked data may include normal waveform data and multiple outliers. However, the multiple outliers include misidentified outliers. That is, there are both misidentified outliers and true outliers among the outliers. After negative value recovery processing and outlier filtering processing, normal waveform data is accurately identified and stored in the normal waveform dataset, and true outliers are removed. Thus, the waveform data with true outliers removed is used as the initial arrival picking result and output for subsequent engineering operation processes.
[0060] The initial arrival picking method provided in this application effectively solves the problems in related technologies where software misjudgments can only be identified manually one by one, and where manual identification may lead to data misjudgments, making automatic identification and correction of misjudgments impossible. The method employs the recovery of negative initial arrival values and the removal of outliers. The overall solution incorporates values with adjustment significance into the valid data area, replacing manual picking, and effectively removes values that cause inaccurate initial arrival picking. This replaces manual identification one by one, improving the quality control level of initial arrival picking, increasing accuracy, and achieving rapid correction of near-shot offset initial arrival picking errors. Simultaneously, it reduces the intensity of manual labor during human-computer interaction, lowers project costs, and improves labor protection levels to meet industry data confidentiality requirements. Furthermore, it reduces the possibility of data deletion due to human factors, increases the total amount of valid data, and is conducive to improving the quality of seismic data, improving picking efficiency and quality, and achieving high-efficiency quality control. This method has strong practical applicability in actual engineering, possesses significant advantages in initial arrival picking quality control, and can meet market demands.
[0061] Furthermore, in practical applications, for projects of the same scale, such as processing data from 50 shots per day, reaching tens of thousands of data points, it would have taken 30 people two months to complete without this method. After applying the initial arrival picking method of this application, manual work was replaced, and the equivalent workload was completed in less than a day. It is evident that the work efficiency is increased to hundreds of times the original daily efficiency per person, greatly improving picking efficiency and quality, and also has a significant advantage in reducing project costs.
[0062] In one instance, using the initial arrival picking method provided in this application embodiment, the following result details can be obtained and returned: the original initial arrival count is 4,047,081, of which 526,338 negative values have been recovered, i.e., recovered outliers, and 164,526 outliers have been removed, i.e., true outliers.
[0063] In one possible implementation, after seismic information from all shot points throughout the entire project has been acquired, offset statistics of the first arrival data can be generated, such as... Figure 8 and Figure 9 As shown, the data statistics before and after implementing this method are compared. For example, Figure 8 This is one of the schematic diagrams illustrating the initial arrival data statistics of an initial arrival picking method according to another embodiment of this application; Figure 9 This is a second schematic diagram of the initial arrival data statistics of the initial arrival picking method according to another embodiment of this application. Figure 9 This represents the statistical results of the initial arrival picking obtained after processing by the initial arrival picking method according to the embodiments of this application. Figure 8This represents the statistics of the initial arrival picking results before processing. By comparison, it is clear that the initial arrival picking method according to the embodiments of this application greatly improves the picking accuracy and enhances the picking quality.
[0064] Combination Figure 8 The image shows the initial data before removal. It's clear that the data before removal contained a large amount of offset data. This offset was caused by software or human error, resulting in the deletion of data that shouldn't have been deleted, leading to inaccurate output data. In comparison, combined with... Figure 9 The diagram shows the initial arrival data after removal. The initial arrival picking method provided in this application eliminates inaccurate picking, realizes automatic identification and correction of misjudgments, and has high accuracy.
[0065] It should be noted that in actual engineering, the initial arrival picking method provided in this application can continuously improve the database for AI learning and use by combining the seed gun to cover various geological conditions during the continuous picking process, thereby achieving a wider range of applications and higher adaptability.
[0066] More details and beneficial effects of this embodiment can be found in the descriptions of the foregoing embodiments, and will not be repeated here.
[0067] Based on the same inventive concept, this application also provides a first arrival picking device, comprising: a first module for acquiring receiver point information and waveform data of receiver points in seismic data, wherein the waveform data includes multiple raw first arrivals; wherein the raw first arrivals include normal waveform data and abnormal waveform data; a second module for automatically picking the waveform data to obtain a first dataset, wherein the first dataset includes multiple outliers; wherein the multiple outliers include misidentified outliers; a third module for processing the multiple outliers based on the waveform data; a fourth module for filtering the waveform data in the first dataset to remove true outliers; and a fifth module for using the waveform data of the first dataset after removing the true outliers as the first arrival picking result.
[0068] More details and beneficial effects of this embodiment can be found in the descriptions of the foregoing embodiments, and will not be repeated here.
[0069] Any number of the functional modules included in the above-described device can be combined into one module, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. At least one of the functional modules included in the above-described device can be at least partially implemented as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a System-on-Chip, a System-on-Substrate, a System-on-Package, an Application-Specific Integrated Circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, at least one of the functional modules included in the device can be at least partially implemented as a computer program module, which, when run, can perform corresponding functions.
[0070] Based on the same inventive concept, in one embodiment, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the first arrival pickup method as described above.
[0071] More details and beneficial effects of this embodiment can be found in the descriptions of the foregoing embodiments, and will not be repeated here.
[0072] Based on the same inventive concept, combined with Figure 10 , Figure 10 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. In one embodiment, this application provides an electronic device including a processor 401, a communication interface 402, a memory 403, and a communication bus 404, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory is used to store computer programs; the processor, when executing the program stored in the memory, implements the method described above. The steps of implementing the initial arrival pickup method in any of the above possible implementations can be equivalent to the initial arrival pickup device described above. Of course, the processor can also be used to process other data or perform calculations. This electronic device can be a PC, server, terminal, or other similar device.
[0073] Further details and beneficial effects of this embodiment can be found in the descriptions of the foregoing embodiments, and will not be repeated here. The processor can also be used to process other data or perform calculations. The electronic device can be a PC, server, terminal, or other similar device.
[0074] It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of this application. Experimental methods in the following embodiments, unless specific conditions are specified, are generally determined according to national standards. If no corresponding national standard exists, then generally accepted international standards, conventional conditions, or conditions recommended by the manufacturer are followed.
[0075] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, 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 said element.
[0076] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.
Claims
1. A method for initial arrival pickup, characterized in that, include: Obtain receiver point information and receiver point waveform data from seismic data. The waveform data includes multiple raw first arrivals; wherein the raw first arrivals include normal waveform data and abnormal waveform data. The waveform data is automatically picked up to obtain a first dataset, which includes multiple outliers; wherein, the multiple outliers include misidentified outliers. Based on the waveform data, the multiple outliers are processed; The waveform data in the first dataset is filtered to remove true outliers; The waveform data of the first dataset after removing the true outliers is used as the initial picking result.
2. The initial arrival picking method according to claim 1, characterized in that, The plurality of outliers includes negative data, and the processing of the plurality of outliers based on the waveform data includes: Based on the waveform data, the negative values are restored to positive values; Save the positive data to the first dataset.
3. The initial arrival picking method according to claim 2, characterized in that, The step of filtering the waveform data in the first dataset to remove true outliers includes: Calculate the waveform data in the first dataset that meet the first condition; The values that meet the first condition are identified as true outliers.
4. The initial arrival picking method according to claim 3, characterized in that, The calculation of waveform data in the first dataset that meets the first condition includes: For each detector point, the weighted standard deviation of the waveform data corresponding to the detector point is calculated using the following formula to obtain waveform data that meets the first condition; wherein, the first condition is that the standard deviation exceeds a preset threshold. Where N represents the total number of detector points selected adjacent to the detector point, M represents the number of detector points whose waveform data has non-negative values before the anomaly processing, and w i x represents the first-arrival data weight vector of the adjacent detector points. i Represents the vector of initial arrival data values. This represents the weighted average of the first arrival data of the adjacent detector points.
5. The initial arrival picking method according to claim 2, characterized in that, The step of restoring the negative data to positive data includes: converting the negative data into positive data while maintaining the absolute values of the positive data and the negative data being equal.
6. The initial arrival picking method according to claim 3, characterized in that, The method further includes: Construct a normal waveform dataset to store the initial arrival picking results; Waveform data that do not meet the first condition in the first dataset are stored in the normal waveform dataset.
7. The initial arrival picking method according to claim 6, characterized in that, The method further includes: retaining the misidentified outliers, wherein the misidentified outliers are waveform data that do not meet the first condition in the processed data of the plurality of outliers, and are stored in the normal waveform dataset.
8. A first-arrival pickup device, characterized in that, include: The first module is used to acquire receiver point information and receiver point waveform data from seismic data. The waveform data includes multiple raw first arrivals; wherein the raw first arrivals include normal waveform data and abnormal waveform data. The second module is used to automatically pick up the waveform data to obtain a first dataset, the first dataset including multiple outliers; wherein, the multiple outliers include misidentified outliers; The third module is used to process the multiple outliers based on the waveform data; The fourth module is used to filter the waveform data in the first dataset and remove real outliers; The fifth module is used to take the waveform data of the first dataset after removing the real outliers as the initial picking result.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the initial arrival picking method as described in any one of claims 1 to 7.
10. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the initial arrival pickup method as described in any one of claims 1 to 7.