Seismic data processing method and device and storage medium

Abstract

The embodiment of the application discloses a kind of seismic data processing method, device and storage medium, belong to seismic exploration technical field.The embodiment of the application is by the sample point value of the part of acquisition equipment collected, using the root mean square value of these sample point values can determine abnormal acquisition information, without according to all acquisition equipment collected seismic data to generate trace gather data or shot gather data, also without in waiting all acquisition equipment can determine abnormal acquisition information after recovery, shorten the time of waiting equipment recovery and data processing time, in this way, the abnormality existing in the process of seismic data acquisition can be determined more timely, it is beneficial to promptly rectify relevant problems, guarantee the quality of subsequent data.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration technology, and in particular to a seismic data processing method, apparatus and storage medium. Background Technology

[0002] Seismic exploration refers to the use of artificially generated seismic waves to explore geological conditions through the propagation of seismic waves in strata, primarily for the search for mineral resources such as oil and gas. During seismic exploration, multiple intersecting survey lines can be laid out on the surface corresponding to the strata. Survey lines in a certain direction, such as transverse survey lines, are used as detector lines, each identified by a line number. Acquisition equipment is deployed at the intersections of each longitudinal survey line and each detector line. The intersection of any longitudinal survey line with multiple detector lines corresponds to the same point number, which is actually the line number of that longitudinal survey line. Thus, the deployment location of each acquisition device can be indicated by the line number of the corresponding detector line and the point number on that detector line. Then, seismic waves are generated at a certain point on the surface. As the seismic waves propagate underground, they encounter interfaces of different elasticity in the strata, producing reflected or refracted waves. Accordingly, the acquisition equipment at each location can receive and record the returning reflected or refracted waves, thereby obtaining seismic data.

[0003] In related technologies, after all acquisition equipment is retrieved, the computing device can download the seismic data collected by all the acquisition devices. Then, the computing device performs format conversion and separation processing on the downloaded seismic data to obtain gather data or shot gather data. Based on the gather data or shot gather data, abnormal seismic data can be identified. By identifying abnormal seismic data, problems with the data can be determined or abnormal acquisition equipment can be located, thus helping the field geophysical exploration team to make relevant rectifications to ensure the quality of subsequently acquired seismic data.

[0004] However, the time required to retrieve all acquisition equipment and obtain gather or shot gather data based on the seismic data collected by all acquisition equipment is relatively long. This makes it impossible to screen out abnormal seismic data in a timely manner. Consequently, it is impossible to determine the problems in the data or locate the abnormal acquisition equipment in a timely manner, thus making it impossible to rectify the relevant problems in a timely manner and affecting the quality of subsequent seismic data. Summary of the Invention

[0005] This application provides a seismic data processing method, apparatus, and storage medium, which can promptly filter out abnormal seismic data, thereby helping field geophysical exploration personnel to quickly locate problems with abnormal acquisition equipment or abnormal data. The technical solution is as follows:

[0006] On the one hand, a seismic data processing method is provided, the method comprising:

[0007] Acquire seismic data files from each of the multiple acquisition devices along multiple target detector lines, wherein the seismic data files include sample point values ​​acquired by the corresponding acquisition device within the acquisition time period;

[0008] Based on the first duration, the collection period is divided into multiple time windows;

[0009] Based on the sample point values ​​collected by each acquisition device within each time window, determine the root mean square value of the sample point values ​​of each acquisition device within each time window.

[0010] Abnormal acquisition information is determined based on the root mean square value of the sample points of each acquisition device within each time window.

[0011] Optionally, determining outlier sample values ​​based on the root mean square value of sample values ​​from each acquisition device within each time window includes:

[0012] Based on the root mean square value of the sample points of each acquisition device within each time window, the average root mean square value of the sample points of each acquisition device is determined.

[0013] Obtain the line number and point number corresponding to each acquisition device. The line number corresponding to each acquisition device is the line number of the detector line where the corresponding acquisition device is located, and the point number corresponding to each acquisition device is the point number of the corresponding acquisition device on the detector line.

[0014] Based on the average root mean square value of the sample points of each acquisition device and the corresponding line number and point number, an attribute set is generated for each acquisition device. The attribute set includes the average root mean square value of the sample points of the corresponding acquisition device, the corresponding line number and point number.

[0015] The abnormal acquisition information is determined based on the attribute set of each acquisition device.

[0016] Optionally, the abnormal acquisition information is used to indicate an abnormal acquisition device among the plurality of acquisition devices, and determining the abnormal acquisition information based on the attribute set of each acquisition device includes:

[0017] Based on the line number and point number corresponding to each acquisition device, the multiple attribute sets of the multiple acquisition devices are arranged to obtain an attribute set matrix. In the attribute set matrix, the acquisition devices corresponding to the attribute sets in the same row have the same line number, and the acquisition devices corresponding to the attribute sets in the same column have the same point number.

[0018] The set of anomalous attributes in the attribute set matrix is ​​determined using the isolated forest algorithm;

[0019] The set of abnormal attributes is determined as the abnormal collection information.

[0020] Optionally, determining abnormal acquisition information based on the root mean square value of sample points from each acquisition device within each time window includes:

[0021] Based on the root mean square value of the sample points of each acquisition device on each target detector line within each time window, the time-series root mean square matrix corresponding to the target detector line is generated.

[0022] Based on the root mean square time matrix corresponding to each target detector line, the abnormal acquisition information corresponding to each target detector line is determined.

[0023] Optionally, generating the time-series root mean square matrix corresponding to the target detector line based on the root mean square value of the sample points of each acquisition device on each target detector line within each time window includes:

[0024] According to the order of the point numbers corresponding to the multiple first acquisition devices on the first target detector line from smallest to largest and the order of the multiple time windows, the root mean square values ​​of the sample points of each first acquisition device on the first target detector line in each time window are arranged to obtain the time series root mean square matrix corresponding to the first target detector line.

[0025] Wherein, the first target detection line is any one of the plurality of target detection lines, the root mean square value of each row in the time-series root mean square matrix corresponding to the first target detection line is the root mean square value of the same first acquisition device in the plurality of time windows, and the plurality of root mean square values ​​are arranged in the order of the plurality of time windows, the root mean square value of each column in the time-series root mean square matrix corresponding to the first target detection line is the root mean square value of each first acquisition device on the first target detection line in the same time window, and the root mean square values ​​of each first acquisition device in the same time window are arranged in ascending order of the point number corresponding to each first acquisition device.

[0026] Optionally, determining the abnormal acquisition information corresponding to each target detector line based on the time-series root mean square matrix corresponding to each target detector line includes:

[0027] The k-means clustering algorithm is used to detect the time-series root mean square matrix corresponding to the first target detector line to obtain anomaly clusters, which include multiple abnormal root mean square values ​​in the time-series root mean square matrix corresponding to the first target detector line.

[0028] The first acquisition device and time window corresponding to the multiple abnormal root mean square values ​​are respectively used as the abnormal acquisition information corresponding to the first target detector line.

[0029] On the other hand, a seismic data processing apparatus is provided, the apparatus comprising:

[0030] The acquisition module is used to acquire the seismic data file of each of the multiple acquisition devices on multiple target detector lines. The seismic data file includes the sample point values ​​acquired by the corresponding acquisition device within the acquisition time period.

[0031] The segmentation module is used to divide the collection time period into multiple time windows based on the first duration;

[0032] The first determining module is used to determine the root mean square value of the sample points collected by each acquisition device in each time window based on the sample point values ​​collected by each acquisition device in each time window.

[0033] The second determination module is used to determine abnormal acquisition information based on the root mean square value of the sample points of each acquisition device within each time window.

[0034] Optionally, the second determining module is mainly used for:

[0035] Based on the root mean square value of the sample points of each acquisition device within each time window, the average root mean square value of the sample points of each acquisition device is determined.

[0036] Obtain the line number and point number corresponding to each acquisition device. The line number corresponding to each acquisition device is the line number of the detector line where the corresponding acquisition device is located, and the point number corresponding to each acquisition device is the point number of the corresponding acquisition device on the detector line.

[0037] Based on the average root mean square value of the sample points of each acquisition device and the corresponding line number and point number, an attribute set is generated for each acquisition device. The attribute set includes the average root mean square value of the sample points of the corresponding acquisition device, the corresponding line number and point number.

[0038] The abnormal acquisition information is determined based on the attribute set of each acquisition device.

[0039] Optionally, the second determining module is mainly used for:

[0040] Based on the line number and point number corresponding to each acquisition device, the multiple attribute sets of the multiple acquisition devices are arranged to obtain an attribute set matrix. In the attribute set matrix, the acquisition devices corresponding to the attribute sets in the same row have the same line number, and the acquisition devices corresponding to the attribute sets in the same column have the same point number.

[0041] The set of anomalous attributes in the attribute set matrix is ​​determined using the isolated forest algorithm;

[0042] The set of abnormal attributes is determined as the abnormal collection information.

[0043] Optionally, the second determining module is mainly used for:

[0044] Based on the root mean square value of the sample points of each acquisition device on each target detector line within each time window, the time-series root mean square matrix corresponding to the target detector line is generated.

[0045] Based on the root mean square time matrix corresponding to each target detector line, the abnormal acquisition information corresponding to each target detector line is determined.

[0046] Optionally, the second determining module is mainly used for:

[0047] According to the order of the point numbers corresponding to the multiple first acquisition devices on the first target detector line from smallest to largest and the order of the multiple time windows, the root mean square values ​​of the sample points of each first acquisition device on the first target detector line in each time window are arranged to obtain the time series root mean square matrix corresponding to the first target detector line.

[0048] Wherein, the first target detection line is any one of the plurality of target detection lines, the root mean square value of each row in the time-series root mean square matrix corresponding to the first target detection line is the root mean square value of the same first acquisition device in the plurality of time windows, and the plurality of root mean square values ​​are arranged in the order of the plurality of time windows, the root mean square value of each column in the time-series root mean square matrix corresponding to the first target detection line is the root mean square value of each first acquisition device on the first target detection line in the same time window, and the root mean square values ​​of each first acquisition device in the same time window are arranged in ascending order of the point number corresponding to each first acquisition device.

[0049] Optionally, the second determining module is mainly used for:

[0050] The k-means clustering algorithm is used to detect the time-series root mean square matrix corresponding to the first target detector line to obtain anomaly clusters, which include multiple abnormal root mean square values ​​in the time-series root mean square matrix corresponding to the first target detector line.

[0051] The first acquisition device and time window corresponding to the multiple abnormal root mean square values ​​are respectively used as the abnormal acquisition information corresponding to the first target detector line.

[0052] On the other hand, a computing device is provided, the computing device comprising:

[0053] processor;

[0054] Memory used to store processor-executable instructions;

[0055] The processor executes executable instructions in the memory to perform the above-described seismic data processing method.

[0056] On the other hand, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when the computer program is executed by a computer, it implements the steps of the earthquake data processing method described above.

[0057] On the other hand, a computer program product containing instructions is provided that, when run on a computer, causes the computer to perform the steps of the aforementioned seismic data processing method.

[0058] The beneficial effects of the technical solutions provided in this application include at least the following:

[0059] In this embodiment, when determining abnormal acquisition information during the seismic data acquisition process, it is not necessary to generate gather data or shot gather data based on the seismic data collected by all acquisition devices. Correspondingly, it is also not necessary to wait for all acquisition devices to be retrieved before determining abnormal acquisition information. That is, in this embodiment, abnormal acquisition information can be determined by using the root mean square value of the sample point values ​​collected by a portion of the acquired acquisition devices, which shortens the waiting time for device retrieval. Moreover, since the amount of data computation required to calculate the root mean square value based on the sample point values ​​collected by the acquisition devices and determine abnormal acquisition information based on the root mean square value is smaller than generating gather data or shot gather data based on the seismic data collected by all acquisition devices, the time required to determine abnormal acquisition information is also shorter. In this way, anomalies existing in the acquisition process can be identified more promptly, which is conducive to timely rectification of related problems and ensures the quality of subsequent data. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a flowchart of a seismic data processing method provided in an embodiment of this application;

[0062] Figure 2 This is a schematic diagram of the deployment of a survey line provided in an embodiment of this application;

[0063] Figure 3 This is a graph showing the change of sample point values ​​in an acquisition device over time, as provided in an embodiment of this application.

[0064] Figure 4 This is a diagram illustrating an attribute set matrix provided in an embodiment of this application;

[0065] Figure 5This is a diagram illustrating the time-series root mean square matrix corresponding to the first target detector line provided in the embodiments of this application;

[0066] Figure 6 This is a schematic diagram of the structure of an earthquake data processing device provided in an embodiment of this application;

[0067] Figure 7 This is a schematic diagram of the structure of a computing device for processing seismic data provided in an embodiment of this application. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0069] Before providing a detailed explanation of the embodiments of this application, let's first introduce the application scenarios involved in the embodiments of this application.

[0070] Currently, in seismic exploration of strata, multiple survey lines are laid out on the surface corresponding to the strata, with multiple acquisition devices evenly spaced along each line. Seismic waves are then generated at a point on the surface. As these waves propagate underground, they encounter interfaces between strata with varying elasticity, producing reflected or refracted waves. The acquisition devices at each location receive and record these reflected or refracted waves, thus obtaining seismic data. By processing the seismic data from each acquisition device and interpreting the results, the geological conditions of the strata can be obtained, providing data support for the search for oil and gas resources or other engineering projects. However, the seismic data acquired by these devices may contain various problems such as weak energy and persistent high noise; therefore, ensuring the quality of the seismic data acquired by these devices is crucial.

[0071] Therefore, considering that acquisition equipment cannot generate shot gathers or shot gathers in real time based on its own recorded seismic data, technicians typically retrieve all acquisition equipment after a certain period of seismic data collection. After all equipment is retrieved, the computing equipment downloads the seismic data collected by all equipment. Then, the downloaded seismic data undergoes format conversion and separation processing to obtain shot gathers or shot gathers. These shot gathers or shot gathers are then used to identify anomalous seismic data. Identifying anomalous seismic data can reveal potential problems such as weak energy or persistent strong noise in the seismic data, helping field geophysical personnel to make appropriate corrections and ensuring the quality of subsequently acquired seismic data. However, because retrieving all acquisition equipment takes a long time, and the process of separating and processing the seismic data from all acquisition equipment to obtain shot gathers or shot gathers is computationally intensive and time-consuming, the discovery of anomalous seismic data is often delayed, failing to provide timely guidance to field geophysical personnel for corrective action, thus affecting the quality of the seismic data. Based on this, the seismic data processing method provided in this application can shorten the time required to detect abnormal acquisition information from the acquisition equipment, thereby guiding field geophysical personnel to rectify related problems as early as possible, and thus better ensuring the quality of subsequent seismic data.

[0072] The seismic data processing method provided in the embodiments of this application will be described next.

[0073] The seismic data processing method provided in this application embodiment can be executed by a computing device, which may have data processing capabilities. Optionally, the computing device may also have a data display function, such as displaying seismic data acquired by the acquisition device, the processing results of the seismic data, and detected abnormal acquisition information. Exemplarily, the computing device may be a terminal device such as a personal computer, tablet computer, or smartphone; of course, it may also be a backend server, which is not limited in this application embodiment.

[0074] Figure 1 This is a seismic data processing method provided in an embodiment of this application. For example... Figure 1 As shown, the method includes the following steps:

[0075] Step 101: Obtain the seismic data file of each of the multiple acquisition devices on the multiple target detector lines. The seismic data file includes the sample point values ​​acquired by the corresponding acquisition device during the acquisition time period.

[0076] In this embodiment, multiple intersecting survey lines can be arranged on the surface corresponding to the stratum to be explored. The transverse survey lines can be used as detector lines, each identified by a different line number. The intersection of each longitudinal survey line and each detector line is used as the location for deploying acquisition equipment. Points located on the same longitudinal survey line can correspond to the same point number, which is the line number of that longitudinal survey line. Thus, the deployment location of each acquisition device can be identified by the line number of the detector line to which the acquisition device is located and the corresponding point number.

[0077] It should be noted that the distance between any two adjacent transverse survey lines can be the same, and the distance between any two adjacent longitudinal survey lines can also be the same. Furthermore, the distance between two adjacent transverse survey lines can be equal to or unequal to the distance between two adjacent longitudinal survey lines; this application does not limit this.

[0078] For example, such as Figure 2 As shown, the five transverse survey lines are numbered L1 to L5, and the four longitudinal survey lines are numbered S1 to S4. Therefore, the intersection point of longitudinal survey line S1 and the five transverse survey lines is numbered S1, the intersection point of longitudinal survey line S2 and the five transverse survey lines is numbered S2, and so on. Figure 2 The location of the data acquisition device deployed at point A can be identified as L3S2.

[0079] In this embodiment, field geophysical personnel can retrieve deployed acquisition equipment in batches. Each batch can retrieve multiple acquisition devices from a portion of the deployed receiver lines, which are the target receiver lines. After each batch of acquisition equipment is retrieved, the computing device can download seismic data files from each retrieved acquisition device and use methods in subsequent steps to detect abnormal acquisition information from that batch.

[0080] For example, assuming a total of 20 geophone lines are deployed, and field geophysical surveyors can retrieve acquisition equipment from 5 geophone lines in a day, then the acquisition equipment retrieved from those 5 lines in one day can be considered as a batch of acquisition equipment. In this case, these 5 geophone lines are the target geophone lines. It should be noted that the number of target geophone lines retrieved in each batch by field geophysical surveyors may be the same or different, depending on the construction efficiency.

[0081] Each acquisition device's seismic data file includes sample values ​​collected by that device within the acquisition period, as well as the acquisition time corresponding to each sample value. The acquisition period refers to the time span during which data is collected from multiple acquisition devices. For example, if each acquisition device begins data collection at 12:00 on July 5, 2022, with a preset acquisition duration of 7 days, then data collection will stop at 12:00 on July 12, 2022. Thus, the acquisition period is from 12:00 on July 5, 2022 to 12:00 on July 12, 2022.

[0082] It should be noted that, in this embodiment, each acquisition device records a sample value at preset time intervals starting from the moment data acquisition begins. For example, this preset time interval is 4 milliseconds. That is, the acquisition time interval between any two adjacent sample values ​​in the seismic data file of each acquisition device is the preset time interval. The sample value in this embodiment can be an amplitude value, which can be used to reflect the energy of the wave received by the acquisition device.

[0083] Step 102: Based on the first duration, divide the collection period into multiple time windows.

[0084] After obtaining seismic data files from each acquisition device on multiple target detector lines, the computing device can determine the acquisition time period for multiple acquisition devices based on the first and last sample values ​​in the seismic data file of any acquisition device. Alternatively, the computing device can also obtain the acquisition time period for the multiple acquisition devices input by the user. Then, the acquisition time period is divided into multiple time windows according to a first duration, where the duration of each time window is the first duration.

[0085] It should be noted that the first duration can be set by comprehensively considering the relevant recording time and data calculation volume of a single gun. Specifically, this first duration is longer than the relevant recording time of a single gun. For example, when the relevant recording time of a single gun is 16 seconds, the first duration can be 60 seconds.

[0086] For example, assuming that the collection period of each acquisition device is 7 days, that is, the collection period is 7*24*3600=604800 seconds. When the first duration is 60 seconds, the collection period can be divided into 604800 / 60=10080 time windows.

[0087] Step 103: Based on the sample values ​​collected by each acquisition device within each time window, determine the root mean square value of the sample values ​​collected by each acquisition device within each time window.

[0088] Taking any one of the multiple acquisition devices as an example, the computing device obtains the sample values ​​collected by the acquisition device in each time window. Then, based on the sample values ​​collected by the acquisition device in each time window, the root mean square value of the sample values ​​of the acquisition device in the corresponding time window is determined.

[0089] For example, in this embodiment of the application, after obtaining the seismic data file of each acquisition device, the computing device can load the seismic data file of each acquisition device to obtain the sample point values ​​and corresponding acquisition times contained in the seismic data file. Then, for any time window, the computing device can obtain the sample point values ​​whose acquisition times are located within that time window.

[0090] For example, assuming the acquisition period of each acquisition device is 7 days, or 604,800 seconds, and the acquisition device records a sample value every 4 milliseconds, then the seismic data file of one acquisition device will contain 604,800 * 1000 / 4 = 151,200,000 sample values. As shown in the previous example, when the first duration is 60 seconds, it can be divided into 10,080 time windows. Thus, each time window will contain 151,200,000 / 10,080 = 15,000 sample values.

[0091] Optionally, in this embodiment of the application, after obtaining the sample point values ​​and corresponding acquisition times contained in the seismic data file, the computing device can also display a curve showing the change of sample point values ​​with the acquisition time, with the acquisition time as the horizontal axis and the sample point values ​​as the vertical axis. For example, Figure 3 This is a curve showing the variation of sample values ​​in the acquisition device as a function of acquisition time, as illustrated in an embodiment of this application. This curve is obtained by connecting sample values ​​corresponding to different acquisition times, where the time windows can be divided as follows: Figure 3 As shown, each time window corresponds to a data segment.

[0092] Taking any one of the multiple time windows as an example, the computing device can calculate the root mean square value of the sample points within the time window using the following formula (1).

[0093]

[0094] Among them, x1 to x n RMS represents the n sample values ​​collected by the acquisition device within the time window, and RMS represents the root mean square value of the sample values ​​collected by the acquisition device within the time window.

[0095] Using the method described above, the computing device can obtain the root mean square (RMS) values ​​of the sample points within multiple time windows. For example, assuming there are k time windows, the computing device will obtain k RMS values ​​from the acquisition device, which are (RMS1, RMS2, ..., RMS...). k ).

[0096] For each of the multiple acquisition devices, the computing device can determine multiple root mean square values ​​of each acquisition device within multiple time windows using the method described above.

[0097] Step 104: Determine abnormal acquisition information based on the root mean square value of the sample points of each acquisition device within each time window.

[0098] After determining the root mean square value of the sample points of each acquisition device within each time window, the computing device can determine abnormal acquisition information based on the root mean square value of the sample points of each acquisition device within each time window. This abnormal acquisition information can indicate the abnormal acquisition device among the multiple acquisition devices, or it can indicate the time window within the acquisition period of the abnormal acquisition device where abnormal sample points exist.

[0099] For example, in the first implementation, the computing device determines the average root mean square value of the sample points of each acquisition device based on the root mean square value of the sample points within each time window; obtains the line number and point number corresponding to each acquisition device, where the line number is the line number of the detector line where the acquisition device is located, and the point number is the point number of the point on the detector line where the acquisition device is located; generates an attribute set for each acquisition device based on the average root mean square value of the sample points of each acquisition device and the corresponding line number and point number, the attribute set including the average root mean square value of the sample points of the corresponding acquisition device, the corresponding line number and point number; and determines abnormal acquisition information based on the attribute set of each acquisition device.

[0100] Taking any one of the multiple acquisition devices as an example, the computing device can calculate the average of the root mean square values ​​of multiple sample points of the acquisition device within multiple time windows, thereby obtaining the average root mean square value of the sample points of the acquisition device.

[0101] For example, suppose the data acquisition device has k root mean square values ​​(RMS1, RMS2, ..., RMS) within k time windows. k If the calculation device can obtain the average root mean square value of the sample points of the acquisition device through the following formula (2), then the computing device can obtain the average root mean square value of the sample points of the acquisition device.

[0102]

[0103] Using the above method, the computing device can determine the root mean square value of the sample points of each acquisition device.

[0104] Furthermore, as can be seen from the deployment method of the acquisition equipment described above, the deployment location of each acquisition device can be identified by the line number of the detector line on which the acquisition device is located and the point number of the point on that detector line. Based on this, in this embodiment of the application, the computing device can also obtain the line number and point number corresponding to each acquisition device. Specifically, the header of the seismic data file of each acquisition device can contain the line number and point number corresponding to that acquisition device. In this case, the computing device can directly read the line number and point number from the header of the seismic data file of each acquisition device to obtain the line number and point number corresponding to that acquisition device.

[0105] After obtaining the average root mean square value of the sample points of each acquisition device, the corresponding line number and point number, the computing device can generate the attribute set of the corresponding acquisition device based on the average root mean square value of the sample points of each acquisition device, the corresponding line number and point number.

[0106] For example, taking any data acquisition device as an example, assume that the average root mean square value of the data acquisition device is RV. i j If the corresponding line number is Li and the corresponding point number is Sj, then the attribute set of this acquisition device is...

[0107] After obtaining the attribute sets of each acquisition device, the computing device can arrange the multiple attribute sets of multiple acquisition devices based on the line number and point number corresponding to each acquisition device to obtain an attribute set matrix. In this attribute set matrix, the acquisition devices corresponding to attribute sets in the same row have the same line number, and the acquisition devices corresponding to attribute sets in the same column have the same point number. Then, the computing device can use the isolated forest algorithm to determine the abnormal attribute sets in the attribute set matrix and identify the abnormal attribute sets as abnormal acquisition information.

[0108] The computing device can identify the acquisition devices with the same line number from multiple acquisition devices, and arrange the attribute sets of acquisition devices with the same line number into rows according to the ascending order of the point numbers corresponding to each acquisition device. Then, the attribute sets of each row corresponding to each line number are arranged into columns according to the ascending order of the line numbers, thus obtaining the attribute set matrix.

[0109] For example, assuming that the line numbers of multiple target detector lines are L1, L2, ..., Li, and the point numbers of the points on each detector line are S1, S2, ..., Sj, then the attribute set matrix can be shown in the following equation (3).

[0110]

[0111] In this attribute set matrix, each element represents an attribute set. The superscript of any attribute set represents the point number corresponding to the acquisition device, and the subscript represents the line number corresponding to the acquisition device. For example, This is the set of attributes of the acquisition device at line number L2 and point number S2.

[0112] After generating the attribute set matrix, the computing device can use the Isolation Forest algorithm to determine anomalous elements from multiple elements of the attribute set matrix, that is, to determine the anomalous attribute set among the multiple attribute sets included in the attribute set matrix. The process of determining the anomalous attribute set using the Isolation Forest algorithm can refer to the implementation methods for detecting anomalous sample points in related technologies, where each attribute set is associated with a sample point; this will not be elaborated further in the embodiments of this application.

[0113] After identifying one or more abnormal attribute sets in the attribute set matrix, the computing device can use these abnormal attribute sets as anomaly acquisition information. Based on this, a data acquisition device can be located using the line number and point number in an abnormal attribute set; this data acquisition device is called the anomaly acquisition device.

[0114] Optionally, after obtaining abnormal acquisition information through the above methods, the computing device can also directly display the abnormal acquisition information, or send the abnormal acquisition information to other devices for display, so as to prompt the field geophysical exploration personnel about the problems in the data acquisition process, so that the field geophysical exploration personnel can make relevant rectifications based on the abnormal acquisition information, such as replacing the abnormal acquisition equipment or processing the corresponding ground sample values.

[0115] Optionally, in this embodiment, the computing device can also display a corresponding matrix diagram based on the attribute set matrix. For example, when the attribute set matrix includes 3 rows and 15 columns, with each row corresponding to a line number and each column corresponding to a point number, assuming the line numbers corresponding to the 3 rows are L1 to L3 and the point numbers corresponding to the 15 columns are S1 to S15, the display diagram of the attribute set matrix can be as follows: Figure 4 As shown, each box in the diagram can represent either the attribute set of the acquisition device at the corresponding location or directly represent the acquisition device itself. Based on this, once the anomaly indication information is determined, the computing device can also display a prompt message in the attribute set matrix display diagram based on the anomaly attribute set contained in the determined anomaly indication information. For example, a box can be marked on the display diagram at the corresponding location, thus more clearly indicating the location of the abnormal acquisition device.

[0116] In the second implementation, the computing device can generate the corresponding time-series root mean square matrix for each target detector line based on the root mean square value of the sample points of each acquisition device on each target detector line within each time window; then, based on the time-series root mean square matrix for each target detector line, the abnormal acquisition information corresponding to each target detector line is determined.

[0117] As described in step 103, for any acquisition device, the computing device can calculate multiple root mean square (RMS) values ​​of that acquisition device within multiple time windows. Based on this, in this step, the computing device first determines multiple acquisition devices located on the same target detector line based on the line number corresponding to each acquisition device. Then, based on the RMS values ​​of the sample points of the multiple acquisition devices located on the same target detector line within each time window, it determines the time-series RMS matrix corresponding to the target detector line.

[0118] For example, taking any one of multiple target detector lines as an example, this detector line is referred to as the first target detector line. The computing device first determines the acquisition device whose line number corresponds to the first target detector line from among multiple acquisition devices; these acquisition devices will be referred to as the first acquisition devices below. Then, the computing device arranges the root mean square values ​​of the sample points of each first acquisition device on the first target detector line in ascending order of their corresponding point numbers and in the order of multiple time windows, thereby obtaining the time-series root mean square matrix corresponding to the first target detector line. In this matrix, the root mean square (RMS) value of each row in the time-series RMMS matrix corresponding to the first target detector line is the RMMS value of the same first acquisition device within multiple time windows, and these RMMS values ​​are arranged in the order of the multiple time windows. The RMMS value of each column in the time-series RMMS matrix corresponding to the first target detector line is the RMMS value of each first acquisition device on the first target detector line within the same time window, and the RMMS values ​​of each first acquisition device within the same time window are arranged in ascending order of the point number corresponding to each first acquisition device.

[0119] In other words, for any first acquisition device on the first target detector line, the computing device first arranges the multiple root mean square values ​​of the first acquisition device within multiple time windows into a row according to the chronological order of the time windows. Then, the multiple rows of root mean square values ​​corresponding to the multiple first acquisition devices are arranged in ascending order according to the point number corresponding to each first acquisition device, thereby obtaining the time-series root mean square matrix.

[0120] For example, for a target detector line Li, assuming that the point numbers of multiple points on the detector line are S1, S2, ..., Sj, and the multiple time windows are T1, T2, ..., Tk, the time-series root mean square matrix corresponding to the target detector line Li is shown in equation (4) below.

[0121]

[0122] In this time-series root mean square matrix, the superscript of each root mean square value represents the time window, and the subscript represents the point number of the acquisition device corresponding to that root mean square value. For example, The root mean square value of the sample point at point S2 on the target detector line Li within the time window T2.

[0123] After determining the time-series root mean square matrix corresponding to the first target detector line, the computing device can use the k-means clustering algorithm to detect the time-series root mean square matrix corresponding to the first target detector line and obtain anomaly clusters. These anomaly clusters include multiple abnormal root mean square values ​​in the time-series root mean square matrix corresponding to the first target detector line. The first acquisition device and time window corresponding to these multiple abnormal root mean square values ​​are used as the abnormal acquisition information corresponding to the first target detector line.

[0124] The computing device first determines the number of clusters K based on the root mean square (RMS) values ​​in the time-series RMMS matrix using the elbow method. Then, the computing device clusters the RMS values ​​in the time-series RMMS matrix according to the number of clusters K, resulting in K clusters. Each cluster includes multiple RMS values. After determining the K clusters, for any given cluster, the computing device calculates the relative distance between the cluster center of that cluster and the cluster centers of all other clusters. If the average of these relative distances is greater than a reference threshold, the cluster is determined to be an anomalous cluster; otherwise, it is determined to be a normal cluster. The reference threshold can be a preset threshold, for example, the average of all RMS values ​​in the time-series RMMS matrix.

[0125] After identifying the anomalous clusters, the computing device can use the root mean square (RMS) values ​​included in these clusters as anomalous RMS values. Then, the first acquisition device and time window corresponding to these anomalous RMS values ​​are used as the anomalous acquisition information for the first target detector line. This anomalous acquisition information can then indicate which acquisition device acquired the sample values ​​within which time window, and in which case the anomalous values ​​were found.

[0126] For example, suppose the identified clusters of anomalies include Based on this root mean square value, it can be determined that the sample value collected by the acquisition device at point S2 on the first target detector line within the time window T2 is abnormal.

[0127] After obtaining abnormal acquisition information through the above methods, the computing device can directly display the abnormal acquisition information or send the abnormal acquisition information to other devices for display, thereby alerting field geophysical exploration personnel to problems in the data acquisition process. This allows field geophysical exploration personnel to rectify related problems based on the abnormal acquisition information, such as replacing the abnormal acquisition equipment or processing the corresponding ground sampling point values.

[0128] Optionally, in this embodiment of the application, after determining the time-series root mean square matrix corresponding to the first target detector line, the computing device can also display a diagram of the time-series root mean square matrix. For example, when the time-series root mean square matrix corresponding to the first target detector line includes 5 rows and 10 columns, each row corresponds to a point number, and each column corresponds to a time window. Assuming that the point numbers corresponding to the 5 rows are S1 to S5, and the time windows corresponding to the 10 columns are T1 to T10, the diagram of the time-series root mean square matrix can be displayed as follows: Figure 5 As shown, at this time, each box in the figure represents the root mean square (RMS) value of the acquisition device at the corresponding point on the first target detector line within the corresponding time window. Based on this, after determining the abnormal acquisition information of the first target detector line, the computing device can also display prompt information on the display diagram of the time-series RMS matrix based on the abnormal acquisition information. For example, a box can be marked at the corresponding position on the display diagram, which can more clearly indicate which acquisition device has an abnormal sample value in which time window.

[0129] The above explanation uses the first target detector line as an example. For each of the multiple target detector lines, the computing device can refer to the above method to determine the abnormal acquisition information corresponding to the target detector line.

[0130] Optionally, in some possible cases, the computing device can determine the set of abnormal attributes using the first implementation method described above, and determine the abnormal acquisition information corresponding to each target detection line using the second implementation method. Then, the computing device can use both the set of abnormal attributes and the abnormal acquisition information corresponding to each target detection line as the detected abnormal acquisition information.

[0131] Optionally, in other possible scenarios, the computing device may first determine the set of anomalous attributes using the first implementation method described above, and then determine the anomalous acquisition device corresponding to the set of anomalous attributes. Next, it may determine the target detector line where the anomalous acquisition device is located, and then determine the anomalous acquisition information corresponding to the target detector line where the anomalous acquisition device is located using the method described in the second implementation method.

[0132] In this embodiment, when determining abnormal acquisition information during the seismic acquisition process, it is not necessary to generate gather data or shot gather data based on the seismic data collected by all acquisition devices. Therefore, it is not necessary to wait for all acquisition devices to be retrieved before determining abnormal acquisition information. That is, in this embodiment, abnormal acquisition information can be determined using the root mean square (RMS) values ​​of sample points collected by the acquisition devices on the retrieved portion of the detector lines. This shortens the waiting time for device retrieval. Furthermore, compared to generating gather data or shot gather data based on the seismic data collected by all acquisition devices, the data computation involved in calculating the RMS value based on the sample points collected by the acquisition devices and determining abnormal acquisition information based on the RMS value is smaller in this embodiment. Therefore, the time required to determine abnormal acquisition information is also shorter. This allows for more timely identification of anomalies during the acquisition process, facilitating timely rectification of related problems and ensuring the quality of subsequent data.

[0133] Next, the seismic data processing apparatus provided in the embodiments of this application will be described.

[0134] See Figure 6 This application provides an earthquake data processing apparatus 600, which includes:

[0135] The acquisition module 601 is used to acquire the seismic data file of each of the multiple acquisition devices on multiple target detector lines. The seismic data file includes the sample point values ​​acquired by the corresponding acquisition device during the acquisition time period.

[0136] The segmentation module 602 is used to divide the collection period into multiple time windows based on the first duration;

[0137] The first determining module 603 is used to determine the root mean square value of the sample points of each acquisition device in each time window based on the sample point values ​​acquired by each acquisition device in each time window.

[0138] The second determining module 604 is used to determine abnormal acquisition information based on the root mean square value of the sample points of each acquisition device within each time window.

[0139] Optionally, the second determining module 604 is mainly used for:

[0140] Based on the root mean square value of the sample points of each acquisition device within each time window, the average root mean square value of the sample points of each acquisition device is determined.

[0141] Obtain the line number and point number corresponding to each acquisition device. The line number corresponding to each acquisition device is the line number of the detector line where the corresponding acquisition device is located, and the point number corresponding to each acquisition device is the point number of the corresponding acquisition device on the detector line.

[0142] Based on the average root mean square value of the sample points of each acquisition device and the corresponding line number and point number, an attribute set is generated for each acquisition device. The attribute set includes the average root mean square value of the sample points of the corresponding acquisition device, the corresponding line number and point number.

[0143] Based on the attribute set of each acquisition device, abnormal acquisition information is determined.

[0144] Optionally, the second determining module 604 is mainly used for:

[0145] Based on the line number and point number corresponding to each acquisition device, multiple attribute sets of multiple acquisition devices are arranged to obtain an attribute set matrix. In the attribute set matrix, the acquisition devices corresponding to the attribute sets in the same row have the same line number, and the acquisition devices corresponding to the attribute sets in the same column have the same point number.

[0146] The isolated forest algorithm is used to identify the set of abnormal attributes in the attribute set matrix.

[0147] The set of abnormal attributes is identified as abnormal data collection information.

[0148] Optionally, the second determining module 604 is mainly used for:

[0149] Based on the root mean square value of the sample points of each acquisition device on each target detector line within each time window, the time-series root mean square matrix corresponding to the target detector line is generated.

[0150] Based on the root mean square time matrix corresponding to each target detector line, the abnormal acquisition information corresponding to each target detector line is determined.

[0151] Optionally, the second determining module 604 is mainly used for:

[0152] According to the order of the point numbers corresponding to the multiple first acquisition devices on the first target detector line from smallest to largest and the order of multiple time windows, the root mean square values ​​of the sample points of each first acquisition device on the first target detector line in each time window are arranged to obtain the time series root mean square matrix corresponding to the first target detector line.

[0153] The first target detection line is any one of multiple target detection lines. The root mean square value of each row in the time-series root mean square matrix corresponding to the first target detection line is the root mean square value of the same first acquisition device in multiple time windows, and the multiple root mean square values ​​are arranged in the order of the multiple time windows. The root mean square value of each column in the time-series root mean square matrix corresponding to the first target detection line is the root mean square value of each first acquisition device on the first target detection line in the same time window, and the root mean square values ​​of each first acquisition device in the same time window are arranged in ascending order of the point number corresponding to each first acquisition device.

[0154] Optionally, the second determining module 604 is mainly used for:

[0155] The k-means clustering algorithm is used to detect the time-series root mean square matrix corresponding to the first target detector line to obtain anomaly clusters, which include multiple abnormal root mean square values ​​in the time-series root mean square matrix corresponding to the first target detector line.

[0156] The first acquisition device and time window corresponding to multiple abnormal root mean square values ​​are used as the abnormal acquisition information corresponding to the first target detector line.

[0157] In summary, in this embodiment, when determining abnormal acquisition information during the seismic acquisition process, it is not necessary to generate gather data or shot gather data based on the seismic data collected by all acquisition devices. Furthermore, it is not necessary to wait for all acquisition devices to be retrieved before determining abnormal acquisition information. That is, in this embodiment, abnormal acquisition information can be determined using the root mean square (RMS) values ​​of sample points collected by the acquisition devices on the retrieved portion of the detector lines. This shortens the waiting time for device retrieval. Moreover, compared to generating gather data or shot gather data based on the seismic data collected by all acquisition devices, the computational workload of calculating the RMS value based on the sample points collected by the acquisition devices and determining abnormal acquisition information based on the RMS value is smaller in this embodiment. Therefore, the time required to determine abnormal acquisition information is also shorter. This allows for more timely identification of anomalies during the acquisition process, facilitating timely rectification of related problems and ensuring the quality of subsequent data.

[0158] It should be noted that the seismic data processing device provided in the above embodiments is only illustrated by the division of the above functional modules when processing seismic data. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the seismic data processing device and the seismic data processing method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0159] Figure 7 This is a structural block diagram illustrating a computing device 700 for processing seismic data according to an exemplary embodiment. The computing device in the above embodiment can be implemented using this computing device 700. The computing device 700 can be a smartphone, tablet computer, laptop computer, desktop computer, etc.

[0160] Typically, computing device 700 includes a processor 701 and a memory 702.

[0161] Processor 701 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 701 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 701 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 701 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 701 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0162] The memory 702 may include one or more computer-readable storage media, which may be non-transitory. The memory 702 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 702 are used to store at least one instruction, wherein the at least one instruction is executed by the processor 701 to implement the seismic data processing method provided in the method embodiments of this application.

[0163] In some embodiments, the computing device 700 may also optionally include a peripheral device interface 703 and at least one peripheral device. The processor 701, memory 702, and peripheral device interface 703 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 703 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of the following: a radio frequency circuit 704, a display screen 705, a camera assembly 706, an audio circuit 707, a positioning assembly 708, and a power supply 709.

[0164] Peripheral device interface 703 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 701 and memory 702. In some embodiments, processor 701, memory 702 and peripheral device interface 703 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 701, memory 702 and peripheral device interface 703 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0165] The radio frequency (RF) circuit 704 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 704 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 704 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 704 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 704 can communicate with other computing devices via at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 704 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0166] Display screen 705 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 705 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 701 for processing. In this case, display screen 705 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen 705, positioned as the front panel of computing device 700; in other embodiments, there may be at least two display screens 705, respectively positioned on different surfaces of computing device 700 or in a folded design; in still other embodiments, display screen 705 may be a flexible display screen, positioned on a curved or folded surface of computing device 700. Furthermore, display screen 705 may be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. Display screen 705 may be made of materials such as LCD (Liquid Crystal Display) or OLED (Organic Light-Emitting Diode).

[0167] The camera assembly 706 is used to acquire images or videos. Optionally, the camera assembly 706 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the computing device, and the rear-facing camera is located on the back of the computing device. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly 706 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cool light flash, which can be used for light compensation at different color temperatures.

[0168] The audio circuit 707 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting the sound waves into electrical signals that are input to the processor 701 for processing, or input to the radio frequency circuit 704 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each located at a different part of the computing device 700. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor 701 or the radio frequency circuit 704 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 707 may also include a headphone jack.

[0169] The positioning component 708 is used to determine the current geographic location of the computing device 700 in order to enable navigation or LBS (Location Based Service). The positioning component 708 can be a positioning component based on GPS (Global Positioning System), BeiDou system, or Galileo system.

[0170] Power supply 709 is used to supply power to the various components in computing device 700. Power supply 709 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 709 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, and a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0171] In some embodiments, the computing device 700 further includes one or more sensors. These sensors include, but are not limited to, accelerometers, gyroscopes, pressure sensors, optical sensors, and proximity sensors.

[0172] This application provides a computing device, including a processor and a memory for storing processor-executable instructions, wherein the processor is configured to execute... Figure 1 The seismic data processing method shown in the present application also includes a computer-readable storage medium storing a computer program that, when executed by a processor, can implement... Figure 1 The earthquake data processing method shown.

[0173] Those skilled in the art will understand that Figure 7The structure shown does not constitute a limitation on the computing device 700, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0174] This application also provides a non-transitory computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor of a computing device, enables the computing device to perform the seismic data processing method provided in the above embodiments.

[0175] This application also provides a computer program product containing instructions that, when run on a computer, cause the computer to execute the seismic data processing method provided in the above embodiments.

[0176] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, and displayed data) and signals involved in the embodiments of this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0177] The above description is not intended to limit the embodiments of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of the embodiments of this application.

Claims

1. A seismic data processing method, characterized in that, The method includes: Acquire seismic data files from each of the multiple acquisition devices along multiple target detector lines, wherein the seismic data files include sample point values ​​acquired by the corresponding acquisition device within the acquisition time period; Based on the first duration, the collection period is divided into multiple time windows; Based on the sample point values ​​collected by each acquisition device within each time window, determine the root mean square value of the sample point values ​​of each acquisition device within each time window. Based on the root mean square value of the sample points of each acquisition device within each time window, the average root mean square value of the sample points of each acquisition device is determined. Obtain the line number and point number corresponding to each acquisition device. The line number corresponding to each acquisition device is the line number of the detector line where the corresponding acquisition device is located, and the point number corresponding to each acquisition device is the point number of the corresponding acquisition device on the detector line. Based on the average root mean square value of the sample points of each acquisition device and the corresponding line number and point number, an attribute set is generated for each acquisition device. The attribute set includes the average root mean square value of the sample points of the corresponding acquisition device, the corresponding line number and point number. Based on the line number and point number corresponding to each acquisition device, the multiple attribute sets of the multiple acquisition devices are arranged to obtain an attribute set matrix. In the attribute set matrix, the acquisition devices corresponding to the attribute sets in the same row have the same line number, and the acquisition devices corresponding to the attribute sets in the same column have the same point number. The set of anomalous attributes in the attribute set matrix is ​​determined using the isolated forest algorithm; The set of abnormal attributes is identified as abnormal acquisition information, which is used to indicate abnormal acquisition devices among the plurality of acquisition devices.

2. The method according to claim 1, characterized in that, The method further includes: Based on the root mean square value of the sample points of each acquisition device on each target detector line within each time window, the time-series root mean square matrix corresponding to the target detector line is generated. Based on the root mean square time matrix corresponding to each target detector line, the abnormal acquisition information corresponding to each target detector line is determined.

3. The method according to claim 2, characterized in that, The step of generating the time-series root mean square matrix corresponding to the target detector line based on the root mean square value of the sample points of each acquisition device on each target detector line within each time window includes: According to the order of the point numbers corresponding to the multiple first acquisition devices on the first target detector line from smallest to largest and the order of the multiple time windows, the root mean square values ​​of the sample points of each first acquisition device on the first target detector line in each time window are arranged to obtain the time series root mean square matrix corresponding to the first target detector line. Wherein, the first target detection line is any one of the plurality of target detection lines, the root mean square value of each row in the time-series root mean square matrix corresponding to the first target detection line is the root mean square value of the same first acquisition device in the plurality of time windows, and the plurality of root mean square values ​​are arranged in the order of the plurality of time windows, the root mean square value of each column in the time-series root mean square matrix corresponding to the first target detection line is the root mean square value of each first acquisition device on the first target detection line in the same time window, and the root mean square values ​​of each first acquisition device in the same time window are arranged in ascending order of the point number corresponding to each first acquisition device.

4. The method according to claim 3, characterized in that, The process of determining the abnormal acquisition information corresponding to each target detector line based on the time-series root mean square matrix corresponding to each target detector line includes: The k-means clustering algorithm is used to detect the time-series root mean square matrix corresponding to the first target detector line to obtain anomaly clusters, which include multiple abnormal root mean square values ​​in the time-series root mean square matrix corresponding to the first target detector line. The first acquisition device and time window corresponding to the multiple abnormal root mean square values ​​are respectively used as the abnormal acquisition information corresponding to the first target detector line.

5. A seismic data processing device, characterized in that, The device includes: The acquisition module is used to acquire the seismic data file of each of the multiple acquisition devices on multiple target detector lines. The seismic data file includes the sample point values ​​acquired by the corresponding acquisition device within the acquisition time period. The segmentation module is used to divide the collection time period into multiple time windows based on the first duration; The first determining module is used to determine the root mean square value of the sample points collected by each acquisition device in each time window based on the sample point values ​​collected by each acquisition device in each time window. The second determination module is used to determine abnormal acquisition information based on the root mean square value of the sample points of each acquisition device within each time window; The second determining module is mainly used for: Based on the root mean square value of the sample points of each acquisition device within each time window, the average root mean square value of the sample points of each acquisition device is determined. Obtain the line number and point number corresponding to each acquisition device. The line number corresponding to each acquisition device is the line number of the detector line where the corresponding acquisition device is located, and the point number corresponding to each acquisition device is the point number of the corresponding acquisition device on the detector line. Based on the average root mean square value of the sample points of each acquisition device and the corresponding line number and point number, an attribute set is generated for each acquisition device. The attribute set includes the average root mean square value of the sample points of the corresponding acquisition device, the corresponding line number and point number. The abnormal acquisition information is determined based on the attribute set of each acquisition device; The second determining module is mainly used for: Based on the line number and point number corresponding to each acquisition device, the multiple attribute sets of the multiple acquisition devices are arranged to obtain an attribute set matrix. In the attribute set matrix, the acquisition devices corresponding to the attribute sets in the same row have the same line number, and the acquisition devices corresponding to the attribute sets in the same column have the same point number. The set of anomalous attributes in the attribute set matrix is ​​determined using the isolated forest algorithm; The set of abnormal attributes is determined as the abnormal collection information.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a computer, implements the seismic data method of any one of claims 1-4.

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