Massive seismic data segmentation quality control method and device, electronic equipment and storage medium

Abstract

The present disclosure relates to a method and device for quality control of mass seismic data segmentation, an electronic device and a storage medium, comprising the steps of: obtaining seismic data of a study area and CPU / GPU nodes, segmenting the headers of the seismic data into regions according to the physical coordinates of the shot points and the detection points, selecting the regions according to a first predetermined rule, and the headers in each selected region are a subtask; using all the CPU / GPU nodes to respectively obtain the surface consistency quality control factor of each subtask, and judging whether each surface consistency quality control factor is less than a predetermined percentage, if yes, the quality control passes, otherwise, the quality control fails. This is to solve the problem that when performing surface consistency amplitude quality control on massive seismic data, all CPU nodes are used to obtain the amplitude of each sample point of each channel of the seismic data at one time, and then determine the global amplitude average value. Due to the large amount of seismic data, the whole process will take a lot of time.

Description

Technical Field

[0001] The present disclosure relates to the technical field of seismic data processing, and in particular to a method and device for quality control of mass seismic data segmentation, an electronic device and a storage medium. Background Art

[0002] In recent years, with the advancement of seismic acquisition technology, a new generation of "two wide and one high" or "two wide and two high" acquisition and observation systems with small facets, high coverage, broadband excitation reception, wide azimuth reception, and high density have been more and more widely used. They can bring more abundant underground information to geological researchers or reservoir forecasters, as well as a larger amount of data. Taking the seismic acquisition of Daqing Oilfield in the past five years as an example, the full coverage area is only 50km 2 The pre-stack data volume of the two-width and one-high data is nearly 10TB, the total data volume of the newly collected 3D seismic work area exceeds 10TB, and the data volume of the continuous pre-stack processing is far more than 100 TB. In the quality control of massive data, the timeliness of surface consistency quality control is a prominent problem.

[0003] At present, the technology or means commonly used in the industry for surface consistency amplitude quality control is to use all CPU nodes to count the amplitude of each sample point of all seismic channels and then find the average value, directly draw it into a plane diagram for display, or calculate the average value of all seismic channels to show the relationship between the amplitude of each channel and the global average value. The necessary processing is to read and calculate the amplitude of each sample point of each channel. This step cannot be omitted and takes a lot of time. Summary of the invention

[0004] The present invention discloses a method and device for quality control of mass seismic data segmentation, an electronic device and a storage medium, so as to solve the problem that when performing surface consistency amplitude quality control on mass seismic data, all CPU nodes are used to obtain the amplitude of each sample point of each channel of seismic data at one time, and then determine the global amplitude average value. Due to the large amount of seismic data, the whole process takes a lot of time.

[0005] According to one aspect of the present disclosure, a method for quality control of massive seismic data segmentation is provided, comprising the steps of:

[0006] Obtaining seismic data and CPU / GPU nodes in the study area, wherein the number of CPU / GPU nodes is at least 2;

[0007] Divide the trace header of the seismic data into a first region according to a first predetermined number of shot point physical coordinates, select the first region according to a first predetermined rule, and each selected trace header in the first region is a first subtask;

[0008] Divide the trace header of the seismic data into a second region according to the physical coordinates of a second predetermined number of detection points, select the second region according to a second predetermined rule, and each selected trace header in the second region is a second subtask;

[0009] Utilize all the CPU / GPU nodes to respectively obtain the surface consistency shot point quality control factor of each first subtask, and respectively determine whether each surface consistency shot point quality control factor is less than a predetermined percentage, if yes, the first subtask corresponding to the surface consistency shot point quality control factor passes the quality control, otherwise, fails the quality control;

[0010] After the quality control of all the first subtasks is completed, all the CPU / GPU nodes are used to respectively calculate the surface consistency detection point quality control factor of each second subtask, and it is determined whether each surface consistency detection point quality control factor is less than a predetermined percentage. If so, the second subtask corresponding to the surface consistency detection point quality control factor passes the quality control, otherwise, the quality control fails.

[0011] Preferably, the first predetermined rule includes:

[0012] Randomly select half of the first areas from among all the first areas;

[0013] The second predetermined rule includes:

[0014] Randomly select half of the second areas from among all the second areas;

[0015] Preferably, the method of using all the CPU / GPU nodes to respectively obtain the surface consistency shot point quality control factor of each first subtask includes: obtaining the average amplitude of the shot point area of ​​the seismic data corresponding to the trace header in the retrieved first subtask; determining the surface consistency shot point quality control factor of each first subtask according to the average amplitude of all the shot point areas;

[0016] The method of using all the CPU / GPU nodes to respectively obtain the surface consistency detection point quality control factor of each second subtask includes: obtaining the detection point area amplitude average value of the seismic data corresponding to the trace header in the retrieved second subtask; and determining the surface consistency detection point quality control factor of each second subtask based on the average amplitude values ​​of all the detection point areas.

[0017] Preferably, the method for determining the surface consistency shot point quality control factor of each first subtask according to the average amplitude values ​​of all the shot point regions comprises: obtaining the sum of the average amplitude values ​​of all the shot point regions, dividing the sum of the average amplitude values ​​of all the shot point regions by the total number of the first subtasks, to obtain the global average amplitude value of the shot points; determining the surface consistency shot point quality control factor of each first subtask according to the global average amplitude value of the shot points;

[0018] The method for determining the surface consistency detection point quality control factor of each second subtask based on the average amplitude values ​​of all the detection point regions includes: calculating the sum of the average amplitude values ​​of all the detection point regions, dividing the sum of the average amplitude values ​​of all the detection point regions by the total number of second subtasks, and obtaining the global average amplitude value of the detection points; and determining the surface consistency detection point quality control factor of each second subtask based on the global average amplitude value of the detection points.

[0019] Preferably, the method for determining the surface consistency shot point quality control factor of each first subtask according to the global average amplitude value of the shot point comprises: calculating the surface consistency shot point quality control factor of each first subtask according to the global average amplitude value of the shot point using a quality control factor calculation formula;

[0020] The method for determining the surface consistency detection point quality control factor of each second subtask based on the global average amplitude value of the detection point includes: calculating the surface consistency detection point quality control factor of each second subtask based on the global average amplitude value of the detection point using the quality control factor calculation formula.

[0021] Preferably, the quality control factor calculation formula includes:

[0022] ei=|(p- j) / j|;

[0023] Where: p is the regional average amplitude, j is the global average amplitude.

[0024] According to one aspect of the present disclosure, a mass seismic data segmentation quality control device is provided, comprising:

[0025] An acquisition unit, used to acquire seismic data of a study area and CPU / GPU nodes, wherein the number of CPU / GPU nodes is at least 2;

[0026] a data segmentation unit, for segmenting the trace header of the seismic data into a first region according to a first predetermined number of shot point physical coordinates, selecting the first region according to a first predetermined rule, each selected trace header in the first region being a first subtask, and for segmenting the trace header of the seismic data into a second region according to a second predetermined number of detection point physical coordinates, selecting the second region according to a second predetermined rule, each selected trace header in the second region being a second subtask;

[0027] The quality control unit is used to use all the CPU / GPU nodes to respectively calculate the surface consistency shot point quality control factor of each first subtask, and respectively determine whether each surface consistency shot point quality control factor is less than a predetermined percentage. If so, the first subtask corresponding to the surface consistency shot point quality control factor passes the quality control; otherwise, the quality control fails; and after the quality control of all the first subtasks is completed, use all the CPU / GPU nodes to respectively calculate the surface consistency detection point quality control factor of each second subtask, and respectively determine whether each surface consistency detection point quality control factor is less than a predetermined percentage. If so, the second subtask corresponding to the surface consistency detection point quality control factor passes the quality control; otherwise, the quality control fails.

[0028] According to one aspect of the present disclosure, there is provided an electronic device, including: a processor;

[0029] a memory for storing processor-executable instructions;

[0030] Wherein, the processor is configured to call the instructions stored in the memory to execute the above-mentioned mass seismic data segmentation quality control method.

[0031] According to one aspect of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the above-mentioned mass seismic data segmentation quality control method is implemented.

[0032] The present invention has at least the following beneficial effects:

[0033] The present invention discloses a method and device for quality control of massive seismic data segmentation, an electronic device and a storage medium. The method forms subtasks by performing regional segmentation on the header of seismic data according to a predetermined rule, and uses a CPU / GPU node to sequentially call the subtasks and obtain the regional amplitude average value of the subtask to obtain a surface consistency quality control factor. The method greatly improves the processing efficiency of massive seismic data by performing regional segmentation on the header of seismic data and synchronously obtains the amplitude average value of each segmented region through multiple CPU / GPU nodes, and shortens the entire surface consistency amplitude quality control time of massive seismic data by several times. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used to illustrate the technical solutions of the present disclosure together with the specification.

[0035] Figure 1 A flow chart of a method for quality control of massive seismic data segmentation according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0036] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0037] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0038] The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set consisting of A, B, and C.

[0039] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0040] It can be understood that the above-mentioned various method embodiments mentioned in the present disclosure can be combined with each other to form a combined embodiment without violating the principle logic. Due to space limitations, the present disclosure will not go into details.

[0041] Figure 1 FIG. 2 is a flow chart showing a method for quality control of mass seismic data segmentation according to an embodiment of the present disclosure. Figure 1As shown, the mass seismic data segmentation quality control method includes: step S101: obtaining seismic data and CPU / GPU nodes in a study area, wherein the number of the CPU / GPU nodes is at least 2; step S102: segmenting the header of the seismic data into a first region according to a first predetermined number of shot point physical coordinates, selecting the first region according to a first predetermined rule, and each selected header in the first region is a first subtask; step S103: segmenting the header of the seismic data into a second region according to a second predetermined number of detection point physical coordinates, selecting the second region according to a second predetermined rule, and each selected header in the second region is a second subtask; step S104: using Use all the CPU / GPU nodes to respectively obtain the surface consistency shot point quality control factor of each first subtask, and respectively determine whether each surface consistency shot point quality control factor is less than a predetermined percentage. If so, the first subtask corresponding to the surface consistency shot point quality control factor passes the quality control, otherwise, the quality control fails. Step S105: After all the first subtask quality control is completed, use all the CPU / GPU nodes to respectively obtain the surface consistency detection point quality control factor of each second subtask, and respectively determine whether each surface consistency detection point quality control factor is less than a predetermined percentage. If so, the second subtask corresponding to the surface consistency detection point quality control factor passes the quality control, otherwise, the quality control fails. This is to solve the problem that when performing surface consistency amplitude quality control on massive seismic data, all CPU nodes are used to obtain the amplitude of each sample point of each channel of seismic data at one time, and then determine the global amplitude average value. Due to the large amount of seismic data, the entire process will take a lot of time.

[0042] The embodiment of the present invention provides a method for quality control of mass seismic data segmentation, which specifically includes the following steps:

[0043] Step S101: Obtain seismic data of a study area and CPU / GPU nodes, wherein the number of the CPU / GPU nodes is at least 2.

[0044] In the disclosed embodiment, the seismic trace is indexed by trace header information, which is equivalent to the index information of the seismic data.

[0045] There can be multiple CPU / GPU nodes for processing massive seismic data, where CPU is a central processing unit (CPU for short) and GPU is a graphics processing unit (GPU for short). Image data or information can be called and related operations can be processed through CPU or GPU; if the number of CPU / GPU nodes that can participate in quality control selected by scanning hardware resources in this embodiment is 8, when processing seismic data, all 8 nodes can be used to process seismic data at the same time. If there is only one available CPU / GPU node, the speed of surface consistency amplitude quality control will not be improved compared with the past.

[0046] S102: Segment the header of the seismic data into first regions according to a first predetermined number of shot point physical coordinates, select the first region according to a first predetermined rule, and each selected header in the first region is a first subtask.

[0047] In the disclosed embodiment, the value range of the first predetermined number is 1 million or less. When more than 1 million seismic data are called and processed at one node, the speed is too slow. If the first predetermined number in this embodiment is 900,000, that is, the headers corresponding to the physical coordinates of every 900,000 shot points are divided into an area, one area is a first subtask, and all first subtasks constitute the first task pool to be processed. Among them, the seismic data may only include representative seismic data, and does not need to include all seismic data. By performing quality control on representative seismic data, the quality control time can be further shortened.

[0048] Step S103: dividing the header of the seismic data into second regions according to the physical coordinates of a second predetermined number of detection points, selecting the second regions according to a second predetermined rule, and each selected header in the second region is a second subtask.

[0049] In the disclosed embodiment, the second predetermined number has a value range of 1 million or less. If the second predetermined number is 900,000 in the present embodiment, the trace headers corresponding to the physical coordinates of every 900,000 detection points are divided into an area, one area is a second subtask, and all the second subtasks constitute a second task pool to be processed.

[0050] In the process of surface consistent amplitude compensation, the effects on seismic amplitude vary greatly due to the different environments of the surface shot points and detection points. Therefore, in order to eliminate the differential effects of each shot point and detection point on the amplitude, the surface consistent amplitude compensation operation is usually performed by using the shot point surface consistent amplitude compensation and the detection point surface consistent amplitude compensation. The quality control method of the present invention also performs quality control on these two aspects separately, that is, after using the physical coordinates of the shot points to divide the track head into regions, the physical coordinates of the detection points are used to divide the track head into regions, and then the quality control is performed separately.

[0051] In the present disclosure, the first predetermined rule includes: randomly selecting half of the first areas among all the first areas; and the second predetermined rule includes: randomly selecting half of the second areas among all the second areas.

[0052] In the disclosed embodiment, after the header data of the seismic data are respectively segmented into regions according to the physical coordinates of the shot points and the physical coordinates of the detection points, half of the first regions and the second regions are randomly selected for subsequent quality control operations after the segmentation is completed by the two segmentation methods. The processing volume of seismic data is often very large, and it takes a lot of time to perform quality control on all seismic data. If only half of the selected seismic data is quality controlled, the quality control time can be effectively saved, and only half of the original quality control time is required. According to the statistical midpoint estimation method, the quality control results of the seismic data of half of the first region and half of the second region randomly selected can represent the quality control results of the overall seismic data.

[0053] Step S103: Utilize all the CPU / GPU nodes to respectively calculate the surface consistency shot point quality control factor of each first subtask, and respectively determine whether each surface consistency shot point quality control factor is less than a predetermined percentage; if so, the first subtask corresponding to the surface consistency shot point quality control factor passes the quality control; otherwise, the quality control fails.

[0054] In the present disclosure, the method of using all the CPU / GPU nodes to respectively obtain the surface consistency shot point quality control factor of each first subtask includes: obtaining the average amplitude of the shot point area of ​​the seismic data corresponding to the trace header in the retrieved first subtask; determining the surface consistency shot point quality control factor of each first subtask according to the average amplitude of all the shot point areas;

[0055] The method of using all the CPU / GPU nodes to respectively obtain the surface consistency detection point quality control factor of each second subtask includes: obtaining the detection point area amplitude average value of the seismic data corresponding to the trace header in the retrieved second subtask; and determining the surface consistency detection point quality control factor of each second subtask based on the average amplitude values ​​of all the detection point areas.

[0056] In an embodiment of the present disclosure, a method for obtaining the amplitude average value of a shot point area of ​​seismic data corresponding to a header in a retrieved first subtask includes: obtaining the sum of the amplitude values ​​of all sample points in the seismic data corresponding to the header in the first subtask, dividing the sum of the amplitude values ​​by the total number of sample points in the seismic data corresponding to the header in the first subtask, and obtaining the amplitude average value of the shot point area of ​​the seismic data corresponding to the header in the first subtask.

[0057] The method for obtaining the amplitude average value of the detection point area of ​​the seismic data corresponding to the track header in the retrieved second subtask includes: obtaining the sum of the amplitude values ​​of all sample points in the seismic data corresponding to the track header in the second subtask, dividing the sum of the amplitude values ​​by the total number of sample points in the seismic data corresponding to the track header in the second subtask, and obtaining the amplitude average value of the detection point area of ​​the seismic data corresponding to the track header in the second subtask.

[0058] In the present disclosure, the method for determining the surface consistency shot point quality control factor of each first subtask according to the average amplitude values ​​of all the shot point regions includes: obtaining the sum of the average amplitude values ​​of all the shot point regions, dividing the sum of the average amplitude values ​​of all the shot point regions by the total number of the first subtasks, and obtaining the global average amplitude value of the shot point; determining the surface consistency shot point quality control factor of each first subtask according to the global average amplitude value of the shot point;

[0059] The method for determining the surface consistency detection point quality control factor of each second subtask based on the average amplitude values ​​of all the detection point regions includes: calculating the sum of the average amplitude values ​​of all the detection point regions, dividing the sum of the average amplitude values ​​of all the detection point regions by the total number of second subtasks, and obtaining the global average amplitude value of the detection points; and determining the surface consistency detection point quality control factor of each second subtask based on the global average amplitude value of the detection points.

[0060] In the disclosed embodiment, after adding up the average amplitude values ​​of all shot point regions, the sum of the average amplitude values ​​of the shot point regions is obtained, and the sum of the average amplitude values ​​of the shot point regions is divided by the total number of the first subtasks to finally obtain the global average amplitude value of the shot points. The global average amplitude value of the shot points is the average amplitude value of the shot point amplitudes of all sample points in the seismic data corresponding to the trace headers in all the first subtasks. According to the global average amplitude value of the shot points, the surface consistency shot point quality control factor can be obtained.

[0061] After adding up the average amplitude values ​​of all the detector points, the sum of the average amplitude values ​​of the detector points is obtained, and the sum of the average amplitude values ​​of the detector points is divided by the total number of the second subtask to finally obtain the global average amplitude value of the detector points. The global average amplitude value of the detector points is the average amplitude value of the detector points of all the sample points in the seismic data corresponding to the trace headers in all the second subtasks. According to the global average amplitude value of the detector points, the quality control factor of the surface consistency detector points can be obtained.

[0062] The volume of seismic data is huge. If the header is not segmented into regions, it will take a lot of time to add the amplitude values ​​of all sample points in all seismic data in sequence using CPU / GPU nodes and then calculate the average value. The time taken to call and read the header information of seismic data is only a few thousandth of the amplitude value of each sample point in the entire seismic data; and the first and second task pools composed of the first and second subtasks formed by the segmented header data occupy very little memory; through parallel calculation of multiple nodes, the average amplitude of the shot point and detection point area of ​​each first and second subtask after segmentation can be quickly obtained, saving quality control time.

[0063] In the present disclosure, the method for determining the surface consistency shot point quality control factor of each first subtask according to the global average amplitude value of the shot point includes: calculating the surface consistency shot point quality control factor of each first subtask according to the global average amplitude value of the shot point using a quality control factor calculation formula;

[0064] The method for determining the surface consistency detection point quality control factor of each second subtask based on the global average amplitude value of the detection point includes: calculating the surface consistency detection point quality control factor of each second subtask based on the global average amplitude value of the detection point using the quality control factor calculation formula.

[0065] In the present disclosure, the quality control factor calculation formula includes:

[0066] ei=|(p- j) / j|;

[0067] Where: p is the regional average amplitude, j is the global average amplitude.

[0068] In the disclosed embodiment, there are multiple regional amplitude averages of shot points and detection points. Substitute the regional amplitude averages of each shot point and detection point and the global average amplitude values ​​of the shot point and detection point into the quality control factor calculation formula, and the surface consistency shot point quality control factor corresponding to each first subtask and the surface consistency detection point quality control factor corresponding to each second subtask will be obtained. Among them, when calculating, the regional amplitude average of the surface consistency shot point quality control factor in the formula corresponds to the regional amplitude average of the shot point, and the global average amplitude value corresponds to the global average amplitude value of the shot point; the regional amplitude average of the surface consistency detection point quality control factor in the formula corresponds to the regional amplitude average of the detection point, and the global average amplitude value corresponds to the global average amplitude value of the detection point.

[0069] Taking the first subtask as an example, if the average amplitude values ​​of the shot point area of ​​the seismic data corresponding to the trace heads in any three first subtasks obtained by the above method are 20, 19, and 18 respectively, and the global average amplitude value obtained by all first subtasks is 19, then substituting it into the quality control factor calculation formula will obtain the three surface consistency shot point quality control factors e1, e2, and e3 respectively:

[0070] e1=|( p- j ) / j|=|(20-19) / 19|=0.526;

[0071] e2=|( p- j ) / j|=|(19-19) / 19|=0;

[0072] e3=|( p- j ) / j|=|(18-19) / 19|=0.526.

[0073] Step S104: Utilize all the CPU / GPU nodes to respectively calculate the surface consistency shot point quality control factor of each first subtask, and respectively determine whether each surface consistency shot point quality control factor is less than a predetermined percentage; if so, the first subtask corresponding to the surface consistency shot point quality control factor passes the quality control; otherwise, the quality control fails.

[0074] Step S105: After the quality control of all the first subtasks is completed, all the CPU / GPU nodes are used to respectively calculate the surface consistency detection point quality control factor of each second subtask, and it is determined whether each surface consistency detection point quality control factor is less than a predetermined percentage. If so, the second subtask corresponding to the surface consistency detection point quality control factor passes the quality control, otherwise, the quality control fails.

[0075] In the embodiment of the present disclosure, the predetermined percentage is: 1%. If the surface consistency shot point quality control factor or the surface consistency shot point quality control factor exceeds 1%, it means that the average amplitude value of the shot point or the detection point area is much larger or smaller than the average amplitude value of the overall data. This means that the energy difference of the detector receiving data caused by factors such as the shot offset and the excitation point energy has not been eliminated, and thus the surface consistency operation is unqualified.

[0076] Determine whether the surface consistency shot point and detection point quality control factors of all the first and second subtasks are less than one percent, that is, whether ei is less than 1%. If it is less than, it means that the seismic data has no surface consistency problem and the quality control passes; if ei is greater than or equal to 1%, the processing results have a surface consistency problem and the quality control fails. According to the surface consistency shot point quality control factors of the seismic data corresponding to the three first subtask areas calculated in the above embodiment are: 0.526, 0, and 0.526 respectively. By judging that the three surface consistency shot point quality control factors are all over 1%, it is shown that the seismic data has a surface consistency problem and the quality control fails.

[0077] In the disclosed embodiment, if the total number of CPU / GPU nodes is 8, then when the first call is made, the 8 CPU / GPU nodes simultaneously call the first pending task pool, and a total of 8 first subtasks will be called for the first time; each CPU / GPU node simultaneously calculates the average amplitude of the shot point area of ​​the seismic data corresponding to the trace headers in the 8 called first subtasks; when a CPU / GPU node calculates the average amplitude of the shot point area of ​​the first subtask called for the first time, it will automatically call the next uncalled first subtask from the first pending task pool, and calculate the average amplitude of the shot point area of ​​the first subtask, that is, a CPU / GPU node After the PU node obtains the regional amplitude average of the seismic data corresponding to the header in the current first subtask, it automatically calls the 9th first subtask from the first pending task pool. The first subtask that has been called will no longer be placed in the first pending task pool, and so on, until there are no uncalled first subtasks in the first pending task pool. The CPU / GPU node determines the global average amplitude value of the shot point based on the regional amplitude average values ​​of all shot points obtained, determines the surface consistency shot point quality control factor corresponding to each first subtask based on the global average amplitude value of the shot point, and judges whether each surface consistency shot point quality control factor is less than the predetermined percentage, thereby obtaining the quality control result. After the quality control of all first subtasks is completed, the CPU / GPU node will automatically call the second subtask from the second pending task pool, and calculate the average amplitude value of the detection point area of ​​the seismic data corresponding to the track header in the second subtask. After the average amplitude value of all detection points is calculated, the global average amplitude value of the detection point is determined, and the surface consistency detection point quality control factor of each second subtask is calculated, and quality control is performed until there is no uncalled second subtask in the second pending task pool. All quality controls are completed.

[0078] The present invention solves the practical problem of surface consistency quality control processing of hundreds of TB of massive seismic data. It uses the principle of pre-action to innovatively divide the track header of seismic data into subtasks instead of directly dividing the seismic data; fully utilizes the CPU / GPU computing nodes to parallelly calculate the amplitude average value in each area after segmentation, and takes the amplitude average value of the segmented area as a representative to obtain the surface consistency quality control factor of the subtask, and uses the surface consistency quality control factor to directly control the surface consistency of the seismic data corresponding to the subtask; the present invention is highly efficient and has strong practicality. By dividing and retrieving track header data with a small amount of data, it does not need to occupy too much memory, so that the processing is fast and accurate, breaking through the previous method of using one or several CPU / GPU nodes to count the amplitude value of each sample point of all seismic data and then obtaining the amplitude average value, which can greatly improve the processing speed. At the same time, the present invention can also be applied to seismic data processing with a small amount of data, providing reliable basic data for subsequent applications.

[0079] The execution subject of the mass seismic data segmentation quality control method can be any processing device, for example, the mass seismic data segmentation quality control method can be executed by a terminal device or a server or other processing device, wherein the terminal device can be a user equipment (User Equipment, UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (Personal Digital Assistant, PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. In some possible implementations, the mass seismic data segmentation quality control method can be implemented by a processor calling a computer-readable instruction stored in a memory.

[0080] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0081] The present disclosure also provides a mass seismic data segmentation quality control device, comprising: an acquisition unit, used to acquire seismic data of a study area and a CPU / GPU node, wherein the number of the CPU / GPU nodes is at least 2; a data segmentation unit, used to segment the header of the seismic data into a first region according to a first predetermined number of shot point physical coordinates, select the first region according to a first predetermined rule, and each selected header in the first region is a first subtask; and to segment the header of the seismic data into a second region according to a second predetermined number of detection point physical coordinates, select the second region according to a second predetermined rule, and each selected header in the second region is a second subtask; a quality control unit, used to Utilize all the CPU / GPU nodes to obtain the surface consistency shot point quality control factor of each first subtask respectively, and judge whether each surface consistency shot point quality control factor is less than a predetermined percentage respectively. If yes, the first subtask corresponding to the surface consistency shot point quality control factor passes the quality control, otherwise, the quality control fails. And, after all the first subtask quality control is completed, utilize all the CPU / GPU nodes to obtain the surface consistency detection point quality control factor of each second subtask respectively, and judge whether each surface consistency detection point quality control factor is less than a predetermined percentage respectively. If yes, the second subtask corresponding to the surface consistency detection point quality control factor passes the quality control, otherwise, the quality control fails. For details, please refer to the detailed description of a method for quality control of massive seismic data segmentation.

[0082] In some embodiments, the functions or modules and units included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments. For the sake of brevity, it will not be repeated here.

[0083] An embodiment of the present disclosure further proposes an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to call the instructions stored in the memory to execute the method described above.

[0084] The embodiment of the present disclosure further provides a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the above method is implemented.

[0085] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for quality control of massive seismic data segmentation, characterized in that: Includes steps: Obtaining seismic data and CPU / GPU nodes in the study area, wherein the number of CPU / GPU nodes is at least 2; The header of the seismic data is divided into first regions according to a first predetermined number of shot point physical coordinates, and the first region is selected according to a first predetermined rule, each header in the selected first region is a first subtask, and all the first subtasks constitute a first task pool to be processed; Divide the header of the seismic data into second regions according to the physical coordinates of a second predetermined number of detection points, select the second region according to a second predetermined rule, each selected header in the second region is a second subtask, and all the second subtasks constitute a second task pool to be processed; Utilize all the CPU / GPU nodes to respectively obtain the surface consistency shot point quality control factor of each first subtask, and respectively determine whether each surface consistency shot point quality control factor is less than a predetermined percentage. If so, the first subtask corresponding to the surface consistency shot point quality control factor passes the quality control, otherwise, the quality control fails. When one of the CPU / GPU nodes has completed the calculation, it will automatically call the next uncalled first subtask from the first pending task pool; After the quality control of all the first subtasks is completed, the CPU / GPU node will automatically retrieve the second subtask from the second pending task pool, and use all the CPU / GPU nodes to respectively calculate the surface consistency detection point quality control factor of each second subtask, and respectively determine whether each surface consistency detection point quality control factor is less than a predetermined percentage. If so, the second subtask corresponding to the surface consistency detection point quality control factor passes the quality control, otherwise, the quality control fails.

2. The method for mass seismic data segmentation quality control according to claim 1, characterized in that: The first predetermined rule includes: Randomly select half of the first areas from among all the first areas; The second predetermined rule includes: Among all the second regions, half of the number of the second regions are randomly selected.

3. The method for mass seismic data segmentation quality control according to claim 1 or 2, characterized in that: The method of using all the CPU / GPU nodes to respectively obtain the surface consistency shot point quality control factor of each first subtask includes: obtaining the average amplitude of the shot point area of ​​the seismic data corresponding to the trace header in the retrieved first subtask; determining the surface consistency shot point quality control factor of each first subtask according to the average amplitude of all the shot point areas; The method of using all the CPU / GPU nodes to respectively obtain the surface consistency detection point quality control factor of each second subtask includes: obtaining the detection point area amplitude average value of the seismic data corresponding to the trace header in the retrieved second subtask; and determining the surface consistency detection point quality control factor of each second subtask based on the average amplitude values ​​of all the detection point areas.

4. The method for mass seismic data segmentation quality control according to claim 3, characterized in that: The method for determining the surface consistency shot point quality control factor of each first subtask based on the average amplitude values ​​of all the shot point regions comprises: obtaining the sum of the average amplitude values ​​of all the shot point regions, dividing the sum of the average amplitude values ​​of all the shot point regions by the total number of the first subtasks, and obtaining a global average amplitude value of the shot point; determining the surface consistency shot point quality control factor of each first subtask based on the global average amplitude value of the shot point; The method for determining the surface consistency detection point quality control factor of each second subtask based on the average amplitude values ​​of all the detection point regions includes: calculating the sum of the average amplitude values ​​of all the detection point regions, dividing the sum of the average amplitude values ​​of all the detection point regions by the total number of second subtasks, and obtaining the global average amplitude value of the detection points; and determining the surface consistency detection point quality control factor of each second subtask based on the global average amplitude value of the detection points.

5. The method for mass seismic data segmentation quality control according to claim 4, characterized in that: The method for determining the surface consistency shot point quality control factor of each first subtask according to the global average amplitude value of the shot point comprises: calculating the surface consistency shot point quality control factor of each first subtask according to the global average amplitude value of the shot point using a quality control factor calculation formula; The method for determining the surface consistency detection point quality control factor of each second subtask based on the global average amplitude value of the detection point includes: calculating the surface consistency detection point quality control factor of each second subtask based on the global average amplitude value of the detection point using the quality control factor calculation formula.

6. The method for mass seismic data segmentation quality control according to claim 5, characterized in that: The quality control factor calculation formula includes: ei=|(p- j) / j|; Where: p is the regional average amplitude, j is the global average amplitude.

7. A mass seismic data segmentation quality control device, characterized in that: include: An acquisition unit, used to acquire seismic data of a study area and CPU / GPU nodes, wherein the number of CPU / GPU nodes is at least 2; a data segmentation unit, for segmenting the trace header of the seismic data into a first region according to a first predetermined number of shot point physical coordinates, selecting the first region according to a first predetermined rule, each selected trace header in the first region being a first subtask, and for segmenting the trace header of the seismic data into a second region according to a second predetermined number of detection point physical coordinates, selecting the second region according to a second predetermined rule, each selected trace header in the second region being a second subtask; The quality control unit is used to use all the CPU / GPU nodes to respectively calculate the surface consistency shot point quality control factor of each first subtask, and respectively determine whether each surface consistency shot point quality control factor is less than a predetermined percentage. If so, the first subtask corresponding to the surface consistency shot point quality control factor passes the quality control; otherwise, the quality control fails; and after the quality control of all the first subtasks is completed, use all the CPU / GPU nodes to respectively calculate the surface consistency detection point quality control factor of each second subtask, and respectively determine whether each surface consistency detection point quality control factor is less than a predetermined percentage. If so, the second subtask corresponding to the surface consistency detection point quality control factor passes the quality control; otherwise, the quality control fails.

8. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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