Primary wave travel time data chromatography method and device based on distributed parallel computing

Abstract

The invention provides a first-motion wave travel time data tomography method and device based on distributed parallel computing, and the method comprises the steps: obtaining seismic first-motion wave data of a target work area, and the seismic first-motion wave data comprises SEG-Y seismic data of the target work area; carrying out load balancing on the seismic first arrival wave data according to a main node and a plurality of pre-developed parallel computing sub-nodes; and utilizing the main node and the plurality of parallel computing sub-nodes to carry out chromatography on the seismic first arrival wave data. The invention provides a distributed calculation seismic first-arrival wave travel time chromatography method with the advantage of memory saving in combination with a big data architecture and thought, and the defect that the memory is insufficient when near-surface velocity modeling is carried out in a large work area under the three-dimensional condition is overcome. Therefore, one-time calculation of all first arrival wave travel time data is achieved, and the partition fusion problem caused by partitioning of the model is avoided under the condition that the cluster utilization rate is increased as much as possible.

Description

Technical Field

[0001] The present application belongs to the technical field of oil and gas exploration, in particular to the technical field of oil and gas exploration seismic data processing, and specifically relates to a first arrival wave travel time data tomography method and device based on distributed parallel computing. Background Art

[0002] In oil exploration, the arrival time and source location of the first-arrival seismic wave are important bases for determining the underground stratum structure and detecting oil reservoirs. By analyzing the waveform characteristics of the first-arrival seismic wave, information about the underground strata can be obtained to help explorers determine the direction and target area of ​​oil exploration.

[0003] In recent years, seismic first-arrival wave travel time tomography has played a vital role in near-surface surveys, first-arrival tomography static correction, and shallow surface modeling in deep-domain velocity modeling. As seismic exploration enters a new stage of full 3D acquisition, processing, and interpretation integration, the requirements for computer hardware are becoming increasingly higher. 3D velocity modeling, especially deep-domain velocity modeling, requires huge amounts of computer resources, especially memory.

[0004] There are various first-arrival travel time tomography technologies used in existing seismic exploration: including shortest path ray tracing from the shot point to the detection point to calculate the ray path to build a tomography matrix, or first calculating the travel time field of the entire work area and then tracing back from the detection point to the shot point to calculate the ray path to build a tomography matrix, or the adjoint state method travel time tomography algorithm proposed in recent years, all of which require reading the first-arrival file into memory, and then establishing a mapping relationship between the shot and detection points to complete the first-arrival tomography algorithm. Taking the MPI parallel framework as an example, the conventional first-arrival travel time tomography has a tight memory because each computing unit reads all one data. And because more variables need to be opened up to store three-dimensional velocity bodies and ray paths, the cluster itself is underutilized. Summary of the invention

[0005] The present invention belongs to the field of computer vision. One purpose of the present invention is to provide a distributed parallel computing based first arrival wave travel time data tomography method, which combines the big data architecture and ideas to propose a distributed computing seismic first arrival wave travel time tomography method with the advantage of memory saving, and solves the disadvantage of insufficient memory when performing near-surface velocity modeling in a large work area under three-dimensional conditions. Thus, all first arrival wave travel time data can be calculated at one time, and the partition fusion problem caused by partitioning the model can be avoided while maximizing the cluster utilization rate.

[0006] Another object of the present invention is to provide a first-arrival travel time data tomography device based on distributed parallel computing. Another object of the present invention is to provide an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the first-arrival travel time data tomography method based on distributed parallel computing when executing the computer program. Another object of the present invention is to provide a readable medium, on which a computer program is stored, and when the computer program is executed by the processor, the steps of the first-arrival travel time data tomography method based on distributed parallel computing are implemented.

[0007] In order to solve the technical problems in the background technology of this application, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a first arrival wave travel time data tomography method based on distributed parallel computing, comprising:

[0009] Acquire seismic first-break wave data of the target work area, wherein the seismic first-break wave data includes SEG-Y seismic data of the target work area;

[0010] Load balancing the earthquake first arrival wave data is performed according to the main node and a plurality of parallel computing sub-nodes opened in advance;

[0011] The main node and the plurality of parallel computing sub-nodes are used to tomographically analyze the earthquake first-arrival wave data.

[0012] In one embodiment of the present invention, the load balancing of the seismic first arrival wave data according to the master node and the plurality of pre-developed parallel computing sub-nodes includes:

[0013] Using the master node to read the number of seismic trace data in the seismic first arrival wave data;

[0014] The seismic first arrival wave data is load balanced according to the seismic trace data and the multiple parallel sub-nodes.

[0015] In one embodiment of the present invention, the step of opening the plurality of parallel computing sub-nodes includes:

[0016] According to the number of CPU cores, memory and the amount of seismic trace data of the main node and the parallel computing sub-node, the multiple parallel computing sub-nodes are opened in the MPI parallel framework model.

[0017] In one embodiment of the present invention, after load balancing the seismic first arrival wave data according to the seismic trace data and the plurality of parallel sub-nodes, the method further includes:

[0018] The multiple seismic trace data are stored according to the operation node identifier of the master node and the operation node identifier of the parallel computing sub-node.

[0019] In one embodiment of the present invention, load balancing the seismic first arrival wave data is performed according to the seismic trace data and the multiple parallel sub-nodes, including:

[0020] Determine the number of tomographic seismic trace data that should be allocated to the parallel computing subnode according to the number of CPU cores and memory of the parallel computing subnode and the number of seismic trace data;

[0021] The seismic first-arrival wave data are load balanced according to the number of tomographic seismic trace data to be allocated to the parallel computing sub-node.

[0022] In one embodiment of the present invention, before load balancing the seismic first arrival wave data according to the master node and the plurality of pre-developed parallel computing sub-nodes, the method further includes:

[0023] Broadcast the earthquake first arrival wave data to all parallel computing sub-nodes according to the master node.

[0024] In one embodiment of the present invention, using the master node and the plurality of parallel computing sub-nodes to tomographically analyze the seismic first arrival wave data includes:

[0025] In the velocity model space of the target work area, the seismic first arrival wave data are tomatomized in the order of the Y direction, the X direction and the Z direction of the velocity model space according to the main node and the plurality of parallel computing sub-nodes.

[0026] In a second aspect, the present invention provides a first arrival wave travel time data tomography device based on distributed parallel computing, the device comprising:

[0027] A first-break wave data acquisition module is used to acquire seismic first-break wave data of a target work area, wherein the seismic first-break wave data includes SEG-Y seismic data of the target work area;

[0028] A first-arrival wave data load balancing module is used to load balance the earthquake first-arrival wave data according to the main node and multiple parallel computing sub-nodes opened in advance;

[0029] The first-break wave data tomography module is used to utilize the main node and the multiple parallel computing sub-nodes to tomography the seismic first-break wave data.

[0030] In one embodiment of the present invention, the first arrival wave data load balancing module includes:

[0031] A seismic trace quantity reading unit, used for reading the quantity of seismic trace data in the seismic first arrival wave data by using the master node;

[0032] A first-break wave data load balancing unit is used to load balance the seismic first-break wave data according to the seismic trace data and the multiple parallel sub-nodes.

[0033] In one embodiment of the present invention, the first arrival wave travel time data tomography device based on distributed parallel computing further includes:

[0034] A parallel computing subnode development module, used for developing the plurality of parallel computing subnodes;

[0035] The parallel computing sub-node development module includes:

[0036] The parallel computing sub-node development unit is used to develop the multiple parallel computing sub-nodes in the MPI parallel framework model according to the number of CPU cores, memory and the amount of seismic trace data of the main node and the parallel computing sub-nodes.

[0037] In one embodiment of the present invention, the first arrival wave travel time data tomography device based on distributed parallel computing further includes:

[0038] The seismic trace data storage module is used to store a plurality of seismic trace data according to the operation node identifier of the master node and the operation node identifier of the parallel computing sub-node.

[0039] In one embodiment of the present invention, the first arrival wave data load balancing unit includes:

[0040] A seismic trace quantity determination unit, used to determine the quantity of tomographic seismic trace data to be allocated to the parallel computing subnode according to the number of CPU cores and memory of the parallel computing subnode and the quantity of seismic trace data;

[0041] The first-arrival wave data load balancing subunit is used to load balance the seismic first-arrival wave data according to the number of tomographic seismic trace data to be allocated to the parallel computing subnode.

[0042] In one embodiment of the present invention, the first arrival wave travel time data tomography device based on distributed parallel computing further includes:

[0043] The earthquake first-break wave broadcasting module is used to broadcast the earthquake first-break wave data to all parallel computing sub-nodes according to the master node.

[0044] In one embodiment of the present invention, the first-break data tomography module includes:

[0045] The first arrival wave data tomography unit is used to tomographically perform the seismic first arrival wave data in the velocity model space of the target work area according to the main node and the plurality of parallel computing sub-nodes in the order of the Y direction, the X direction and the Z direction of the velocity model space.

[0046] In a third aspect, the present invention provides a computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the steps of a first arrival wave travel time data tomography method based on distributed parallel computing.

[0047] In a fourth aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of a first arrival wave travel time data tomography method based on distributed parallel computing are implemented.

[0048] In a fifth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a first-arrival travel time data tomography method based on distributed parallel computing.

[0049] From the above description, it can be seen that an embodiment of the present invention provides a first-arrival wave travel time data tomography method and device based on distributed parallel computing, and the corresponding first-arrival wave travel time data tomography method based on distributed parallel computing includes: firstly, obtaining the earthquake first-arrival wave data of the target work area, and the earthquake first-arrival wave data includes the SEG-Y earthquake data of the target work area; then, load balancing the earthquake first-arrival wave data according to the main node and multiple parallel computing sub-nodes opened in advance; finally, using the main node and multiple parallel computing sub-nodes to tomography the earthquake first-arrival wave data.

[0050] The corresponding first-arrival wave travel time data tomography device based on distributed parallel computing includes: a first-arrival wave data acquisition module, which is used to acquire the earthquake first-arrival wave data of the target work area, and the earthquake first-arrival wave data includes the SEG-Y earthquake data of the target work area; a first-arrival wave data load balancing module, which is used to load balance the earthquake first-arrival wave data according to the main node and multiple parallel computing sub-nodes opened in advance; a first-arrival wave data tomography module, which is used to use the main node and multiple parallel computing sub-nodes to tomography the earthquake first-arrival wave data.

[0051] The first arrival wave travel time data tomography method and device based on distributed parallel computing provided by the embodiment of the present invention, combined with the big data architecture and ideas, proposes a distributed computing seismic first arrival wave travel time tomography method and device with the advantage of memory saving, which solves the disadvantage of insufficient memory when performing near-surface velocity modeling in a large work area under three-dimensional conditions. Thus, all first arrival wave travel time data can be calculated at one time, and the partition fusion problem caused by partitioning the model can be avoided while maximizing the cluster utilization rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 A schematic diagram of a flow chart of a first arrival wave travel time data tomography method based on distributed parallel computing in an embodiment of the present invention;

[0054] Figure 2 It is a flowchart diagram of step 200 of the first arrival wave travel time data tomography method based on distributed parallel computing in an embodiment of the present invention;

[0055] Figure 3 It is another flow chart of the first arrival wave travel time data tomography method based on distributed parallel computing in an embodiment of the present invention;

[0056] Figure 4 It is a flowchart diagram of step 400 of the first arrival wave travel time data tomography method based on distributed parallel computing in an embodiment of the present invention;

[0057] Figure 5 It is a flowchart diagram of step 200 of the first arrival wave travel time data tomography method based on distributed parallel computing in an embodiment of the present invention;

[0058] Figure 6 It is a flow chart of step 202 of the first arrival wave travel time data tomography method based on distributed parallel computing in an embodiment of the present invention;

[0059] Figure 7 It is a third flow chart of the first arrival wave travel time data tomography method based on distributed parallel computing in an embodiment of the present invention;

[0060] Figure 8 It is a flowchart diagram of step 300 of the first arrival wave travel time data tomography method based on distributed parallel computing in an embodiment of the present invention;

[0061] Fig. 9 It is a flow chart of a first arrival wave travel time data tomography method based on distributed parallel computing in a specific embodiment of the present invention;

[0062] Fig.10 It is a schematic diagram of the tomographic inversion result of seismic first arrival wave data in a specific implementation manner of the present invention;

[0063] Fig.11It is a block diagram of a first arrival wave travel time data tomography device based on distributed parallel computing in a specific embodiment of the present invention;

[0064] Fig.12 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0067] It should be noted that the terms "including" and "having" in the specification and claims of the present application and the above-mentioned drawings and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. In the absence of conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0068] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of laws and regulations.

[0069] Embodiment 1:

[0070] In recent years, the emerging big data technology adopts the idea of ​​data localization, that is, the data does not move, but the calculation moves. Based on this, the present invention combines the architecture and idea of ​​big data to propose a memory-saving distributed seismic first-arrival wave travel time tomography method to solve the problem of insufficient memory when modeling near-surface velocity in large work areas in three-dimensional conditions.

[0071] The embodiment of the present invention provides a specific implementation method of the first arrival wave travel time data tomography method based on distributed parallel computing, see Figure 1 , specifically including the following:

[0072] Step 100: Acquire seismic first-break wave data of a target work area, wherein the seismic first-break wave data includes SEG-Y seismic data of the target work area;

[0073] Step 200: Load balancing the earthquake first arrival wave data according to the main node and multiple parallel computing sub-nodes opened in advance;

[0074] Step 300: Utilize the main node and the plurality of parallel computing sub-nodes to tomograph the seismic first-arrival wave data.

[0075] From the above description, it can be seen that an embodiment of the present invention provides a first-arrival wave travel time data tomography method based on distributed parallel computing, including: first obtaining the earthquake first-arrival wave data of the target work area, the earthquake first-arrival wave data including the SEG-Y earthquake data of the target work area; then, load balancing the earthquake first-arrival wave data according to the main node and multiple parallel computing sub-nodes opened in advance; finally, using the main node and multiple parallel computing sub-nodes to tomography the earthquake first-arrival wave data.

[0076] The first arrival wave travel time data tomography method based on distributed parallel computing provided in the embodiment of the present invention combines the big data architecture and ideas to propose a distributed computing seismic first arrival wave travel time tomography method with the advantage of memory saving, which solves the disadvantage of insufficient memory when performing near-surface velocity modeling in a large work area under three-dimensional conditions. Thus, all first arrival wave travel time data can be calculated at one time, and the partition fusion problem caused by partitioning the model can be avoided while maximizing the cluster utilization rate.

[0077] Embodiment 2:

[0078] It is understandable that the SEG-Y seismic data in step 100 is a standard format for storing and exchanging seismic data, which is called Seismic Exploration Geophysics-Y format.

[0079] Seismic data in SEG-Y format is in binary format, usually including information such as seismic waveform data, detector location, acquisition parameters, etc. These seismic data are collected by seismic instruments on the ground or underwater, and are stored in SEG-Y format after processing. The SEG-Y format has many advantages, the most important of which is that it is a universal and extensible standard format that facilitates data exchange and sharing between different seismic instruments, software, and processing systems. In addition, the SEG-Y format can also store a variety of common data types, such as multi-channel seismic data, three-dimensional seismic data, etc.

[0080] Preferably, step 200 can be performed under the MPI parallel framework. MPI (Message Passing Interface) is a standard application program interface for writing parallel programs. It allows programs to run in parallel on a computer cluster and realizes the distribution of computing tasks through inter-process communication. The main features and functions of MPI are as follows:

[0081] MPI defines a set of function libraries for communication and synchronization between processes. Developers implement distributed computing by calling these functions. MPI programs can run in parallel on multiple computers, with each process running on a computing node to complete computing tasks together. MPI supports point-to-point communication (send / receive) and collective communication (broadcast, convergence, etc.). MPI provides functions for managing process groups, such as creating, joining, and leaving process groups. The MPI standard does not depend on specific hardware or operating systems and is cross-platform. The main implementations include Open MPI, MPICH, etc.

[0082] When step 200 is implemented, the specific process includes: designing a parallel algorithm based on the seismic first arrival wave volume of the target work area and the predetermined tomography time. Using the MPI function library to write source code, realize task decomposition and inter-process communication. Running the program on the computing cluster, the MPI runtime system is responsible for process management and communication. Each process calculates its own task in parallel and completes the overall task through communication coordination.

[0083] For step 300, first-arrival seismic tomography analysis is used to determine the structure and characteristics of underground strata. This method is based on the propagation speed and path of the seismic first-arrival wave and infers the distribution of underground strata by analyzing seismic record data.

[0084] Specifically, in first-arrival seismic tomography analysis, seismic data must first be acquired, usually by deploying a network of seismic instruments for seismic exploration measurements. The seismic instruments record the arrival time and waveform of the first-arrival seismic waves. These recorded data are then used for data processing and analysis.

[0085] During data processing, a series of calculations and model inferences are performed. By comparing the arrival times of the first arrival waves at different receivers, the distance from the source to the receiver can be calculated. Then, based on the wave velocity model, the velocity distribution of the underground strata can be inferred. Using this information, first arrival wave seismic tomography can be performed.

[0086] The principle of first-arrival seismic tomography is to use the difference in the velocity of seismic waves propagating in different underground media to convert seismic record data into a velocity profile of underground strata through an inversion algorithm. This velocity profile can show information such as the distribution of underground strata, interface location, and lithology changes.

[0087] First-arrival seismic tomography analysis can help explorers determine the location, thickness and shape of oil reservoirs, providing a reliable basis for oil exploration. At the same time, through first-arrival seismic tomography analysis, it is also possible to understand the structural characteristics of underground strata, including faults, folds and lithology changes, providing support for geological research and resource evaluation.

[0088] In some embodiments of the present invention, see Figure 2 , step 200 comprises:

[0089] Step 201: using the master node to read the number of seismic trace data in the seismic first-break wave data;

[0090] Step 202: Load balancing the seismic first-arrival wave data according to the seismic trace data and the multiple parallel sub-nodes.

[0091] In step 201 and step 202, the master node first initializes the MPI parallel framework to obtain the MPI action space and the total number of running nodes; the MPI parallel framework initialization includes: introducing the MPI library: introducing the MPI library in the code, for example, using #include in C / C++<mpi.h> . Initialize the MPI environment: Call the MPI_Init function at the beginning of the code to initialize the MPI environment. This function allocates the required resources to each process and establishes a communication mechanism between processes. Get the number of processes and process numbers: After calling MPI_Init, you can use the MPI_Comm_size function to get the number of processes in the current parallel calculation, and use the MPI_Comm_rank function to get the number of the current process. This information can be used for task division and inter-process communication in subsequent parallel calculations. Execute parallel calculations: After the MPI environment is initialized, parallel computing tasks can be executed. According to the specific algorithm and task division, various communication and synchronization functions provided by MPI can be used to achieve inter-process communication and data exchange. Terminate the MPI environment: After the parallel calculation is completed, you need to call the MPI_Finalize function to terminate the MPI environment. This function releases MPI resources and ensures that all processes exit normally.

[0092] It should be noted that when performing MPI parallel computing, each process will execute the same code, and different tasks and data can be distinguished based on the process number.

[0093] Next, load-balanced data decomposition is carried out, and the master node reads the SEG-Y format seismic data containing the first arrival data.

[0094] In some embodiments of the present invention, see Figure 3 , a first arrival wave travel time data tomography method based on distributed parallel computing, further comprising:

[0095] Step 400: opening the plurality of parallel computing sub-nodes;

[0096] Specifically, the master node opens N+1 parallel child nodes, which are recorded as N+1 computing nodes. Furthermore, in the MPI framework, some functions and methods can be used to create and manage MPI child processes, also called child nodes. A child process is a group of tasks that are executed in parallel and can communicate and coordinate in the MPI program.

[0097] First, call the MPI_Init function to initialize the MPI library. Then, use the MPI_Comm_size and MPI_Comm_rank functions to obtain the total number of processes in the current MPI communication domain and the number of the current process respectively. Finally, you can use the MPI_Comm_split function to divide the process into several sub-communication domains, each of which corresponds to a child node. For example:

[0098] MPI_Comm_split(MPI_Comm comm,int color,int key,MPI_Comm*newcomm);

[0099] Among them: comm: current communication domain. color: specifies the color used to divide the sub-communication domain. Processes with the same color will be assigned to the same sub-communication domain. key: specifies the key value used to divide the sub-communication domain, which is used to determine the order of processes in the sub-communication domain. newcomm: the handle of the sub-communication domain, through which communication and coordination can be carried out in the child nodes.

[0100] Next, the newcomm handle is used in the child node for inter-process communication and coordination. Finally, at the end of the program, the MPI_Finalize function is called to terminate the MPI library.

[0101] Continue to see Figure 4 , step 400 comprises:

[0102] Step 401: According to the number of CPU cores, memory and the amount of seismic trace data of the main node and the parallel computing sub-node, the plurality of parallel computing sub-nodes are opened in the MPI parallel framework model.

[0103] That is, taking into account the hardware configuration of the parallel computing sub-nodes, multiple seismic channel data are distributed to multiple parallel computing sub-nodes instead of being evenly distributed according to the number of parallel computing sub-nodes.

[0104] In some embodiments of the present invention, see Figure 5 In the first arrival wave travel time data tomography method based on distributed parallel computing, after step 202, the method further includes:

[0105] Step 203: storing the plurality of seismic trace data according to the computing node identifier of the master node and the computing node identifier of the parallel computing sub-node.

[0106] Specifically, the computing units are load balanced according to the total number of seismic data read in, and the decomposed files are stored according to the computing node identifiers, so that each computing node can process the same number of seismic data at the same time;

[0107] In some embodiments of the present invention, see Figure 6 , step 202 comprises:

[0108] Step 2021: determining the number of tomographic seismic trace data to be allocated to the parallel computing subnode according to the number of CPU cores and memory of the parallel computing subnode and the number of seismic trace data;

[0109] Step 2022: Load balancing the seismic first-arrival wave data is performed according to the number of tomographic seismic trace data to be allocated to the parallel computing sub-node.

[0110] In some embodiments of the present invention, see Figure 7 In the first arrival wave travel time data tomography method based on distributed parallel computing, before step 200, the method further includes:

[0111] Step 500: Broadcast the earthquake first-arrival wave data to all parallel computing sub-nodes according to the master node.

[0112] Specifically, the master node broadcasts all file names, and each computing unit receives its own file name and processes the corresponding seismic data according to the node identifier; preferably, the MPI_Bcast function can be used for broadcasting:

[0113] int MPI_Bcast(void*buffer,int count,MPI_Datatype datatype,int root,MPI_Commcomm)

[0114] buffer is the data buffer to be broadcast, count is the number of data elements, datatype is the data type, root is the broadcast source node number, and comm is the communication group. This function will broadcast the buffer content on the root node to all other nodes.

[0115] You can also use the MPI_Scatterv+MPI_Gatherv function for broadcasting:

[0116] int MPI_Scatterv(void*sendbuf,int*sendcounts,int*displs,MPI_Datatypesendtype,

[0117] void*recvbuf,int recvcount,MPI_Datatype recvtype,int root,MPI_Commcomm)

[0118] int MPI_Gatherv(void*sendbuf,int sendcount,MPI_Datatype sendtype,

[0119] void*recvbuf,int*recvcounts,int*displs,MPI_Datatype recvtype,

[0120] int root,MPI_Comm comm)

[0121] Specifically, the root node first uses MPI_Scatterv to distribute the data to other nodes, and then other nodes use MPI_Gatherv to aggregate the received data back to the root node to achieve a broadcast effect.

[0122] In some embodiments of the present invention, see Figure 8 , step 300 comprises:

[0123] Step 301: In the velocity model space of the target work area, the seismic first arrival wave data are tomatomized in the order of the Y direction, the X direction and the Z direction of the velocity model space according to the main node and the plurality of parallel computing sub-nodes.

[0124] Specifically, each parallel child node starts to process its own file, and performs loop processing according to the total amount of data in the read data. The model space performs three-dimensional processing in the order of Y\X\Z until the first round of inversion is completed and the result is reduced; then, the next loop is performed until the inversion result meets the conditions

[0125] From the above description, it can be seen that an embodiment of the present invention provides a first-arrival wave travel time data tomography method based on distributed parallel computing, including: first obtaining the earthquake first-arrival wave data of the target work area, the earthquake first-arrival wave data including the SEG-Y earthquake data of the target work area; then, load balancing the earthquake first-arrival wave data according to the main node and multiple parallel computing sub-nodes opened in advance; finally, using the main node and multiple parallel computing sub-nodes to tomography the earthquake first-arrival wave data.

[0126] The first arrival wave travel time data tomography method based on distributed parallel computing provided in the embodiment of the present invention combines the big data architecture and ideas to propose a distributed computing seismic first arrival wave travel time tomography method with the advantage of memory saving, which solves the disadvantage of insufficient memory when performing near-surface velocity modeling in a large work area under three-dimensional conditions. Thus, all first arrival wave travel time data can be calculated at one time, and the partition fusion problem caused by partitioning the model can be avoided while maximizing the cluster utilization rate.

[0127] Embodiment three:

[0128] In a specific embodiment, the present invention also provides a specific embodiment of a first arrival wave travel time data tomography method based on distributed parallel computing, see Fig. 9 , specifically including the following steps.

[0129] S1: The master node initializes the parallel environment;

[0130] The master node initializes the MPI parallel framework and obtains the MPI action space and the total number of running nodes. Preferably, before step S1, it is also necessary to pick up the travel time data of the earthquake first arrival wave and configure other processing parameters.

[0131] For the picking of seismic first-arrival wave data, first filter and denoise: filter and denoise the seismic waveform. Low-frequency filtering can help highlight the seismic signal and reduce noise and other interference. Next, look for changes in the STA / LTA ratio: in many cases, the ratio of the signal average to the long-term average (STA / LTA) can be used to detect the first arrival wave. STA represents the short-term average and LTA represents the long-term average. When the seismic wave arrives, the sudden increase will increase the STA / LTA ratio, which can be used as an indication of the arrival of the first arrival wave. Finally, perform a threshold judgment: set a threshold, and when the STA / LTA ratio exceeds this threshold, it is considered that the first arrival wave has arrived. This threshold may need to be determined through experiments.

[0132] S2: The master node opens N+1 computing units;

[0133] Specifically, the master node opens up N+1 parallel child nodes, recorded as N+1 computing nodes.

[0134] S3: The master node reads the processing parameters and passes them to N+1 computing units;

[0135] The main node reads the processing parameters and passes them to each child node. Perform uniform meshing of the entire work area model on the operation node: complete the full space meshing in the X, Y, and Z directions.

[0136] S4: The master node performs load balancing and outputs load-balanced data;

[0137] Carry out load-balanced data decomposition. The master node reads the SEG-Y format seismic data containing the first arrival data. The computing units are load-balanced according to the total number of seismic data channels. The decomposed files are stored according to the computing node identifier, so that each computing node can process the same number of seismic data channels at the same time.

[0138] S5: The master node broadcasts the data name after load balancing;

[0139] The master node broadcasts all file names, and each computing unit receives its own file name and processes the corresponding seismic data according to the node identifier;

[0140] S6: The computing units process their own data, and all nodes perform normalization after the tomography of the earthquake first-arrival wave data is completed;

[0141] Each computing unit starts to process its own file, and processes it in a loop according to the total amount of data in the read data. The model space performs three-dimensional processing in the order of Y direction, X direction and Z direction until the first round of inversion is completed and the result is reduced. The next cycle is performed until the inversion result meets the conditions. Finally, the tomographic inversion results of the seismic first arrival wave data are output, see Fig.10 .

[0142] Effect demonstration: The first-arrival wave tomography modeling test was performed on the seismic first-arrival wave data collected in a certain exploration area using the methods in steps S1 to S6. The total size of the test data in this exploration area is 72GB, and the total memory of a single node in the operating environment is 248GB. 32 computing units can be opened. If each computing unit is fully read, a very large amount of memory will be consumed, and all computing units cannot be fully utilized. Under the premise that there are many computing units in the computing cluster and limited memory, the memory utilization rate of the traditional parallel method is very low. Table 1 (resource usage of the current single computing unit job only) and Table 2 (memory size occupied by all jobs of the current node (1 computing unit) only) show the usage of computing resources when the traditional parallel method is used to process the data.

[0143] Table 1

[0144] PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 20 0 15.3g 14g 5968 R 99.6 5.8 29:01.50 FirstArrivalTom

[0145] Table 2

[0146] total used free Shared Buffers Cached Mem: 252G 194G 57G 232K 19M 177G - / +buffers / cache: 17G 234G Swap: 0B 0B 0B

[0147] Table 3 (resource usage of the current computing unit job only) and Table 4 (memory usage of all jobs of the current node (21 computing units) only) demonstrate the advantage of the present invention in that the memory usage of a single computing unit is low. Therefore, the number of computing units can be increased and the cluster utilization can be improved.

[0148] Table 3

[0149] PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 20 0 1662m 1.4g 6392 R 103.0 0.6 5:38.33 FirstArrivalTom

[0150] Table 4

[0151] total used free Shared Buffers Cached Mem: 252G 210G 41G 5.5M 20M 177G - / +buffers / cache: 132G 219G Swap: 0B 0B 0B

[0152] From the above description, it can be seen that an embodiment of the present invention provides a first-arrival wave travel time data tomography method based on distributed parallel computing, including: first obtaining the earthquake first-arrival wave data of the target work area, the earthquake first-arrival wave data including the SEG-Y earthquake data of the target work area; then, load balancing the earthquake first-arrival wave data according to the main node and multiple parallel computing sub-nodes opened in advance; finally, using the main node and multiple parallel computing sub-nodes to tomography the earthquake first-arrival wave data.

[0153] The first arrival wave travel time data tomography method based on distributed parallel computing provided in the embodiment of the present invention combines the big data architecture and ideas to propose a distributed computing seismic first arrival wave travel time tomography method with the advantage of memory saving, which solves the disadvantage of insufficient memory when performing near-surface velocity modeling in a large work area under three-dimensional conditions. Thus, all first arrival wave travel time data can be calculated at one time, and the partition fusion problem caused by partitioning the model can be avoided while maximizing the cluster utilization rate.

[0154] Embodiment 4:

[0155] Based on the same inventive concept, the embodiment of the present application also provides a first arrival wave travel time data tomography device based on distributed parallel computing, which can be used to implement the method described in the above embodiment, such as the following embodiment. Since the principle of solving the problem by the first arrival wave travel time data tomography device based on distributed parallel computing is similar to the first arrival wave travel time data tomography method based on distributed parallel computing, the implementation of the first arrival wave travel time data tomography device based on distributed parallel computing can refer to the implementation of the first arrival wave travel time data tomography method based on distributed parallel computing, and the repeated parts will not be repeated. As used below, the term "unit" or "module" can be a combination of software and / or hardware that implements a predetermined function. Although the system described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0156] The embodiment of the present invention provides a specific implementation of a first arrival wave travel time data tomography device based on distributed parallel computing, which can realize a first arrival wave travel time data tomography method based on distributed parallel computing, see Fig.11 , the first arrival wave travel time data tomography device based on distributed parallel computing includes:

[0157] A first-break wave data acquisition module 10 is used to acquire seismic first-break wave data of a target work area, wherein the seismic first-break wave data includes SEG-Y seismic data of the target work area;

[0158] A first-arrival wave data load balancing module 20 is used to load balance the earthquake first-arrival wave data according to the main node and a plurality of parallel computing sub-nodes opened in advance;

[0159] The first-break wave data tomography module 30 is used to tomography the seismic first-break wave data using the main node and the multiple parallel computing sub-nodes.

[0160] In one embodiment of the present invention, the first arrival wave data load balancing module includes:

[0161] A seismic trace quantity reading unit, used for reading the quantity of seismic trace data in the seismic first arrival wave data by using the master node;

[0162] A first-break wave data load balancing unit is used to load balance the seismic first-break wave data according to the seismic trace data and the multiple parallel sub-nodes.

[0163] In one embodiment of the present invention, the first arrival wave travel time data tomography device based on distributed parallel computing further includes:

[0164] A parallel computing subnode development module, used for developing the plurality of parallel computing subnodes;

[0165] The parallel computing sub-node development module includes:

[0166] The parallel computing sub-node development unit is used to develop the multiple parallel computing sub-nodes in the MPI parallel framework model according to the number of CPU cores, memory and the amount of seismic trace data of the main node and the parallel computing sub-nodes.

[0167] In one embodiment of the present invention, the first arrival wave travel time data tomography device based on distributed parallel computing further includes:

[0168] The seismic trace data storage module is used to store a plurality of seismic trace data according to the operation node identifier of the master node and the operation node identifier of the parallel computing sub-node.

[0169] In one embodiment of the present invention, the first arrival wave data load balancing unit includes:

[0170] A seismic trace quantity determination unit, used to determine the quantity of tomographic seismic trace data to be allocated to the parallel computing subnode according to the number of CPU cores and memory of the parallel computing subnode and the quantity of seismic trace data;

[0171] The first-arrival wave data load balancing subunit is used to load balance the seismic first-arrival wave data according to the number of tomographic seismic trace data to be allocated to the parallel computing subnode.

[0172] In one embodiment of the present invention, the first arrival wave travel time data tomography device based on distributed parallel computing further includes:

[0173] The earthquake first-break wave broadcasting module is used to broadcast the earthquake first-break wave data to all parallel computing sub-nodes according to the master node.

[0174] In one embodiment of the present invention, the first-break data tomography module includes:

[0175] The first arrival wave data tomography unit is used to tomographically perform the seismic first arrival wave data in the velocity model space of the target work area according to the main node and the plurality of parallel computing sub-nodes in the order of the Y direction, the X direction and the Z direction of the velocity model space.

[0176] From the above description, it can be seen that an embodiment of the present invention provides a first-arrival wave travel time data tomography device based on distributed parallel computing, including: a first-arrival wave data acquisition module, used to acquire the seismic first-arrival wave data of the target work area, the seismic first-arrival wave data includes the SEG-Y seismic data of the target work area; a first-arrival wave data load balancing module, used to load balance the seismic first-arrival wave data according to the main node and multiple parallel computing sub-nodes opened in advance; a first-arrival wave data tomography module, used to use the main node and multiple parallel computing sub-nodes to tomography the seismic first-arrival wave data.

[0177] The first arrival wave travel time data tomography device based on distributed parallel computing provided by the embodiment of the present invention combines the big data architecture and ideas to propose a distributed computing seismic first arrival wave travel time tomography device with the advantage of memory saving, which solves the disadvantage of insufficient memory when performing near-surface velocity modeling in a large work area under three-dimensional conditions. Thus, all first arrival wave travel time data can be calculated at one time, and the partition fusion problem caused by partitioning the model can be avoided while maximizing the cluster utilization rate.

[0178] Embodiment five:

[0179] The embodiments of the present application also provide a specific implementation of an electronic device capable of implementing all steps in the first arrival wave travel time data tomography method based on distributed parallel computing in the above embodiments, see Fig.12 , electronic equipment specifically includes the following:

[0180] Processor (processor) 1201, memory (memory) 1202, communication interface (CommunicationsInterface) 1203 and bus 1204;

[0181] The processor 1201, the memory 1202, and the communication interface 1203 communicate with each other through the bus 1204; the communication interface 1203 is used to realize information transmission between the server device and the client device and other related devices;

[0182] The processor 1201 is used to call the computer program in the memory 1202. When the processor executes the computer program, all the steps in the first arrival wave travel time data tomography method based on distributed parallel computing in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0183] Obtain the earthquake first-arrival wave data of the target work area, the earthquake first-arrival wave data includes the SEG-Y earthquake data of the target work area;

[0184] Load balancing of earthquake first arrival wave data is performed based on the main node and multiple parallel computing sub-nodes opened in advance;

[0185] The main node and multiple parallel computing sub-nodes are used to compute the tomographic seismic first arrival wave data.

[0186] In one embodiment, load balancing of seismic first arrival wave data is performed based on a master node and a plurality of pre-developed parallel computing sub-nodes, including:

[0187] Use the master node to read the number of seismic trace data in the earthquake first arrival wave data;

[0188] Load balancing of earthquake first-arrival wave data is performed based on seismic trace data and multiple parallel sub-nodes.

[0189] In one embodiment, the step of opening a plurality of parallel computing sub-nodes includes:

[0190] According to the number of CPU cores, memory and the amount of seismic data of the main node and parallel computing sub-nodes, multiple parallel computing sub-nodes are opened in the MPI parallel framework model.

[0191] In one embodiment, after load balancing the seismic first arrival wave data according to the seismic trace data and the multiple parallel sub-nodes, the method further includes:

[0192] The multiple seismic trace data are stored according to the operation node identifier of the main node and the operation node identifier of the parallel computing sub-node.

[0193] In one embodiment, load balancing of seismic first arrival wave data is performed based on seismic trace data and multiple parallel sub-nodes, including:

[0194] Determine the number of tomographic seismic trace data that should be allocated to the parallel computing subnode according to the number of CPU cores, memory and the number of seismic trace data of the parallel computing subnode;

[0195] The load of seismic first arrival wave data is balanced according to the number of tomographic seismic trace data that should be allocated to the parallel computing sub-nodes.

[0196] In one embodiment, before load balancing the seismic first arrival wave data according to the main node and the plurality of parallel computing sub-nodes opened in advance, the method further includes:

[0197] Broadcast earthquake first-arrival wave data to all parallel computing sub-nodes based on the master node.

[0198] In one embodiment, using a main node and multiple parallel computing sub-nodes tomographic seismic first arrival wave data includes:

[0199] In the velocity model space of the target work area, the seismic first arrival wave data are sequentially sliced ​​in the Y direction, X direction and Z direction of the velocity model space according to the main node and multiple parallel computing sub-nodes.

[0200] Embodiment six:

[0201] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps in the first-arrival travel time data tomography method based on distributed parallel computing in the above embodiments. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all the steps of the first-arrival travel time data tomography method based on distributed parallel computing in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0202] Obtain the earthquake first-arrival wave data of the target work area, the earthquake first-arrival wave data includes the SEG-Y earthquake data of the target work area;

[0203] Load balancing of earthquake first arrival wave data is performed based on the main node and multiple parallel computing sub-nodes opened in advance;

[0204] The main node and multiple parallel computing sub-nodes are used to compute the tomographic seismic first arrival wave data.

[0205] In one embodiment, load balancing of seismic first arrival wave data is performed based on a master node and a plurality of pre-developed parallel computing sub-nodes, including:

[0206] Use the master node to read the number of seismic trace data in the earthquake first arrival wave data;

[0207] Load balancing of earthquake first-arrival wave data is performed based on seismic trace data and multiple parallel sub-nodes.

[0208] In one embodiment, the step of opening a plurality of parallel computing sub-nodes includes:

[0209] According to the number of CPU cores, memory and the amount of seismic data of the main node and parallel computing sub-nodes, multiple parallel computing sub-nodes are opened in the MPI parallel framework model.

[0210] In one embodiment, after load balancing the seismic first arrival wave data according to the seismic trace data and the multiple parallel sub-nodes, the method further includes:

[0211] The multiple seismic trace data are stored according to the operation node identifier of the main node and the operation node identifier of the parallel computing sub-node.

[0212] In one embodiment, load balancing of seismic first arrival wave data is performed based on seismic trace data and multiple parallel sub-nodes, including:

[0213] Determine the number of tomographic seismic trace data that should be allocated to the parallel computing subnode according to the number of CPU cores, memory and the number of seismic trace data of the parallel computing subnode;

[0214] The load of seismic first arrival wave data is balanced according to the number of tomographic seismic trace data that should be allocated to the parallel computing sub-nodes.

[0215] In one embodiment, before load balancing the seismic first arrival wave data according to the main node and the plurality of parallel computing sub-nodes opened in advance, the method further includes:

[0216] Broadcast earthquake first-arrival wave data to all parallel computing sub-nodes based on the master node.

[0217] In one embodiment, using a main node and multiple parallel computing sub-nodes tomographic seismic first arrival wave data includes:

[0218] In the velocity model space of the target work area, the seismic first arrival wave data are sequentially sliced ​​in the Y direction, X direction and Z direction of the velocity model space according to the main node and multiple parallel computing sub-nodes.

[0219] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0220] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0221] Although the present application provides method operation steps such as embodiments or flow charts, more or fewer operation steps may be included based on conventional or non-creative labor. The order of steps listed in the embodiments is only one way of executing the order of many steps and does not represent the only execution order. When the actual device or client product is executed, it can be executed in the order of the method shown in the embodiments or the drawings or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0222] For the convenience of description, the above devices are described in various modules according to their functions. Of course, when implementing the embodiments of this specification, the functions of each module can be implemented in the same or more software and / or hardware, or the module implementing the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0223] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules for implementing the method and structures within the hardware component.

[0224] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0225] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0226] Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. In the description of this specification, the description of the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of this specification. In this specification, the schematic representation of the above terms does not necessarily target the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, in the absence of contradiction, a person skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0227] The above is only an example of the embodiment of the present specification and is not intended to limit the embodiment of the present specification. For those skilled in the art, the embodiment of the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiment of the present specification shall be included in the scope of the claims of the embodiment of the present specification.

Claims

1. A first arrival wave travel time data tomography method based on distributed parallel computing, characterized in that: include: Acquire seismic first-break wave data of the target work area, wherein the seismic first-break wave data includes SEG-Y seismic data of the target work area; Load balancing the earthquake first arrival wave data is performed according to the main node and a plurality of parallel computing sub-nodes opened in advance; The main node and the plurality of parallel computing sub-nodes are used to tomographically analyze the earthquake first-arrival wave data.

2. The first arrival wave travel time data tomography method according to claim 1, characterized in that: The load balancing of the earthquake first arrival wave data according to the main node and the plurality of parallel computing sub-nodes opened in advance includes: Using the master node to read the number of seismic trace data in the seismic first arrival wave data; The seismic first arrival wave data is load balanced according to the seismic trace data and the multiple parallel sub-nodes.

3. The first arrival wave travel time data tomography method according to claim 2, characterized in that: The step of opening the plurality of parallel computing sub-nodes comprises: According to the number of CPU cores, memory and the amount of seismic trace data of the main node and the parallel computing sub-node, the multiple parallel computing sub-nodes are opened in the MPI parallel framework model.

4. The first arrival wave travel time data tomography method according to claim 2, characterized in that: After load balancing the seismic first arrival wave data according to the seismic trace data and the multiple parallel sub-nodes, the method further includes: The multiple seismic trace data are stored according to the operation node identifier of the master node and the operation node identifier of the parallel computing sub-node.

5. The first arrival wave travel time data tomography method according to claim 3, characterized in that: Load balancing the seismic first arrival wave data according to the seismic trace data and the multiple parallel sub-nodes includes: Determine the number of tomographic seismic trace data that should be allocated to the parallel computing subnode according to the number of CPU cores and memory of the parallel computing subnode and the number of seismic trace data; The seismic first-arrival wave data are load balanced according to the number of tomographic seismic trace data to be allocated to the parallel computing sub-node.

6. The first arrival wave travel time data tomography method according to any one of claims 1 to 5, characterized in that: Before load balancing the seismic first arrival wave data according to the master node and the plurality of pre-developed parallel computing sub-nodes, the method further includes: Broadcast the earthquake first arrival wave data to all parallel computing sub-nodes according to the master node.

7. The first arrival wave travel time data tomography method according to claim 1, characterized in that: Utilizing the master node and the plurality of parallel computing sub-nodes to tomograph the seismic first arrival wave data includes: In the velocity model space of the target work area, the seismic first arrival wave data are tomatomized in the order of the Y direction, the X direction and the Z direction of the velocity model space according to the main node and the plurality of parallel computing sub-nodes.

8. A first arrival wave travel time data tomography device based on distributed parallel computing, characterized in that: include: A first-break wave data acquisition module is used to acquire seismic first-break wave data of a target work area, wherein the seismic first-break wave data includes SEG-Y seismic data of the target work area; A first-arrival wave data load balancing module is used to load balance the earthquake first-arrival wave data according to the main node and multiple parallel computing sub-nodes opened in advance; The first-break wave data tomography module is used to utilize the main node and the multiple parallel computing sub-nodes to tomography the seismic first-break wave data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the first arrival wave travel time data tomography method based on distributed parallel computing described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the first arrival wave travel time data tomography method based on distributed parallel computing described in any one of claims 1 to 7 are implemented.

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