CN112230284A

CN112230284A - Parallel random noise attenuation method, monitoring method and node for three-dimensional pre-stack data

Info

Publication number: CN112230284A
Application number: CN201910634865.6A
Authority: CN
Inventors: 薛贵仁; 张旭东; 李军茹; 张建磊; 杨志昱; 薛红刚
Original assignee: China National Petroleum Corp; BGP Inc
Current assignee: China National Petroleum Corp; BGP Inc
Priority date: 2019-07-15
Filing date: 2019-07-15
Publication date: 2021-01-15

Abstract

The application provides a parallel random noise attenuation method, a monitoring method and nodes of three-dimensional pre-stack data, wherein the parallel random noise attenuation method comprises the following steps: receiving three-dimensional pre-stack data corresponding to other computing nodes in parallel; based on the size of a preset target space window, a preset BT protocol is applied to read corresponding target seismic data in the three-dimensional pre-stack data in parallel with other computing nodes; and carrying out noise attenuation processing on the corresponding target seismic data in parallel with other computing nodes, and outputting the corresponding noise attenuation results to the master node in parallel with other computing nodes, so that the master node carries out data summarization on the noise attenuation results output by the computing nodes and sends the corresponding summarization results to the corresponding database. The method and the device can effectively improve the efficiency of random noise attenuation of the three-dimensional pre-stack data, and further can effectively improve the efficiency of seismic exploration by using the seismic data after the random noise attenuation.

Description

Parallel random noise attenuation method, monitoring method and node for three-dimensional pre-stack data

Technical Field

The application relates to the technical field of seismic exploration, in particular to a parallel random noise attenuation method, a monitoring method and nodes of three-dimensional pre-stack data.

Background

In the process of acquiring seismic data, due to the complexity of the earth surface conditions of a work area and the influence of the external environment where a seismic source and a demodulator probe are located, seismic data often contain a lot of random noises, such as microseisms, background interference and the like. The random noise which is represented as disorder on the seismic record is widely distributed, the signal-to-noise ratio of the data is seriously influenced, and great difficulty is brought to the processing and interpretation work of the seismic data. Therefore, effective methods for removing random noise have been sought.

Random Noise Attenuation (RNA for short) based on prediction theory is one of the more effective methods, which was first proposed by canales in 1984 (F-X domain), and is rapidly applied to seismic data digital processing due to its theoretical tightness and significance in practical effect. Through the long-term continuous development, the development from the early two-dimensional to the current four-dimensional (three-dimensional prestack) has been carried out. The frequency space F-XYZ domain prediction denoising technology based on the prediction theory defines X, Y, Z dimensions as different LINEs in the CROSSLINE direction, different CMPs in the INLINE direction and offsets (or track numbers) in a common CMP track set respectively, applies the prediction denoising technology to the CMP domain three-dimensional prestack seismic record and realizes the random noise attenuation of the three-dimensional prestack seismic record.

However, the three-dimensional pre-stack random noise attenuation based on the F-XYZ domain prediction denoising technology considers the three-dimensional seismic data as a whole, fully utilizes three-dimensional space information, completely retains the structural form, and has an obvious denoising effect. However, since it needs to perform overall prediction on seismic data in a large spatial range, the amount of data processed at a time is large, and the amount of calculation is also large. In the process of processing data with large data volume, the increase of cluster computing performance speed is far larger than that of centralized storage bandwidth, and the requirement of node parallel computing on data bandwidth is far larger than that which can be provided by centralized storage, that is, the existing random noise attenuation processing mode of three-dimensional pre-stack data has low processing efficiency and cannot meet the processing requirement of seismic data with large data volume.

Disclosure of Invention

Aiming at the problems in the prior art, the application provides a parallel random noise attenuation method, a monitoring method and nodes for three-dimensional pre-stack data, which can effectively improve the efficiency of random noise attenuation of the three-dimensional pre-stack data and further can effectively improve the efficiency of seismic exploration by using seismic data after random noise attenuation.

In order to solve the technical problem, the application provides the following technical scheme:

in a first aspect, the present application provides a method for attenuating parallel random noise of three-dimensional prestack data, including:

receiving three-dimensional pre-stack data corresponding to other computing nodes in parallel;

based on the size of a preset target space window, a preset BT protocol is applied to read corresponding target seismic data in the three-dimensional pre-stack data in parallel with other computing nodes;

and carrying out noise attenuation processing on the corresponding target seismic data in parallel with other computing nodes, and outputting the corresponding noise attenuation results to the master node in parallel with other computing nodes, so that the master node carries out data summarization on the noise attenuation results output by the computing nodes and sends the corresponding summarization results to the corresponding database.

Further, before the applying a preset BT protocol and reading target seismic data in the corresponding three-dimensional prestack data in parallel with other computing nodes in the method for parallel random noise attenuation of three-dimensional prestack data, the method further includes:

establishing a corresponding distributed memory;

correspondingly, the reading of the corresponding target seismic data in the three-dimensional prestack data in parallel by applying a preset BT protocol and other computing nodes based on the size of the preset target space window includes:

and based on the size of a preset target space window, reading target seismic data in the corresponding three-dimensional pre-stack data in the corresponding distributed memory by applying a preset BT protocol and other computing nodes in parallel.

Further, the method for parallel random noise attenuation of three-dimensional prestack data further comprises:

receiving data sharing requests sent by other computing nodes;

and acquiring and sending corresponding seismic data to other computing nodes in the distributed memory corresponding to the data sharing request based on the BT protocol.

Further, before the parallel noise attenuation processing is performed on the corresponding target seismic data in parallel with other computing nodes in the parallel random noise attenuation method for three-dimensional pre-stack data, the method further includes:

and establishing at least three control threads for parallel execution, wherein at least one control thread is used for receiving the target seismic data, at least one control thread is used for carrying out noise attenuation processing on the target seismic data, and at least one control thread is used for outputting the noise attenuation result to the main node.

establishing a plurality of computing threads according to a CPU processing threshold value preset by the CPU;

correspondingly, the noise attenuation processing is performed on the target seismic data corresponding to each of the other computing nodes in parallel, and the noise attenuation processing includes:

and carrying out noise attenuation processing on the target seismic data corresponding to each computing thread by applying the plurality of computing threads corresponding to each computing thread in parallel with other computing nodes.

Further, the noise attenuation processing in the parallel random noise attenuation method for the three-dimensional prestack data comprises: fourier transform processing, operator solving processing, seismic deconvolution processing and inverse fast Fourier transform processing.

In a second aspect, the present application provides a data monitoring method, including:

monitoring each computing node belonging to the same Linux cluster in real time;

if a task request of any one computing node is received, distributing a task to the corresponding computing node according to the task request, wherein the task comprises three-dimensional pre-stack data to be processed, so that the computing node realizes the parallel random noise attenuation method of the three-dimensional pre-stack data aiming at the three-dimensional pre-stack data;

receiving noise attenuation results sent by each computing node;

and summarizing the noise attenuation results output by each computing node for many times, and sending the corresponding summarized results to the corresponding database.

Further, the task request in the data monitoring method includes a task identifier and a noise attenuation result of the last completion of the corresponding computing node; the data monitoring method further comprises the following steps:

and recording the task identification and the noise attenuation result which are finished last time by the computing node in a recording file.

Further, the data monitoring method further comprises:

if any computing node is detected to be out of order, fault result inspection is carried out on the computing node according to the record file, and tasks which are not completed by the computing node are distributed to other computing nodes to be completed.

In a third aspect, the present application provides a computing node, comprising:

the data parallel receiving module is used for receiving the three-dimensional pre-stack data corresponding to the other computing nodes in parallel;

the data parallel reading module is used for reading corresponding target seismic data in the three-dimensional pre-stack data in parallel with other computing nodes by applying a preset BT protocol based on the size of a preset target space window;

and the data parallel processing module is used for carrying out noise attenuation processing on the corresponding target seismic data in parallel with other computing nodes and outputting the corresponding noise attenuation results to the master node in parallel with the other computing nodes, so that the master node carries out data summarization on the noise attenuation results output by the computing nodes and sends the corresponding summarization results to the corresponding database.

Further, the computing node further comprises:

the memory establishing module is used for establishing a corresponding distributed memory;

correspondingly, the data parallel reading module comprises:

and the target seismic data parallel reading unit is used for reading the corresponding target seismic data in the three-dimensional prestack data in the corresponding distributed memory in parallel by applying a preset BT protocol and other computing nodes based on the size of a preset target space window.

Further, the computing node further comprises:

the sharing request receiving module is used for receiving data sharing requests sent by other computing nodes;

and the data sharing module is used for acquiring and sending corresponding seismic data to other computing nodes in the distributed memory corresponding to the data sharing module based on the BT protocol according to the data sharing request.

Further, the computing node further comprises:

and the control thread establishing module is used for establishing at least three control threads for parallel execution, wherein at least one control thread is used for receiving the target seismic data, at least one control thread is used for carrying out noise attenuation processing on the target seismic data, and at least one control thread is used for outputting the noise attenuation result to the main node.

Further, the computing node further comprises:

the computing thread establishing module is used for establishing a plurality of computing threads according to a CPU processing threshold value preset by the computing thread establishing module;

correspondingly, the data parallel processing module comprises:

and the noise attenuation processing unit is used for applying a plurality of corresponding calculation threads in parallel with other calculation nodes to perform noise attenuation processing on the corresponding target seismic data.

Further, the noise attenuation processing in the computing node comprises: fourier transform processing, operator solving processing, seismic deconvolution processing and inverse fast Fourier transform processing.

In a fourth aspect, the present application provides a first electronic device, including a first memory, a first processor, and a computer program stored in the first memory and executable on the first processor, where the first processor implements the steps of the method for parallel random noise attenuation of three-dimensional pre-stack data when executing the program.

In a fifth aspect, the present application provides a first computer readable storage medium having stored thereon a first computer program which, when executed by a processor, performs the steps of the method for parallel random noise attenuation of three-dimensional prestack data.

In a sixth aspect, the present application provides a master node, comprising:

the real-time monitoring module is used for monitoring each computing node belonging to the same Linux cluster in real time;

the task allocation module is used for allocating a task to a corresponding computing node according to a task request if the task request of any computing node is received, wherein the task contains three-dimensional pre-stack data to be processed, so that the computing node realizes a parallel random noise attenuation method of the three-dimensional pre-stack data aiming at the three-dimensional pre-stack data;

a processing result receiving module, configured to receive the noise attenuation result sent by each computing node;

and the data summarizing module is used for summarizing the noise attenuation results output by each computing node for many times and sending the corresponding summarizing results to the corresponding database.

Further, the task request in the master node includes a task identifier and a noise attenuation result of the last completion of the corresponding computing node; the master node further comprises:

and the data recording module is used for recording the task identifier and the noise attenuation result which are finished last time by the computing node in a recording file.

Further, the master node further includes:

and the fault processing module is used for carrying out fault result check on the computing node according to the record file and distributing the tasks which are not completed by the computing node to other computing nodes to complete the tasks if any computing node is detected to have a fault.

In a seventh aspect, the present application provides a second electronic device, which includes a second memory, a second processor, and a computer program stored in the second memory and executable on the second processor, where the second processor implements the steps of the data monitoring method when executing the program.

In an eighth aspect, the present application provides a second computer readable storage medium having stored thereon a second computer program which, when executed by a second processor, performs the steps of the data monitoring method.

In a ninth aspect, the present application provides a parallel random noise attenuation processing system for three-dimensional pre-stack data, comprising: at least one said master node, and, a plurality of said compute nodes;

and each computing node is in communication connection with the main node.

According to the technical scheme, the application provides a parallel random noise attenuation method, a monitoring method and a node for three-dimensional pre-stack data, and the method comprises the following steps: receiving three-dimensional pre-stack data corresponding to other computing nodes in parallel; based on the size of a preset target space window, a preset BT protocol is applied to read corresponding target seismic data in the three-dimensional pre-stack data in parallel with other computing nodes; the method carries out noise attenuation processing on target seismic data corresponding to each other in parallel with other computing nodes, outputs noise attenuation results corresponding to each other to a main node in parallel with other computing nodes, enables the main node to carry out data summarization on the noise attenuation results output by each computing node and sends corresponding summarized results to a corresponding database, has high reliability and high accuracy in the process of random noise attenuation processing, can effectively improve the processing performance of a processor aiming at the seismic data, particularly the seismic data processing with ultra-large data volume, solves the input or output bottleneck of centralized storage by fully utilizing the storage resources such as internal memories, SSD, temporary disks and the like of a plurality of nodes of a Linux cluster, effectively shortens RNA time, can effectively improve the efficiency of random noise attenuation of three-dimensional pre-stack data, and further can meet the requirements of geophysical exploration data processing of petroleum, the time of seismic exploration is effectively shortened, and the efficiency of seismic exploration by using seismic data after random noise attenuation can be improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic structural diagram of a random noise attenuation system for three-dimensional prestack data according to the present application.

Fig. 2 is a schematic flow chart of a parallel random noise attenuation method for three-dimensional prestack data in the embodiment of the present application.

Fig. 3 is a schematic flow chart of a data monitoring method in the embodiment of the present application.

Fig. 4 is a schematic diagram of a comprehensive application flow of a method for implementing parallel random noise attenuation of three-dimensional prestack data by using a compute node and a master node in a specific application example of the present application.

Fig. 5 is a schematic diagram of node parallel logic processing in an embodiment of the present application.

Fig. 6 is a schematic diagram of a logical structure of a distributed memory in a specific application example of the present application.

Fig. 7 is a schematic structural diagram of a computing node in the embodiment of the present application.

Fig. 8 is a schematic structural diagram of a first electronic device in this embodiment.

Fig. 9 is a schematic structural diagram of a master node in the embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Firstly, a serial implementation process of three-dimensional pre-stack RNA is explained, and a three-dimensional pre-stack RNA serial implementation algorithm specifically comprises the following steps:

(1) dividing the whole data into a plurality of small space window data;

(2) reading data of a small space window;

(3) RNA calculation, including FFT conversion, operator calculation, DECON and inverse FFT conversion;

(4) overlapping in the Z direction;

(5) overlapping in the Y direction;

(6) and overlapping in the X direction, outputting X data, and turning to the step 2.

In a large-scale Linux cluster, with the rapid increase of seismic data volume, which is hundreds of T, the centralized storage bandwidth cannot meet the increase of the calculation performance speed.

In view of the above, in the conventional random noise attenuation processing method for three-dimensional prestack data, the processing efficiency is low and the processing requirement of seismic data with large data volume cannot be met, an IO bottleneck in the RNA parallel computing process needs to be solved, the RNA parallel efficiency in production is improved, and the cost reduction and efficiency improvement requirements in the processing of petroleum geophysical exploration data are met. Based on the above, the present application provides a parallel random noise attenuation method for three-dimensional pre-stack data, a data monitoring method, a computing node, a master node, an electronic device, a computer-readable storage medium, and a parallel random noise attenuation system for three-dimensional pre-stack data. Seismic data are divided into a plurality of data files according to three-dimensional keyword parameters given by a user and stored on different nodes, space window data required by calculation are broadcasted and read on each node according to a BT mechanism, and overlapped data parts among the space windows are processed according to the BT mechanism. The data IO time in the seismic data RNA processing is greatly shortened, a high-expansibility parallel method for seismic data RNA processing is formed, and the method has the advantages of node fault tolerance, fault recovery and the like.

A method of random noise attenuation of three-dimensional prestack data, as referred to in one or more embodiments of the present application, comprises: preprocessing data, wherein seismic data are stored in each node in a multi-file mode; constructing a distributed Cache by using a memory, an SSD and a temporary disk; sharing data on the distributed Cache based on a BT mechanism; reading space window data in a BT mode; RNA calculation, including FFT conversion, operator solution, DECON and inverse FFT conversion; z, Y, X direction overlapping is respectively made; and carrying out IO on data between different nodes by using a BT mechanism.

In the random noise attenuation of the three-dimensional prestack data in one or more embodiments of the application, the distributed Cache data of the BT mechanism is fully utilized among all nodes, the processing of each node is fully parallel, and simultaneously, in each node, the RNA calculation, the overlapping processing and the output are parallel, so that the RNA processing time is greatly shortened, and meanwhile, the random noise attenuation of the three-dimensional prestack data has the node fault tolerance and breakpoint protection functions, and meets the requirements of actual industrial production.

That is to say, the purpose of the present application is to accelerate the three-dimensional pre-stack RNA processing process, improve the processing performance, especially to process seismic data with a very large data volume, fully utilize the storage resources of Linux cluster multi-node memory, SSD, temporary disk, etc., solve the IO bottleneck of centralized storage, effectively shorten the RNA time, and meet the demand of processing oil geophysical exploration data.

The parallel random noise attenuation method for the three-dimensional prestack data mainly solves the IO bottleneck problem of large data centralized storage. The parallel strategy of the invention fully utilizes the memory resources of Linux cluster multi-node, SSD, temporary disk and the like, and inside each node, the multithreading is used for RNA calculation, and simultaneously the threads are started for overlapping window processing and output.

The parallel random noise attenuation method of the three-dimensional prestack data comprises the following steps:

(1) distributing the seismic data which are stored in a centralized way to different nodes according to the three-dimensional keyword parameters given by the user;

(2) constructing a distributed Cache by using a memory, an SSD and a temporary disk;

(3) sharing data on the distributed Cache based on a BT mechanism;

(4) reading space window data in a BT mode through a distributed Cache;

(5) RNA calculation, including FFT conversion, operator solution, DECON and inverse FFT conversion;

(6) z, Y, X direction overlapping is respectively made;

(7) and carrying out IO on the data between the nodes by using a BT mechanism.

Since the processing of each small spatial window data is relatively independent, the processing of a plurality of small spatial window data can be paralleled, and the parallel method of the present invention is based on the parallel method. The parallel strategy of the invention is to distribute the space window data of the first three directions to each node for parallel processing, perform data reconstruction calculation by using multiple threads in each node, and start the threads to perform overlapping processing and output while performing calculation.

Based on the above, the present application further provides a random noise attenuation system for three-dimensional pre-stack data, referring to fig. 1, the random noise attenuation system for three-dimensional pre-stack data includes at least one master node 1 and a plurality of computing nodes 2, where the master node 1 and the computing nodes 2 may both be servers or client devices, the master node 1 and each of the computing nodes 2 are in communication connection, and the master node 1 may also be in communication connection with at least one corresponding client device and at least one database, respectively. The master node 1 may obtain a monitoring instruction from a client device, where the computing node 2 is configured to execute all or part of the contents of the random noise attenuation method for three-dimensional prestack data in one or more embodiments of the present application, and the master node 1 is configured to execute all or part of the contents of the data monitoring method in one or more embodiments of the present application.

It is understood that the client devices may include smart phones, tablet electronic devices, network set-top boxes, portable computers, desktop computers, Personal Digital Assistants (PDAs), in-vehicle devices, smart wearable devices, and the like. Wherein, intelligence wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..

In practical applications, the random noise attenuation part of the three-dimensional prestack data may be performed on the server side as described above, or all operations may be performed in the client device. The selection may be specifically performed according to the processing capability of the client device, the limitation of the user usage scenario, and the like. This is not a limitation of the present application. The client device may further include a processor if all operations are performed in the client device.

The client device may have a communication module (i.e., a communication unit), and may be communicatively connected to a remote server to implement data transmission with the server. The server may include a server on the task scheduling center side, and in other implementation scenarios, the server may also include a server on an intermediate platform, for example, a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may include a single computer device, or may include a server cluster formed by a plurality of servers, or a server structure of a distributed apparatus.

The server and the client device may communicate using any suitable network protocol, including network protocols not yet developed at the filing date of this application. The network protocol may include, for example, a TCP/IP protocol, a UDP/IP protocol, an HTTP protocol, an HTTPS protocol, or the like. Of course, the network Protocol may also include, for example, an RPC Protocol (Remote Procedure Call Protocol), a REST Protocol (Representational State Transfer Protocol), and the like used above the above Protocol.

In one or more embodiments of the present application, the compute nodes and the master node both belong to the same Linux cluster (load balancing cluster). The operating system of the Linux cluster is a low-level support software that is used to interface with hardware and provide a limited set of services for user programs. A computer system is a co-organism of hardware and software that are interdependent, not separable. The hardware of the computer comprises peripheral equipment, a processor, a memory, a hard disk and other electronic equipment which form a motor of the computer. But has no software to operate and control it and is not functional by itself. The software that performs this control task is called the operating system, which in Linux terminology is called the "kernel" and may also be called the "kernel". The main modules (or components) of the Linux kernel are divided into the following parts: storage management, CPU and process management, file systems, device management and drivers, network communications, and initialization (boot) of the system, system calls, and the like.

The following examples are intended to illustrate the details.

In order to effectively improve the efficiency of random noise attenuation of three-dimensional prestack data and further effectively improve the efficiency of seismic exploration by using seismic data after random noise attenuation, the application provides an embodiment of a parallel random noise attenuation method of three-dimensional prestack data, and referring to fig. 2, the parallel random noise attenuation method of three-dimensional prestack data specifically includes the following contents:

step 100: and receiving the three-dimensional pre-stack data corresponding to the other computing nodes in parallel.

Step 200: and based on the size of a preset target space window, reading corresponding target seismic data in the three-dimensional pre-stack data in parallel by applying a preset BT protocol and other computing nodes.

Step 300: and carrying out noise attenuation processing on the corresponding target seismic data in parallel with other computing nodes, and outputting the corresponding noise attenuation results to the master node in parallel with other computing nodes, so that the master node carries out data summarization on the noise attenuation results output by the computing nodes and sends the corresponding summarization results to the corresponding database.

In order to further improve the processing efficiency of the parallel random noise attenuation of the three-dimensional prestack data, in an embodiment of the application, before step 200, the method for parallel random noise attenuation of three-dimensional prestack data further includes: step 001: corresponding distributed memories are established.

Correspondingly, the step 200 specifically includes the following steps:

step 201: and based on the size of a preset target space window, reading target seismic data in the corresponding three-dimensional pre-stack data in the corresponding distributed memory by applying a preset BT protocol and other computing nodes in parallel.

It will be appreciated that the BT protocol is known collectively as BitTorren, and belongs to a P2P download protocol.

In order to be able to further improve the processing efficiency of parallel random noise attenuation of three-dimensional prestack data,

step 202: and receiving data sharing requests sent by other computing nodes.

Step 203: and acquiring and sending corresponding seismic data to other computing nodes in the distributed memory corresponding to the data sharing request based on the BT protocol.

In an embodiment of the present application, before step 300, the method for parallel random noise attenuation of three-dimensional prestack data further includes:

step 204: and establishing at least three control threads for parallel execution, wherein at least one control thread is used for receiving the target seismic data, at least one control thread is used for carrying out noise attenuation processing on the target seismic data, and at least one control thread is used for outputting the noise attenuation result to the main node.

Step 205: and establishing a plurality of computing threads according to the CPU processing threshold value preset by the CPU.

Correspondingly, the step 300 further includes the following steps:

step 301: and carrying out noise attenuation processing on the target seismic data corresponding to each computing thread by applying the plurality of computing threads corresponding to each computing thread in parallel with other computing nodes.

It is understood that the noise attenuation process in the present application includes: fourier transform processing, operator solving processing, seismic deconvolution processing and inverse fast Fourier transform processing.

From the above description, it can be seen that the parallel random noise attenuation method for three-dimensional prestack data provided in the embodiments of the present application has high reliability and high accuracy in the random noise attenuation processing process, and can effectively improve the processing performance of a processor for seismic data, especially the processing of seismic data with an ultra-large data volume.

In order to effectively improve the efficiency and processing reliability of random noise attenuation of three-dimensional prestack data and further effectively improve the efficiency of seismic exploration by using seismic data after random noise attenuation, the application provides an embodiment of a data monitoring method with an execution main body as a main node, and the data monitoring method specifically includes the following contents:

step 400: monitoring each computing node belonging to the same Linux cluster in real time;

step 500: if a task request of any one computing node is received, distributing a task to the corresponding computing node according to the task request, wherein the task comprises three-dimensional pre-stack data to be processed, so that the computing node realizes the parallel random noise attenuation method of the three-dimensional pre-stack data aiming at the three-dimensional pre-stack data.

Step 600: and receiving the noise attenuation result sent by each computing node.

Step 700: and summarizing the noise attenuation results output by each computing node for many times, and sending the corresponding summarized results to the corresponding database.

It can be understood that the task request includes a task identifier and a noise attenuation result of the last completion of the corresponding computing node; the data monitoring method further specifically comprises the following contents:

step 800: and recording the task identification and the noise attenuation result which are finished last time by the computing node in a recording file.

In an embodiment of the present application, the data monitoring method further includes the following steps:

step 900: if any computing node is detected to be out of order, fault result inspection is carried out on the computing node according to the record file, and tasks which are not completed by the computing node are distributed to other computing nodes to be completed.

As can be seen from the above description, in the data monitoring method provided in the embodiment of the present application, the seismic data is divided into a plurality of data files according to the three-dimensional keyword parameter given by the user, the data are stored in different nodes, the spatial window data required for the operation is broadcast and read on each node according to the BT mechanism, and the overlapped data portions between the spatial windows are also processed according to the BT mechanism. The data IO time in the seismic data RNA processing is greatly shortened, a high-expansibility parallel method for seismic data RNA processing is formed, and the method has the advantages of node fault tolerance, fault recovery and the like.

In order to further explain the scheme, the application also provides a specific application example of the data monitoring method comprising the step process of the parallel random noise attenuation method of the three-dimensional pre-stack data. And each node reads data according to the size (X, Y, Z three directions) of the space window, and the accelerated RNA calculation is realized by utilizing multiple calculation nodes in the cluster and the parallel calculation of multiple cores in the nodes. Referring to fig. 4 and 5, the specific steps and implementation process are as follows:

s1: data distribution

Distributing the seismic data which are stored in a centralized manner to different nodes according to three-dimensional keyword parameters given by a user, and reading the corresponding seismic data in parallel by each node, namely reading the seismic data which are stored in the centralized manner in parallel by each node according to the distributed data range, wherein the range of the X direction of the seismic data is 1-M, and the size of the read data range of each node is (1-M)/N if N nodes are arranged.

S2: building distributed memory

The distributed storage Cache as shown in fig. 6 is constructed using a memory, an SSD, and a temporary disk.

S3: data sharing and reading

Each node shares data on the Cache of the distributed storage based on a preset BT (also called as BT mechanism, BT download or P2P download) protocol; and each node reads data according to the preset size (X, Y, Z three directions) of the target space window based on the preset BT protocol.

S4: intra-node multithreading

And establishing three control threads in each computing node by utilizing pthread, wherein the control threads are respectively used for task request, data reading, computing control and result output. Therefore, data pre-reading, the output of the previous calculation result and the subsequent calculation are simultaneously carried out, and the full parallelism of the input, calculation and output processes of a task is achieved.

Specifically, the input, calculation, and output processes in S4 are respectively implemented as follows:

(1) data input:

the computing node reads data according to the preset size (X, Y, Z three directions) of the target space window based on the preset BT protocol.

(2) And (3) data calculation:

and multithread parallel computing in the nodes is to establish a computing thread according with the number of CPU cores for parallel computing so as to fully utilize the processing capacity (CPU core) of each node. Creating a thread by using pthread, and performing thread control by using pthread _ mutex _ t and pthread _ cond _ t, wherein the RNA calculation comprises the following steps: FFT, operator calculation, DECON and inverse FFT are respectively carried out for Z, Y, X direction overlapping.

(3) And (3) data output:

and after the computing node finishes a computing task, enabling the output control thread to output a result.

S5: the main node records files:

when a computing node requests a task, the ID and result information of the last completed task are transmitted to the main node, and the main node is responsible for storing an information record file (as a fault recovery proof) transmitted back from the node and then continuously distributing the task to the computing node completing the task. And recording the file name as the job name under the directory of the user sending the job so as to avoid job conflict among users.

S6: node fault tolerance

In the calculation process, if the main node detects that a certain calculation node has a fault, the main node performs result check according to the recorded file information and delivers the unfinished tasks to other nodes to finish.

S7: fault recovery

In the process of executing the job, when the job execution fails due to various machine reasons, the main node rebuilds the task list according to the record file after restarting the job and checks the correctness of the calculation result. Upon a failover restart, the job parameter restart should be selected as yes.

S8: result merging

In the calculation process, each node is responsible for outputting a calculation result in order to fully parallel the nodes. After the computing task is completed, the main node is responsible for merging the output results of the computing nodes. And when a final result is generated, the main node writes data information into the database.

S9: performance testing

The test cluster environment comprises a hardware environment, a software environment and running software, wherein the hardware environment is a Linux cluster with 20 nodes, each node comprises 16 cores, and the nodes are connected through a gigabit network (two network cables are bound); running software including an RNA parallel program; the test data adopts data of a certain three-dimensional work area, and specific parameters are shown in a table 1.

TABLE 1 test Environment

In order to ensure the stability and reliability of the test performance result, 20 nodes are adopted to perform the test three times respectively, and the specific results are shown in table 2:

TABLE 2 test results

In order to effectively improve the efficiency and processing reliability of random noise attenuation of three-dimensional prestack data and further effectively improve the efficiency of seismic exploration by using seismic data after random noise attenuation, the application provides an embodiment of a computing node capable of executing all or part of the contents in a parallel random noise attenuation method of the three-dimensional prestack data, and the computing node specifically includes the following contents, referring to fig. 7:

the data parallel receiving module 10 is used for receiving the three-dimensional pre-stack data corresponding to each other in parallel with other computing nodes;

the data parallel reading module 20 is configured to apply a preset BT protocol to read, in parallel with other computing nodes, target seismic data in the corresponding three-dimensional pre-stack data based on the size of a preset target space window;

the data parallel processing module 30 is configured to perform noise attenuation processing on the respective corresponding target seismic data in parallel with other computing nodes, and output respective corresponding noise attenuation results to the master node in parallel with the other computing nodes, so that the master node performs data summarization on the noise attenuation results output by the computing nodes and sends corresponding summarization results to corresponding databases.

In a specific embodiment, the computing node further includes the following:

and the memory establishing module 01 is used for establishing a corresponding distributed memory.

Correspondingly, the data parallel reading module 20 specifically includes the following contents:

and the target seismic data parallel reading unit 21 is configured to apply a preset BT protocol and other computing nodes to read target seismic data in the corresponding three-dimensional prestack data in parallel in the corresponding distributed memory based on the size of a preset target spatial window.

The sharing request receiving module 22 is configured to receive a data sharing request sent by another computing node;

and the data sharing module 23 is configured to acquire and send corresponding seismic data to other computing nodes in the distributed storage corresponding to the data sharing module based on the BT protocol according to the data sharing request.

A control thread establishing module 24, configured to establish at least three control threads for parallel execution, where at least one control thread is used to receive the target seismic data, at least one control thread is used to perform noise attenuation processing on the target seismic data, and at least one control thread is used to output the noise attenuation result to the master node.

The computing thread establishing module 25 is configured to establish a plurality of computing threads according to a CPU processing threshold preset by the computing thread establishing module;

correspondingly, the data parallel processing module 30 specifically includes the following contents: and the noise attenuation processing unit 31 is configured to apply, in parallel with other computing nodes, the plurality of corresponding computing threads to perform noise attenuation processing on the corresponding target seismic data.

It is to be understood that the noise attenuation process includes: fourier transform processing, operator solving processing, seismic deconvolution processing and inverse fast Fourier transform processing.

From the above description, it can be seen that the reliability and the accuracy of the random noise attenuation processing process of the computing node provided in the embodiment of the present application are high, the processing performance of the processor for seismic data, especially the seismic data processing with an ultra-large data volume, can be effectively improved, by fully utilizing the storage resources of the Linux cluster multi-node memory, SSD, temporary disk, and the like, the bottleneck of input or output of centralized storage is solved, the RNA time is effectively shortened, the efficiency of random noise attenuation of three-dimensional pre-stack data can be effectively improved, the requirement of processing oil geophysical exploration data can be further met, the time of seismic exploration is effectively shortened, and the efficiency of seismic exploration by applying the seismic data after random noise attenuation can be improved.

From a hardware aspect, an embodiment of the present application further provides a specific implementation manner of an electronic device, which is capable of implementing all steps in the parallel random noise attenuation method for three-dimensional prestack data in the foregoing embodiment, and with reference to fig. 8, the electronic device specifically includes the following contents:

a processor (processor)601, a memory (memory)602, a communication Interface (Communications Interface)603, and a bus 604;

the processor 601, the memory 602 and the communication interface 603 complete mutual communication through the bus 604; the communication interface 603 is used for realizing information transmission among the computing nodes, the client terminals, the main nodes, the relevant databases and other participating mechanisms;

the processor 601 is configured to call a computer program in the memory 602, and the processor executes the computer program to implement all the steps in the method for parallel random noise attenuation of three-dimensional prestack data in the foregoing embodiments, for example, when executing the computer program, the processor implements the following steps:

Step 200: and reading the corresponding target seismic data in the three-dimensional pre-stack data by applying a preset BT protocol based on the size of a preset target space window.

From the above description, it can be known that the electronic device provided in the embodiment of the present application has high reliability and high accuracy in the random noise attenuation processing process, and can effectively improve the processing performance of the processor for seismic data, especially the seismic data processing with an ultra-large data volume, and by fully utilizing the storage resources of the Linux cluster multi-node memory, SSD, temporary disk, and the like, the input or output bottleneck of centralized storage is solved, the RNA time is effectively shortened, the efficiency of random noise attenuation of three-dimensional pre-stack data can be effectively improved, and then the requirement of processing oil geophysical exploration data can be met, the time of seismic exploration is effectively shortened, and the efficiency of seismic exploration using seismic data after random noise attenuation can be improved.

Embodiments of the present application further provide a computer-readable storage medium capable of implementing all steps in the parallel random noise attenuation method for three-dimensional pre-stack data in the above embodiments, where the computer-readable storage medium stores thereon a computer program, and when the computer program is executed by a processor, the computer program implements all steps of the parallel random noise attenuation method for three-dimensional pre-stack data in the above embodiments, for example, when the processor executes the computer program, the processor implements the following steps:

From the above description, it can be seen that the computer-readable storage medium provided in the embodiment of the present application has high reliability and high accuracy in the random noise attenuation processing process, and can effectively improve the processing performance of the processor for seismic data, especially the processing of seismic data with an ultra-large data volume, and solve the input or output bottleneck of centralized storage by fully utilizing the storage resources of the Linux cluster multi-node memory, SSD, temporary disk, and the like, effectively shorten the RNA time, effectively improve the efficiency of random noise attenuation of three-dimensional pre-stack data, and further can meet the requirement of processing petroleum geophysical exploration data, effectively shorten the seismic exploration time, and improve the efficiency of seismic exploration using seismic data after random noise attenuation.

In order to effectively improve the efficiency and processing reliability of random noise attenuation of three-dimensional prestack data and further effectively improve the efficiency of seismic exploration by using seismic data after random noise attenuation, the present application provides an embodiment of a master node for implementing all or part of the contents in the data monitoring method, and referring to fig. 9, the master node specifically includes the following contents:

and the real-time monitoring module 40 is used for monitoring each computing node belonging to the same Linux cluster in real time.

The task allocation module 50 is configured to, if a task request of any one of the computing nodes is received, allocate a task to the corresponding computing node according to the task request, where the task includes three-dimensional pre-stack data to be processed, so that the computing node implements the parallel random noise attenuation method for the three-dimensional pre-stack data with respect to the three-dimensional pre-stack data.

A processing result receiving module 60, configured to receive noise attenuation results sent by each of the computing nodes;

and the data summarizing module 70 is configured to perform data summarization on the noise attenuation results output by each computing node for multiple times, and send corresponding summarization results to a corresponding database.

The task request comprises a task identifier and a noise attenuation result which are finished last time by a corresponding computing node; the master node further includes:

and the data recording module 80 is configured to record the task identifier and the noise attenuation result that are completed by the computing node last time in a record file.

And the fault processing module 90 is configured to, if it is detected that any one of the computing nodes fails, perform fault result checking on the computing node according to the record file, and allocate tasks that are not completed by the computing node to other computing nodes to complete the tasks.

As can be seen from the above description, the master node provided in the embodiment of the present application splits seismic data into a plurality of data files according to a three-dimensional keyword parameter given by a user, stores the data files in different nodes, performs broadcast reading on each node according to a BT mechanism on spatial window data required for operation, and processes an overlapped data portion between spatial windows according to the BT mechanism. The data IO time in the seismic data RNA processing is greatly shortened, a high-expansibility parallel method for seismic data RNA processing is formed, and the method has the advantages of node fault tolerance, fault recovery and the like.

From a hardware aspect, an embodiment of the present application further provides a specific implementation manner of an electronic device capable of implementing all steps in the data monitoring method in the foregoing embodiment, where the structure and composition of the electronic device for implementing the data monitoring method are the same as those of the electronic device for implementing the parallel random noise attenuation method for three-dimensional prestack data, and the electronic device specifically includes the following contents:

a processor (processor), a memory (memory), a communication Interface (Communications Interface), and a bus;

the processor, the memory and the communication interface complete mutual communication through the bus; the communication interface is used for realizing information transmission among the computing node, the client terminal, the main node, the related database and other participating mechanisms;

the processor is configured to call a computer program in the memory, and when the processor executes the computer program, all the steps in the data monitoring method in the foregoing embodiments are implemented, for example, when the processor executes the computer program, the following steps are implemented:

As can be seen from the above description, in the electronic device provided in the embodiment of the present application, the seismic data is divided into a plurality of data files according to the three-dimensional keyword parameter given by the user, the data are stored in different nodes, the spatial window data required for the operation is broadcast and read on each node according to the BT mechanism, and the overlapped data portions between the spatial windows are also processed according to the BT mechanism. The data IO time in the seismic data RNA processing is greatly shortened, a high-expansibility parallel method for seismic data RNA processing is formed, and the method has the advantages of node fault tolerance, fault recovery and the like.

An embodiment of the present application further provides a computer-readable storage medium capable of implementing all the steps in the data monitoring method in the foregoing embodiment, where the computer-readable storage medium stores a computer program, and the computer program implements all the steps of the data monitoring method in the foregoing embodiment when executed by a processor, for example, the processor implements the following steps when executing the computer program:

As can be seen from the above description, in the computer-readable storage medium provided in this embodiment of the present application, the seismic data is divided into a plurality of data files according to the three-dimensional keyword parameter given by the user, the data are stored in different nodes, the spatial window data required for the operation are broadcast and read on each node according to the BT mechanism, and the overlapping data portions between the spatial windows are also processed according to the BT mechanism. The data IO time in the seismic data RNA processing is greatly shortened, a high-expansibility parallel method for seismic data RNA processing is formed, and the method has the advantages of node fault tolerance, fault recovery and the like.

In order to effectively improve the efficiency and the processing reliability of random noise attenuation of three-dimensional pre-stack data and further effectively improve the efficiency of seismic exploration by using seismic data after random noise attenuation, the application provides an embodiment of a parallel random noise attenuation processing system of three-dimensional pre-stack data, and the parallel random noise attenuation processing system of three-dimensional pre-stack data specifically comprises the following contents:

at least one of the aforementioned master nodes, and a plurality of the aforementioned compute nodes; and each computing node is in communication connection with the main node.

From the above description, it can be seen that the parallel random noise attenuation processing system for three-dimensional pre-stack data provided in the embodiment of the present application has high reliability and high accuracy in the random noise attenuation processing process, and can effectively improve the processing performance of a processor for seismic data, especially the processing of seismic data with an ultra-large data volume.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class embodiment, since it is substantially similar to the method embodiment, the description is simple, and the relevant points can be referred to the partial description of the method embodiment.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

Although the present application provides method steps as described in an embodiment or flowchart, additional or fewer steps may be included based on conventional or non-inventive efforts. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or client product executes, it may execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) according to the embodiments or methods shown in the figures.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a vehicle-mounted human-computer interaction device, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

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

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.

The embodiments of this specification may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The described embodiments may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of an embodiment of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and variations to the embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present specification should be included in the scope of the claims of the embodiments of the present specification.

Claims

1. A parallel random noise attenuation method for three-dimensional prestack data is characterized by comprising the following steps:

2. The method for parallel random noise attenuation of three-dimensional prestack data according to claim 1, further comprising, before the applying the preset BT protocol to read the corresponding target seismic data in the three-dimensional prestack data in parallel with other computing nodes:

establishing a corresponding distributed memory;

3. The method of parallel random noise attenuation of three-dimensional prestack data according to claim 2, characterized by further comprising:

receiving data sharing requests sent by other computing nodes;

4. The method of claim 1, wherein prior to performing noise attenuation processing on the respective target seismic data in parallel with the other computing nodes, the method further comprises:

5. The method of claim 1, wherein prior to performing noise attenuation processing on the respective target seismic data in parallel with the other computing nodes, the method further comprises:

6. The method of parallel random noise attenuation of three-dimensional prestack data according to any of claims 1 to 5, characterized in that the noise attenuation process comprises: fourier transform processing, operator solving processing, seismic deconvolution processing and inverse fast Fourier transform processing.

7. A method for monitoring data, comprising:

if a task request of any one computing node is received, distributing a task to the corresponding computing node according to the task request, wherein the task comprises three-dimensional pre-stack data to be processed, so that the computing node realizes the parallel random noise attenuation method of the three-dimensional pre-stack data according to any one of claims 1 to 6 aiming at the three-dimensional pre-stack data;

receiving noise attenuation results sent by each computing node;

8. The data monitoring method according to claim 7, wherein the task request includes a task identifier and a noise attenuation result of a last completion of the corresponding computing node; the data monitoring method further comprises the following steps:

9. The data monitoring method of claim 8, further comprising:

10. A computing node, comprising:

11. The computing node of claim 10, further comprising:

correspondingly, the data parallel reading module comprises:

12. The computing node of claim 11, further comprising:

13. The computing node of claim 10, further comprising:

14. The computing node of claim 10, further comprising:

correspondingly, the data parallel processing module comprises:

15. The computing node of any of claims 10 to 14, wherein the noise attenuation process comprises: fourier transform processing, operator solving processing, seismic deconvolution processing and inverse fast Fourier transform processing.

16. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the program, implements the steps of the method of parallel random noise attenuation of three-dimensional pre-stack data according to any of claims 1 to 6.

17. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for parallel random noise attenuation of three-dimensional prestack data according to any one of claims 1 to 6.

18. A master node, comprising:

the task allocation module is used for allocating a task to a corresponding computing node according to a task request if the task request of any computing node is received, wherein the task contains three-dimensional pre-stack data to be processed, so that the computing node can realize the parallel random noise attenuation method of the three-dimensional pre-stack data according to any one of claims 1 to 6 aiming at the three-dimensional pre-stack data;

19. The master node of claim 18, wherein the task request includes a task identifier and a noise attenuation result of a last completion of the corresponding computing node; the master node further comprises:

20. The master node of claim 19, further comprising:

21. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the data monitoring method according to any of claims 7 to 9 are implemented when the processor executes the program.

22. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the data monitoring method of any one of claims 7 to 9.

23. A system for parallel random noise attenuation processing of three-dimensional prestack data, comprising: at least one master node as claimed in any one of claims 18 to 20, and a plurality of computing nodes as claimed in any one of claims 10 to 15;

and each computing node is in communication connection with the main node.