CN114076977A - Seismic data processing method and device based on block chain - Google Patents
Seismic data processing method and device based on block chain Download PDFInfo
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Abstract
The invention provides a seismic data processing method and a device based on a block chain, wherein the method comprises the following steps: a first node in the block chain sends a seismic data processing request aiming at a target block to the block chain; in response to the seismic data processing request, a second node in the blockchain processes the seismic data with a data processing machine model to generate a processing result; and the first node performs consensus with the second node according to the processing result. In the mining area circulation process, the seismic data processing is carried out through the block chain, so that the utilization of processing models and computing resources is improved, the optimal machine learning models and computing resources can be utilized in the seismic data processing process, and meanwhile, the fairness of seismic data processing charging is guaranteed.
Description
Technical Field
The invention relates to the technical field of oil exploration, in particular to the technical field of seismic data processing, and specifically relates to a seismic data processing method and device based on a block chain.
Background
This section is intended to provide a background or context to the embodiments of the application that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
As is known, the blockchain is derived from a transaction mode of the bitcoin, has the characteristics of decentralization, openness, independence, safety and anonymity, and a consensus mechanism is an important component of the blockchain and is also an important factor for implementing the characteristics of the blockchain. Machine learning is an artificial intelligence implementation means and is widely applied in various fields, a seismic processing method adopting machine learning has the characteristic of automatic processing, massive seismic data can be processed on the premise of no human intervention, however, the seismic data is greatly limited and different by acquisition conditions and underground structures, a massive sample library is required to be established for model training when the seismic data processing under the generalized conditions is completed under the normal condition, the massive sample library can hardly be realized, and if the sample libraries are close to each other under the condition of seismic data acquisition or approximate underground structures, the same processing model can be commonly used.
Different oil fields or processing centers usually carry out seismic data processing work in a certain area for a long time when processing seismic data, that is, the acquisition conditions or underground structures of the seismic data processed by the different oil fields or the processing centers are approximate, so that a sample library and a processing model corresponding to specific acquisition conditions or structural styles are easy to establish, once a block is replaced, the processing model needs to be reestablished, in addition, the requirement of seismic data processing on computing resources is huge, the seismic data processing period can be greatly shortened and the precision is improved through the existing processing model and idle computing resources, and therefore a set of reasonable processing model and a computing resource metering and trading method need to be established.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a seismic data processing method and device based on a block chain, which uses the block chain technology to reprocess seismic data and process hardware resources for transaction, thereby reducing the hardware equipment resetting cost and the seismic data reprocessing cost caused in the process of mining right circulation.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, a method for processing seismic data based on a blockchain is provided, including:
a first node in the block chain sends a seismic data processing request aiming at a target block to the block chain;
in response to the seismic data processing request, a second node in the blockchain processes the seismic data with a data processing machine model to generate a processing result;
and the first node performs consensus with the second node according to the processing result.
Further, the data processing machine learning model is used for performing static correction, denoising, consistency processing, prestack migration and velocity modeling on the seismic data of the target work area.
Further, the sending, by a first node in the blockchain, a seismic data processing request for a target block into the blockchain includes:
the first node acquires the target block information and the sample data of the seismic data in the block chain;
the first node generates a request authorization code according to the target block information and the sample data;
the first node sends a request authorization code to the block chain.
Further, said processing the seismic data with a data processing machine model by a second node in the blockchain in response to the seismic data processing request to generate a processing result, comprising:
the second node downloads the target block information and the sample data in the block chain according to the request authorization code;
the second node judges whether the data processing machine model accords with the request authorization code according to the target block information and the sample data;
if so, the second node processes the seismic data by using a data processing machine model to generate a processing result;
and the second node generates a result authorization code according to the request authorization code and the processing result, and sends the result authorization code to the block chain.
Further, the consensus between the first node and the second node according to the processing result includes:
the first node acquires the processing result according to the result authorization code;
the first node evaluates the processing result;
and if the evaluation is passed, the first node sends a result passing authorization code to the second node through the block chain.
Further, the block chain stores the request authorization code, the result authorization code, and the result passing authorization code.
Further, the determining, by the second node, whether the data processing machine model conforms to the request authorization code according to the target block information and the sample data includes:
the second node judges whether the target block is in accordance with the data processing machine model or not according to the target block information and the heading geographical position information in the sample information;
and the second node judges whether the sample information belongs to the seismic data of the second node.
In a second aspect, there is provided a blockchain-based seismic data processing apparatus comprising:
the processing request sending unit is used for sending a seismic data processing request aiming at a target block to the block chain by a first node in the block chain;
the data processing unit is used for responding to the seismic data processing request, and a second node in the block chain processes the seismic data by using a data processing machine model to generate a processing result;
and the result consensus unit is used for the first node to perform consensus with the second node according to the processing result.
Further, the data processing machine learning model is used for performing static correction, denoising, consistency processing, prestack migration and velocity modeling on the seismic data of the target work area.
Further, the processing request transmitting unit includes:
the sample data acquisition module is used for acquiring the target block information and the sample data of the seismic data in the block chain by the first node;
a request code generation module, configured to generate, by the first node, a request authorization code according to the target block information and the sample data;
a request code sending module, configured to send a request authorization code to the block chain by the first node.
Further, the data processing unit includes:
an information downloading module, configured to download, by the second node, the target block information and the sample data in the block chain according to the request authorization code;
a request code judging module, configured to judge, by the second node, whether the data processing machine model conforms to the request authorization code according to the target block information and the sample data;
a processing result generation module, configured to process the seismic data by the second node using a data processing machine model to generate the processing result;
and the result code generating module is used for generating a result authorization code by the second node according to the request authorization code and the processing result, and sending the result authorization code to the block chain.
Further, the result consensus unit includes:
a result obtaining module, configured to obtain, by the first node, the processing result according to the result authorization code;
a result evaluation module for evaluating the processing result by the first node;
and the pass code sending module is used for sending the result to the second node through the block chain by the first node through the authorization code.
Further, the block chain-based seismic data processing apparatus further includes: and the authorization code storage unit is used for storing the request authorization code, the result authorization code and the result passing authorization code in the block chain.
Further, the request code determining module includes:
the position information judging module is used for judging whether the target block is in accordance with the data processing machine model or not by the second node according to the target block information and the heading geographical position information in the sample information;
and the seismic data judgment module is used for judging whether the sample information belongs to the seismic data of the second node by the second node.
In a third aspect, an electronic device is provided, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above-mentioned method for processing seismic data based on blockchains when executing the program.
In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the above-mentioned blockchain-based seismic data processing method.
The embodiment of the invention provides a seismic data processing method and a device based on a block chain, wherein the method comprises the following steps: firstly, a first node in a block chain sends a seismic data processing request aiming at a target block to the block chain; then, responding to the seismic data processing request, and processing the seismic data by a second node in the block chain by using the data processing machine model to generate a processing result; and finally, the first node is identified with the second node according to the processing result. In the mining area circulation process, the seismic data processing is carried out through the block chain, so that the utilization of processing models and computing resources is improved, the optimal machine learning models and computing resources can be utilized in the seismic data processing process, and meanwhile, the fairness of seismic data processing charging is guaranteed.
In order to make the aforementioned and other objects, features and advantages of the invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
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. In the drawings:
FIG. 1 is a first schematic flow chart of a method of processing seismic data based on blockchains in an embodiment of the invention;
fig. 2 shows the specific steps of step S100 in fig. 1;
fig. 3 shows the specific steps of step S200 in fig. 1;
fig. 4 shows the specific steps of step S300 in fig. 1;
FIG. 5 is a second schematic flow chart of a method of processing seismic data based on blockchains in an embodiment of the invention;
FIG. 6 shows a schematic flow chart of step 202 in an embodiment of the invention;
FIG. 7 is a schematic flow chart of a method for multi-domain processing of seismic data based on machine learning in an embodiment of the present invention;
FIG. 8 is a first block chain-based seismic data processing apparatus in an embodiment of the invention;
FIG. 9 is a diagram illustrating a structure of a processing request sending unit according to an embodiment of the present invention;
FIG. 10 is a block diagram of a data processing unit according to an embodiment of the present invention;
FIG. 11 is a diagram illustrating a structure of a result consensus unit according to an embodiment of the present invention;
FIG. 12 is a block chain-based seismic data processing apparatus in an embodiment of the invention;
FIG. 13 is a block diagram of a request code determination module according to an embodiment of the present invention;
fig. 14 is a block diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, 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 only partial 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.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
It should be noted that the terms "comprises" and "comprising," and any variations thereof, in the description and claims of this application and the above-described drawings, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
In view of the prior art, the following problems exist: different oil fields or processing centers usually carry out seismic data processing work in a certain work area for a long time when processing seismic data, namely the acquisition conditions or underground structures of the processed seismic data are similar, so that a sample library and a processing model corresponding to specific acquisition conditions or structure styles are easy to establish, once a block is replaced, the processing model needs to be reestablished, in addition, the requirement of seismic data processing on computing resources is huge, the seismic data processing period can be greatly shortened by utilizing the existing processing model and idle computing resources, the processing precision can be greatly improved, and therefore a set of reasonable processing model and computing resource metering and trading method needs to be established. Based on this, the present application provides a seismic data processing method based on a block chain, as shown in fig. 1, the method may include the following:
step S100: a first node in the blockchain sends a seismic data processing request for a target block into the blockchain.
It is understood that the first node in step S100 refers to a party called a service demander after the operator obtains the mining area of the target work area, and a second node which is a party participating in the service after the operator loses the mining right. Specifically, the demand side firstly releases transaction information in a block chain mode so as to obtain a data processing machine learning model and seismic data corresponding to a target work area from the participation side.
Step S200: in response to the seismic data processing request, a second node in the blockchain processes the seismic data with a data processing machine model to generate a processing result.
It is understood that only the node having the data processing machine model corresponding to the target block and the seismic data may accept the seismic data processing request in step S100 and process the seismic data to generate a processing result.
Step S300: and the first node performs consensus with the second node according to the processing result.
It is to be understood that consensus refers to the agreement in some respect of the common knowledge, results, and data required by different nodes in a blockchain. There is no centralized mechanism in the blockchain, so that when information transmission and value transfer are carried out, the consistency and correctness of each transaction on all accounting nodes are commonly solved and guaranteed. The new consensus method of the block chain enables the participants and the demanders to perform large-scale and efficient operation on seismic data processing and hardware resource maximization without relying on centralized organization.
The seismic data processing method based on the block chain provided by the embodiment of the invention comprises the following steps: firstly, a first node in a block chain sends a seismic data processing request aiming at a target block to the block chain; then, responding to the seismic data processing request, and processing the seismic data by a second node in the block chain by using the data processing machine model to generate a processing result; and finally, the first node is identified with the second node according to the processing result. In the mining area circulation process, the seismic data processing is carried out through the block chain, so that the utilization of processing models and computing resources is improved, the optimal machine learning models and computing resources can be utilized in the seismic data processing process, and meanwhile, the fairness of seismic data processing charging is guaranteed.
In an alternative embodiment, the data processing machine learning model is used for performing static correction, denoising, consistency processing, prestack migration and velocity modeling on the seismic data of the target work area.
It is understood that the data processing machine learning model can cover the whole process of seismic data processing, including a static correction machine learning model, a denoising machine learning model, a consistency processing machine learning model, a prestack migration machine learning model, and a velocity modeling machine learning model. Taking a static correction machine learning model as an example, the model is a machine learning model established when a seismic data owner performs static correction processing, is used for performing static correction processing on the seismic block, is suitable for seismic data of the seismic block, and is obtained by deep learning training through a static correction sample and a tag library in seismic data processing of the same position in the past by a previous operator or a business server. Specifically, an operator firstly generates an authorization code for seismic block information and seismic sample data, and then issues the authorization code and a service requirement to a block chain for a participant to look up.
In an embodiment, referring to fig. 2, step S100 further includes:
step 101: the first node acquires the target block information and the sample data of the seismic data in the block chain;
step 102: the first node generates a request authorization code according to the target block information and the sample data;
step 103: the first node sends a request authorization code to the block chain.
In steps 101 to 103, optionally, the requester node generates a request authorization code of the seismic data to be processed; and the requiring party issues the request authorization code of the seismic data to be processed, the request authorization code of the seismic data processing machine learning model, the integrity check code of the seismic data processing machine learning model and the processing requirement to a block chain for the participating party to look up.
In an embodiment, referring to fig. 3, step S200 further includes:
step 201: the second node downloads the target block information and the sample data in the block chain according to the request authorization code;
step 202: the second node judges whether the data processing machine model accords with the request authorization code according to the target block information and the sample data;
step 203: if so, the second node processes the seismic data by using a data processing machine model to generate a processing result;
step 204: and the second node generates a result authorization code according to the request authorization code and the processing result, and sends the result authorization code to the block chain.
In steps 201 to 204, the participant judges whether the data processing machine model and the seismic data of the participant accord with the target block according to the request authorization code, and optionally, the participant verifies the integrity of the downloaded model according to the seismic data processing machine learning model integrity verification code; then, after the participant node passes through model integrity check, generating a model authentication code and writing the model authentication code into a block chain, and then configuring a seismic data processing machine learning model by the participant to process seismic data according to the processing requirement of a demand side to obtain processed seismic data; then the participator generates result authorization codes for the processed seismic data; and finally, the participator uploads the result authorization code to the block chain for the requiring party to look up.
In an embodiment, referring to fig. 4, step S300 further includes:
step 301: the first node acquires the processing result according to the result authorization code;
step 302: the first node evaluates the processing result;
step 303: and if the evaluation is passed, the first node sends a result passing authorization code to the second node through the block chain.
In steps 301 to 303, the requiring party obtains a seismic data processing result according to the result authorization code; then, the demand side evaluates the seismic data processing result by adopting a general quality control method to determine whether the processing task is finished; and finally, the requiring party takes the authorization code corresponding to the processing result (the result passes through the authorization code) as the seismic data processing machine learning model authentication code passing through the consensus authentication.
In one embodiment, referring to fig. 5, the method for processing seismic data based on blockchains further comprises:
step S400: the block chain stores the request authorization code, the result authorization code, and the result pass authorization code.
Specifically, the demand side writes the seismic data processing result authentication code after passing the consensus authentication into the block chain; and then the demand side counts the number of model authentication codes generated by the participants after the participants pass the model integrity check and writes the number into the block chain. It can be understood that the above operations can ensure that the data in the blockchain is consistent and the transaction records of each node are accurately recorded.
In one embodiment, referring to fig. 6, step 202 further comprises:
step 2021: the second node judges whether the target block is in accordance with the data processing machine model or not according to the target block information and the heading geographical position information in the sample information;
step 2022: and the second node judges whether the sample information belongs to the seismic data of the second node.
Specifically, after downloading sample data, the participating party compares the geographical position information in the seismic data track head with the geographical position information of the owned seismic data processing machine learning model, if the participating party is at the same position, the seismic data is further input and processed by using the existing seismic data processing machine learning model, and the processed data is configured with access authority and can be accessed by the same authorization code.
The embodiment of the application provides a seismic data processing method based on a block chain and the seismic data processing is carried out through the block chain, so that the utilization of a processing model and computing resources is improved. Specifically, the method comprises the following steps: firstly, acquiring a seismic data processing machine learning model in a block chain mode; and then obtaining a seismic data processing result in a block chain mode. The invention ensures that the fairness of the seismic data processing charging can be ensured while the best machine learning model and computing resources are utilized in the seismic data processing process.
To further illustrate the present solution, the present invention provides a specific application example of the seismic data processing method based on the block chain, and the specific application example specifically includes the following contents, see fig. 7.
It will be appreciated that seismic data is subject to different surface conditions and that its interfering signals exhibit different characteristics. The difference of the surface conditions has larger correlation with the geographic position, so that the processing flow and parameters of the seismic blocks at different positions have larger difference. Earthquake blocks in different geographic positions belong to different operators, and after a period of exploration, the mine right can be transferred to other operators for exploration. The process of the mining weight circulation is accompanied with the circulation and the reprocessing of the seismic data in the seismic block and the redeployment of processing hardware resources. Seismic data is reprocessed and hardware resources are processed for transactions using blockchain techniques, thereby reducing hardware equipment movement costs and seismic data reprocessing costs.
S1: the demand party issues the transaction information in a blockchain manner.
The demander needs to obtain a seismic data processing machine learning model from the participator; and then the demand party issues transaction information to the participants with processing resources in a block chain mode and obtains a seismic data processing result.
S2: and the participator inquires and processes the transaction information published in the blockchain.
It will be appreciated that the transaction information includes a seismic data processing machine learning model, which was previously obtained via deep learning training when processing seismic data for the same location block, and corresponding seismic data.
Optionally, the participating party may upload the seismic data processing machine learning model and the corresponding seismic data to the block chain in advance, or may notify each node in the block chain that the participating party can provide the model and the data, and in the former case, the participating party checks the integrity of the downloaded model according to the seismic data processing machine learning model integrity check code; then, after the participant node passes through the model integrity check, generating a model authentication code and writing the model authentication code into a block chain, so that the participant completes the data download and marks the block chain; then, the participator configures a seismic data processing machine learning model to process the seismic data according to the processing requirements of the demander to obtain processed seismic data; then the participant generates an authorization code for the processed seismic data; finally, the participant uploads the authorization code to a block chain for the demand side to look up;
s3: and the demander identifies the seismic data processing machine learning model and the seismic data.
Specifically, after the requiring party obtains the processed seismic data through the network by means of the authorization code, the requiring party evaluates the returned seismic data processing result, and selects the participant providing the best processing result to initiate consensus processing. And finally, generating an authorization code for the seismic data processing machine learning model passing the consensus verification and storing the authorization code into a block chain. Specifically, if the consensus between the demand party and the participants passes, the participants whose demand direction achieves the consensus send seismic data processing machine learning model authorization code acquisition requests, the demand party confirms that the seismic data processing machine learning model can access and generate model integrity check codes after obtaining the machine learning model authorization code of the participants, and the demand party writes information of the demand party, the participants, the model integrity check codes and the seismic data processing machine learning model authorization codes into a block chain for storage.
The seismic data processing method based on the block chain provided by the specific application example of the invention can improve the utilization rate of a seismic data processing model and seismic data processing resources; the method ensures the fairness of seismic data processing charging while utilizing the best machine learning model and computing resources in the seismic data processing process. And can greatly shorten the seismic data processing period and improve the precision by reasonably utilizing the existing processing model and idle computing resources,
based on the same inventive concept, the embodiment of the present application further provides a seismic data processing apparatus based on a block chain, which can be used to implement the method described in the above embodiment, as described in the following embodiment. Because the principle of solving the problems of the seismic data processing device based on the block chain is similar to that of the method, the implementation of the seismic data processing device based on the block chain can refer to the implementation of the method, and repeated details are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
In an embodiment, as shown in fig. 8, the seismic data processing apparatus based on a blockchain specifically includes:
a processing request sending unit 10, configured to send a seismic data processing request for a target block to a block chain by a first node in the block chain;
a data processing unit 20, configured to, in response to the seismic data processing request, process the seismic data by a second node in the blockchain using a data processing machine model to generate a processing result;
and a result consensus unit 30, configured to perform consensus on the first node and the second node according to the processing result.
Further, the data processing machine learning model is used for performing static correction, denoising, consistency processing, prestack migration and velocity modeling on the seismic data of the target work area.
Further, as shown in fig. 9, the processing request transmitting unit 10 includes:
a sample data obtaining module 101, configured to obtain, by the first node, the target block information and sample data of the seismic data in the block chain;
a request code generation module 102, configured to generate, by the first node, a request authorization code according to the target block information and the sample data;
a request code sending module 103, configured to send a request authorization code to the block chain by the first node.
Further, as shown in fig. 10, the data processing unit 20 includes:
an information downloading module 201, configured to download, by the second node, the target block information and the sample data in the block chain according to the request authorization code;
a request code determining module 202, configured to determine, by the second node, whether the data processing machine model conforms to the request authorization code according to the target block information and the sample data;
a processing result generating module 203, configured to process the seismic data by the second node using a data processing machine model to generate the processing result;
a result code generating module 204, configured to generate, by the second node, a result authorization code according to the request authorization code and the processing result, and send the result authorization code to the block chain.
Further, as shown in fig. 11, the result consensus unit 30 includes:
a result obtaining module 301, configured to obtain the processing result according to the result authorization code by the first node;
a result evaluation module 302, configured to evaluate the processing result by the first node;
a pass code sending module 303, configured to send, by the first node, the result pass authorization code to the second node through the blockchain.
Further, as shown in fig. 12, the block chain-based seismic data processing apparatus further includes: an authorization code storage unit 40, configured to store the request authorization code, the result authorization code, and the result passing authorization code in the block chain.
Further, as shown in fig. 13, the request code determining module 202 includes:
a position information determining module 2021, configured to determine, by the second node, whether the target block and the data processing machine model conform to each other according to the target block information and the heading geographical position information in the sample information;
a seismic data determining module 2022, configured to determine, by the second node, whether the sample information belongs to the seismic data of the second node itself.
The seismic data processing device based on the block chain provided by the embodiment of the invention comprises: firstly, a first node in a block chain sends a seismic data processing request aiming at a target block to the block chain; then, responding to the seismic data processing request, and processing the seismic data by a second node in the block chain by using the data processing machine model to generate a processing result; and finally, the first node is identified with the second node according to the processing result. In the mining area circulation process, the seismic data processing is carried out through the block chain, so that the utilization of processing models and computing resources is improved, the optimal machine learning models and computing resources can be utilized in the seismic data processing process, and meanwhile, the fairness of seismic data processing charging is guaranteed.
The apparatuses, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or implemented by a product with certain functions. A typical implementation device is an electronic device, which may be, for example, a personal computer, a laptop computer, 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.
In a typical example, the electronic device specifically comprises a memory, a processor and a computer program stored on the memory and executable on the processor, which when executed by the processor implements the steps of the above-described blockchain-based seismic data processing method.
Referring now to FIG. 14, shown is a schematic diagram of an electronic device 600 suitable for use in implementing embodiments of the present application.
As shown in fig. 14, the electronic apparatus 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate works and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM)) 603. In the RAM603, various programs and data necessary for the operation of the system 600 are also stored. The CPU601, ROM602, and RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted as necessary on the storage section 608.
In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, an embodiment of the invention includes a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above-described blockchain-based seismic data processing method.
In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611.
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.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
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.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The application 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 application 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.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (16)
1. A method of seismic data processing based on blockchains, comprising:
a first node in the block chain sends a seismic data processing request aiming at a target block to the block chain;
in response to the seismic data processing request, a second node in the blockchain processes the seismic data with a data processing machine model to generate a processing result;
and the first node performs consensus with the second node according to the processing result.
2. The seismic data processing method of claim 1, wherein the data processing machine learning model is used for static correction, denoising, consistency processing, prestack migration, and velocity modeling of the seismic data of the target work area.
3. The seismic data processing method of claim 1, wherein sending a seismic data processing request for a target block into a blockchain by a first node in the blockchain comprises:
the first node acquires the target block information and the sample data of the seismic data in the block chain;
the first node generates a request authorization code according to the target block information and the sample data;
the first node sends a request authorization code to the block chain.
4. The seismic data processing method of claim 3, wherein said processing the seismic data with a data processing machine model by a second node in a blockchain in response to the seismic data processing request to generate a processing result comprises:
the second node downloads the target block information and the sample data in the block chain according to the request authorization code;
the second node judges whether the data processing machine model accords with the request authorization code according to the target block information and the sample data;
if so, the second node processes the seismic data by using a data processing machine model to generate a processing result;
and the second node generates a result authorization code according to the request authorization code and the processing result, and sends the result authorization code to the block chain.
5. The seismic data processing method of claim 4, wherein the first node agrees with the second node based on the processing result, comprising:
the first node acquires the processing result according to the result authorization code;
the first node evaluates the processing result;
and if the evaluation is passed, the first node sends a result passing authorization code to the second node through the block chain.
6. The seismic data processing method of claim 5, further comprising: the block chain stores the request authorization code, the result authorization code, and the result pass authorization code.
7. The seismic data processing method of claim 4, wherein the second node determining whether the data processing machine model conforms to the request authorization code based on the target block information and the sample data comprises:
the second node judges whether the target block is in accordance with the data processing machine model or not according to the target block information and the heading geographical position information in the sample information;
and the second node judges whether the sample information belongs to the seismic data of the second node.
8. A blockchain-based seismic data processing apparatus, comprising:
the processing request sending unit is used for sending a seismic data processing request aiming at a target block to the block chain by a first node in the block chain;
the data processing unit is used for responding to the seismic data processing request, and a second node in the block chain processes the seismic data by using a data processing machine model to generate a processing result;
and the result consensus unit is used for the first node to perform consensus with the second node according to the processing result.
9. The seismic data processing apparatus of claim 8, wherein the data processing machine learning model is used for static correction, de-noising, consistency processing, pre-stack migration, and velocity modeling of the seismic data of the target work area.
10. The seismic data processing apparatus of claim 8, wherein the processing request transmitting unit comprises:
the sample data acquisition module is used for acquiring the target block information and the sample data of the seismic data in the block chain by the first node;
a request code generation module, configured to generate, by the first node, a request authorization code according to the target block information and the sample data;
a request code sending module, configured to send a request authorization code to the block chain by the first node.
11. The seismic data processing apparatus of claim 10, wherein the data processing unit comprises:
an information downloading module, configured to download, by the second node, the target block information and the sample data in the block chain according to the request authorization code;
a request code judging module, configured to judge, by the second node, whether the data processing machine model conforms to the request authorization code according to the target block information and the sample data;
a processing result generation module, configured to process the seismic data by the second node using a data processing machine model to generate the processing result;
and the result code generating module is used for generating a result authorization code by the second node according to the request authorization code and the processing result, and sending the result authorization code to the block chain.
12. The seismic data processing apparatus of claim 11, wherein the result consensus unit comprises:
a result obtaining module, configured to obtain, by the first node, the processing result according to the result authorization code;
a result evaluation module for evaluating the processing result by the first node;
and the pass code sending module is used for sending the result to the second node through the block chain by the first node through the authorization code.
13. The seismic data processing apparatus of claim 12, further comprising: and the authorization code storage unit is used for storing the request authorization code, the result authorization code and the result passing authorization code in the block chain.
14. The seismic data processing apparatus of claim 11, wherein the request code determination module comprises:
the position information judging module is used for judging whether the target block is in accordance with the data processing machine model or not by the second node according to the target block information and the heading geographical position information in the sample information;
and the seismic data judgment module is used for judging whether the sample information belongs to the seismic data of the second node by the second node.
15. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of blockchain based seismic data processing according to any of claims 1 to 7 are implemented when the program is executed by the processor.
16. 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 blockchain-based seismic data processing according to any one of claims 1 to 7.
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