CN106250101A - Migration before stack method for parallel processing based on MapReduce and device - Google Patents
Migration before stack method for parallel processing based on MapReduce and device Download PDFInfo
- Publication number
- CN106250101A CN106250101A CN201510320301.7A CN201510320301A CN106250101A CN 106250101 A CN106250101 A CN 106250101A CN 201510320301 A CN201510320301 A CN 201510320301A CN 106250101 A CN106250101 A CN 106250101A
- Authority
- CN
- China
- Prior art keywords
- offset distance
- group
- offset
- distance group
- primary
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Landscapes
- Image Processing (AREA)
Abstract
Providing a kind of migration before stack method for parallel processing based on MapReduce and device, the method includes: seismic channel set data sorted according to offset distance size, generates common offset road collection data;According to the offset distance packet parameters pre-set, the collection packet of common offset road is become offset distance group;Perform to map Map operation for each offset distance group, obtain imaging space;And merge each imaging space and obtain imaging results data.The method and device, by utilizing MapReduce parallel model, improve migration before stack Parallel Implementation mode, improve computational efficiency and the large-scale data adaptability of migration before stack, provide the seismic imaging data of efficiently and accurately for seismic prospecting.
Description
Technical field
The invention belongs to Seismic Data Processing Technique field in field of seismic exploration, be specifically related to a kind of based on
The migration before stack method for parallel processing of MapReduce and device.
Background technology
Migration before stack processes and has high requirements Large-scale parallel computing all the time, along with the expansion of exploration scale
Greatly, in the face of the huge size data volume in thousands of square kilometres of exploratory areas, existing integration method pre-stack time migration face
Face computational efficiency to reduce, parallel processing small scale, the problem high to the requirement of hardware.When how to improve prestack
Between skew scale adaptability, improve extensive data process time computational efficiency and system suitability, improve
The economic benefit that pre-stack time migration processes, is that pre-stack time migration process suffers from a problem that.
Summary of the invention
The purpose of the disclosure is to solve a difficult problem present in above-mentioned prior art, it is provided that a kind of based on
The migration before stack method for parallel processing of MapReduce and device, improve the computational efficiency that migration before stack processes,
It is allowed to be adapted to large-scale calculations.
On the one hand providing a kind of migration before stack method for parallel processing based on MapReduce, the method includes:
Seismic channel set data are sorted according to offset distance size, generates common offset road collection data;According to pre-setting
Offset distance packet parameters, the collection packet of common offset road is become offset distance group;For each offset distance group
Perform to map Map operation, obtain imaging space;Merge each imaging space and obtain imaging results data.
On the other hand a kind of migration before stack parallel processing apparatus based on MapReduce, this device bag are provided
Include: for seismic channel set data being sorted according to offset distance size, generate the parts of common offset road collection data;
For according to the offset distance packet parameters pre-set, the collection packet of common offset road being become offset distance group
Parts;For performing to map Map operation for each offset distance group, obtain the parts of imaging space;For
Merge each imaging space and obtain the parts of imaging results data.
Each aspect of the present invention, by utilizing MapReduce parallel model, improves migration before stack Parallel Implementation
Mode, improves computational efficiency and the large-scale data adaptability of migration before stack, provides efficiently for seismic prospecting
Seismic imaging data accurately.
Accompanying drawing explanation
By combining accompanying drawing, disclosure illustrative embodiments is described in more detail, the disclosure above-mentioned
And other purpose, feature and advantage will be apparent from, wherein, in disclosure illustrative embodiments
In, identical reference number typically represents same parts.
Fig. 1 shows the schematic diagram of handling process based on MapReduce.
Fig. 2 shows that migration before stack based on MapReduce according to an embodiment of the invention is located parallel
The flow chart of reason method
Fig. 3 shows the schematic diagram of an example of offset distance component prescription case.
Fig. 4 shows the processing procedure schematic diagram of a concrete example of the present invention.
Fig. 5 shows the treatment effect schematic diagram of the embodiment of the present invention.
Detailed description of the invention
It is more fully described the preferred implementation of the disclosure below with reference to accompanying drawings.Although accompanying drawing shows
The preferred implementation of the disclosure, however, it is to be appreciated that may be realized in various forms the disclosure and should be by
Embodiments set forth herein is limited.On the contrary, it is provided that these embodiments are to make the disclosure more saturating
Thorough and complete, and the scope of the present disclosure intactly can be conveyed to those skilled in the art.
The present invention utilizes MapReduce parallel computational model to improve the Parallel Implementation side that existing migration before stack processes
Formula, utilizes efficient packet scheme to realize Large-scale parallel computing.By the transformation of parallel mode, carry
The computational efficiency that high migration before stack processes.
MapReduce is a kind of parallel processing computation model of Google company exploitation, due at magnanimity number
According to process in there is good autgmentability and good fault-tolerance, be widely used in web page index, data
Excavate, science is simulated and in cloud computing platform.
MapReduce provides one, on large-scale computer cluster, mass data is carried out distributed treatment
Method, it, by two functions that have been abstracted into of complicated parallel computation process height, maps Map and reduction
Reduce.Process by mass data collection is divided into small data set give different computers, thus
Realize parallelization.For data, MapReduce is regarded as a series of < key, and value > is to (key
Be worth to), data handling procedure is then simplified to Map map and two stages of Reduce reduction.?
In MapReduce platform, often the input data set of an operation is divided into several independent data bases,
By Map parallel processing, by one group of < key, value > to being mapped to one group of new < key, value > couple,
As the input of Reduce, perform associative operation.Handling process schematic diagram such as Fig. 1 institute of MapReduce
Show, be made up of input (piecemeal), Map task, Reduce task, output (result) four part.
MapReduce is the technology of the big Data processing being commonly used to the Internet, but inventor's understanding
Arrive, the mass data processing advantage of MapReduce parallel model, it is possible to realize on a large scale for migration before stack process,
Efficiently calculate and a kind of effective solution is provided.
Embodiment 1
Fig. 2 shows that migration before stack based on MapReduce according to an embodiment of the invention is located parallel
The flow chart of reason method, the method includes:
Seismic channel set data are sorted by step 201 according to offset distance size, generate common offset road collection data;
Step 202, according to the offset distance packet parameters pre-set, becomes the collection packet of common offset road partially
Move away from group;
Step 203, performs to map Map operation for each offset distance group, obtains imaging space;And
Step 204, merges each imaging space and obtains imaging results data.
The present embodiment, by utilizing MapReduce parallel model to improve migration before stack Parallel Implementation mode, carries
The computational efficiency of high migration before stack and large-scale data adaptability, provide the ground of efficiently and accurately for seismic prospecting
Shake imaging data.
Generate common offset road collection data
In one example, first to seismic channel set data (such as CMP seismic channel set data) according to
Offset distance size is ranked up, such as, can by seismic channel set data according to offset distance from small to large (or from
Big to little) order arrangement, after obtained arrangement, data are referred to herein as common offset road collection data.
It is grouped into offset distance group
In one example, the collection packet of common offset road become offset distance group comprise the steps that according to setting in advance
Common offset road collection data are once grouped and obtain primary offset distance group by the offset distance packet parameters put;With
And in the case of the number of channels of primary offset distance group is less than threshold value, using primary offset distance group as described skew
Away from group, in the case of the number of channels of primary offset distance group exceedes threshold value, carry out for described primary offset distance group
Secondary is grouped, and obtains secondary offset distance group, as described offset distance group.
1, once it is grouped
In one example, can be according to the offset distance packet parameters pre-set, by common offset road collection data
Once being grouped and obtain primary offset distance group, the most each primary offset distance group is once grouped corresponding to one piece
After common offset road collection data (also referred to as data block).
In one example, offset distance packet parameters can be pre-set as required by user.Such as, use
Family can carry out examination process by different packet parameters, the packet ginseng that Selection effect is good from examination result
Number is as offset distance packet parameters.
The example of a kind of packet mode given below, wherein, with maximum offset value Fmax, smallest offset is away from value
Fmin, offset distance interval DfAs offset distance packet parameters.It will be understood by those skilled in the art that offset distance is grouped
Parameter is not limited in this example listed these, as long as it can as the foundation of offset distance component prescription case i.e.
Can.
In this example, the once packet scheme of common offset road collection data can be expressed by following formula:
Formula (1) represents total packet number, FNmaxRepresent packet count, FmaxRepresent maximum offset value, FminRepresent
Smallest offset is away from value, DfRepresent the offset distance interval between primary offset distance group.
Formula (2) represents the group number of each primary offset distance group.FiFor forming the institute of a primary offset distance group
There are the offset distance of seismic channel set data, FNGroup number for primary offset distance group.It is to say, offset distance is Fi
All seismic channel set data, composition group number is FNPrimary offset distance group.The offset distance component group of this example
Scheme can keep the basic structure that original place is managed to greatest extent, it is ensured that calculates the consistent of effect before and after packet
Property.
It will be understood by those skilled in the art that packet scheme is not limited to above example, those skilled in the art can
To select other packet schemes as required, as long as be capable of common offset road collection data are grouped
Purpose.
Fig. 3 shows a concrete example of the offset distance packet scheme according to the present embodiment.Art technology
Personnel should be understood that this example is only for the purposes of understanding, including concrete numerical value in interior any details the most not
It is intended to limit the invention.
Fig. 3 gives with smallest offset away from value 100, maximum offset value 900, offset distance class interval 200
As a example by grouping process, in Fig. 3, chequered with black and white block represents the earthquake number after arranging from big to small by offset distance
According to, the data after CIG represent the offset distance value in the primary offset distance group correspondence imaging space after packet, at this
In example, take the offset distance intermediate value of primary offset distance group as the offset distance after this primary offset distance composition picture
Value.
In this example, offset distance divides positive and negative, and the division of offset bank uses absolute value to process.
In the case of the number of channels of primary offset distance group is less than threshold value, can be using primary offset distance group as
The offset distance group obtained eventually.
2, secondary packet
In another example, if the number of channels of the primary offset distance group after being once grouped exceedes threshold value (such as 5000
Other threshold values that road or other users are arranged according to actual needs), primary offset distance group can be carried out secondary and divide
Group processes, say, that can be using the primary offset distance group after once packet as packet object, to each
Primary offset distance group is grouped again according to number of channels, obtains secondary offset distance group (the most sub-offset distance group)
As the offset distance group finally given.The block number of each primary offset distance group secondary packet can depend on that this is first
Total number of channels that level offset distance group comprises.It is as a example by 5000 roads by threshold value, when the primary total number of channels of offset distance group is less than
During 5000 road, do not carry out secondary packet;When the primary total number of channels of offset distance group more than 5000 roads less than 10000 roads
Time, each primary offset distance group can be grouped into two pieces, such as one piece 5000 road, another part is remaining part
Number of channels;When the primary total number of channels of offset distance group is big more than 10000, during less than 15000 road, three pieces can be grouped into,
Two pieces is 5000 roads, and the 3rd piece is remaining number of channels, the like, inclined to each primary more than 5000
Shifting group carries out secondary packet.Secondary offset distance group in same primary offset distance group has identical offset distance group
Number.
Perform Map operation
After completing the packet transaction of above-mentioned offset distance group, can perform to map Map operation for each offset distance group,
Obtain imaging space.A Map is i.e. utilized to process, an offset distance group be once grouped
In the case of, this offset distance group is primary offset distance group, in the case of carrying out secondary packet, this offset distance group
It it is secondary offset distance group.Each imaging space corresponds to a primary offset distance group, say, that at the beginning of one
No matter being grouped into how many secondary offset distance group in level offset distance group again, they both correspond to an identical imaging
Space.The size of the imaging space so obtained depends on the scale of the imaging data of a primary offset distance group,
Compared with this processing mode imaging space whole with overall calculation, memory usage reduces.
Map calculates three processes that are generally divided into: data are read in, skew kernel function calculates, result of calculation output.
Data are read in various ways, can once read in a track data, read in next track data after having calculated again;
Can also once read in many track datas.In order to improve the computational efficiency of kernel function, data read in mode can be adopted
Take the processing mode once reading in a full block of data.
Reduction operation
In one example, in the case of carrying out secondary packet, the result that can operate each Map is carried out
Reduction Reduce operates, so that the result belonging to same primary offset distance group is carried out reduction process.Reduction principle
For: the result of the Map operation for same primary offset distance group is carried out reduction process, same to generate
Imaging space.Complete the imaging data file number after reduction process consistent with the packet count of primary offset distance group.
Afterwards, can merge each Reduce operation result as final process result, i.e. obtain complete imaging road
Collection data.
Imaging space piecemeal processes
In one example, it is also possible to carry out piecemeal process according to the hardware resource imaging space to obtaining.
As it was noted above, a corresponding imaging space of primary offset distance group, the process of this embodiment obtains
Complete imaging results data be the summation after imaging space corresponding to all primary offset distance groups merges.So
And, in the case of each Map only calculates the imaging space of a primary offset distance group, memory usage is still
The space being improved, in the face of big work area, when mass data calculates, internal memory yet suffers from the situation of deficiency.
In order to solve mass data processing problem, imaging space piecemeal can be taked to process.This piecemeal processes can
To be the internal processes for single map, namely map is calculating a corresponding primary offset distance
During the imaging space organized, if processing inadequate resource, then this imaging space can be carried out piecemeal process,
Through repeatedly calculating, complete the process of an imaging space.
The piecemeal of imaging space refers to the process resource status according to the equipment of calculating, to each primary offset distance group
Corresponding imaging space carries out piecemeal, to process the imaging space after piecemeal respectively.The size of imaging space is
According to processing what requirement determined, the parameter set determine, unrelated with the size of offset distance group, with offset distance
Group packet count is the most unrelated.In one example, for imaging space too big in the case of, calculate the place of equipment
Reason resource (the such as storage such as internal memory resource) state may not meet the requirement simultaneously processing (such as depositing),
Therefore can carry out imaging space piecemeal process, the size of piecemeal stores resource with internal memory etc. or other process money
Be correlated with in source, unrelated with the size of offset distance group.
Below for ease of understanding, provide the concrete example that an imaging space piecemeal processes.In this example,
After offset distance group packet transaction, when processing large-scale data, first obtain in calculating equipment available
Process resource information, such as amount of ram, contrast imaging space and memory size, within imaging space exceedes
During storage, imaging space is carried out piecemeal process, process a part of imaging space every time.Can be according to internal memory
Size and the size of imaging space carry out piecemeal, such as in save as 1GB, imaging space is 4GB, internal memory one
The secondary imaging space that can only process four point, therefore imaging space is divided into four pieces, processes one piece every time, passes through
The process that four times have processed an imaging space, after having processed, result exports hard disk, internal memory
Vacate, process for next block imaging space, through recycled for multiple times internal memory, it is achieved mass data is once
Property disposal ability.
For ease of understanding, Fig. 4 gives the secondary packet of the offset distance group comprised in above-mentioned example, imaging sky
Between the processing procedure schematic diagram of one concrete example of the present invention of piecemeal and reduction operation.Those skilled in the art
Should be understood that the purpose of this schematic diagram is only that the scheme helping to understand above-mentioned example, wherein, offset distance group
Secondary packet, imaging space packet and reduction operation are not to realize the step that the purpose of the disclosure is had to carry out
Suddenly.
Embodiment 2
According to another embodiment of the present invention, it is provided that a kind of migration before stack based on MapReduce is located parallel
Reason device, this device includes: for seismic channel set data being sorted according to offset distance size, generates skew altogether
Parts away from road collection data;For according to the offset distance packet parameters pre-set, by common offset road collection number
According to the parts being grouped into offset distance group;For performing to map Map operation for each offset distance group, become
The parts of image space;And obtain the parts of imaging results data for merging each imaging space.
In one example, the collection packet of common offset road become offset distance group may include that according in advance
Common offset road collection data are once grouped and obtain primary offset distance group by the offset distance packet parameters arranged;
And in the case of the number of channels of primary offset distance group is less than threshold value, using primary offset distance group as described partially
Move away from group, in the case of the number of channels of primary offset distance group exceedes threshold value, enter for described primary offset distance group
Row secondary is grouped, and obtains secondary offset distance group, as described offset distance group.
In one example, this device may also include that in the case of carrying out secondary packet, to each
The result of Map operation carries out the operation of reduction Reduce, to carry out the result belonging to same primary offset distance group
The parts of reduction process.
In one example, according to following formula, common offset road collection data once can be grouped:
Wherein, FNmaxRepresent packet count, FmaxRepresent maximum offset value, FminRepresent that smallest offset is away from value, Df
Represent the offset distance interval between primary offset distance group, FiFor forming all seismic channels of a primary offset distance group
The offset distance of collection data, FNGroup number for primary offset distance group.
In one example, this device may also include that for the process resource status according to the equipment of calculating, right
Imaging space carries out piecemeal, to process the parts of the imaging space after piecemeal respectively.
Application example
For ease of understanding scheme and the effect thereof of the embodiment of the present invention, a concrete application example given below.
It will be understood by those skilled in the art that this example is merely illustrative, its any detail is not intended to limit
The present invention.
In this application example, utilize seismic exploration data that the scheme of the embodiment of the present invention is tested.Survey
Examination data volume 2.1TB, test node 67,20 physical computing cores of each node.Fig. 5 is prestack time
Migration result profile, which illustrates the treatment effect of the embodiment of the present invention.Table 1 is at different parallel schema
Reason time and memory usage compare.Test result shows, the embodiment of the present invention significantly reduces the interior of program
Deposit utilization rate, improve computational efficiency, it is achieved that the massive processing power of mass data, improve earthquake
The economic benefit that survey data processes.
Tupe | The process time | Single node memory requirements |
MPI parallel processing | 92 hours | 48.5GB |
MapReduce parallel processing | 86 hours | 8.4GB |
Table 1
The disclosure refer to " imaging road collection ", " imaging space ", " imaging data " three concepts.Such as nothing
Being particularly limited to, the calculated imaging data of each map is commonly referred to as imaging space, is made up of imaging space
Result data be commonly referred to as imaging road collection (such as by after imaging space is combined according to imaging road collection
Put in order and after carrying out data rearrangement, obtain imaging road collection), no matter imaging road collection or imaging space or
The intermediate object program (result after such as stipulations) obtained in processing procedure can be referred to as imaging data.
The disclosure can be system, method and/or computer program.Computer program can include
Computer-readable recording medium, containing for making processor realize the computer of various aspects of the disclosure
Readable program instructions.
Computer-readable recording medium can be to keep and to store the instruction used by instruction execution equipment
Tangible device.Computer-readable recording medium such as may be-but not limited to-storage device electric,
Magnetic storage apparatus, light storage device, electromagnetism storage device, semiconductor memory apparatus or above-mentioned any conjunction
Suitable combination.The more specifically example (non exhaustive list) of computer-readable recording medium includes: portable
Formula computer disks, hard disk, random access memory (RAM), read only memory (ROM), erasable type can be compiled
Journey read only memory (EPROM or flash memory), static RAM (SRAM), Portable compressed dish are only
Read memorizer (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanical coding equipment, example
As stored the punch card or groove internal projection structure and the combination of above-mentioned any appropriate having instruction on it.
Computer-readable recording medium used herein above is not construed as instantaneous signal itself, such as radio wave or
The electromagnetic wave of other Free propagations of person, the electromagnetic wave propagated by waveguide or other transmission mediums are (such as, logical
Cross the light pulse of fiber optic cables) or by the signal of telecommunication of wire transfer.
Computer-readable program instructions as described herein can download to each from computer-readable recording medium
Calculating/processing equipment, or by under network, such as the Internet, LAN, wide area network and/or wireless network
It is downloaded to outer computer or External memory equipment.Network can include copper transmission cable, fiber-optic transfer, wireless
Transmission, router, fire wall, switch, gateway computer and/or Edge Server.Each calculating/place
Adapter or network interface in reason equipment receive computer-readable program instructions from network, and forward
This computer-readable program instructions, for the computer-readable storage medium being stored in each calculating/processing equipment
In matter.
Can be assembly instruction, instruction set architecture for performing the computer program instructions of disclosure operation
(ISA) instruction, machine instruction, machine-dependent instructions, microcode, firmware instructions, condition setup data,
Or the source code write with the combination in any of one or more programming languages or object code, described programming language
Speech includes OO programming language such as Smalltalk, C++ etc., and the process type programming of routine
Language such as " C " language or similar programming language.Computer-readable program instructions can fully with
Perform on the computer of family, perform the most on the user computer, perform as independent software kit,
Part part on the user computer performs or on the remote computer completely in remote computer or service
Perform on device.In the situation relating to remote computer, remote computer can be by the network of any kind
It is connected to subscriber computer including LAN (LAN) or wide area network (WAN), or, it may be connected to outer
Portion's computer (such as utilizes ISP to pass through Internet connection).In certain embodiments,
Personalized customization electronic circuit is carried out by the status information utilizing computer-readable program instructions, the most able to programme
Logic circuit, field programmable gate array (FPGA) or programmable logic array (PLA), this electronic circuit
Computer-readable program instructions can be performed, thus realize various aspects of the disclosure.
Referring herein to the method according to disclosure embodiment, device (system) and the stream of computer program
Journey figure and/or block diagram describe various aspects of the disclosure.Should be appreciated that each of flow chart and/or block diagram
The combination of each square frame in square frame and flow chart and/or block diagram, can be real by computer-readable program instructions
Existing.
These computer-readable program instructions can be supplied to general purpose computer, special-purpose computer or other can compile
The processor of journey data processing equipment, thus produce a kind of machine so that computer is being passed through in these instructions
Or other programmable data processing means processor perform time, create in flowchart and/or block diagram
The device of the function/action of regulation in one or more square frames.Can also be these computer-readable program instructions
Storage in a computer-readable storage medium, these instruction make computer, programmable data processing means and/
Or other equipment work in a specific way, thus, storage has the computer-readable medium of instruction then to include one
Manufacture, it includes the function/action of regulation in the one or more square frames in flowchart and/or block diagram
The instruction of various aspects.
Can also computer-readable program instructions be loaded into computer, other programmable data processing means,
Or on miscellaneous equipment so that on computer, other programmable data processing means or miscellaneous equipment, perform one
Series of operative steps, to produce computer implemented process, so that at computer, other number able to programme
According to the one or more sides in the instruction flowchart performed in processing means or miscellaneous equipment and/or block diagram
Function/the action of regulation in frame.
Flow chart and block diagram in accompanying drawing show the system of multiple embodiments, method and meter according to the disclosure
Architectural framework in the cards, function and the operation of calculation machine program product.In this, flow chart or block diagram
In each square frame can represent a module, program segment or a part for instruction, described module, program segment
Or a part for instruction comprises the executable instruction of one or more logic function for realizing regulation.Having
In a little realizations as replacement, the function marked in square frame can also be to be different from marked in accompanying drawing suitable
Sequence occurs.Such as, two continuous print square frames can essentially perform substantially in parallel, and they sometimes can also
Performing in the opposite order, this is depending on involved function.It is also noted that block diagram and/or flow chart
In each square frame and the combination of square frame in block diagram and/or flow chart, can be by the function performing regulation
Or the special hardware based system of action realizes, or can be with specialized hardware and computer instruction
Combination realizes.
Being described above the presently disclosed embodiments, described above is exemplary, and non-exclusive,
And it is also not necessarily limited to disclosed each embodiment.In the scope and spirit without departing from illustrated each embodiment
In the case of, many modifications and changes will be apparent from for those skilled in the art.
The selection of term used herein, it is intended to explain that the principle of each embodiment, reality are applied or to market best
In the technological improvement of technology, or make other those of ordinary skill of the art be understood that to disclose herein
Each embodiment.
Claims (10)
1. a migration before stack method for parallel processing based on MapReduce, the method includes:
Seismic channel set data are sorted according to offset distance size, generates common offset road collection data;
According to the offset distance packet parameters pre-set, the collection packet of common offset road is become offset distance group;
Perform to map Map operation for each offset distance group, obtain imaging space;And
Merge each imaging space and obtain imaging results data.
Migration before stack method for parallel processing based on MapReduce the most according to claim 1, wherein,
Offset distance group is become to include the collection packet of common offset road:
According to the offset distance packet parameters pre-set common offset road collection data are once grouped at the beginning of obtaining
Level offset distance group;And
In the case of the number of channels of primary offset distance group is less than threshold value, using primary offset distance group as described partially
Move away from group, in the case of the number of channels of primary offset distance group exceedes threshold value, enter for described primary offset distance group
Row secondary is grouped, and obtains secondary offset distance group, as described offset distance group.
Migration before stack method for parallel processing based on MapReduce the most according to claim 2, also wraps
Include:
In the case of carrying out secondary packet, the result operating each Map carries out the operation of reduction Reduce,
So that the result belonging to same primary offset distance group is carried out reduction process.
Migration before stack method for parallel processing based on MapReduce the most according to claim 2, wherein,
According to following formula, common offset road collection data are once grouped:
Wherein, FNmaxRepresent packet count, FmaxRepresent maximum offset value, FminRepresent that smallest offset is away from value, Df
Represent the offset distance interval between primary offset distance group, FiFor forming all seismic channels of a primary offset distance group
The offset distance of collection data, FNGroup number for primary offset distance group.
5. locate parallel according to the migration before stack based on MapReduce described in any one in claim 1-4
Reason method, also includes:
According to the process resource status of the equipment of calculating, imaging space is carried out piecemeal, and after processing piecemeal respectively
Imaging space.
6. a migration before stack parallel processing apparatus based on MapReduce, this device includes:
For seismic channel set data being sorted according to offset distance size, generate the parts of common offset road collection data;
For according to the offset distance packet parameters pre-set, the collection packet of common offset road being become offset distance
The parts of group;
For performing to map Map operation for each offset distance group, obtain the parts of imaging space;And
The parts of imaging results data are obtained for merging each imaging space.
Migration before stack parallel processing apparatus based on MapReduce the most according to claim 6, wherein,
Offset distance group is become to include the collection packet of common offset road:
According to the offset distance packet parameters pre-set common offset road collection data are once grouped at the beginning of obtaining
Level offset distance group;And
In the case of the number of channels of primary offset distance group is less than threshold value, using primary offset distance group as described partially
Move away from group, in the case of the number of channels of primary offset distance group exceedes threshold value, enter for described primary offset distance group
Row secondary is grouped, and obtains secondary offset distance group, as described offset distance group.
Migration before stack parallel processing apparatus based on MapReduce the most according to claim 7, also wraps
Include:
For in the case of carrying out secondary packet, the result operating each Map carries out reduction Reduce behaviour
Make, the result belonging to same primary offset distance group to be carried out the parts of reduction process.
Migration before stack parallel processing apparatus based on MapReduce the most according to claim 7, wherein,
According to following formula, common offset road collection data are once grouped:
Wherein, FNmaxRepresent packet count, FmaxRepresent maximum offset value, FminRepresent that smallest offset is away from value, Df
Represent the offset distance interval between primary offset distance group, FiFor forming all seismic channels of a primary offset distance group
The offset distance of collection data, FNGroup number for primary offset distance group.
10. parallel according to the migration before stack based on MapReduce described in any one in claim 5-9
Processing means, also includes:
For the process resource status according to the equipment of calculating, imaging space is carried out piecemeal, to process respectively point
The parts of the imaging space after block.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510320301.7A CN106250101A (en) | 2015-06-12 | 2015-06-12 | Migration before stack method for parallel processing based on MapReduce and device |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510320301.7A CN106250101A (en) | 2015-06-12 | 2015-06-12 | Migration before stack method for parallel processing based on MapReduce and device |
Publications (1)
Publication Number | Publication Date |
---|---|
CN106250101A true CN106250101A (en) | 2016-12-21 |
Family
ID=57626763
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201510320301.7A Pending CN106250101A (en) | 2015-06-12 | 2015-06-12 | Migration before stack method for parallel processing based on MapReduce and device |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN106250101A (en) |
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109657197A (en) * | 2017-10-10 | 2019-04-19 | 中国石油化工股份有限公司 | A kind of pre-stack depth migration calculation method and system |
CN110426736A (en) * | 2019-08-02 | 2019-11-08 | 中国地质大学(北京) | A kind of acquisition methods and device of offset gather |
CN111025400A (en) * | 2018-10-10 | 2020-04-17 | 中国石油化工股份有限公司 | Hadoop-based seismic migration imaging operation endurance method and system |
CN112444851A (en) * | 2019-08-30 | 2021-03-05 | 中国石油化工股份有限公司 | Reverse time migration imaging method based on MapReduce parallel framework and storage medium |
CN113051074A (en) * | 2021-03-19 | 2021-06-29 | 大庆油田有限责任公司 | Method for extracting mass shot domain efficient common imaging point offset gather |
Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6178381B1 (en) * | 1998-01-27 | 2001-01-23 | Shell Oil Company | Method of geophysical exploration |
US6330512B1 (en) * | 2000-07-25 | 2001-12-11 | Phillips Petroleum Company | Hybrid gather ground-roll suppression |
US20100118652A1 (en) * | 2008-11-13 | 2010-05-13 | Jorg Friedrich Schneider | Determination of depth moveout and of residual radii of curvature in the common angle domain |
CN102141633A (en) * | 2010-12-10 | 2011-08-03 | 中国科学院地质与地球物理研究所 | Anisotropic three-dimensional prestack time migration method |
CN104133240A (en) * | 2014-07-29 | 2014-11-05 | 中国石油天然气集团公司 | Large-scale collateral kirchhoff prestack depth migration method and device |
CN104570081A (en) * | 2013-10-29 | 2015-04-29 | 中国石油化工股份有限公司 | Pre-stack reverse time migration seismic data processing method and system by integral method |
CN104570073A (en) * | 2013-10-16 | 2015-04-29 | 中国石油化工股份有限公司 | Bi-reflection seismic wave imaging method applicable to complex, high and steep structure |
-
2015
- 2015-06-12 CN CN201510320301.7A patent/CN106250101A/en active Pending
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6178381B1 (en) * | 1998-01-27 | 2001-01-23 | Shell Oil Company | Method of geophysical exploration |
US6330512B1 (en) * | 2000-07-25 | 2001-12-11 | Phillips Petroleum Company | Hybrid gather ground-roll suppression |
US20100118652A1 (en) * | 2008-11-13 | 2010-05-13 | Jorg Friedrich Schneider | Determination of depth moveout and of residual radii of curvature in the common angle domain |
CN102141633A (en) * | 2010-12-10 | 2011-08-03 | 中国科学院地质与地球物理研究所 | Anisotropic three-dimensional prestack time migration method |
CN104570073A (en) * | 2013-10-16 | 2015-04-29 | 中国石油化工股份有限公司 | Bi-reflection seismic wave imaging method applicable to complex, high and steep structure |
CN104570081A (en) * | 2013-10-29 | 2015-04-29 | 中国石油化工股份有限公司 | Pre-stack reverse time migration seismic data processing method and system by integral method |
CN104133240A (en) * | 2014-07-29 | 2014-11-05 | 中国石油天然气集团公司 | Large-scale collateral kirchhoff prestack depth migration method and device |
Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109657197A (en) * | 2017-10-10 | 2019-04-19 | 中国石油化工股份有限公司 | A kind of pre-stack depth migration calculation method and system |
CN109657197B (en) * | 2017-10-10 | 2022-11-22 | 中国石油化工股份有限公司 | Pre-stack depth migration calculation method and system |
CN111025400A (en) * | 2018-10-10 | 2020-04-17 | 中国石油化工股份有限公司 | Hadoop-based seismic migration imaging operation endurance method and system |
CN110426736A (en) * | 2019-08-02 | 2019-11-08 | 中国地质大学(北京) | A kind of acquisition methods and device of offset gather |
CN110426736B (en) * | 2019-08-02 | 2020-04-07 | 中国地质大学(北京) | Offset gather acquisition method and device |
CN112444851A (en) * | 2019-08-30 | 2021-03-05 | 中国石油化工股份有限公司 | Reverse time migration imaging method based on MapReduce parallel framework and storage medium |
CN113051074A (en) * | 2021-03-19 | 2021-06-29 | 大庆油田有限责任公司 | Method for extracting mass shot domain efficient common imaging point offset gather |
CN113051074B (en) * | 2021-03-19 | 2022-08-19 | 大庆油田有限责任公司 | Method for extracting mass shot domain efficient common imaging point offset gather |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
JP7087079B2 (en) | Robust gradient weight compression scheme for deep learning applications | |
CN106250101A (en) | Migration before stack method for parallel processing based on MapReduce and device | |
CN104570081A (en) | Pre-stack reverse time migration seismic data processing method and system by integral method | |
CN109872311A (en) | A kind of Rock Mass Integrality sentences knowledge method | |
WO2015000029A1 (en) | Evacuation plan design | |
CN106095942A (en) | Strong variable extracting method and device | |
CN106452884A (en) | A data distribution method and apparatus in a block chain system | |
CN106886535A (en) | A kind of data pick-up method and apparatus for being adapted to multiple data sources | |
Romanowska et al. | Dispersal and the Movius line: testing the effect of dispersal on population density through simulation | |
Samadi Alinia et al. | Tehran’s seismic vulnerability classification using granular computing approach | |
TW201732655A (en) | Mining method and device for target characteristic data | |
CN105445792A (en) | Pre-stack multiple reverse time migration earthquake data processing method and system | |
CN110309947A (en) | Complete vehicle logistics order forecast method and device, logistics system and computer-readable medium | |
Giordano et al. | Parallel execution of cellular automata through space partitioning: the landslide simulation sciddicas3-hex case study | |
Witte et al. | Industry-scale CO2 flow simulations with model-parallel fourier neural operators | |
Zhao et al. | LandSys II: Agent-based land use–forecast model with artificial neural networks and multiagent model | |
Wu et al. | Research on fault cutting algorithm of the three-dimensional numerical manifold method | |
CN107656307B (en) | Full waveform inversion calculation method and system | |
CN106886510A (en) | The method and device that a kind of chart shows | |
CN115375867B (en) | Method, system, equipment and medium for calculating geothermal resource quantity by using grid model | |
Lim et al. | Evolutionary optimization with dynamic fidelity computational models | |
CN106250380A (en) | The self-defined method of partition of Hadoop file system data | |
Carr et al. | Hybrid data-parallel contour tree computation | |
CN109582476A (en) | Data processing method, apparatus and system | |
CN105447251B (en) | A kind of verification method based on transaction types excitation |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
RJ01 | Rejection of invention patent application after publication | ||
RJ01 | Rejection of invention patent application after publication |
Application publication date: 20161221 |