CN105403913A - Pre-stack depth migration method and device - Google Patents
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Abstract
The invention provides a pre-stack depth migration method and device. The method comprises the following steps: each calculation node gets an imaging task and reads a travel time table corresponding to the imaging task to a CPU memory, calculation nodes transmit the travel time tables in the CPU memories to GPU graphics cards in the corresponding calculation nodes, CPUs and GPUs in the calculation nodes perform migration imaging calculations for read seismic data until migration of to-be-migrated data allocated to the corresponding calculation nodes is completed, the CPUs in the calculation nodes add migration imaging results in the CPU memories to migration imaging results in the GPU graphics cards to be taken as migration imaging results of the corresponding calculation nodes. The technical problem that a pure CPU calculation cluster is difficult to complete pre-stack depth migration on the condition of large data in the prior art is solved, and the technical effect that the pre-stack depth migration is completed rapidly and efficiently is achieved.
Description
Technical field
The present invention relates to technical field of geological exploration, particularly a kind of prestack depth migration method and device.
Background technology
Kirchhoff prestack depth migration technology is a kind of Depth Domain formation method of outbalance in geophysical survey seismic data process process; the method has higher image quality relative to time migration; and not by the restriction of field data observed pattern; the method can also export the common imaging gather based on geophone offset in addition; wherein, common imaging gather is the important information carrying out further velocity analysis.
Flatly expressing one's feelings under condition, Kirchhoff (kirchhoff) integral method offset equation is:
Wherein, imaging point ξ=(x
ξ, y
ξ, z
ξ), I (ξ) is expressed as the imaging results of picture point ξ
,d [t
,m, h] represent the geological data of field inspection, m represents common midpoint, and h represents half geophone offset, Ω
ξrepresent migration aperture.And migration before stack process nature is the process a series of observation data being weighted to summation, wherein, the W (ξ, m, h) in above formula just represents weighting factor, t
d(ξ, m, h) represent by shot point to imaging point again to the hourage of acceptance point.
Although the mathematical expression of Kirchhoff prestack depth migration method is comparatively abstract, but its physics realization process can simply be described as: seismic trace is from treating the mapping of offset data space to migration result data space multi-to-multi, mapping relations are eikonal equation whilst on tour computing formula, when not considering variable orifice footpath condition under, Kirchhoff prestack depth migration be exactly every one geophone offset be h treat that migrating seismic data is mapped in an Elliptic Cylinder of migration result data centralization, wherein, the axle center of this Elliptic Cylinder is that current waiting offsets seismic trace by the projected position of terrestrial coordinate in migration result space.If migration aperture adopts circular and aperture value is 300, so treat that skew earthquake sampled point can be mapped on 282600 (3.14*300*300) individual migration result earthquake sampled point for one.
Flatly expressing one's feelings under condition; Kirchhoff Summation Method of Migration adopts above-mentioned Kirchhoff integral method offset equation; this formula can carry out depth shift; when carrying out depth shift, employing mode as shown in Figure 1 calculates whilst on tour; particularly, following two formula combined calculation whilst on tours can be adopted:
T＝T
s+T
r
Wherein, T
srepresent shot point and the geophone station whilst on tour to imaging point, T
rrepresent that geophone station is to the whilst on tour of imaging point, t represents shot point or the geophone station whilst on tour to imaging point, and V represents the interval velocity of medium.
As can be seen from above-mentioned three formula, the pre-stack depth migration completing an earthquake sampling point needs to comprise: the calculating such as whilst on tour calculating, amplitude weight, anti-alias-filtering and integration summation.Usually, seismic trace comprises several thousand earthquake sampling points, a work area comprises several ten million to several hundred million seismic traces, therefore the calculated amount of the pre-stack depth migration in a work area is extremely huge, and the computing power of traditional computer is difficult to the requirement meeting pre-stack depth migration calculated amount.
For the problems referred to above, at present effective solution is not yet proposed.
Summary of the invention
Embodiments provide a kind of prestack depth migration method, to reach the object rapidly and efficiently completing pre-stack depth migration, the method comprises:
Computing node gets an imaging task, and reads whilst on tour table corresponding to this imaging task in CPU internal memory;
Whilst on tour table in CPU internal memory transfers in the GPU video card in this computing node by described computing node;
Described computing node read be dispensed to this computing node treat migrating seismic data;
CPU and GPU in described computing node respectively from be dispensed to this computing node treat read geological data migrating seismic data, and according to described whilst on tour table, migration imaging calculating is carried out to the geological data read, until be dispensed to this computing node treat that offset data offset completes;
Imaging results in GPU video card is passed to the CPU in described computing node by the GPU in described computing node, this CPU by the migration imaging result in CPU internal memory and the migration imaging results added in GPU video card, as the migration imaging result of this computing node.
In one embodiment, the whilst on tour table in CPU internal memory transfers in the GPU video card in this computing node by described computing node, comprising:
Adopt the mode of copy to be copied in GPU video card by the whilst on tour table in CPU internal memory, make the whilst on tour table in CPU internal memory identical with the whilst on tour table in GPU video card.
In one embodiment, CPU and GPU in described computing node respectively from be dispensed to this computing node treat read geological data migrating seismic data, and according to described whilst on tour table, migration imaging calculating is carried out to the geological data read, until be dispensed to this computing node treat that offset data offset completes, comprising:
CPU and GPU in described computing node according to respective computing power adaptively from be dispensed to this computing node treat read geological data migrating seismic data, line displacement imaging of going forward side by side calculates, until be dispensed to this computing node treat that offset data has all offset.
In one embodiment, get an imaging task at computing node, and before reading in this imaging task corresponding whilst on tour table to CPU internal memory, described method also comprises:
Described computing node is got a whilst on tour and is calculated starting point;
Described computing node calculates starting point as starting point using the whilst on tour got, using migration aperture as lateral extent, using the peak excursion degree of depth as longitudinal extent, calculate the time of any point in this starting point to described lateral extent and longitudinal extent limited range space, to obtain the whilst on tour table of this starting point.
In one embodiment, according to following formula determination starting point number:
Starting point number=(that treats migrating seismic data is maximum along line direction wire size-treat the minimum along line direction wire size of migrating seismic data) × (treat maximum No. CMP of migrating seismic data-treat minimum No. CMP of migrating seismic data);
Wherein, the minimum minimum maximum diameter of hole/distance between centers of tracks along line direction wire size-cross line direction along between line direction wire size=current imaging area of migrating seismic data is treated);
Maximum maximum along line direction wire size+(maximum diameter of hole/distance between centers of tracks of cross line direction) along between line direction wire size=current imaging area treating migrating seismic data;
Minimum No. CMP between the minimum No. CMP=current imaging area for the treatment of migrating seismic data-(maximum diameter of hole/CMP spacing along straight line direction);
Maximum No. CMP between the maximum No. CMP=current imaging area for the treatment of migrating seismic data+(maximum diameter of hole/CMP spacing along straight line direction).
In one embodiment, described computing node is group leader's node, or group member's node, wherein, all computing nodes participating in calculating are divided into N group, and from every group, choose a computing node as group leader's node, the computing node except group leader's node is as group member's node;
Accordingly, imaging results in GPU video card is passed to the CPU in computing node by the GPU in described computing node, this CPU is by the migration imaging result in CPU internal memory and the migration imaging results added in GPU video card, and after the migration imaging result as this computing node, described method also comprises:
The group leader's node often organized reclaims the migration imaging result of all group member's nodes in the grouping of this group leader's node place and exports.
In one embodiment, according to following principle, the computing node participating in calculating is divided into N group:
When the number of imaging task is greater than 4, adjacent 4 computing nodes are divided into one group;
When the number of imaging task is less than 4 and is greater than 1, an imaging task computing node is divided into one group;
When the number of imaging task is less than 1,1 computing node is divided into one group.
In one embodiment, according to the number of following formula determination imaging task:
The number of the storable whilst on tour table of total bin number/single computing node of the number=imaging space of imaging task;
Wherein, the storage size shared by number=(physical memory × 0.8 of single GPU video card)/single imaging space whilst on tour table of single computing node storable whilst on tour table;
Wherein, the storage size shared by single imaging space whilst on tour table=(migration aperture/distance between centers of tracks) × (migration aperture/CMP spacing) × (excursions depths/depth sampling interval) × 4 bytes.
The embodiment of the present invention additionally provides a kind of pre-stack depth migration device, and to reach the object rapidly and efficiently completing pre-stack depth migration, this device is arranged in computing node, comprising:
First reading unit, for getting an imaging task, and reads whilst on tour table corresponding to this imaging task in CPU internal memory;
Transmission unit, for transferring in the GPU video card in this computing node by the whilst on tour table in CPU internal memory;
Second reading unit, for read be dispensed to this computing node treat migrating seismic data;
First control module, for control CPU and GPU in described computing node respectively from be dispensed to this computing node treat read geological data migrating seismic data, and according to described whilst on tour table, migration imaging calculating is carried out to the geological data read, until be dispensed to this computing node treat that offset data offset completes;
Second control module, imaging results in GPU video card passed to CPU in described computing node for controlling GPU in described computing node, this CPU by the migration imaging result in CPU internal memory and the migration imaging results added in GPU video card, as the migration imaging result of this computing node.
In one embodiment, the whilst on tour table in CPU internal memory is copied in GPU video card specifically for adopting the mode of copy by described transmission unit, makes the whilst on tour table in CPU internal memory identical with the whilst on tour table in GPU video card.
In embodiments of the present invention, computing node does not carry out migration imaging by means of only CPU, also adopts GPU to carry out migration imaging, and that is, computing node adopts the parallel mode of CPU and GPU to carry out pre-stack seismic migration imaging.Because GPU possesses very high calculated performance, the mode adopting this CPU and GPU parallel can to solve in prior art effectively under large data cases, pure CPU computing cluster has been difficult to the technical matters of pre-stack depth migration, reaches the technique effect rapidly and efficiently completing pre-stack depth migration.
Accompanying drawing explanation
Accompanying drawing described herein is used to provide a further understanding of the present invention, forms a application's part, does not form limitation of the invention.In the accompanying drawings:
Fig. 1 is that horizontal earth's surface whilst on tour calculates schematic diagram;
Fig. 2 is the method flow diagram according to prestack depth migration method of the invention process;
Fig. 3 treats migrating seismic data segmentation figure according to the embodiment of the present invention;
Fig. 4 is the bin number of the imaging space according to the embodiment of the present invention;
Fig. 5 be determine between the imaging area according to the embodiment of the present invention treat migrating seismic data scope;
Fig. 6 is the extraction figure of the common imaging point whilst on tour table according to the embodiment of the present invention;
Fig. 7 is transferred in GPU video memory according to the whilst on tour table of the embodiment of the present invention from CPU internal memory;
Fig. 8 carries out treating that migrating seismic data offsets according to CPU and GPU of the embodiment of the present invention is collaborative;
Fig. 9 be according to the embodiment of the present invention to treat in migrating seismic data task pool after skew that CPU and GPU carries out migration result synchronous;
Figure 10 is the speed field pattern of the model according to the embodiment of the present invention;
Figure 11 is according to the employing of embodiment of the present invention migration result figure of the present invention;
Figure 12 is the structured flowchart according to pre-stack depth migration device of the invention process.
Embodiment
For making the object, technical solutions and advantages of the present invention clearly understand, below in conjunction with embodiment and accompanying drawing, the present invention is described in further details.At this, exemplary embodiment of the present invention and illustrating for explaining the present invention, but not as a limitation of the invention.
Inventor considers, along with the appearance of high density wide-azimuth data acquisition modes, geological data amount sharply increases, and pure CPU computing cluster has been difficult to the Kirchhoff prestack depth migration process of tens of TB geological data.Therefore, can in conjunction with the advantage of GPU video card at high-performance computing sector, solve by the mode of CPU-GPU cooperated computing the data scale sharply expanded and the excessive problem of the geological data amount caused.
A kind of prestack depth migration method that Fig. 2 provides for the embodiment of the present application.Although hereafter describe flow process to comprise the multiple operations occurred with particular order, but should have a clear understanding of, these processes can comprise more or less operation, and these operations can sequentially perform or executed in parallel (such as using parallel processor or multi-thread environment).As shown in Figure 2, described method comprises:
Step 201: computing node gets an imaging task, and read whilst on tour table corresponding to this imaging task in CPU internal memory;
Step 202: the whilst on tour table in CPU internal memory transfers in the GPU video card in this computing node by computing node;
Step 203: computing node read be dispensed to this computing node treat migrating seismic data;
Step 204: CPU and GPU in computing node respectively from be dispensed to this computing node treat read geological data migrating seismic data, and according to described whilst on tour table, migration imaging calculating is carried out to the geological data read, until be dispensed to this computing node treat that offset data offset completes;
Step 205: the imaging results in GPU video card is passed to the CPU in described computing node by the GPU in computing node, this CPU by the migration imaging result in CPU internal memory and the migration imaging results added in GPU video card, as the migration imaging result of this computing node.
That is, in this example, computing node does not carry out migration imaging by means of only CPU, also adopts GPU to carry out migration imaging, and that is, computing node adopts the parallel mode of CPU and GPU to carry out pre-stack seismic migration imaging.Because GPU possesses very high calculated performance, the mode adopting this CPU and GPU parallel can to solve in prior art effectively under large data cases, pure CPU computing cluster has been difficult to the technical matters of pre-stack depth migration, reaches the technique effect rapidly and efficiently completing pre-stack depth migration.
In the concrete process implemented, can adopt residence time table, the pattern of flowing geological data carries out pre-stack depth migration in single computing node.Such as, the mode of copy can be adopted to be copied in GPU video card by the whilst on tour table in CPU internal memory, thus ensure that the whilst on tour table in CPU internal memory is identical with the whilst on tour table in GPU video card.Like this follow-up carry out pre-stack depth migration in, CPU and GPU only needs to read whilst on tour from the internal memory of self, decrease data transmission and access time.Further, what so-called flowing geological data referred to is exactly, for CPU and GPU in each computing node, can according to respective computing power adaptively from be dispensed to this computing node treat read geological data migrating seismic data, line displacement imaging of going forward side by side calculate.Namely, the multiprocessing that in CPU and GPU, computing power is strong, few process that computing power is weak, that is the data to be offset of getting that in CPU and GPU, computing power is strong carry out migration imaging more, weak the lacking of computing power is got data to be offset and is carried out migration imaging, until treat that offset data has all offset by what be dispensed to this computing node.
Above-mentioned whilst on tour table can obtain in such a way: computing node is got a whilst on tour and calculated starting point; Computing node calculates starting point as starting point using the whilst on tour got, using migration aperture as lateral extent, using the peak excursion degree of depth as longitudinal extent, calculate the time of any point in this starting point to described lateral extent and longitudinal extent limited range space, to obtain the whilst on tour table of this starting point.
Wherein, starting point number can be determined according to following formula:
Starting point number=(that treats migrating seismic data is maximum along line direction wire size-treat the minimum along line direction wire size of migrating seismic data) × (treat maximum No. CMP of migrating seismic data-treat minimum No. CMP of migrating seismic data);
Wherein, the minimum minimum maximum diameter of hole/distance between centers of tracks along line direction wire size-cross line direction along between line direction wire size=current imaging area of migrating seismic data is treated);
Maximum maximum along line direction wire size+(maximum diameter of hole/distance between centers of tracks of cross line direction) along between line direction wire size=current imaging area treating migrating seismic data;
Minimum No. CMP between the minimum No. CMP=current imaging area for the treatment of migrating seismic data-(maximum diameter of hole/CMP spacing along straight line direction);
Maximum No. CMP between the maximum No. CMP=current imaging area for the treatment of migrating seismic data+(maximum diameter of hole/CMP spacing along straight line direction).
Carrying out, in the concrete process calculated, to divide into groups to computing node, namely all computing nodes participating in calculating can be divided into groups.Such as can limit the node name order of the node in same group continuously, particularly, grouping group number can carry out according to following principle:
When the number of imaging task is greater than 4, adjacent 4 computing nodes are divided into one group;
When the number of imaging task is less than 4 and is greater than 1, an imaging task computing node is divided into one group;
When the number of imaging task is less than 1,1 computing node is divided into one group.
After grouping, can using first computing node often organizing as group leader's node, using other node in group as group member's node.Group leader's node of each group can read single geophone offset from this domain by road and treat offset address data file, and is broadcast to other computing node in group.Each computing node simultaneously in group gets an imaging task, and reads in the whilst on tour table internal memory that this imaging thinks corresponding.Above-mentioned group leader's node can also arrange an effect, is exactly that the group leader's node often organized also is responsible for the migration imaging result of all group member's nodes reclaimed in the grouping of this group leader's node place and exports.
Wherein, the number of imaging task can be determined according to following formula:
The number of the storable whilst on tour table of total bin number/single computing node of the number=imaging space of imaging task;
Wherein, the storage size shared by number=(physical memory × 0.8 of single GPU video card)/single imaging space whilst on tour table of single computing node storable whilst on tour table;
Wherein, the storage size shared by single imaging space whilst on tour table=(migration aperture/distance between centers of tracks) × (migration aperture/CMP spacing) × (excursions depths/depth sampling interval) × 4 bytes.
In order to be described above-mentioned prestack depth migration method better, additionally provide a specific embodiment, namely complete prestack depth migration method is described said method.But it should be noted that this specific embodiment is only to better the present invention is described, do not form inappropriate limitation of the present invention.
The method is mainly based on CPU-GPU isomerization hardware cooperated computing; take into full account the feature of Kirchhoff prestack depth migration, adopted resident whilst on tour table, the pattern of flowing geological data; avoid the I/O bottleneck of PCI-E bus as far as possible, can comprise the following steps:
Step 1: static correction, denoising, deconvolution process are carried out to the geological data that field acquisition obtains, and after carrying out above-mentioned process, pre-service is carried out to the data obtained.
Wherein, pre-service can comprise:
Step 1-1: collect the sp location X-coordinate of each track data, sp location Y-coordinate, geophone station position X-coordinate, geophone station position Y-coordinate, wire size along line direction (Inline), the wire size etc. of cross line direction (Xline);
Step 1-2: migrating seismic data will be treated according to geophone offset (offset), No. CMP, line direction, cross line direction wire size according to three grades of key word sortings, and each offset data be saved as an independently seismic data acquisition;
Step 1-3: the header word information in establishment step 1-1 and the seismic data acquisition corresponding relation each other in step 1-2.
Step 2: according to minimum geophone offset, maximum offset, geophone offset incrementation parameter, according to following formulae discovery geophone offset number:
Geophone offset number=(maximum offset-minimum geophone offset)/geophone offset increment;
Step 3: with work area scope (Inline line number × CMP number) size for standard, with as shown in Figure 3 treat that migrating seismic data segmentation figure determines the number of offset distance file.
Wherein, can the determining according to following principle of number of offset distance file:
CMP number × single track the size of data of offset data amount of treating=Inline line number × work area, work area every bar Inline line of single temporary disk (T-disk)
The data volume treating offset data amount/mono-offset distance of offset distance file number=single temporary disk (T-disk).
Further, if offset distance file number is less than 1, be then multiple file by single offset distance file declustering; If offset distance file number is greater than 1, be then a file by multiple offset distance Piece file mergence.
Step 4: determine the bin number (Fig. 3) between imaging area as shown in Figure 4, particularly, can be specified to the bin number of image space in such a way:
CMP number (X) on survey line number (L) between the total bin number=imaging area between imaging area × every bar survey line
Step 5: according to the scope treating migrating seismic data required between the current imaging area that migration aperture is determined as shown in Figure 5, particularly, the scope treating migrating seismic data can be determined in such a way:
Treat the minimum minimum maximum diameter of hole/distance between centers of tracks along line direction wire size-cross line direction along between line direction wire size=current imaging area of migrating seismic data;
Treat the maximum maximum maximum diameter of hole/distance between centers of tracks along line direction wire size+cross line direction along between line direction wire size=current imaging area of migrating seismic data;
Minimum No. CMP between the minimum No. CMP=current imaging area for the treatment of migrating seismic data-along the maximum diameter of hole/CMP spacing of straight line direction;
Maximum No. CMP between the maximum No. CMP=current imaging area for the treatment of migrating seismic data+along the maximum diameter of hole/CMP spacing of straight line direction;
Step 6: determine the starting point number that total whilst on tour calculates according to the scope of migrating seismic data for the treatment of determined, particularly, can determine in such a way to calculate starting point number:
The starting point number that total whilst on tour calculates=(that treats migrating seismic data is maximum along line direction wire size-treat the minimum along line direction wire size of migrating seismic data) × (treat maximum No. CMP of migrating seismic data-treat minimum No. CMP of migrating seismic data);
Step 7: all computing nodes participating in calculating, each computing node is got a whilst on tour and is calculated starting point, using this point as calculating starting point, take migration aperture as lateral extent, with the peak excursion degree of depth for longitudinal extent, calculate this calculating starting point treats arbitrfary point, space within the scope of migrating seismic data time to this.After calculating, with this calculating starting point for label, ray tracing starting point whilst on tour table is stored in and shares on dish.Then, get new ray tracing starting point, until all ray tracing starting points all calculate complete;
Step 8: determine the storage size shared by single imaging space whilst on tour table, particularly, the storage space can determining shared by single imaging space whilst on tour table according to following formula:
Storage size shared by single imaging space whilst on tour table=(migration aperture/distance between centers of tracks) × (migration aperture/CMP spacing) × (excursions depths/depth sampling interval) × 4 bytes;
Step 9: determine the whilst on tour table number that single computing node stores, particularly, can determine according to following formula the whilst on tour table number that single computing node stores:
The storage size shared by whilst on tour table number=(physical memory × 0.8 of single GPU card)/single imaging space whilst on tour table that single computing node stores;
Step 10: the general assignment number being specified to image space, particularly, can be specified to the general assignment number of image space according to following formula:
The whilst on tour table number that total bin number between the general assignment number=imaging area of imaging space/individual node stores;
Step 11: all computing nodes participating in calculating are divided into groups, the node name order of the computing node in same group is continuously;
Particularly, can divide into groups according to following principle to computing node: if the general assignment number of imaging space is greater than 4, then to be one group by adjacent 4 node division, if not whole point, last remaining node is one group; If the general assignment number of imaging space is less than 4 and is greater than 1, then it is one group with the general assignment of an imaging space several node division; If the general assignment number of imaging space is less than 1, then it is one group with 1 node.
Step 12: first computing node step 11 being divided into groups to obtain is defined as group leader's node, in group, other computing node is as group member's node;
Step 13: group leader's node of each group reads single geophone offset from this domain by road and treats migrating seismic data file, and be broadcast to this and organize all group member's nodes;
Step 14: in group, each computing node gets an imaging task, and reads common imaging point whilst on tour table corresponding to this imaging task in internal memory according to mode as shown in Figure 6;
Particularly, whilst on tour table can be read according to following principle: determine all ray tracing starting point scopes to this imaging point; The whilst on tour table of this imaging point is drawn into, composition imaging point whilst on tour table from the ray tracing starting point whilst on tour table treating in the scope of migrating seismic data determined;
Step 15: the whilst on tour table in internal memory is transferred to respectively two pieces of GPU cards as shown in Figure 7, can adopts, by PCI-E, whilst on tour table be transferred to GPU video card from CPU internal memory;
Particularly, transmitting procedure can be carried out according to following principle:
Whilst on tour table in internal memory is copied in two GPU video cards respectively, and copy procedure order is carried out, thus ensure that the whilst on tour table in internal memory is identical with the whilst on tour table in two GPU video memorys.
Step 16: each computing node sets up the task pool that is treated migrating seismic data, CPU and two GPU video card carries out calculations of offset according to shown in Fig. 8 respectively to this task pool application geological data, until the institute's offset data offset that needs in task pool is complete;
Step 17: when in task pool after migrating seismic data is complete by whole calculations of offset, it is synchronous that GPU and CPU carries out a migration result;
Particularly, synchronizing process can be carried out according to following principle:
As shown in Figure 9, the imaging results in video memory is passed to CPU by GPU video card, and CPU is responsible for the imaging results imaging results in GPU and the imaging results in CPU are added as this computing node;
Step 18: after calculations of offset task completes, group leader's node reclaims the imaging results of this group member's node and exports;
Step 19: in group, all computing nodes get remaining calculations of offset task, in the completed, reclaim migration result and export by group leader's node, then in group, all computing nodes get remaining calculation task, again until all calculations of offset tasks all complete;
Step 20: the Kirchhoff prestack depth migration that the CPU-GPU that repetition above-mentioned steps 13 to step 19 completes next geophone offset file works in coordination with calculates, until all geophone offset files all complete calculations of offset.
The advantage and the effect that show the example method are described to the principle of above-mentioned prestack depth migration method below:
If: treat that offset data space is:
Wherein, S----treats the total size of offset data
L----treats offset data L bar Inline line
X----treats offset data X CMP point
O----treats offset data O offset distance road
R
ijk---geological data size in-the i-th line jth CMP point kth offset gather
If: single whilst on tour table space is:
Wherein, the total size of T----whilst on tour table
The whilst on tour table line number that the migration aperture in A----Inline line direction is determined
The whilst on tour table CMP that the migration aperture in B----Crossline line direction is determined counts
If: migration result data space is:
Wherein, the total size of M----migration result data
L----migration result L bar Inline line
X----every bar Inline line X crp point
O offset gather data in each crp bin of O----
In upper example, adopt the pattern of CPU-GPU cooperated computing, in order to the efficient calculation performance of GPU hardware can be used better, adopt the pattern of fixing whilst on tour table circulation geological data, if all geological datas all complete calculations of offset by GPU card, so geological data needs from internal memory, to pass through PCI-E bus transfer in video memory.Total volume of transmitted data is:
G＝S*(L*X*T)/(100T)+L*X*T＝S*L*X/100+L*X*T
Wherein, the PCI-E total flow that S* (L*X*T)/(100T) is geological data, L*X*T is whilst on tour table total flow, and 100T is the capacity can deposited in video memory.
If adopt the pattern of fixing earthquake datacycle whilst on tour table, then the transmission total amount of PCI-E that GPU card completes required for all earthquake data offsets is;
G＝S+8*L*X*T*(S/100T)＝S+8*L*X*S/100
Wherein, S is the PCI-E total flow of geological data, and 8*L*X*S/100 is whilst on tour table total flow, and 100T is the capacity can deposited in video memory.
Generally speaking, treat that the size of offset data S and whilst on tour table total amount (L*X*T) is substantially suitable, therefore the Part II of above-mentioned first volume of transmitted data formula is equivalent to the Part I of second volume of transmitted data formula, and the Part II of above-mentioned second volume of transmitted data formula is 8 times of the Part I of first volume of transmitted data formula.As can be seen here, in this example, the mode utilizing GPU card to complete calculations of offset greatly reduces the I/O flow of PCI-E bus, thus solves the I/O crucial problem of Kichhoff integral pre-stack depth migration CPU-GPU cooperated computing PCI-E bus.
In order to verify effect of the present invention, a gross data has been selected to carry out deflection test, as shown in Figure 10, for rate pattern schematic diagram, be migration result schematic diagram of the present invention as shown in figure 11, compare can find out both, mode of the present invention is right-on.
In order to verify effect of the present invention, the three-dimensional work area of a certain reality is selected to test, this chosen three-dimensional work area treat offset data size 300GB, computer cluster carries out the test of large-scale parallel Kichhoff integral pre-stack depth migration network traffics, this computer cluster comprises 64 nodes, and this test employs 30 nodes.Final discovery; CPU and GPU utilization factor is all higher; local disk flow is lower; network traffics only occur when internal memory switches; therefore the problem that when present invention successfully solves CPU-GPU cooperated computing in Kirchhoff prestack depth migration, PCI-E flow bus is excessive can be described; GPU computational resource can be made full use of, thus significantly promote counting yield.
In order to verify the effect that the present invention reaches, adopt following hardware environment to test, test cluster has 256 computing nodes, and each node has 24 cores.The test data adopted is: the geological data of test is three three-dimensional work areas of reality, and design parameter is as shown in table 1:
Table 1
| Test block | Migration aperture | Excursions depths | Export CMP line |
| Qi mouth is three-dimensional | 13000×13000 | 8000 | 850×430 |
| Jilin is three-dimensional | 14000 meters × 14000 meters | 8000 | 180×1120 |
| Gold credit is three-dimensional | 10000×10000 | 13000 | 300×1317 |
Inclined contrast working time of body be respectively by CPU-GPU cooperated computing pattern of the present invention and pure CPU computation schema as shown in table 2, as can be seen from the comparing result of three real data, faster than pure CPU more than 1.3 times of the counting yield of CPU-GPU cooperated computing version.
Table 2
By above test result can sufficient proof CPU-GPU cooperated computing version relative to the counting yield advantage of pure CPU calculated version, also demonstrate advantage place of the present invention.
In upper example; provide a kind of hardware extendible CPU-GPU isomerization hardware cooperated computing Kirchhoff prestack depth migration parallel method; carry out by the method the feature that pre-stack depth migration has fast operation, be suitable for the industrial treatment of large work area, mass data pre-stack depth migration imaging.Particularly, the method has taken into full account the feature of Kirchhoff prestack depth migration, adopts resident whilst on tour table, and the pattern of flowing geological data, avoids the I/O bottleneck of PCI-E bus as far as possible.CPU-GPU cooperated computing pattern according to the automatic Distribution Calculation task of CPU and GPU computing power separately, has the feature of " able people should do more work " completely.Thus solve the impact because calculated performance difference, overall computational performance produced between isomerization hardware.
Further; in this example; on the basis of extensive Kirchhoff prestack depth migration framework; propose a kind of CPU-GPU heterogeneous polynuclear pre-stack depth migration skew implementation framework; further increase counting yield, shorten computation period, make the pre-stack depth migration of tens of TB TB data even up to a hundred become possibility.
Based on same inventive concept, additionally provide a kind of pre-stack depth migration device in the embodiment of the present invention, as described in the following examples.The principle of dealing with problems due to pre-stack depth migration device is similar to prestack depth migration method, and therefore the enforcement of pre-stack depth migration device see the enforcement of prestack depth migration method, can repeat part and repeat no more.Following used, term " unit " or " module " can realize the software of predetermined function and/or the combination of hardware.Although the device described by following examples preferably realizes with software, hardware, or the realization of the combination of software and hardware also may and conceived.Figure 12 is a kind of structured flowchart of the pre-stack depth migration device of the embodiment of the present invention, this pre-stack depth migration is arranged in computing node as shown in figure 12, comprise: the first reading unit 1201, transmission unit 1202, second reading unit 1203, first control module 1204 and the second control module 1205, be described this structure below.
First reading unit 1201, for getting an imaging task, and reads whilst on tour table corresponding to this imaging task in CPU internal memory;
Transmission unit 1202, for transferring in the GPU video card in this computing node by the whilst on tour table in CPU internal memory;
Second reading unit 1203, for read be dispensed to this computing node treat migrating seismic data;
First control module 1204, for control CPU and GPU in described computing node respectively from be dispensed to this computing node treat read geological data migrating seismic data, and according to described whilst on tour table, migration imaging calculating is carried out to the geological data read, until be dispensed to this computing node treat that offset data offset completes;
Second control module 1205, imaging results in GPU video card passed to CPU in described computing node for controlling GPU in described computing node, this CPU by the migration imaging result in CPU internal memory and the migration imaging results added in GPU video card, as the migration imaging result of this computing node.
In one embodiment, transmission unit 1202 specifically can adopt the mode of copy to be copied in GPU video card by the whilst on tour table in CPU internal memory, makes the whilst on tour table in CPU internal memory identical with the whilst on tour table in GPU video card.
The specific implementation of above-mentioned pre-stack depth migration device and step see the above-mentioned description to prestack depth migration method, can not repeat at this.
In another embodiment, additionally provide a kind of software, this software is for performing the technical scheme described in above-described embodiment and preferred implementation.
In another embodiment, additionally provide a kind of storage medium, store above-mentioned software in this storage medium, this storage medium includes but not limited to: CD, floppy disk, hard disk, scratch pad memory etc.
From above description, can find out, the embodiment of the present invention achieves following technique effect: computing node does not carry out migration imaging by means of only CPU, also adopts GPU to carry out migration imaging, that is, computing node adopts the parallel mode of CPU and GPU to carry out pre-stack seismic migration imaging.Because GPU possesses very high calculated performance, the mode adopting this CPU and GPU parallel can to solve in prior art effectively under large data cases, pure CPU computing cluster has been difficult to the technical matters of pre-stack depth migration, reaches the technique effect rapidly and efficiently completing pre-stack depth migration.
Obviously, those skilled in the art should be understood that, each module of the above-mentioned embodiment of the present invention or each step can realize with general calculation element, they can concentrate on single calculation element, or be distributed on network that multiple calculation element forms, alternatively, they can realize with the executable program code of calculation element, thus, they can be stored and be performed by calculation element in the storage device, and in some cases, step shown or described by can performing with the order be different from herein, or they are made into each integrated circuit modules respectively, or the multiple module in them or step are made into single integrated circuit module to realize.Like this, the embodiment of the present invention is not restricted to any specific hardware and software combination.
The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, for a person skilled in the art, the embodiment of the present invention can have various modifications and variations.Within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (10)
1. a prestack depth migration method, is characterized in that, comprising:
Computing node gets an imaging task, and reads whilst on tour table corresponding to this imaging task in CPU internal memory;
Whilst on tour table in CPU internal memory transfers in the GPU video card in this computing node by described computing node;
Described computing node read be dispensed to this computing node treat migrating seismic data;
CPU and GPU in described computing node respectively from be dispensed to this computing node treat read geological data migrating seismic data, and according to described whilst on tour table, migration imaging calculating is carried out to the geological data read, until be dispensed to this computing node treat that offset data offset completes;
Imaging results in GPU video card is passed to the CPU in described computing node by the GPU in described computing node, this CPU by the migration imaging result in CPU internal memory and the migration imaging results added in GPU video card, as the migration imaging result of this computing node.
2. method according to claim 1, is characterized in that, the whilst on tour table in CPU internal memory transfers in the GPU video card in this computing node by described computing node, comprising:
Adopt the mode of copy to be copied in GPU video card by the whilst on tour table in CPU internal memory, make the whilst on tour table in CPU internal memory identical with the whilst on tour table in GPU video card.
3. method according to claim 1, it is characterized in that, CPU and GPU in described computing node respectively from be dispensed to this computing node treat read geological data migrating seismic data, and according to described whilst on tour table, migration imaging calculating is carried out to the geological data read, until be dispensed to this computing node treat that offset data offset completes, comprising:
CPU and GPU in described computing node according to respective computing power adaptively from be dispensed to this computing node treat read geological data migrating seismic data, line displacement imaging of going forward side by side calculates, until be dispensed to this computing node treat that offset data has all offset.
4. method according to claim 1, is characterized in that, gets an imaging task at computing node, and before reading in this imaging task corresponding whilst on tour table to CPU internal memory, described method also comprises:
Described computing node is got a whilst on tour and is calculated starting point;
Described computing node calculates starting point as starting point using the whilst on tour got, using migration aperture as lateral extent, using the peak excursion degree of depth as longitudinal extent, calculate the time of any point in this starting point to described lateral extent and longitudinal extent limited range space, to obtain the whilst on tour table of this starting point.
5. method according to claim 4, is characterized in that, according to following formula determination starting point number:
Starting point number=(that treats migrating seismic data is maximum along line direction wire size-treat the minimum along line direction wire size of migrating seismic data) × (treat maximum No. CMP of migrating seismic data-treat minimum No. CMP of migrating seismic data);
Wherein, the minimum minimum maximum diameter of hole/distance between centers of tracks along line direction wire size-cross line direction along between line direction wire size=current imaging area of migrating seismic data is treated);
Maximum maximum along line direction wire size+(maximum diameter of hole/distance between centers of tracks of cross line direction) along between line direction wire size=current imaging area treating migrating seismic data;
Minimum No. CMP between the minimum No. CMP=current imaging area for the treatment of migrating seismic data-(maximum diameter of hole/CMP spacing along straight line direction);
Maximum No. CMP between the maximum No. CMP=current imaging area for the treatment of migrating seismic data+(maximum diameter of hole/CMP spacing along straight line direction).
6. the method according to claim 4 or 5, it is characterized in that, described computing node is group leader's node, or group member's node, wherein, all computing nodes participating in calculating are divided into N group, and from every group, choose a computing node as group leader's node, the computing node except group leader's node is as group member's node;
Accordingly, imaging results in GPU video card is passed to the CPU in computing node by the GPU in described computing node, this CPU is by the migration imaging result in CPU internal memory and the migration imaging results added in GPU video card, and after the migration imaging result as this computing node, described method also comprises:
The group leader's node often organized reclaims the migration imaging result of all group member's nodes in the grouping of this group leader's node place and exports.
7. method according to claim 6, is characterized in that, according to following principle, the computing node participating in calculating is divided into N group:
When the number of imaging task is greater than 4, adjacent 4 computing nodes are divided into one group;
When the number of imaging task is less than 4 and is greater than 1, an imaging task computing node is divided into one group;
When the number of imaging task is less than 1,1 computing node is divided into one group.
8. method according to claim 1, is characterized in that, the number according to following formula determination imaging task:
The number of the storable whilst on tour table of total bin number/single computing node of the number=imaging space of imaging task;
Wherein, the storage size shared by number=(physical memory × 0.8 of single GPU video card)/single imaging space whilst on tour table of single computing node storable whilst on tour table;
Wherein, the storage size shared by single imaging space whilst on tour table=(migration aperture/distance between centers of tracks) × (migration aperture/CMP spacing) × (excursions depths/depth sampling interval) × 4 bytes.
9. a pre-stack depth migration device, is arranged in computing node, it is characterized in that, comprising:
First reading unit, for getting an imaging task, and reads whilst on tour table corresponding to this imaging task in CPU internal memory;
Transmission unit, for transferring in the GPU video card in this computing node by the whilst on tour table in CPU internal memory;
Second reading unit, for read be dispensed to this computing node treat migrating seismic data;
First control module, for control CPU and GPU in described computing node respectively from be dispensed to this computing node treat read geological data migrating seismic data, and according to described whilst on tour table, migration imaging calculating is carried out to the geological data read, until be dispensed to this computing node treat that offset data offset completes;
Second control module, imaging results in GPU video card passed to CPU in described computing node for controlling GPU in described computing node, this CPU by the migration imaging result in CPU internal memory and the migration imaging results added in GPU video card, as the migration imaging result of this computing node.
10. device according to claim 9, it is characterized in that, whilst on tour table in CPU internal memory is copied in GPU video card specifically for adopting the mode of copy by described transmission unit, makes the whilst on tour table in CPU internal memory identical with the whilst on tour table in GPU video card.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106094035A (en) * | 2016-08-18 | 2016-11-09 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | A kind of residual static correction computational methods quickly processed based on unit |
| CN106405641A (en) * | 2016-10-26 | 2017-02-15 | 中国石油天然气集团公司 | Pre-stack time migration method and device |
| CN106526666A (en) * | 2016-09-29 | 2017-03-22 | 中国石油天然气集团公司 | Method, device and system for prestack depth migration |
| CN106526667A (en) * | 2016-10-26 | 2017-03-22 | 中国石油天然气集团公司 | Pre-stack time migration method and device |
| CN109100804A (en) * | 2018-06-11 | 2018-12-28 | 中国石油天然气集团有限公司 | Improve data re-establishing method, the apparatus and system of seismic data spatial sampling attribute |
| CN111965699A (en) * | 2020-09-09 | 2020-11-20 | 中国海洋石油集团有限公司 | Method and system for processing kirchhoff prestack depth migration seismic data |
| WO2024087126A1 (en) * | 2022-10-28 | 2024-05-02 | Saudi Arabian Oil Company | Method and system for determining migration data using cross-spread gathers and parallel processing |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5153857A (en) * | 1991-07-09 | 1992-10-06 | Conoco Inc. | Method for selecting seismic traces for higher efficiency of pre-stack two dimensional or three dimensional depth migration |
| CN102565854A (en) * | 2011-12-27 | 2012-07-11 | 中国科学院地质与地球物理研究所 | Mass data GPU (graphics processing unit) wave equation reverse time migration imaging 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 |
| CN104635258A (en) * | 2013-11-06 | 2015-05-20 | 中国石油天然气集团公司 | Method of using CPU-GPU platform for seismic wave reverse-time migration imaging |
-
2015
- 2015-10-30 CN CN201510726034.3A patent/CN105403913A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5153857A (en) * | 1991-07-09 | 1992-10-06 | Conoco Inc. | Method for selecting seismic traces for higher efficiency of pre-stack two dimensional or three dimensional depth migration |
| CN102565854A (en) * | 2011-12-27 | 2012-07-11 | 中国科学院地质与地球物理研究所 | Mass data GPU (graphics processing unit) wave equation reverse time migration imaging method |
| 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 |
| CN104635258A (en) * | 2013-11-06 | 2015-05-20 | 中国石油天然气集团公司 | Method of using CPU-GPU platform for seismic wave reverse-time migration imaging |
| 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 |
Non-Patent Citations (2)
| Title |
|---|
| 杜宏: "基于GPU/CPU协同并行计算的叠前时间偏移技术在煤炭地震数据处理中的应用", 《科技信息》 * |
| 王延光 等: "起伏地表叠前逆时深度偏移与并行实现", 《石油地球物理勘探》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106094035A (en) * | 2016-08-18 | 2016-11-09 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | A kind of residual static correction computational methods quickly processed based on unit |
| CN106094035B (en) * | 2016-08-18 | 2017-11-14 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | A kind of residual static correction computational methods quickly handled based on unit |
| CN106526666A (en) * | 2016-09-29 | 2017-03-22 | 中国石油天然气集团公司 | Method, device and system for prestack depth migration |
| CN106526666B (en) * | 2016-09-29 | 2019-04-12 | 中国石油天然气集团公司 | Prestack depth migration method, apparatus and system |
| CN106405641A (en) * | 2016-10-26 | 2017-02-15 | 中国石油天然气集团公司 | Pre-stack time migration method and device |
| CN106526667A (en) * | 2016-10-26 | 2017-03-22 | 中国石油天然气集团公司 | Pre-stack time migration method and device |
| CN106526667B (en) * | 2016-10-26 | 2018-10-16 | 中国石油天然气集团公司 | Prestack time migration method and device |
| CN109100804A (en) * | 2018-06-11 | 2018-12-28 | 中国石油天然气集团有限公司 | Improve data re-establishing method, the apparatus and system of seismic data spatial sampling attribute |
| CN111965699A (en) * | 2020-09-09 | 2020-11-20 | 中国海洋石油集团有限公司 | Method and system for processing kirchhoff prestack depth migration seismic data |
| WO2024087126A1 (en) * | 2022-10-28 | 2024-05-02 | Saudi Arabian Oil Company | Method and system for determining migration data using cross-spread gathers and parallel processing |
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