CN110308483A - Reflection coefficient solving method and device based on multitask Bayes compressed sensing - Google Patents
Reflection coefficient solving method and device based on multitask Bayes compressed sensing Download PDFInfo
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- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/282—Application of seismic models, synthetic seismograms
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- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
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- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
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Abstract
The embodiment of the invention provides a reflection coefficient solving method and a device based on multitask Bayes compressed sensing, wherein the method comprises the following steps: extracting seismic wavelets from the seismic data using a three-spectrum mixed-phase wavelet estimation method, wherein the seismic wavelets are extracted from a plurality of seismic traces; selecting a fixed window or a sliding window, and extracting the adjacent seismic channels from a plane after the size of the window is determined; and based on a convolution model, performing simultaneous inversion on a plurality of seismic traces by using a fast sequence sparse Bayesian learning algorithm to obtain a reflection coefficient of multi-task Bayesian compressed sensing.
Description
Technical field
The present invention relates to technical field of petrochemical industry, are based on multitask Bayes compressed sensing in particular to one kind
Reflection coefficient acquiring method and device.
Background technique
At present in the method for improving seismic data resolution, the Method of Deconvolution to the with strong applicability of data, using compared with
It is extensive.But since there are noises in actual seismic data, while deconvolution improves resolution ratio, noise is also amplified, and is made
Obtaining signal-to-noise ratio reduces；For the seismic signal with limit, deconvolution can only improve resolution ratio in limited frequency range, therefore anti-
Convolution can not achieve its original intention, obtain reflection coefficient sequence.And under the hypothesis that reflection coefficient adds sparsity constraints, there is L1
The methods of norm deconvolution, minimum entropy deconvolution, the sparse inversion based on bayesian theory, but the precision of inversion result is inadequate,
Small and weak dropout is more, and lateral continuity is poor, and availability is low, unsatisfactory especially for the treatment effect of three-dimensional data.
For the problem of the not high availability difference of inversion result precision in seismic data analysis in the prior art, there has been no reasonable
Solution.
Summary of the invention
The embodiment of the invention provides a kind of reflection coefficient acquiring method and dress based on multitask Bayes's compressed sensing
It sets, at least to solve the problems, such as that high availability is not poor for inversion result precision in seismic data analysis in the related technology.
According to one embodiment of present invention, a kind of reflection coefficient based on multitask Bayes's compressed sensing is provided to ask
Take method, comprising: seismic wavelet is extracted from seismic data using the three spectrum mixed phase wavelet estimations technique, wherein the earthquake
Wavelet extraction is from multiple seismic channels；Fixed window or sliding window are selected, extracts adjacent institute from plane after determining window size
State seismic channel；Based on convolution model, while carrying out multiple seismic channels using rapid serial management loading algorithm
Inverting obtains the reflection coefficient of multitask Bayes's compressed sensing.
Preferably, fixed window or sliding window are selected, extracts adjacent seismic channel packet from plane after determining window size
It includes: when the seismic data is 3D seismic data, seismic channel is chosen from plane, the size of the window is according to adjacent institute
State the spacing size setting of seismic channel.
It is preferably based on convolution model, carries out multiple seismic channels using rapid serial management loading algorithm
While inverting include: that reflection coefficient is sought problem to be converted to sparse Bayesian regression problem, utilize multitask Bayes pressure
Contracting cognitive method seek while multiple seismic channels.
Preferably, after the reflection coefficient for obtaining multitask Bayes's compressed sensing, the method also includes: utilize wideband
Four parameter Morlet wavelets of band and the reflection coefficient convolution generate high-resolution seismic exploration section.
According to another aspect of the present invention, a kind of reflection coefficient based on multitask Bayes's compressed sensing is additionally provided
Seek device, comprising: the first extraction module, for using the three spectrum mixed phase wavelet estimations technique to extract earthquake from seismic data
Wavelet, wherein the seismic wavelet extraction is from multiple seismic channels；Second extraction module, for selecting fixed window or sliding window
Mouthful, the adjacent seismic channel is extracted from plane after determining window size；Inverting module, for being based on convolution model, using fast
Fast sequence management loading algorithm carries out inverting while multiple seismic channels, obtains multitask Bayes's compressed sensing
Reflection coefficient.
Preferably, second extraction module includes: selection unit, for being 3D seismic data when the seismic data
When, seismic channel is chosen from plane, the size of the window is set according to the spacing size of the adjacent seismic channel.
Preferably, the inverting module includes: to seek unit, is converted to sparse pattra leaves for reflection coefficient to be sought problem
This regression problem is sought while carrying out multiple seismic channels using multitask Bayes's compression sensing method.
Preferably, described device further include: convolution module, for using wide band four parameters Morlet wavelet with it is described
Reflection coefficient convolution generates high-resolution seismic exploration section.
Other side according to an embodiment of the present invention additionally provides a kind of storage medium, stores in the storage medium
There is computer program, wherein the computer program is arranged to execute the step in any of the above-described embodiment of the method when operation
Suddenly.
According to another embodiment of the invention, a kind of electronic device, including memory and processor are additionally provided, it is described
Computer program is stored in memory, the processor is arranged to run the computer program to execute any of the above-described
Step in embodiment of the method.
Through the embodiment of the present invention, it solves under the hypothesis that reflection coefficient adds sparsity constraints, the inverting of conventional method
As a result precision is inadequate, and small and weak dropout is more, and lateral continuity is poor, the low problem of availability.It is pressed based on multitask Bayes
Contract the reflection coefficient inversion method perceived, the defect of conventional method is overcome, using adjacent seismic channel similitude, using multitask
Bayes's compression sensing method multiple tracks carries out inverting simultaneously, and chooses adjacent seismic channel from plane by fixed or sliding window,
While improving seismic data resolution, the continuity of seismic data is improved.
Detailed description of the invention
The drawings described herein are used to provide a further understanding of the present invention, constitutes part of this application, this hair
Bright illustrative embodiments and their description are used to explain the present invention, and are not constituted improper limitations of the present invention.In the accompanying drawings:
Fig. 1 is a kind of shifting of reflection coefficient acquiring method based on multitask Bayes's compressed sensing of the embodiment of the present invention
The hardware block diagram of dynamic terminal；
Fig. 2 be according to embodiments of the present invention in the reflection coefficient acquiring method based on multitask Bayes's compressed sensing stream
Cheng Tu；
Fig. 3 is the knot that the reflection coefficient according to an embodiment of the present invention based on multitask Bayes's compressed sensing seeks device
Structure block diagram；
Fig. 4 is the another stream of the reflection coefficient acquiring method based on multitask Bayes's compressed sensing of the embodiment of the present invention
Cheng Tu；
Fig. 5 is the two-dimension speed model schematic that the embodiment of the present invention is established；
Fig. 6 is the forward modeling result containing noise of Fig. 5 of the embodiment of the present invention；
Fig. 7 is the reflection coefficient section of Fig. 5 of the embodiment of the present invention；
Fig. 8 is the reflection coefficient section that the embodiment of the present invention is obtained based on conventional single task Bayes inversion method；
Fig. 9 is the reflection coefficient section obtained using present invention method, but using fixed window；
Figure 10 is the reflection coefficient section obtained using present invention method, but using sliding window；
Figure 11 is the practical two-dimentional poststack seismic data of the embodiment of the present invention；
Figure 12 is the seismic wavelet that the embodiment of the present invention is extracted from Figure 11 using the three spectrum mixed phase wavelet estimations technique；
Figure 13 is the high-resolution that the reflection coefficient obtained using present invention method and Morlet wavelet convolution are generated
Rate seismic profile；
Figure 14 is the actual poststack 3D data volume of the embodiment of the present invention；
Figure 15 is the schematic diagram that seismic channel is extracted in direction using fixed window along the embodiment of the present invention, and using the present invention
Method processing 3D data volume (Figure 14) obtains practical 3D data volume；
Figure 16 is the embodiment of the present invention along the schematic diagram for extracting seismic channel using sliding window from face, and using the present invention
Method processing 3D data volume (Figure 14) obtains practical 3D data volume；
Figure 17 is the seismic profile that the embodiment of the present invention is intercepted from 3D data volume (Figure 15) along horizontal line direction；
Figure 18 is the seismic profile that the embodiment of the present invention is intercepted from 3D data volume (Figure 16) along horizontal line direction, intercepts position
It sets identical as the position of Figure 17；
Figure 19 is the seismic profile that the embodiment of the present invention is intercepted from 3D data volume (Figure 15) along vertical line direction；
Figure 20 is the seismic profile that the embodiment of the present invention is intercepted from 3D data volume (Figure 16) along vertical line direction, intercepts position
It sets identical as the position of Figure 19.
Specific embodiment
Hereinafter, the present invention will be described in detail with reference to the accompanying drawings and in combination with Examples.It should be noted that not conflicting
In the case of, the features in the embodiments and the embodiments of the present application can be combined with each other.
It should be noted that description and claims of this specification and term " first " in above-mentioned attached drawing, "
Two " etc. be to be used to distinguish similar objects, without being used to describe a particular order or precedence order.
Embodiment 1
Embodiment of the method provided by the embodiment of the present application one can be in mobile terminal, terminal or similar fortune
It calculates and is executed in device.For running on mobile terminals, Fig. 1 is that one kind of the embodiment of the present invention is pressed based on multitask Bayes
The hardware block diagram of the mobile terminal of the reflection coefficient acquiring method of contracting perception.As shown in Figure 1, mobile terminal 100 can wrap
Including one or more (one is only shown in Fig. 1) processors 1002, (processor 1002 can include but is not limited to Micro-processor MCV
Or the processing unit of programmable logic device FPGA etc.) and memory 1004 for storing data, optionally, it is above-mentioned it is mobile eventually
End can also include the transmission device 1006 and input-output equipment 1008 for communication function.Those of ordinary skill in the art
It is appreciated that structure shown in FIG. 1 is only to illustrate, the structure of above-mentioned mobile terminal is not caused to limit.For example, mobile whole
End 100 may also include than shown in Fig. 1 more perhaps less component or with the configuration different from shown in Fig. 1.
The embodiment of the invention provides a kind of reflection coefficient acquiring methods based on multitask Bayes's compressed sensing.Fig. 2
Be according to embodiments of the present invention in the reflection coefficient acquiring method based on multitask Bayes's compressed sensing flow chart, such as Fig. 2
It is shown, this method comprises:
Step S201 extracts seismic wavelet using the three spectrum mixed phase wavelet estimations technique, wherein earthquake from seismic data
Wavelet extraction is from multiple seismic channels；
Step S203 selects fixed window or sliding window, extracts adjacent seismic channel from plane after determining window size；
Step S205 is based on convolution model, carries out multiple seismic channels using rapid serial management loading algorithm
Simultaneous Inversion obtains the reflection coefficient of multitask Bayes's compressed sensing.
Method through the embodiment of the present invention can select the drilling fluid suppression formulation to match on different stratum, be
The design of drilling fluid suppression formulation provides scientific basis, improves working efficiency, saves experimental expenses.Suitable for water-base drilling fluid and
The mudstone stratum drilling fluid suppression formulation of similar geological conditions designs.Can optimization design process, improve working efficiency, realize fast
Speed accurately completes the design of drilling fluid suppression formulation.
It should be noted that multitask Bayes's compression sensing method itself needs to extract multiple seismic channels, calculated result
The reflection coefficient that exactly extracted earthquake is arrived, so being Simultaneous Inversion." multitask " is exactly the data using multiple seismic channels
Carry out inverting.Compared to one of data inversion is utilized in the past, this method is by extracting adjacent multiple tracks data and utilizing adjacent seismic channel
Data have similitude to improve the precision of inverting.
Preferably, above-mentioned steps S203 can be realized by following steps: when seismic data is 3D seismic data, from
Seismic channel is chosen in plane, the size of window is set according to the spacing size of adjacent seismic channel.
Preferably, above-mentioned steps S205 can be realized by following steps: by reflection coefficient seek problem be converted to it is sparse
Bayesian regression problem is sought while carrying out multiple seismic channels using multitask Bayes's compression sensing method.
Preferably, after the reflection coefficient for obtaining multitask Bayes's compressed sensing, the method also includes: utilize wideband
The four parameter Morlet wavelets and reflection coefficient convolution of band, generate high-resolution seismic exploration section.
It should be noted that the phase spectrum to estimate wavelet is composed in three spectrum mixed phase wavelet estimations by phase three first, so
It is combined afterwards with the amplitude spectrum of wavelet, mixed-phase seismic wavelet is obtained by Fourier transformation, the amplitude spectrum of wavelet can lead to
The autocorrelative amplitude spectrum of earthquake record is crossed to obtain.
Convolution model is a kind of model of production synthesis (theory) earthquake record, it is assumed that per pass earthquake record is by earthquake
Wavelet and the convolution of the reflection coefficient of each layer of subsurface model are constituted, it may also be necessary to add random noise.
Wide band four parameters Morlet wavelet is one of wavelet function, and small echo refers to having limit for length or quickly decline
The concussion waveform that subtracts indicates signal.Morlet wavelet expression are as follows:
Wherein, ωm,χ,u,The respectively average angular frequency of Morlet wavelet, time width scale factor, time location delay
And initial phase.By adjusting four parameter ωm,χ,u,It can be to time width, secondary lobe, dominant frequency, bandwidth and the height of control wavelet
The energy of (low) frequency vibration width is capable of the frequency and amplitude energy of analog quantization seismic data well, generates the high score of high-quality
Resolution earthquake record.
And convolution is exactly the convolution inside mathematics, this is the professional saying of synthetic seismogram in earthquake, is exactly digital reflex
The data that coefficient and this wavelet expression come out carry out convolution algorithm, and operation result is exactly the earthquake record synthesized.
A kind of reflection coefficient based on multitask Bayes's compressed sensing is additionally provided in the present embodiment and seeks device, is used
In executing the step in any of the above-described embodiment of the method, details are not described herein again for the content having been noted above.Fig. 3 is according to the present invention
The reflection coefficient based on multitask Bayes's compressed sensing of embodiment seeks the structural block diagram of device, as shown in figure 3, the device
It include: the first extraction module 30, for using the three spectrum mixed phase wavelet estimations technique to extract seismic wavelet from seismic data,
In, seismic wavelet extraction is from multiple seismic channels；Second extraction module 32 determines window for selecting fixed window or sliding window
Adjacent seismic channel is extracted from plane after mouth size；Inverting module 34 utilizes the sparse shellfish of rapid serial for being based on convolution model
This learning algorithm of leaf carries out inverting while multiple seismic channels, obtains the reflection coefficient of multitask Bayes's compressed sensing.
By above-mentioned apparatus, the drilling fluid suppression formulation to match can be selected on different stratum, inhibited for drilling fluid
The design of formula provides scientific basis, improves working efficiency, saves experimental expenses.Suitable for water-base drilling fluid and similar geology item
The mudstone stratum drilling fluid suppression formulation of part designs.Can optimization design process, improve working efficiency, realize fast and accurately
Complete the design of drilling fluid suppression formulation.
Preferably, the second extraction module 32 includes: selection unit, is used for when seismic data is 3D seismic data, from
Seismic channel is chosen in plane, the size of window is set according to the spacing size of adjacent seismic channel.
Preferably, inverting module 34 includes: to seek unit, is converted to sparse Bayesian for reflection coefficient to be sought problem
Regression problem is sought while carrying out multiple seismic channels using multitask Bayes's compression sensing method.
Preferably, described device further include: convolution module, for utilizing wide band four parameters Morlet wavelet and reflection
Coefficient convolution generates high-resolution seismic exploration section.
The purpose of the embodiment of the present invention is that providing a kind of raising seismic data resolution, improving the successional side of inversion result
Method is established the model with very strong generalization ability by multi-task learning, is improved since adjacent earthquake stage property has similitude
The precision of inverting；Seismic channel is extracted using fixed or sliding window from plane simultaneously, improves the continuity of 3D data volume.
The purpose of the embodiment of the present invention can be achieved by the following technical measures: be based on multitask Bayes compressed sensing
Reflection coefficient acquiring method, inversion step includes: step 1, using the three spectrum mixed phase wavelet estimations technique from seismic data
Middle extraction seismic wavelet is extracted by multiple tracks and is averaged the stability that seismic wavelet can be improved；Step 2, fixed window is selected
Mouth or sliding window, determine window size, adjacent seismic channel are extracted from plane；Step 3, it is based on convolution model, is by reflection
Number seeks problem and is converted to sparse Bayesian regression problem, and anti-simultaneously using rapid serial management loading algorithm multiple tracks
It drills；Step 4, using wide band four parameters Morlet wavelet and reflection coefficient convolution, high-resolution seismic exploration section is generated.
The reflection coefficient acquiring method based on multitask Bayes in the embodiment of the present invention, by utilizing adjacent seismic channel
Similitude, establish in the refutation process with very strong generalization ability, compared to conventional inversion method, inverting knot can be improved
The precision of fruit protects small and weak signal.When extracting multichannel seismic data, by, using fixed or sliding window, changing from plane
The continuity of kind inversion result.The reflection coefficient and wide band four parameters Morlet wavelet convolution finally obtained using inverting is defeated
High-resolution seismic exploration data out.
To enable the above content of the embodiment of the present invention, feature and advantage to be clearer and more comprehensible, preferably implementation is cited below particularly out
Example, and cooperate institute's accompanying drawings, it is described in detail below.
As shown in figure 4, Fig. 4 is the reflection coefficient side of seeking based on multitask Bayes's compressed sensing of the embodiment of the present invention
The another flow chart of method.In step 401, seismic data is inputted, Figure 11 is actual two-dimentional stacked seismic data.Process enters
Step 402.
In step 402, earthquake is extracted using the three spectrum mixed phase wavelet estimations technique from actual seismic data (Figure 11)
Wave (Figure 12).Process enters step 403.
In step 403, selection is fixed as needed or sliding window, setting window size extract seismic channel from plane.
Process enters step 404.
In step 404, it is contemplated that the similitude of adjacent seismic channel will be compressed using of the invention based on multitask Bayes
Perceived reflection coefficient acquiring method handles seismic data.Process enters step 405.
In step 405, the high-resolution seismic exploration of the reflection coefficient obtained using step 404 and the generation of Morlet wavelet convolution
Section (Figure 13).Process enters step 406.
In order to embody advantage and flexibility of the present invention when handling 3D data volume, to 3D data volume shown in Figure 14
It is handled.When extracting seismic channel using sliding window along single direction, the 3D data volume such as Figure 15 is obtained；When from plane
When the upper extraction seismic channel using sliding window, the 3D data volume such as Figure 16 is obtained.Along horizontal line direction respectively from Figure 15 and figure
It is as shown in Figure 17 and Figure 18 that seismic profile is cut at the same position of 16 two data volumes, along vertical line direction respectively from Figure 15 and
Seismic profile is cut as illustrated in figures 19 and 20 at the same position of two data volumes of Figure 16.It can be seen that from black rectangle frame
Seismic channel is extracted using sliding window from plane and carries out treated seismic data cube in horizontal line direction and vertical line direction
Continuity it is more preferable.
Embodiment 2
The embodiments of the present invention also provide a kind of storage medium, computer program is stored in the storage medium, wherein
The computer program is arranged to execute the step in any of the above-described embodiment of the method when operation.
Optionally, in the present embodiment, above-mentioned storage medium can be set to store by executing based on following steps
Calculation machine program:
Step S1 extracts seismic wavelet using the three spectrum mixed phase wavelet estimations technique, wherein earthquake from seismic data
Wave extracts from multiple seismic channels；
Step S2 selects fixed window or sliding window, extracts adjacent seismic channel from plane after determining window size；
Step S3 is based on convolution model, carries out the same of multiple seismic channels using rapid serial management loading algorithm
When inverting, obtain the reflection coefficient of multitask Bayes's compressed sensing.
Optionally, in the present embodiment, above-mentioned storage medium can include but is not limited to: USB flash disk, read-only memory (Read-
Only Memory, referred to as ROM), it is random access memory (Random Access Memory, referred to as RAM), mobile hard
The various media that can store computer program such as disk, magnetic or disk.
The embodiments of the present invention also provide a kind of electronic device, including memory and processor, stored in the memory
There is computer program, which is arranged to run computer program to execute the step in any of the above-described embodiment of the method
Suddenly.
Optionally, above-mentioned electronic device can also include transmission device and input-output equipment, wherein the transmission device
It is connected with above-mentioned processor, which connects with above-mentioned processor.
Optionally, in the present embodiment, above-mentioned processor can be set to execute following steps by computer program:
Step S1 extracts seismic wavelet using the three spectrum mixed phase wavelet estimations technique, wherein earthquake from seismic data
Wave extracts from multiple seismic channels；
Step S2 selects fixed window or sliding window, extracts adjacent seismic channel from plane after determining window size；
Step S3 is based on convolution model, carries out the same of multiple seismic channels using rapid serial management loading algorithm
When inverting, obtain the reflection coefficient of multitask Bayes's compressed sensing.
Specific example in the present embodiment can refer to example described in above-described embodiment and optional embodiment, this
Details are not described herein for embodiment.
Obviously, those skilled in the art should be understood that each module of the above invention or each step can be with general
Computing device realize that they can be concentrated on a single computing device, or be distributed in multiple computing devices and formed
Network on, optionally, they can be realized with the program code that computing device can perform, it is thus possible to which they are stored
It is performed by computing device in the storage device, and in some cases, it can be to be different from shown in sequence execution herein
Out or description the step of, perhaps they are fabricated to each integrated circuit modules or by them multiple modules or
Step is fabricated to single integrated circuit module to realize.In this way, the present invention is not limited to any specific hardware and softwares to combine.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.It is all within principle of the invention, it is made it is any modification, etc.
With replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of reflection coefficient acquiring method based on multitask Bayes's compressed sensing characterized by comprising
Seismic wavelet is extracted from seismic data using the three spectrum mixed phase wavelet estimations technique, wherein the seismic wavelet extraction
From multiple seismic channels；
Fixed window or sliding window are selected, extracts the adjacent seismic channel from plane after determining window size；
Based on convolution model, inverting while carrying out multiple seismic channels using rapid serial management loading algorithm,
Obtain the reflection coefficient of multitask Bayes's compressed sensing.
2. determining window size the method according to claim 1, wherein selecting fixed window or sliding window
Extracting the adjacent seismic channel from plane afterwards includes:
When the seismic data is 3D seismic data, seismic channel is chosen from plane, the size of the window is according to adjacent
The spacing size of the seismic channel is set.
3. utilizing rapid serial sparse Bayesian the method according to claim 1, wherein being based on convolution model
Learning algorithm carry out multiple seismic channels while inverting include:
Reflection coefficient is sought into problem and is converted to sparse Bayesian regression problem, using multitask Bayes compression sensing method into
It is sought while row multiple seismic channels.
4. method according to any one of claims 1 to 3, which is characterized in that obtain multitask Bayes's compressed sensing
After reflection coefficient, the method also includes:
Using wide band four parameters Morlet wavelet and the reflection coefficient convolution, high-resolution seismic exploration section is generated.
5. a kind of reflection coefficient based on multitask Bayes's compressed sensing seeks device characterized by comprising
First extraction module, for using the three spectrum mixed phase wavelet estimations technique to extract seismic wavelet from seismic data, wherein
The seismic wavelet extraction is from multiple seismic channels；
Second extraction module extracts adjacent institute after determining window size for selecting fixed window or sliding window from plane
State seismic channel；
Inverting module carries out multiple earthquakes using rapid serial management loading algorithm for being based on convolution model
Inverting while road obtains the reflection coefficient of multitask Bayes's compressed sensing.
6. device according to claim 5, which is characterized in that second extraction module includes:
Selection unit, for choosing seismic channel from plane when the seismic data is 3D seismic data, the window
Size is set according to the spacing size of the adjacent seismic channel.
7. device according to claim 5, which is characterized in that the inverting module includes:
Unit is sought, sparse Bayesian regression problem is converted to for reflection coefficient to be sought problem, utilizes multitask Bayes
Compression sensing method seek while multiple seismic channels.
8. according to the described in any item devices of claim 5 to 7, which is characterized in that described device further include:
Convolution module, for generating high-resolution using wide band four parameters Morlet wavelet and the reflection coefficient convolution
Seismic profile.
9. a kind of storage medium, which is characterized in that be stored with computer program in the storage medium, wherein the computer
Program is arranged to execute method described in any one of Claims 1-4 when operation.
10. a kind of electronic device, including memory and processor, which is characterized in that be stored with computer journey in the memory
Sequence, the processor are arranged to run the computer program to execute side described in any one of Claims 1-4
Method.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111025395A (en) * | 2020-01-06 | 2020-04-17 | 中国石油化工股份有限公司 | Method for improving thin inter-layer resolution based on compressed sensing Gaussian frequency domain |
| CN111291898A (en) * | 2020-02-17 | 2020-06-16 | 哈尔滨工业大学 | Multi-task sparse Bayesian extreme learning machine regression method |
| CN112558161A (en) * | 2020-12-24 | 2021-03-26 | 中国石油大学(华东) | Well constraint earthquake low-frequency recovery method based on compressed sensing |
Citations (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2005066660A2 (en) * | 2004-01-09 | 2005-07-21 | Statoil Asa | Processing seismic data representing a physical system |
| CN101221249A (en) * | 2007-01-10 | 2008-07-16 | 中国石油集团东方地球物理勘探有限责任公司 | Seismic data quality analyzing technique |
| CN102096101A (en) * | 2010-11-24 | 2011-06-15 | 中国石油天然气集团公司 | Method and device for extracting hybrid-phase seismic wavelets |
| CN102736109A (en) * | 2012-06-08 | 2012-10-17 | 中国石油大学(华东) | Method for de-noising, correcting and superposing CRP (Common Reflection Point) gather |
| CN102819688A (en) * | 2012-08-29 | 2012-12-12 | 电子科技大学 | Two-dimensional seismic data full-layer tracking method based on semi-supervised classification |
| CN103439740A (en) * | 2013-07-24 | 2013-12-11 | 中国石油天然气股份有限公司 | Method and device for predicting relative impedance based on dipole seismic wavelet multiple integral |
| CN103440493A (en) * | 2013-02-27 | 2013-12-11 | 中国人民解放军空军装备研究院侦察情报装备研究所 | Hyperspectral image blur classification method and device based on related vector machine |
| CN103632160A (en) * | 2012-08-24 | 2014-03-12 | 孙琤 | Combination-kernel-function RVM (Relevance Vector Machine) hyperspectral classification method integrated with multi-scale morphological characteristics |
| CN104360382A (en) * | 2014-10-31 | 2015-02-18 | 中国石油化工股份有限公司 | Method for detecting oil and gas by aid of stacked seismic data |
| CN104459779A (en) * | 2014-12-09 | 2015-03-25 | 中国石油天然气集团公司 | Method and device for automatically identifying abnormal seismic traces |
| CN104749553A (en) * | 2015-04-10 | 2015-07-01 | 西安电子科技大学 | Fast sparse Bayesian learning based direction-of-arrival estimation method |
| WO2015175851A1 (en) * | 2014-05-16 | 2015-11-19 | Schlumberger Canada Limited | Waveform-based seismic localization with quantified uncertainty |
| CN105334532A (en) * | 2015-10-21 | 2016-02-17 | 中国石油化工股份有限公司 | Seismic wavelet estimation method |
| WO2016061293A1 (en) * | 2014-10-15 | 2016-04-21 | Westerngeco Llc | Noise model estimation in multi-measurement data |
| CN105572727A (en) * | 2014-10-16 | 2016-05-11 | 中国石油化工股份有限公司 | Reservoir fluid identification method based on pore fluid parameter frequency dependence inversion |
| CN105842732A (en) * | 2016-03-16 | 2016-08-10 | 中国石油大学(北京) | Inversion method of multichannel sparse reflection coefficient and system thereof |
| CN106249281A (en) * | 2015-06-10 | 2016-12-21 | 中国石油化工股份有限公司 | Inversion method that the viscoelastic medium amplitude of pre-stack seismic changes with frequency and system |
| CN106250988A (en) * | 2016-07-28 | 2016-12-21 | 武汉理工大学 | Relevance vector regression Incremental Learning Algorithm based on sample properties and system |
| CN107238862A (en) * | 2016-03-29 | 2017-10-10 | 中国石油化工股份有限公司 | Reflectance factor method of estimation and device based on Bayes's inverting framework |
| CN107239589A (en) * | 2017-04-07 | 2017-10-10 | 西安理工大学 | Reliability of slope analysis method based on MRVM AFOSM |
| CN107589448A (en) * | 2017-07-13 | 2018-01-16 | 西安交通大学 | A kind of multitrace seismogram reflection coefficient sequence Simultaneous Inversion method |
| WO2018183415A1 (en) * | 2017-03-29 | 2018-10-04 | Westerngeco Llc | Compressive sensing imaging |
| CN108717165A (en) * | 2018-05-28 | 2018-10-30 | 桂林电子科技大学 | Lithium ion battery SOC on-line prediction methods based on data-driven method |
| CN109669213A (en) * | 2019-02-25 | 2019-04-23 | 中国石油化工股份有限公司 | Frequency dividing diffusing filter tomography intensifying method based on optimization Morlet small echo |
-
2019
- 2019-05-23 CN CN201910435339.7A patent/CN110308483A/en active Pending
Patent Citations (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2005066660A2 (en) * | 2004-01-09 | 2005-07-21 | Statoil Asa | Processing seismic data representing a physical system |
| CN101221249A (en) * | 2007-01-10 | 2008-07-16 | 中国石油集团东方地球物理勘探有限责任公司 | Seismic data quality analyzing technique |
| CN102096101A (en) * | 2010-11-24 | 2011-06-15 | 中国石油天然气集团公司 | Method and device for extracting hybrid-phase seismic wavelets |
| CN102736109A (en) * | 2012-06-08 | 2012-10-17 | 中国石油大学(华东) | Method for de-noising, correcting and superposing CRP (Common Reflection Point) gather |
| CN103632160A (en) * | 2012-08-24 | 2014-03-12 | 孙琤 | Combination-kernel-function RVM (Relevance Vector Machine) hyperspectral classification method integrated with multi-scale morphological characteristics |
| CN102819688A (en) * | 2012-08-29 | 2012-12-12 | 电子科技大学 | Two-dimensional seismic data full-layer tracking method based on semi-supervised classification |
| CN103440493A (en) * | 2013-02-27 | 2013-12-11 | 中国人民解放军空军装备研究院侦察情报装备研究所 | Hyperspectral image blur classification method and device based on related vector machine |
| CN103439740A (en) * | 2013-07-24 | 2013-12-11 | 中国石油天然气股份有限公司 | Method and device for predicting relative impedance based on dipole seismic wavelet multiple integral |
| WO2015175851A1 (en) * | 2014-05-16 | 2015-11-19 | Schlumberger Canada Limited | Waveform-based seismic localization with quantified uncertainty |
| WO2016061293A1 (en) * | 2014-10-15 | 2016-04-21 | Westerngeco Llc | Noise model estimation in multi-measurement data |
| CN105572727A (en) * | 2014-10-16 | 2016-05-11 | 中国石油化工股份有限公司 | Reservoir fluid identification method based on pore fluid parameter frequency dependence inversion |
| CN104360382A (en) * | 2014-10-31 | 2015-02-18 | 中国石油化工股份有限公司 | Method for detecting oil and gas by aid of stacked seismic data |
| CN104459779A (en) * | 2014-12-09 | 2015-03-25 | 中国石油天然气集团公司 | Method and device for automatically identifying abnormal seismic traces |
| CN104749553A (en) * | 2015-04-10 | 2015-07-01 | 西安电子科技大学 | Fast sparse Bayesian learning based direction-of-arrival estimation method |
| CN106249281A (en) * | 2015-06-10 | 2016-12-21 | 中国石油化工股份有限公司 | Inversion method that the viscoelastic medium amplitude of pre-stack seismic changes with frequency and system |
| CN105334532A (en) * | 2015-10-21 | 2016-02-17 | 中国石油化工股份有限公司 | Seismic wavelet estimation method |
| CN105842732A (en) * | 2016-03-16 | 2016-08-10 | 中国石油大学(北京) | Inversion method of multichannel sparse reflection coefficient and system thereof |
| CN107238862A (en) * | 2016-03-29 | 2017-10-10 | 中国石油化工股份有限公司 | Reflectance factor method of estimation and device based on Bayes's inverting framework |
| CN106250988A (en) * | 2016-07-28 | 2016-12-21 | 武汉理工大学 | Relevance vector regression Incremental Learning Algorithm based on sample properties and system |
| WO2018183415A1 (en) * | 2017-03-29 | 2018-10-04 | Westerngeco Llc | Compressive sensing imaging |
| CN107239589A (en) * | 2017-04-07 | 2017-10-10 | 西安理工大学 | Reliability of slope analysis method based on MRVM AFOSM |
| CN107589448A (en) * | 2017-07-13 | 2018-01-16 | 西安交通大学 | A kind of multitrace seismogram reflection coefficient sequence Simultaneous Inversion method |
| CN108717165A (en) * | 2018-05-28 | 2018-10-30 | 桂林电子科技大学 | Lithium ion battery SOC on-line prediction methods based on data-driven method |
| CN109669213A (en) * | 2019-02-25 | 2019-04-23 | 中国石油化工股份有限公司 | Frequency dividing diffusing filter tomography intensifying method based on optimization Morlet small echo |
Non-Patent Citations (2)
| Title |
|---|
| MORLET J.等: ""Wave propagation and sampling theory：part II，sampling theory and complex waves"", 《GEOPHYSICS》 * |
| 陈祖庆 等: ""基于压缩感知的系数脉冲反射系数谱反演方法研究"", 《石油物探》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111025395A (en) * | 2020-01-06 | 2020-04-17 | 中国石油化工股份有限公司 | Method for improving thin inter-layer resolution based on compressed sensing Gaussian frequency domain |
| CN111291898A (en) * | 2020-02-17 | 2020-06-16 | 哈尔滨工业大学 | Multi-task sparse Bayesian extreme learning machine regression method |
| CN112558161A (en) * | 2020-12-24 | 2021-03-26 | 中国石油大学(华东) | Well constraint earthquake low-frequency recovery method based on compressed sensing |
| CN112558161B (en) * | 2020-12-24 | 2022-07-26 | 青岛职业技术学院 | Well constraint earthquake low-frequency recovery method based on compressed sensing |
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