CN109001805A - A kind of earthquake source inverting Uncertainty Analysis Method, storage medium and server - Google Patents
A kind of earthquake source inverting Uncertainty Analysis Method, storage medium and server Download PDFInfo
- Publication number
- CN109001805A CN109001805A CN201810506557.0A CN201810506557A CN109001805A CN 109001805 A CN109001805 A CN 109001805A CN 201810506557 A CN201810506557 A CN 201810506557A CN 109001805 A CN109001805 A CN 109001805A
- Authority
- CN
- China
- Prior art keywords
- source
- uncertainty
- function
- source inversion
- earthquake
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 54
- 238000013076 uncertainty analysis Methods 0.000 title claims abstract description 27
- 238000003860 storage Methods 0.000 title claims abstract description 17
- 230000006870 function Effects 0.000 claims abstract description 54
- 230000019771 cognition Effects 0.000 claims abstract description 22
- 238000007619 statistical method Methods 0.000 claims abstract description 4
- 238000004891 communication Methods 0.000 claims description 11
- 238000003325 tomography Methods 0.000 claims description 10
- 238000009499 grossing Methods 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 5
- 238000013480 data collection Methods 0.000 claims description 4
- 238000011158 quantitative evaluation Methods 0.000 abstract description 4
- 238000005315 distribution function Methods 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 241000208340 Araliaceae Species 0.000 description 2
- 238000000342 Monte Carlo simulation Methods 0.000 description 2
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 2
- 235000003140 Panax quinquefolius Nutrition 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 235000008434 ginseng Nutrition 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 238000005553 drilling Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000002401 inhibitory effect Effects 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000035939 shock Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01V1/30—Analysis
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Acoustics & Sound (AREA)
- Environmental & Geological Engineering (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Geophysics (AREA)
- Geophysics And Detection Of Objects (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention discloses a kind of earthquake source inverting Uncertainty Analysis Method, storage medium and server, the method includes the steps: source inversion parameter is carried out using Bayes statistical method to resolve the priori probability density function for obtaining corresponding source inversion parameter；Seismic source information amount is obtained according to the priori probability density function of the source inversion parameter；Stochastic uncertainty function and cognition uncertainty function are measured out according to the seismic source information；Assessment judgement is carried out to source inversion PRELIMINARY RESULTS in conjunction with the stochastic uncertainty function and cognition uncertainty function, obtains accurate source inversion interpretation result.These two types of uncertainties are unified on the benchmark of information content and measure, to realize two class uncertainty quantitative evaluations and uniformly, correctly interpret for InSAR earthquake source inversion result and provide scientific basis by introducing information entropy theory by the present invention.
Description
Technical field
The present invention relates to source inversion field more particularly to a kind of earthquake source inverting Uncertainty Analysis Methods, storage
Medium and server.
Background technique
InSAR is the advanced space to ground measuring technique of recent decades development, is traditional SAR remote sensing technology and radio day
The product that literary interference technique combines, the technology can obtain the closely observation data with high density earthquake displacement field.It is based on
The focus spatial information that InSAR is obtained is more fine accurate, also functions to very positive work in existing earthquake engineering related application
With.
Although the earthquake information that InSAR is obtained has very big advantage in spatial resolution, it must be recognized that InSAR
Fault slip inverting still has multi-solution, and the gliding model difference obtained based on distinct methods and data inversion is very big.?
In seismic risk analysis, the conclusion obtained based on different InSAR inversion results is widely different.It is published in top periodical Nature
Two articles about Chilean 8.8 grades of violent earthquakes in 2010 on Geoscience and Natrue, it is whether complete for the earthquake
It releases the energy accumulated between shake and gives completely different conclusion.
Therefore, the base that analysis and research are the buildings of InSAR focus target criteriaization is carried out to InSAR earthquake source uncertainty
Plinth, to subsequent applications reliability important in inhibiting such as guarantee Assessment of The Earthquake Risk In Future.
Summary of the invention
In view of above-mentioned deficiencies of the prior art, the purpose of the present invention is to provide a kind of earthquake source inverting uncertainties point
Analysis method, storage medium and server, it is intended to solve the prior art and carry out earthquake solution using InSAR earthquake source inversion result
When translating, not while considering that the stochastic uncertainty of inversion result and cognition are uncertain, cause interpretation result inaccurate, obstruction pair
The problem of earthquake recognizes.
Technical scheme is as follows:
A kind of earthquake source inverting Uncertainty Analysis Method, wherein comprising steps of
The priori for resolve the corresponding source inversion parameter of acquisition to source inversion parameter using Bayes statistical method is general
Rate density function；
Seismic source information amount is obtained according to the priori probability density function of the source inversion parameter；
Stochastic uncertainty function and cognition uncertainty function are measured out according to the seismic source information；
Source inversion PRELIMINARY RESULTS is commented in conjunction with the stochastic uncertainty function and cognition uncertainty function
Estimate judgement, obtains accurate source inversion interpretation result.
The earthquake source inverting Uncertainty Analysis Method, wherein the source inversion parameter includes: that tomography block is sliding
Dynamic distribution vector s, tomography geometric parameter m, InSAR data collection weight σ 2 and smoothing factor α.
The earthquake source inverting Uncertainty Analysis Method, wherein the seismic source information amount includes: source inversion ginseng
The information content utilized in the information content and source inversion of information content, InSAR data offer that number needs.
The earthquake source inverting Uncertainty Analysis Method, wherein the information content that the source inversion parameter needs
For the comentropy of source inversion parameter priori probability density function；The information content that the InSAR data provides is source inversion ginseng
Mutual information between several and InSAR observation data；The information content utilized in the source inversion is InSAR inverted parameters and focus
Mutual information between inverted parameters.
The earthquake source inverting Uncertainty Analysis Method, wherein the stochastic uncertainty function is that focus is anti-
The information content for drilling parameter needs subtracts the information content of InSAR data offer.
The earthquake source inverting Uncertainty Analysis Method, wherein the cognition uncertainty function is InSAR number
The information content utilized in source inversion is subtracted according to the information content of offer.
The earthquake source inverting Uncertainty Analysis Method, wherein stochastic uncertainty function described in the combination
And cognition uncertainty function carries out assessment judgement to source inversion PRELIMINARY RESULTS, obtains accurate source inversion interpretation result
The step of specifically include:
When carrying out earthquake interpretation to InSAR earthquake source inverting PRELIMINARY RESULTS, in conjunction with the stochastic uncertainty function
And cognition uncertainty function carries out assessment judgement to source inversion PRELIMINARY RESULTS；
Determine that result effectively accepts or rejects interpretation result according to assessment, finally obtains accurate source inversion interpretation knot
Fruit.
A kind of computer readable storage medium, wherein the computer-readable recording medium storage has one or more
Program, one or more of programs are executed by one or more processors, to realize that a kind of earthquake source inverting is uncertain
The step of property analysis method.
A kind of application server, wherein including at least one processor, display screen, memory and communication interface and always
Line, the processor, display screen, memory and communication interface complete mutual communication by bus, and the processor calls
The step of logical order in memory is to execute a kind of earthquake source inverting Uncertainty Analysis Method.
The utility model has the advantages that these two types of uncertainties are unified in information by introducing stochastic variable information entropy theory by the present invention
It is measured on the benchmark of amount, stochastic uncertainty subtracts InSAR data offer by the information content that source inversion parameter needs
Information content indicates that the uncertain information content provided by InSAR data of cognition subtracts the information content utilized in source inversion and indicates,
To realize two class uncertainty quantitative evaluations and uniformly, correctly interprets and provide for InSAR earthquake source inversion result
Scientific basis.
Detailed description of the invention
Fig. 1 is a kind of flow chart of earthquake source inverting Uncertainty Analysis Method preferred embodiment of the present invention.
Fig. 2 is a kind of structural block diagram of application server preferred embodiment of the present invention.
Specific embodiment
The present invention provides a kind of earthquake source inverting Uncertainty Analysis Method, storage medium and server, to make this hair
Bright purpose, technical solution and effect are clearer, clear, and the present invention is described in more detail below.It should be appreciated that herein
Described specific embodiment is only used to explain the present invention, is not intended to limit the present invention.
Referring to Fig. 1, Fig. 1 is a kind of earthquake source inverting Uncertainty Analysis Method preferred embodiment provided by the invention
Flow chart, wherein as shown, comprising steps of
S10, source inversion parameter is carried out using Bayes statistical method to resolve the elder generation for obtaining corresponding source inversion parameter
Test probability density function；
S20, seismic source information amount is obtained according to the priori probability density function of the source inversion parameter；
S30, stochastic uncertainty function and cognition uncertainty function are measured out according to the seismic source information；
S40, in conjunction with the stochastic uncertainty function and cognition uncertainty function to source inversion PRELIMINARY RESULTS into
Row assessment determines, obtains accurate source inversion interpretation result.
Specifically, by InSAR observation noise and inverting resolution capability double influence, source inversion result exist simultaneously with
Machine uncertainty (observation causes) and uncertain (inverting ability causes) the two kinds of uncertainties of cognition.Using InSAR earthquake
When source inversion result carries out earthquake interpretation, if not considering these two types of uncertainties, interpretation result inaccuracy will be often resulted in,
Hinder the cognition to earthquake.
Based on this, the present invention studies the InSAR source inversion based on comentropy by introducing stochastic variable information entropy theory
Uncertain Unified Expression function, the expression of seismic source information amount develop the quick analysis side of InSAR source inversion information content
Method realizes the stochastic uncertainty to InSAR source inversion result and recognizes probabilistic Unified Expression, is InSAR earthquake
Source inversion result, which correctly interprets, provides scientific basis.
Further, under Bayes's source inversion frame, research considers that the fault slip priori of more regularization coefficients is general
It is incomplete can to overcome the problems, such as that uniform and smooth constraint factor slides heterogeneous description to fault plane for rate modeling method.Bayes's system
Focal shock parameter variable and InSAR data weight, smoothing factor isoinversion model coefficient can be processed into random change by meter method
Amount obtains the prior probability distribution of these variables by resolving.Based on these features, the present invention will be carried out using bayes method
InSAR source inversion calculates, and Bayes's source inversion model merges linear dimensions simultaneously and resolves with nonlinear parameter.Its
In, linear dimensions is that tomography block slides distribution vector s, and nonlinear parameter is tomography geometric parameter m, InSAR data collection weight σ2,
And smoothing factor α.
To avoid smooth or owing smooth phenomenon, the priori probability density function of tomography block sliding distribution vector s is quasi- to be adopted
With the spatial smoothness constraints of more smoothing factors.Second order Laplacian Matrix D is introduced, so that DjS~N (0, αj), wherein j is tomography
Sub-block number, αjFor the corresponding smoothing factor of j tomography sub-block.Tomography geometric parameter m, InSAR data collection weight σ2, and it is smooth
Equal distribution density letter of the priori probability density function proposed adoption of factor alpha nonlinear parameter in a certain experience value range
Number.
When being modeled to InSAR data error, show preferably to characterize the anisotropy of InSAR atmosphere delay equal error
As the present invention uses anisotropic covarianceAnd QkRespectively k-th of data set weight and covariance matrix.
For the anisotropic variogram fitting of realization, it is intended that the rotation with propositions such as Knospe and Extension algorithm, it will be anisotropic
Random signal is transformed into isotropism to handle.
Higher-dimension, multiple extremum characteristic in view of the parameter priori probability density function in Bayes's inverse model, use horse
The possible calculation amount of Er Kefu monte carlo method (MCMC) is huge, solves difficult.Here the substep of proposed adoption Fukuda et al. is asked
Solution method, by priori probability density function p (m, s, σ, α | d) be decomposed into two probability density functions:
P (m, s, σ, α | d)=P (s | d, m, σ, α) P (m, σ, α | d) wherein, first probability density function p (s | d, m, σ,
It is α) the linear dimensions prior distribution after given nonlinear parameter, can be calculated by least square method, and second probability is close
The prior distribution that function p (m, σ, α | d]) is nonlinear parameter is spent, Markov Monte Carlo method can be used to calculate.
Specifically, InSAR source inversion uncertainty can be divided into independently of inversion method stochastic uncertainty and according to
Rely the cognition in inversion method uncertain.The research achievement that information theory is used for reference in present invention research, measures three kinds of seismic source informations
The information utilized in the information content and source inversion of information content, InSAR data offer that amount, i.e. source inversion parameter need
Amount.
For InSAR source inversion, source inversion parameter z=[mT, sT, σT, αT] priori probability density distribution
Comentropy H (z) is exactly the information content that source inversion parameter needs, mutual between source inversion parameter and InSAR observation data (d)
Information I (d；Z) be InSAR data provide information content, InSAR inverted parameters (zinv) and source inversion parameter (z) between
Mutual information I (zinv；It z) is the information content utilized in source inversion.
Further, the difference of both information content that the information content and InSAR data that source inversion parameter needs provide is shake
Source result needs and information content that InSAR data does not contain, referred to as stochastic uncertainty；And the information that InSAR data provides
The difference of both information content utilized in amount and source inversion is the information content that InSAR has contained and inversion method does not utilize,
Referred to as cognition is uncertain.
That is, the stochastic uncertainty function subtracts InSAR data by the information content that source inversion parameter needs
The information content of offer indicates；The cognition uncertainty function is subtracted sharp in source inversion by the information content that InSAR data provides
Information content indicates.
Further, it is quantitative description stochastic uncertainty function and cognition uncertainty function, needs to H (z), I
(d；And I (z z)inv；Z) three variables carry out asking calculation.According to the definition of random variable of continuous type comentropy, source inversion parameter z
Comentropy H (z) can be obtained by following formula: H (z)=- ∫V1P (z) logp (z) dz is wherein the domain of V1 parameter z, p (z)
It is the priori probability density of z.It being modeled according to front prior probability, p (z) can be described by fixed analytical expression, therefore for
The calculating of comentropy H (z) can substitute into definition and carry out direct solution.
It is mutual between source inversion parameter and InSAR observation data (d) according to the definition of random variable of continuous type mutual information
Information I (d；Z) it can be obtained by following formula:Wherein, V2
The domain of parameter d and z, pd, z (d, z) is the Joint Distribution of d and z, pd(d) and pz(z) be respectively d and z edge distribution.This
In Joint Distribution pD, z(d, z) can not use fixed analytic expression expression, two kinds of strategies of proposed adoption, the first strategy assumes p in advanceD, z
(d, z) probability-distribution function form, the parameters of probability-distribution function are fitted by parametric method；Second of strategy does not assume that
The form of probability-distribution function, the approximation of distribution function is provided by nonparametric technique, and nonparametric method includes histogram method, core
Function method and average histogram method etc..
Further, InSAR inverted parameters (zinv) and source inversion parameter (z) between mutual information I (zinv；Z) same
Sample can be provided by above-mentioned mutual information formula.(zinv) for probability density without analytical expression, the mode that sample set generates is as follows:
Firstly, carrying out MCMC sampling according to Z priori density function, the sample set of initial actual parameter Z is generated, then according to actual parameter
Sample set generates the sample set of corresponding InSAR observation d, finally carries out Inversion Calculation to observation sample collection, it is anti-to obtain InSAR
Drill parameter (zinv) sample set.This partial content is related to bigger calculation amount, and leonenko method can be used, around joint
Distribution function directly calculates mutual information, so that the sample space volume of higher-dimension parameter be avoided to ask with what dimension growth was exponentially exploded
Topic.
For the prior art when carrying out earthquake interpretation using InSAR earthquake source inversion result, tending to ignore result can
Thus the problem of by property, often results in interpretation result inaccuracy, the inconsistent phenomenon of conclusion.And the present invention is by introducing comentropy
These two types of uncertainties are unified on the benchmark of information content and measure by theory, and stochastic uncertainty is by source inversion parameter
The information content that the information content needed subtracts InSAR data offer indicates, recognizes the uncertain information content provided by InSAR data
Subtracting the information content that utilizes in source inversion indicates, to realize two class uncertainty quantitative evaluations and uniformly, is
InSAR earthquake source inversion result, which correctly interprets, provides scientific basis.
Based on above-mentioned earthquake source inverting Uncertainty Analysis Method, the present invention also provides a kind of computer-readable storages
Medium, the computer-readable recording medium storage have one or more program, and one or more of programs can be by one
A or multiple processors execute, to realize in earthquake source inverting Uncertainty Analysis Method described in any embodiment as above
The step of.
Based on above-mentioned earthquake source inverting Uncertainty Analysis Method, the present invention also provides a kind of application servers, such as
Shown in Fig. 2 comprising at least one processor (processor) 20；Display screen 21；And memory (memory) 22, may be used also
To include communication interface (Communications Interface) 23 and bus 24.Wherein, processor 20, display screen 21, deposit
Reservoir 22 and communication interface 23 can complete mutual communication by bus 24.Display screen 21 is set as display initial setting up mould
Preset user guides interface in formula.Communication interface 23 can transmit information.Processor 20 can call patrolling in memory 22
Instruction is collected, to execute the method in above-described embodiment.
In addition, the logical order in above-mentioned memory 22 can be realized and as only by way of SFU software functional unit
Vertical product when selling or using, can store in a computer readable storage medium.
Memory 22 is used as a kind of computer readable storage medium, and it is executable to may be configured as storage software program, computer
Program, such as the corresponding program instruction of method or module in the embodiment of the present disclosure.Processor 30 is stored in memory by operation
Software program, instruction or module in 22, thereby executing functional application and data processing, i.e. side in realization above-described embodiment
Method.
Memory 22 may include storing program area and storage data area, wherein storing program area can storage program area, extremely
Application program needed for a few function；Storage data area, which can be stored, uses created data etc. according to terminal device.This
Outside, memory 22 may include high-speed random access memory, can also include nonvolatile memory.For example, USB flash disk, movement
Hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory,
RAM), a variety of media that can store program code such as magnetic or disk, are also possible to transitory memory medium.
In addition, a plurality of instruction processing unit in above-mentioned storage medium and mobile terminal loads and the detailed process executed exists
It has been described in detail in the above method, has just no longer stated one by one herein.
In conclusion earthquake source inverting Uncertainty Analysis Method provided by the invention, by introducing stochastic variable letter
Entropy theory is ceased, these two types of uncertainties are unified on the benchmark of information content and are measured, stochastic uncertainty is by source inversion
The information content that the information content that parameter needs subtracts InSAR data offer indicates, recognizes the uncertain letter provided by InSAR data
Breath amount subtracts the information content that utilizes in source inversion and indicates, so that two class uncertainty quantitative evaluations and uniformly are realized,
It is correctly interpreted for InSAR earthquake source inversion result and scientific basis is provided.
It should be understood that the application of the present invention is not limited to the above for those of ordinary skills can
With improvement or transformation based on the above description, all these modifications and variations all should belong to the guarantor of appended claims of the present invention
Protect range.
Claims (9)
1. a kind of earthquake source inverting Uncertainty Analysis Method, which is characterized in that comprising steps of
The prior probability for resolve the corresponding source inversion parameter of acquisition to source inversion parameter using Bayes statistical method is close
Spend function；
Seismic source information amount is obtained according to the priori probability density function of the source inversion parameter；
Stochastic uncertainty function and cognition uncertainty function are measured out according to the seismic source information；
Assessment is carried out to source inversion PRELIMINARY RESULTS in conjunction with the stochastic uncertainty function and cognition uncertainty function to sentence
It is fixed, obtain accurate source inversion interpretation result.
2. earthquake source inverting Uncertainty Analysis Method according to claim 1, which is characterized in that the source inversion
Parameter includes: tomography block sliding distribution vector s, tomography geometric parameter m, InSAR data collection weight σ2And smoothing factor a.
3. earthquake source inverting Uncertainty Analysis Method according to claim 1, which is characterized in that the seismic source information
Amount includes: the information utilized in the information content of source inversion parameter needs, the information content of InSAR data offer and source inversion
Amount.
4. earthquake source inverting Uncertainty Analysis Method according to claim 3, which is characterized in that the source inversion
The information content that parameter needs is the comentropy of source inversion parameter priori probability density function；The letter that the InSAR data provides
Breath amount is the mutual information between source inversion parameter and InSAR observation data；The information content utilized in the source inversion is
Mutual information between InSAR inverted parameters and source inversion parameter.
5. earthquake source inverting Uncertainty Analysis Method according to claim 4, which is characterized in that described random not true
Qualitative function is that the information content that source inversion parameter needs subtracts the information content that InSAR data provides.
6. earthquake source inverting Uncertainty Analysis Method according to claim 5, which is characterized in that the cognition is not true
Qualitative function is that the information content that InSAR data provides subtracts the information content utilized in source inversion.
7. earthquake source inverting Uncertainty Analysis Method according to claim 6, which is characterized in that described in the combination
Stochastic uncertainty function and cognition uncertainty function carry out assessment judgement to source inversion PRELIMINARY RESULTS, obtain accurately
The step of source inversion interpretation result, specifically includes:
When carrying out earthquake interpretation to InSAR earthquake source inverting PRELIMINARY RESULTS, in conjunction with the stochastic uncertainty function and
Cognition uncertainty function carries out assessment judgement to source inversion PRELIMINARY RESULTS；
Determine that result effectively accepts or rejects interpretation result according to assessment, finally obtains accurate source inversion interpretation result.
8. a kind of computer readable storage medium, which is characterized in that the computer-readable recording medium storage have one or
Multiple programs, one or more of programs are executed by one or more processors, to realize that the claims 1-7 is any
A kind of the step of earthquake source inverting Uncertainty Analysis Method.
9. a kind of application server, which is characterized in that including at least one processor, display screen, memory and communication interface
And bus, the processor, display screen, memory and communication interface complete mutual communication, the processor by bus
Call the logical order in memory to execute any one the earthquake source inverting analysis of uncertainty side the claims 1-7
The step of method.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810506557.0A CN109001805B (en) | 2018-05-24 | 2018-05-24 | Seismic source inversion uncertainty analysis method, storage medium and server |
| PCT/CN2019/087040 WO2019223585A1 (en) | 2018-05-24 | 2019-05-15 | Seismic source inversion uncertainty analysis method, storage medium, and server |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810506557.0A CN109001805B (en) | 2018-05-24 | 2018-05-24 | Seismic source inversion uncertainty analysis method, storage medium and server |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109001805A true CN109001805A (en) | 2018-12-14 |
| CN109001805B CN109001805B (en) | 2020-03-31 |
Family
ID=64572876
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810506557.0A Active CN109001805B (en) | 2018-05-24 | 2018-05-24 | Seismic source inversion uncertainty analysis method, storage medium and server |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN109001805B (en) |
| WO (1) | WO2019223585A1 (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019223585A1 (en) * | 2018-05-24 | 2019-11-28 | 深圳大学 | Seismic source inversion uncertainty analysis method, storage medium, and server |
| CN115730424A (en) * | 2022-10-17 | 2023-03-03 | 南方科技大学 | Finite fault inversion method, device and terminal based on multi-source geodetic data |
| CN116127247A (en) * | 2023-02-14 | 2023-05-16 | 中国地震局地球物理研究所 | Probability risk analysis and calculation method for coupling multiple seismic source models |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030212568A1 (en) * | 2002-05-10 | 2003-11-13 | Ian Oliver | Method for measuring mutual understanding |
| US20140122381A1 (en) * | 2012-10-25 | 2014-05-01 | Microsoft Corporation | Decision tree training in machine learning |
| CN106844945A (en) * | 2017-01-19 | 2017-06-13 | 电子科技大学 | One kind considers cognitive probabilistic multistate system probabilistic compct analysis method |
| CN106991446A (en) * | 2017-04-06 | 2017-07-28 | 哈尔滨理工大学 | A kind of embedded dynamic feature selection method of the group policy of mutual information |
| CN107316321A (en) * | 2017-06-22 | 2017-11-03 | 电子科技大学 | Multiple features fusion method for tracking target and the Weight number adaptively method based on comentropy |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2009353036B2 (en) * | 2009-09-25 | 2015-04-30 | Landmark Graphics Corporation | Systems and methods for the quantitative estimate of production-forecast uncertainty |
| WO2015168114A1 (en) * | 2014-05-01 | 2015-11-05 | Conocophillips Company | Deterministic phase correction and application |
| CN104749624B (en) * | 2015-03-03 | 2017-07-07 | 中国石油大学(北京) | A kind of seismic facies identification and its uncertain quantitative assessment synchronization realizing method |
| CN109001805B (en) * | 2018-05-24 | 2020-03-31 | 深圳大学 | Seismic source inversion uncertainty analysis method, storage medium and server |
| CN108828662B (en) * | 2018-05-24 | 2020-04-07 | 深圳大学 | Seismic source inversion visual analysis method, storage medium and server |
-
2018
- 2018-05-24 CN CN201810506557.0A patent/CN109001805B/en active Active
-
2019
- 2019-05-15 WO PCT/CN2019/087040 patent/WO2019223585A1/en active Application Filing
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030212568A1 (en) * | 2002-05-10 | 2003-11-13 | Ian Oliver | Method for measuring mutual understanding |
| US20140122381A1 (en) * | 2012-10-25 | 2014-05-01 | Microsoft Corporation | Decision tree training in machine learning |
| CN106844945A (en) * | 2017-01-19 | 2017-06-13 | 电子科技大学 | One kind considers cognitive probabilistic multistate system probabilistic compct analysis method |
| CN106991446A (en) * | 2017-04-06 | 2017-07-28 | 哈尔滨理工大学 | A kind of embedded dynamic feature selection method of the group policy of mutual information |
| CN107316321A (en) * | 2017-06-22 | 2017-11-03 | 电子科技大学 | Multiple features fusion method for tracking target and the Weight number adaptively method based on comentropy |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019223585A1 (en) * | 2018-05-24 | 2019-11-28 | 深圳大学 | Seismic source inversion uncertainty analysis method, storage medium, and server |
| CN115730424A (en) * | 2022-10-17 | 2023-03-03 | 南方科技大学 | Finite fault inversion method, device and terminal based on multi-source geodetic data |
| CN115730424B (en) * | 2022-10-17 | 2023-08-04 | 南方科技大学 | Finite fault inversion method, device and terminal based on multi-source geodetic data |
| CN116127247A (en) * | 2023-02-14 | 2023-05-16 | 中国地震局地球物理研究所 | Probability risk analysis and calculation method for coupling multiple seismic source models |
| CN116127247B (en) * | 2023-02-14 | 2023-08-18 | 中国地震局地球物理研究所 | Probability risk analysis and calculation method for coupling multiple seismic source models |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109001805B (en) | 2020-03-31 |
| WO2019223585A1 (en) | 2019-11-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110275148B (en) | Sea clutter amplitude distribution parameter estimation method and system | |
| Lahdelma et al. | Prospect theory and stochastic multicriteria acceptability analysis (SMAA) | |
| Garcin | Estimation of time-dependent Hurst exponents with variational smoothing and application to forecasting foreign exchange rates | |
| Wang et al. | Spatial and temporal prediction and uncertainty of soil loss using the revised universal soil loss equation: a case study of the rainfall–runoff erosivity R factor | |
| CN109001805A (en) | A kind of earthquake source inverting Uncertainty Analysis Method, storage medium and server | |
| Younes et al. | Use of global sensitivity analysis to help assess unsaturated soil hydraulic parameters | |
| CN103077537B (en) | Novel L1 regularization-based real-time moving target tracking method | |
| Taylor et al. | Investigating improvements in the accuracy of prediction intervals for combinations of forecasts: A simulation study | |
| Molkenthin et al. | Derivative‐based global sensitivity analysis: Upper bounding of sensitivities in seismic‐hazard assessment using automatic differentiation | |
| CN113687433A (en) | Bi-LSTM-based magnetotelluric signal denoising method and system | |
| Zhou et al. | A Two-Component $ K $–Lognormal Mixture Model and Its Parameter Estimation Method | |
| Garcin et al. | Wavelet shrinkage of a noisy dynamical system with non-linear noise impact | |
| CN112257021A (en) | Selectable kriging spatial interpolation rainfall estimation method | |
| CN115545334A (en) | Land use type prediction method, land use type prediction device, electronic device, and storage medium | |
| CN107632010A (en) | A kind of quantitative approach of combination LIBS to steel samples | |
| Bonamente | Distribution of the C statistic with applications to the sample mean of Poisson data | |
| CN111830481B (en) | Radar echo single-component amplitude distribution model parameter estimation method and device | |
| CA2716841C (en) | Relative noise of a measured signal | |
| Arbogast et al. | Object‐oriented processing of CRM precipitation forecasts by stochastic filtering | |
| CN113538239B (en) | Interpolation method based on space-time autoregressive neural network model | |
| WO2023050649A1 (en) | Esg index determination method based on data complementing, and related product | |
| Stringari et al. | A New Probabilistic Wave Breaking Model for Dominant Wind‐Sea Waves Based on the Gaussian Field Theory | |
| CN116777452B (en) | Prepayment system and method for intelligent ammeter | |
| Conze et al. | The forward Kolmogorov equation for two dimensional options | |
| CN104517288A (en) | Remote sensing data statistical property estimation algorithm based on WT (wavelet transform) |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |