AustralieFrV1, PascalFrancis, Curation, bibRecord, 004C37

Transdimensional inversion of receiver functions and surface wave dispersion

Identifieur interne : 004C37 ( PascalFrancis/Curation ); précédent : 004C36; suivant : 004C38

Transdimensional inversion of receiver functions and surface wave dispersion

Auteurs : T. Bodin [Australie] ; M. Sambridge [Australie] ; H. Tkalcic [Australie] ; P. Arroucau [États-Unis] ; K. Gallagher [France] ; N. Rawlinson [Australie]

Source :

Journal of geophysical research [ 0148-0227 ] ; 2012.

RBID : Pascal:12-0253616

Descripteurs français

Pascal (Inist)
- Problème inverse, Onde surface, Dispersion onde, Diaclase, Algorithme, Modèle 1 dimension, Analyse Chaîne Markov, Chaîne Markov, Méthode Monte Carlo, Solution, Alizé, Bruit, Matrice covariance, Erreur, Complexité, Forme onde, Variance, Covariance, Incertitude, Pondération, Onde S, Vitesse onde, Australie.
Wicri :
- geographic : Australie.
- topic : Bruit.

English descriptors

KwdEn :
- Australia, Complexity, Covariance matrix, Markov chain, Markov chain analysis, Monte Carlo analysis, S-waves, Trade wind, Variance, Wave velocity, Weighting, algorithms, covariance, errors, inverse problem, joints, noise, one-dimensional models, solution, surface waves, uncertainties, wave dispersion, waveforms.

Abstract

We present a novel method for joint inversion of receiver functions and surface wave dispersion data, using a transdimensional Bayesian formulation. This class of algorithm treats the number of model parameters (e.g. number of layers) as an unknown in the problem. The dimension of the model space is variable and a Markov chain Monte Carlo (McMC) scheme is used to provide a parsimonious solution that fully quantifies the degree of knowledge one has about seismic structure (i.e constraints on the model, resolution, and trade-offs). The level of data noise (i.e. the covariance matrix of data errors) effectively controls the information recoverable from the data and here it naturally determines the complexity of the model (i.e. the number of model parameters). However, it is often difficult to quantify the data noise appropriately, particularly in the case of seismic waveform inversion where data errors are correlated. Here we address the issue of noise estimation using an extended Hierarchical Bayesian formulation, which allows both the variance and covariance of data noise to be treated as unknowns in the inversion. In this way it is possible to let the data infer the appropriate level of data fit. In the context of joint inversions, assessment of uncertainty for different data types becomes crucial in the evaluation of the misfit function. We show that the Hierarchical Bayes procedure is a powerful tool in this situation, because it is able to evaluate the level of information brought by different data types in the misfit, thus removing the arbitrary choice of weighting factors. After illustrating the method with synthetic tests, a real data application is shown where teleseismic receiver functions and ambient noise surface wave dispersion measurements from the WOMBAT array (South-East Australia) are jointly inverted to provide a probabilistic 1D model of shear-wave velocity beneath a given station.

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A03		`1`		`@0 J. geophys. res.`
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A11	`02`	`1`		`@1 SAMBRIDGE (M.)`
A11	`03`	`1`		`@1 TKALCIC (H.)`
A11	`04`	`1`		`@1 ARROUCAU (P.)`
A11	`05`	`1`		`@1 GALLAGHER (K.)`
A11	`06`	`1`		`@1 RAWLINSON (N.)`
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A14	`02`			`@1 Environmental, Earth and Geospatial Sciences, North Carolina Central University @2 Durham, North Carolina @3 USA @Z 4 aut.`
A14	`03`			`@1 Géosciences Rennes, Université de Rennes 1 @2 Rennes @3 FRA @Z 5 aut.`
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C01	`01`		`ENG`	@0 We present a novel method for joint inversion of receiver functions and surface wave dispersion data, using a transdimensional Bayesian formulation. This class of algorithm treats the number of model parameters (e.g. number of layers) as an unknown in the problem. The dimension of the model space is variable and a Markov chain Monte Carlo (McMC) scheme is used to provide a parsimonious solution that fully quantifies the degree of knowledge one has about seismic structure (i.e constraints on the model, resolution, and trade-offs). The level of data noise (i.e. the covariance matrix of data errors) effectively controls the information recoverable from the data and here it naturally determines the complexity of the model (i.e. the number of model parameters). However, it is often difficult to quantify the data noise appropriately, particularly in the case of seismic waveform inversion where data errors are correlated. Here we address the issue of noise estimation using an extended Hierarchical Bayesian formulation, which allows both the variance and covariance of data noise to be treated as unknowns in the inversion. In this way it is possible to let the data infer the appropriate level of data fit. In the context of joint inversions, assessment of uncertainty for different data types becomes crucial in the evaluation of the misfit function. We show that the Hierarchical Bayes procedure is a powerful tool in this situation, because it is able to evaluate the level of information brought by different data types in the misfit, thus removing the arbitrary choice of weighting factors. After illustrating the method with synthetic tests, a real data application is shown where teleseismic receiver functions and ambient noise surface wave dispersion measurements from the WOMBAT array (South-East Australia) are jointly inverted to provide a probabilistic 1D model of shear-wave velocity beneath a given station.
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C03	`13`	`X`	`FRE`	`@0 Matrice covariance @5 13`
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C03	`14`	`2`	`FRE`	`@0 Erreur @5 14`
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C03	`14`	`2`	`SPA`	`@0 Error @5 14`
C03	`15`	`X`	`FRE`	`@0 Complexité @5 15`
C03	`15`	`X`	`ENG`	`@0 Complexity @5 15`
C03	`15`	`X`	`SPA`	`@0 Complejidad @5 15`
C03	`16`	`2`	`FRE`	`@0 Forme onde @5 16`
C03	`16`	`2`	`ENG`	`@0 waveforms @5 16`
C03	`17`	`X`	`FRE`	`@0 Variance @5 17`
C03	`17`	`X`	`ENG`	`@0 Variance @5 17`
C03	`17`	`X`	`SPA`	`@0 Variancia @5 17`
C03	`18`	`2`	`FRE`	`@0 Covariance @5 18`
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C03	`19`	`2`	`FRE`	`@0 Incertitude @5 19`
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C03	`20`	`X`	`FRE`	`@0 Pondération @5 20`
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C03	`20`	`X`	`SPA`	`@0 Ponderación @5 20`
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C03	`21`	`2`	`SPA`	`@0 Onda S @5 21`
C03	`22`	`X`	`FRE`	`@0 Vitesse onde @5 22`
C03	`22`	`X`	`ENG`	`@0 Wave velocity @5 22`
C03	`22`	`X`	`SPA`	`@0 Velocidad onda @5 22`
C03	`23`	`2`	`FRE`	`@0 Australie @2 NG @5 61`
C03	`23`	`2`	`ENG`	`@0 Australia @2 NG @5 61`
C03	`23`	`2`	`SPA`	`@0 Australia @2 NG @5 61`
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Le document en format XML

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<front><div type="abstract" xml:lang="en">We present a novel method for joint inversion of receiver functions and surface wave dispersion data, using a transdimensional Bayesian formulation. This class of algorithm treats the number of model parameters (e.g. number of layers) as an unknown in the problem. The dimension of the model space is variable and a Markov chain Monte Carlo (McMC) scheme is used to provide a parsimonious solution that fully quantifies the degree of knowledge one has about seismic structure (i.e constraints on the model, resolution, and trade-offs). The level of data noise (i.e. the covariance matrix of data errors) effectively controls the information recoverable from the data and here it naturally determines the complexity of the model (i.e. the number of model parameters). However, it is often difficult to quantify the data noise appropriately, particularly in the case of seismic waveform inversion where data errors are correlated. Here we address the issue of noise estimation using an extended Hierarchical Bayesian formulation, which allows both the variance and covariance of data noise to be treated as unknowns in the inversion. In this way it is possible to let the data infer the appropriate level of data fit. In the context of joint inversions, assessment of uncertainty for different data types becomes crucial in the evaluation of the misfit function. We show that the Hierarchical Bayes procedure is a powerful tool in this situation, because it is able to evaluate the level of information brought by different data types in the misfit, thus removing the arbitrary choice of weighting factors. After illustrating the method with synthetic tests, a real data application is shown where teleseismic receiver functions and ambient noise surface wave dispersion measurements from the WOMBAT array (South-East Australia) are jointly inverted to provide a probabilistic 1D model of shear-wave velocity beneath a given station.</div>
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</fA43>
<fA44><s0>0000</s0>
<s1>© 2012 INIST-CNRS. All rights reserved.</s1>
</fA44>
<fA45><s0>1 p.3/4</s0>
</fA45>
<fA47 i1="01" i2="1"><s0>12-0253616</s0>
</fA47>
<fA60><s1>P</s1>
</fA60>
<fA61><s0>A</s0>
</fA61>
<fA64 i1="01" i2="1"><s0>Journal of geophysical research</s0>
</fA64>
<fA66 i1="01"><s0>USA</s0>
</fA66>
<fC01 i1="01" l="ENG"><s0>We present a novel method for joint inversion of receiver functions and surface wave dispersion data, using a transdimensional Bayesian formulation. This class of algorithm treats the number of model parameters (e.g. number of layers) as an unknown in the problem. The dimension of the model space is variable and a Markov chain Monte Carlo (McMC) scheme is used to provide a parsimonious solution that fully quantifies the degree of knowledge one has about seismic structure (i.e constraints on the model, resolution, and trade-offs). The level of data noise (i.e. the covariance matrix of data errors) effectively controls the information recoverable from the data and here it naturally determines the complexity of the model (i.e. the number of model parameters). However, it is often difficult to quantify the data noise appropriately, particularly in the case of seismic waveform inversion where data errors are correlated. Here we address the issue of noise estimation using an extended Hierarchical Bayesian formulation, which allows both the variance and covariance of data noise to be treated as unknowns in the inversion. In this way it is possible to let the data infer the appropriate level of data fit. In the context of joint inversions, assessment of uncertainty for different data types becomes crucial in the evaluation of the misfit function. We show that the Hierarchical Bayes procedure is a powerful tool in this situation, because it is able to evaluate the level of information brought by different data types in the misfit, thus removing the arbitrary choice of weighting factors. After illustrating the method with synthetic tests, a real data application is shown where teleseismic receiver functions and ambient noise surface wave dispersion measurements from the WOMBAT array (South-East Australia) are jointly inverted to provide a probabilistic 1D model of shear-wave velocity beneath a given station.</s0>
</fC01>
<fC02 i1="01" i2="3"><s0>001E</s0>
</fC02>
<fC02 i1="02" i2="2"><s0>001E01</s0>
</fC02>
<fC02 i1="03" i2="2"><s0>220</s0>
</fC02>
<fC03 i1="01" i2="2" l="FRE"><s0>Problème inverse</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="2" l="ENG"><s0>inverse problem</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="2" l="SPA"><s0>Problema inverso</s0>
<s5>01</s5>
</fC03>
<fC03 i1="02" i2="2" l="FRE"><s0>Onde surface</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="2" l="ENG"><s0>surface waves</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="2" l="SPA"><s0>Onda superficie</s0>
<s5>02</s5>
</fC03>
<fC03 i1="03" i2="2" l="FRE"><s0>Dispersion onde</s0>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="2" l="ENG"><s0>wave dispersion</s0>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="2" l="SPA"><s0>Dispersión onda</s0>
<s5>03</s5>
</fC03>
<fC03 i1="04" i2="2" l="FRE"><s0>Diaclase</s0>
<s5>04</s5>
</fC03>
<fC03 i1="04" i2="2" l="ENG"><s0>joints</s0>
<s5>04</s5>
</fC03>
<fC03 i1="04" i2="2" l="SPA"><s0>Diaclasa</s0>
<s5>04</s5>
</fC03>
<fC03 i1="05" i2="2" l="FRE"><s0>Algorithme</s0>
<s5>05</s5>
</fC03>
<fC03 i1="05" i2="2" l="ENG"><s0>algorithms</s0>
<s5>05</s5>
</fC03>
<fC03 i1="05" i2="2" l="SPA"><s0>Algoritmo</s0>
<s5>05</s5>
</fC03>
<fC03 i1="06" i2="2" l="FRE"><s0>Modèle 1 dimension</s0>
<s5>06</s5>
</fC03>
<fC03 i1="06" i2="2" l="ENG"><s0>one-dimensional models</s0>
<s5>06</s5>
</fC03>
<fC03 i1="06" i2="2" l="SPA"><s0>Modelo 1 dimensión</s0>
<s5>06</s5>
</fC03>
<fC03 i1="07" i2="2" l="FRE"><s0>Analyse Chaîne Markov</s0>
<s5>07</s5>
</fC03>
<fC03 i1="07" i2="2" l="ENG"><s0>Markov chain analysis</s0>
<s5>07</s5>
</fC03>
<fC03 i1="08" i2="X" l="FRE"><s0>Chaîne Markov</s0>
<s5>08</s5>
</fC03>
<fC03 i1="08" i2="X" l="ENG"><s0>Markov chain</s0>
<s5>08</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA"><s0>Cadena Markov</s0>
<s5>08</s5>
</fC03>
<fC03 i1="09" i2="2" l="FRE"><s0>Méthode Monte Carlo</s0>
<s5>09</s5>
</fC03>
<fC03 i1="09" i2="2" l="ENG"><s0>Monte Carlo analysis</s0>
<s5>09</s5>
</fC03>
<fC03 i1="10" i2="2" l="FRE"><s0>Solution</s0>
<s5>10</s5>
</fC03>
<fC03 i1="10" i2="2" l="ENG"><s0>solution</s0>
<s5>10</s5>
</fC03>
<fC03 i1="11" i2="X" l="FRE"><s0>Alizé</s0>
<s5>11</s5>
</fC03>
<fC03 i1="11" i2="X" l="ENG"><s0>Trade wind</s0>
<s5>11</s5>
</fC03>
<fC03 i1="11" i2="X" l="SPA"><s0>Alisio</s0>
<s5>11</s5>
</fC03>
<fC03 i1="12" i2="2" l="FRE"><s0>Bruit</s0>
<s5>12</s5>
</fC03>
<fC03 i1="12" i2="2" l="ENG"><s0>noise</s0>
<s5>12</s5>
</fC03>
<fC03 i1="13" i2="X" l="FRE"><s0>Matrice covariance</s0>
<s5>13</s5>
</fC03>
<fC03 i1="13" i2="X" l="ENG"><s0>Covariance matrix</s0>
<s5>13</s5>
</fC03>
<fC03 i1="13" i2="X" l="SPA"><s0>Matriz covariancia</s0>
<s5>13</s5>
</fC03>
<fC03 i1="14" i2="2" l="FRE"><s0>Erreur</s0>
<s5>14</s5>
</fC03>
<fC03 i1="14" i2="2" l="ENG"><s0>errors</s0>
<s5>14</s5>
</fC03>
<fC03 i1="14" i2="2" l="SPA"><s0>Error</s0>
<s5>14</s5>
</fC03>
<fC03 i1="15" i2="X" l="FRE"><s0>Complexité</s0>
<s5>15</s5>
</fC03>
<fC03 i1="15" i2="X" l="ENG"><s0>Complexity</s0>
<s5>15</s5>
</fC03>
<fC03 i1="15" i2="X" l="SPA"><s0>Complejidad</s0>
<s5>15</s5>
</fC03>
<fC03 i1="16" i2="2" l="FRE"><s0>Forme onde</s0>
<s5>16</s5>
</fC03>
<fC03 i1="16" i2="2" l="ENG"><s0>waveforms</s0>
<s5>16</s5>
</fC03>
<fC03 i1="17" i2="X" l="FRE"><s0>Variance</s0>
<s5>17</s5>
</fC03>
<fC03 i1="17" i2="X" l="ENG"><s0>Variance</s0>
<s5>17</s5>
</fC03>
<fC03 i1="17" i2="X" l="SPA"><s0>Variancia</s0>
<s5>17</s5>
</fC03>
<fC03 i1="18" i2="2" l="FRE"><s0>Covariance</s0>
<s5>18</s5>
</fC03>
<fC03 i1="18" i2="2" l="ENG"><s0>covariance</s0>
<s5>18</s5>
</fC03>
<fC03 i1="19" i2="2" l="FRE"><s0>Incertitude</s0>
<s5>19</s5>
</fC03>
<fC03 i1="19" i2="2" l="ENG"><s0>uncertainties</s0>
<s5>19</s5>
</fC03>
<fC03 i1="20" i2="X" l="FRE"><s0>Pondération</s0>
<s5>20</s5>
</fC03>
<fC03 i1="20" i2="X" l="ENG"><s0>Weighting</s0>
<s5>20</s5>
</fC03>
<fC03 i1="20" i2="X" l="SPA"><s0>Ponderación</s0>
<s5>20</s5>
</fC03>
<fC03 i1="21" i2="2" l="FRE"><s0>Onde S</s0>
<s5>21</s5>
</fC03>
<fC03 i1="21" i2="2" l="ENG"><s0>S-waves</s0>
<s5>21</s5>
</fC03>
<fC03 i1="21" i2="2" l="SPA"><s0>Onda S</s0>
<s5>21</s5>
</fC03>
<fC03 i1="22" i2="X" l="FRE"><s0>Vitesse onde</s0>
<s5>22</s5>
</fC03>
<fC03 i1="22" i2="X" l="ENG"><s0>Wave velocity</s0>
<s5>22</s5>
</fC03>
<fC03 i1="22" i2="X" l="SPA"><s0>Velocidad onda</s0>
<s5>22</s5>
</fC03>
<fC03 i1="23" i2="2" l="FRE"><s0>Australie</s0>
<s2>NG</s2>
<s5>61</s5>
</fC03>
<fC03 i1="23" i2="2" l="ENG"><s0>Australia</s0>
<s2>NG</s2>
<s5>61</s5>
</fC03>
<fC03 i1="23" i2="2" l="SPA"><s0>Australia</s0>
<s2>NG</s2>
<s5>61</s5>
</fC03>
<fC07 i1="01" i2="2" l="FRE"><s0>Australasie</s0>
</fC07>
<fC07 i1="01" i2="2" l="ENG"><s0>Australasia</s0>
</fC07>
<fC07 i1="01" i2="2" l="SPA"><s0>Australasia</s0>
</fC07>
<fN21><s1>191</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
</standard>
</inist>
</record>

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	Serveur d'exploration sur les relations entre la France et l'Australie
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Transdimensional inversion of receiver functions and surface wave dispersion

Transdimensional inversion of receiver functions and surface wave dispersion

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