Bayesian inference of preferential seepage path by gradient-based Markov Chain Monte Carlo

Kazunori Fujisawa; Michael C. Koch; Akira Murakami

doi:10.3208/jgssp.v08.j44

Bayesian inference of preferential seepage path by gradient-based Markov Chain Monte Carlo

Kazunori Fujisawa, Michael C. Koch, Akira Murakami

Research output: Contribution to journal › Conference article › peer-review

Abstract

The region or the path of preferential seepage flow is inversely identified by a gradient-based Markov Chain Monte Carlo method called Hamiltonian Monte Carlo (HMC). Observing hydraulic head and discharge rate of seepage water, HMC method estimates the domain or the path of preferential seepage flow by changing the shapes of finite elements over which the seepage flow is numerically solved by the finite element method. One simple synthetic example is solved in this article, and the numerical result shows that HMC method with a moving mesh performs well for this geometric inverse problem.

Original language	English
Pages (from-to)	59-63
Number of pages	5
Journal	Japanese Geotechnical Society Special Publication
Volume	8
Issue number	3
DOIs	https://doi.org/10.3208/jgssp.v08.j44
Publication status	Published - 14 Mar 2020
Externally published	Yes
Event	8th Japan-China Geotechnical Symposium: Challenges to Breakthrough in Geotechnic - Kyoto, Japan Duration: 28 Sept 2020 → 29 Sept 2020

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title = "Bayesian inference of preferential seepage path by gradient-based Markov Chain Monte Carlo",

abstract = "The region or the path of preferential seepage flow is inversely identified by a gradient-based Markov Chain Monte Carlo method called Hamiltonian Monte Carlo (HMC). Observing hydraulic head and discharge rate of seepage water, HMC method estimates the domain or the path of preferential seepage flow by changing the shapes of finite elements over which the seepage flow is numerically solved by the finite element method. One simple synthetic example is solved in this article, and the numerical result shows that HMC method with a moving mesh performs well for this geometric inverse problem.",

keywords = "Hamiltonian monte carlo method, Inverse problem, Moving mesh, Preferential seepage flow",

author = "Kazunori Fujisawa and Koch, \{Michael C.\} and Akira Murakami",

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doi = "10.3208/jgssp.v08.j44",

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TY - JOUR

T1 - Bayesian inference of preferential seepage path by gradient-based Markov Chain Monte Carlo

AU - Fujisawa, Kazunori

AU - Koch, Michael C.

AU - Murakami, Akira

PY - 2020/3/14

Y1 - 2020/3/14

N2 - The region or the path of preferential seepage flow is inversely identified by a gradient-based Markov Chain Monte Carlo method called Hamiltonian Monte Carlo (HMC). Observing hydraulic head and discharge rate of seepage water, HMC method estimates the domain or the path of preferential seepage flow by changing the shapes of finite elements over which the seepage flow is numerically solved by the finite element method. One simple synthetic example is solved in this article, and the numerical result shows that HMC method with a moving mesh performs well for this geometric inverse problem.

AB - The region or the path of preferential seepage flow is inversely identified by a gradient-based Markov Chain Monte Carlo method called Hamiltonian Monte Carlo (HMC). Observing hydraulic head and discharge rate of seepage water, HMC method estimates the domain or the path of preferential seepage flow by changing the shapes of finite elements over which the seepage flow is numerically solved by the finite element method. One simple synthetic example is solved in this article, and the numerical result shows that HMC method with a moving mesh performs well for this geometric inverse problem.

KW - Hamiltonian monte carlo method

KW - Inverse problem

KW - Moving mesh

KW - Preferential seepage flow

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EP - 63

JO - Japanese Geotechnical Society Special Publication

JF - Japanese Geotechnical Society Special Publication

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T2 - 8th Japan-China Geotechnical Symposium: Challenges to Breakthrough in Geotechnic

Y2 - 28 September 2020 through 29 September 2020

ER -

Bayesian inference of preferential seepage path by gradient-based Markov Chain Monte Carlo

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