A modular methodology for time-domain stochastic seismic wave propagation

Journal Article · 27 August 2021 · Computers and Geotechnics

DOI: https://doi.org/10.1016/j.compgeo.2021.104409 · OSTI ID:1902113

Wang, Fangbo  ^[1]; Wang, Hexiang ^[2]; Yang, Han  ^[2]; Feng, Yuan  ^[2]; Jeremić, Boris  ^[3]

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1. Tianjin Univ. (China)
2. Univ. of California, Davis, CA (United States)
3. Univ. of California, Davis, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Presented here is a modular methodology for time-domain stochastic seismic wave propagation analysis. Presented methodology is designed to analyse uncertain seismic motions as an input, propagating through uncertain material. Traditional approach for uncertain wave propagation relies on models that include deep bedrock, local soil site, and their random process and random field information. Such models can become quite large and computationally intractable. The modular approach proposed herein features two step approach that allows separate consideration of the deep bedrock and local site along with corresponding random field information. In this work, the first step considers an auxiliary stochastic motions problem in the bedrock. Stochastic local site response can then be simulated in a reduced domain within certain depth from the surface. Application of uncertain seismic motions at depth, for local uncertain site response is done using stochastic effective forces developed through the Domain Reduction Method. By using Hermite polynomial chaos expansion to represent the non-Gaussian random field of material parameters and non-stationary random process of seismic motion, the proposed modular methodology is formulated using intrusive stochastic Galerkin approach, as seen in the Stochastic Elastic–Plastic Finite Element Method (SEPFEM). Developed modular methodology is illustrated using a 1-D stochastic seismic wave propagation analysis with three cases, and simulation results are also verified with results from conventional approach.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1902113
• Journal Information: Computers and Geotechnics, Journal Name: Computers and Geotechnics Vol. 139; ISSN 0266-352X
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English