Please use this identifier to cite or link to this item: https://hdl.handle.net/11681/45300
Title: Short-range near-surface seismic ensemble predictions and uncertainty quantification for layered medium
Authors: Vecherin, Sergey N.
Ketcham, Stephen A. (Stephen Alan)
Meyer, Aaron C.
Dunn, Kyle G.
Desmond, Jacob R.
Parker, Michael W.
Keywords: Ensemble predictions
Monte-Carlo simulations
Near-surface seismic modeling
Probabilistic modeling
Sensitivity analysis
Shortrange seismic propagation
Uncertainty quantification
Publisher: Engineer Research and Development Center (U.S.)
Series/Report no.: Miscellaneous Paper (Engineer Research and Development Center (U.S.)) ; no. ERDC/CRREL MP-22-12
Is Version Of: Vecherin, S., S. Ketcham, A. Meyer, K. Dunn, J. Desmond, and M. Parker. "Short-range near-surface seismic ensemble predictions and uncertainty quantification for layered medium." Journal of Applied Geophysics 204 (2022): 104735. https://doi.org/10.1016/j.jappgeo.2022.104735
Abstract: To make a prediction for seismic signal propagation, one needs to specify physical properties and subsurface ground structure of the site. This information is frequently unknown or estimated with significant uncertainty. This paper describes a methodology for probabilistic seismic ensemble prediction for vertically stratified soils and short ranges with no in situ site characterization. Instead of specifying viscoelastic site properties, the methodology operates with probability distribution functions of these properties taking into account analytical and empirical relationships among viscoelastic variables. This yields ensemble realizations of signal arrivals at specified locations where statistical properties of the signals can be estimated. Such ensemble predictions can be useful for preliminary site characterization, for military applications, and risk analysis for remote or inaccessible locations for which no data can be acquired. Comparison with experiments revealed that measured signals are not always within the predicted ranges of variability. Variance-based global sensitivity analysis has shown that the most significant parameters for signal amplitude predictions in the developed stochastic model are the uncertainty in the shear quality factor and the Poisson ratio above the water table depth.
Description: Miscellaneous Paper
Gov't Doc #: ERDC/CRREL MP-22-12
Rights: Approved for Public Release; Distribution is Unlimited
URI: https://hdl.handle.net/11681/45300
http://dx.doi.org/10.21079/11681/45300
Appears in Collections:Miscellaneous Paper

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