Model-data fusion for spatial and statistical characterization of soil parameters from geophysical measurements

Siddharth S. Parida, Kallol Sett, Puneet Singla

Research output: Contribution to journalArticle

Abstract

A recently developed PDE-constrained stochastic inverse analysis algorithm for spatial and statistical characterization of soil parameters from geophysical measurements, considering uncertainty due to limited measurements and sensor noise, is exemplified and validated. A 60m × 60 m geotechnical site in Garner Valley, CA is used as the validation testbed. Advanced geophysical test measurements – in terms of velocity waveforms at a few locations on the surface due to surface excitations using a mobile shaker – are available for the site. The algorithm inversely analyzes the available measurements to probabilistically estimate the elastic parameters of the soil at the site up to a depth of 40 m. The algorithm relies on (1) hypothesizing the soil parameters to be heterogeneous, anisotropic random fields, (2) making prior assumptions on them, (3) numerically simulating the geophysical experiment using the finite element method in conjunction with a stochastic collocation approach, and (4) fusing simulated measurements with experimental measurements using a minimum variance framework to update the prior assumptions on the soil parameter random fields. The estimated elastic parameters of the soil are presented in terms of marginal mean and marginal standard deviation profiles of the soil's P- and S-wave velocities as well as their correlation structures in the x-, y-, and z-direction. In ascertaining the accuracy of the inverse analysis algorithm, the geophysical experiment is numerically re-simulated with the estimated P- and S-wave velocity profiles and the model predicted velocity waveforms are compared against the field observations at all the measurement locations. Comments are made at appropriate places regarding several aspects of the algorithm in highlighting the lessons learned through this validation effort towards accurate stochastic full waveform inversion of geophysical measurements.

Original languageEnglish (US)
Pages (from-to)35-57
Number of pages23
JournalSoil Dynamics and Earthquake Engineering
Volume124
DOIs
StatePublished - Sep 1 2019

Fingerprint

Data fusion
Soils
inverse analysis
soil
P-wave
wave velocity
S-wave
velocity profile
finite element method
parameter
experiment
Testbeds
sensor
soil profiles
valley
uncertainty
valleys
Experiments
Finite element method
Sensors

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Geotechnical Engineering and Engineering Geology
  • Soil Science

Cite this

@article{0be7d4688b7b4864a87b45b09a03b0ec,
title = "Model-data fusion for spatial and statistical characterization of soil parameters from geophysical measurements",
abstract = "A recently developed PDE-constrained stochastic inverse analysis algorithm for spatial and statistical characterization of soil parameters from geophysical measurements, considering uncertainty due to limited measurements and sensor noise, is exemplified and validated. A 60m × 60 m geotechnical site in Garner Valley, CA is used as the validation testbed. Advanced geophysical test measurements – in terms of velocity waveforms at a few locations on the surface due to surface excitations using a mobile shaker – are available for the site. The algorithm inversely analyzes the available measurements to probabilistically estimate the elastic parameters of the soil at the site up to a depth of 40 m. The algorithm relies on (1) hypothesizing the soil parameters to be heterogeneous, anisotropic random fields, (2) making prior assumptions on them, (3) numerically simulating the geophysical experiment using the finite element method in conjunction with a stochastic collocation approach, and (4) fusing simulated measurements with experimental measurements using a minimum variance framework to update the prior assumptions on the soil parameter random fields. The estimated elastic parameters of the soil are presented in terms of marginal mean and marginal standard deviation profiles of the soil's P- and S-wave velocities as well as their correlation structures in the x-, y-, and z-direction. In ascertaining the accuracy of the inverse analysis algorithm, the geophysical experiment is numerically re-simulated with the estimated P- and S-wave velocity profiles and the model predicted velocity waveforms are compared against the field observations at all the measurement locations. Comments are made at appropriate places regarding several aspects of the algorithm in highlighting the lessons learned through this validation effort towards accurate stochastic full waveform inversion of geophysical measurements.",
author = "Parida, {Siddharth S.} and Kallol Sett and Puneet Singla",
year = "2019",
month = "9",
day = "1",
doi = "10.1016/j.soildyn.2019.05.010",
language = "English (US)",
volume = "124",
pages = "35--57",
journal = "Soil Dynamics and Earthquake Engineering",
issn = "0267-7261",
publisher = "Elsevier BV",

}

Model-data fusion for spatial and statistical characterization of soil parameters from geophysical measurements. / Parida, Siddharth S.; Sett, Kallol; Singla, Puneet.

In: Soil Dynamics and Earthquake Engineering, Vol. 124, 01.09.2019, p. 35-57.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Model-data fusion for spatial and statistical characterization of soil parameters from geophysical measurements

AU - Parida, Siddharth S.

AU - Sett, Kallol

AU - Singla, Puneet

PY - 2019/9/1

Y1 - 2019/9/1

N2 - A recently developed PDE-constrained stochastic inverse analysis algorithm for spatial and statistical characterization of soil parameters from geophysical measurements, considering uncertainty due to limited measurements and sensor noise, is exemplified and validated. A 60m × 60 m geotechnical site in Garner Valley, CA is used as the validation testbed. Advanced geophysical test measurements – in terms of velocity waveforms at a few locations on the surface due to surface excitations using a mobile shaker – are available for the site. The algorithm inversely analyzes the available measurements to probabilistically estimate the elastic parameters of the soil at the site up to a depth of 40 m. The algorithm relies on (1) hypothesizing the soil parameters to be heterogeneous, anisotropic random fields, (2) making prior assumptions on them, (3) numerically simulating the geophysical experiment using the finite element method in conjunction with a stochastic collocation approach, and (4) fusing simulated measurements with experimental measurements using a minimum variance framework to update the prior assumptions on the soil parameter random fields. The estimated elastic parameters of the soil are presented in terms of marginal mean and marginal standard deviation profiles of the soil's P- and S-wave velocities as well as their correlation structures in the x-, y-, and z-direction. In ascertaining the accuracy of the inverse analysis algorithm, the geophysical experiment is numerically re-simulated with the estimated P- and S-wave velocity profiles and the model predicted velocity waveforms are compared against the field observations at all the measurement locations. Comments are made at appropriate places regarding several aspects of the algorithm in highlighting the lessons learned through this validation effort towards accurate stochastic full waveform inversion of geophysical measurements.

AB - A recently developed PDE-constrained stochastic inverse analysis algorithm for spatial and statistical characterization of soil parameters from geophysical measurements, considering uncertainty due to limited measurements and sensor noise, is exemplified and validated. A 60m × 60 m geotechnical site in Garner Valley, CA is used as the validation testbed. Advanced geophysical test measurements – in terms of velocity waveforms at a few locations on the surface due to surface excitations using a mobile shaker – are available for the site. The algorithm inversely analyzes the available measurements to probabilistically estimate the elastic parameters of the soil at the site up to a depth of 40 m. The algorithm relies on (1) hypothesizing the soil parameters to be heterogeneous, anisotropic random fields, (2) making prior assumptions on them, (3) numerically simulating the geophysical experiment using the finite element method in conjunction with a stochastic collocation approach, and (4) fusing simulated measurements with experimental measurements using a minimum variance framework to update the prior assumptions on the soil parameter random fields. The estimated elastic parameters of the soil are presented in terms of marginal mean and marginal standard deviation profiles of the soil's P- and S-wave velocities as well as their correlation structures in the x-, y-, and z-direction. In ascertaining the accuracy of the inverse analysis algorithm, the geophysical experiment is numerically re-simulated with the estimated P- and S-wave velocity profiles and the model predicted velocity waveforms are compared against the field observations at all the measurement locations. Comments are made at appropriate places regarding several aspects of the algorithm in highlighting the lessons learned through this validation effort towards accurate stochastic full waveform inversion of geophysical measurements.

UR - http://www.scopus.com/inward/record.url?scp=85066262581&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85066262581&partnerID=8YFLogxK

U2 - 10.1016/j.soildyn.2019.05.010

DO - 10.1016/j.soildyn.2019.05.010

M3 - Article

AN - SCOPUS:85066262581

VL - 124

SP - 35

EP - 57

JO - Soil Dynamics and Earthquake Engineering

JF - Soil Dynamics and Earthquake Engineering

SN - 0267-7261

ER -