Induced Earthquake Source Characterization Using Multi-stage of Hamiltonian Monte Carlo Algorithm

Poster (2022)

Authors

La ODE Marzujriban Masfara Applied Geophysics and Petrophysics - CEG
Thomas Cullison Universiteit Utrecht, Stanford University
J. Ruan Applied Geophysics and Petrophysics - CEG
C. Weemstra Royal Netherlands Meteorological Institute (KNMI), Applied Geophysics and Petrophysics - CEG
Research Group
Applied Geophysics and Petrophysics (CEG) (TU Delft)
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Published Date

2022

Language

English

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Faculty

Civil Engineering & Geosciences

Department

Geoscience and Engineering

Research Group

Applied Geophysics and Petrophysics

Abstract

Estimating earthquake parameters, including their uncertainty, requires
probabilistic sampling or inversion using Bayesian algorithms. One such
Bayesian algorithm known to be highly efficient is the Hamiltonian Monte
Carlo (HMC) algorithm, and modifying the algorithm with an additional
linearization step can further increase this efficiency. However, the
modified HMC relies heavily on accurate prior information to effectively
sample non-linear earthquake parameters (e.g., hypocenter and origin
time). Furthermore, the ability of the modified HMC to estimate
non-linear parameters diminishes with respect to the high degree of
non-linearity that is inherent to some types of events, such as induced
earthquakes. To address this, we adjust the modified HMC to be run in
multiple stages, combined with pre-determined initial prior sets. We
test this adjustment using synthetic and real data from an induced
earthquake event in the Groningen gas field in the Netherlands. We start
by obtaining an initial estimate of the prior information and use it to
draw multiple initial prior sets. We then run the HMC for each initial
prior set in multiple stages where the results from the current stage
serve as the prior for the next stage. As the final step, we form the
final posterior distributions by selecting results that give the best
fit between the observed and modeled data. Within this approach, we
estimate ten earthquake parameters those are the six components of a
full moment tensor solution, the centroid (three coordinate components),
and the earthquake's origin time (including the static time corrections
for each recording station). After obtaining the final results, we
compare our findings with those of an existing earthquake catalog and
several other research results. Given the available fault map of
Groningen's subsurface, we found that our results have a higher degree
of correlation with respect to the major subsurface faults.