Reflection Full Waveform Inversion With Second‐Order Optimization Using the Adjoint‐State Method

Wang, Tengfei; Cheng, Jiubing; Geng, Jianhua

doi:10.1029/2021jb022135

Cited by 15 publications

(1 citation statement)

References 118 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These results can be applied when the rock-physics models used here are combined with observations that follow a trend like the data used in the work presented in this paper. Deterministic or probabilistic approaches can be used to solve geophysical inverse problems 9,10,11,12,13 . The Bayesian framework is particularly conducive to solving seismic and rockphysics inversion problems in a probabilistic fashion 7,14,15,16,17,18,19,20,21 .…”

Section: Introductionmentioning

confidence: 99%

Multi-ensemble Bayesian Analysis of Inclusion-based Rock-physics Models

Spikes,

Sen

2024

Preprint

View full text Add to dashboard Cite

A statistics-based approach to rock physics often includes the calculation of a series of simulations to fit data along with probability associated with the modeling to characterize uncertainty. We present a Bayesian approach to determine the most probable rock-physics model (e.g., inclusion based). Our results also feature combinations of highly probable model inputs from posterior distributions; these combinations result from using of many different sets of input values from a prior distribution where each set corresponds to an ensemble. Exhaustive sampling allows for the calculation of the full posterior distribution for each ensemble. We demonstrate this method using two inclusion-based rock-physics models, the self-consistent and differential effective medium models along with a carbonate laboratory data set. Results indicate that the latter of the two models is the most probable. Analyses of the underlying model inputs indicate multiple but distinct clusters among those inputs. The problem is computationally demanding and requires parallel computation for tractability. Results from this work are applicable to data sets with similar velocity-porosity trends. More generally, the method is applicable to any other data set and relevant rock-physics models of interest.

show abstract