Geological realism in hydrogeological and geophysical inverse modeling: A review
By Niklas Linde, Philippe Renard, Tapan Mukerji, Jef Caers. 2015
195 citations recorded by the source corpus.
Abstract Scientific curiosity, exploration of georesources and environmental concerns are pushing the geoscientific research community toward subsurface investigations of ever-increasing complexity. This review explores various approaches to formulate and solve inverse problems in ways that effectively integrate geological concepts with geophysical and hydrogeological data. Modern geostatistical simulation algorithms can produce multiple subsurface realizations that are in agreement with conceptual geological models and statistical rock physics can be used to map these realizations into physical properties that are sensed by the geophysical or hydrogeological data. The inverse problem consists of finding one or an ensemble of such subsurface realizations that are in agreement with the data. The most general inversion frameworks are presently often computationally intractable when applied to large-scale problems and it is necessary to better understand the implications of simplifying (1) the conceptual geological model (e.g., using model compression); (2) the physical forward problem (e.g., using proxy models); and (3) the algorithm used to solve the inverse problem (e.g., Markov chain Monte Carlo or local optimization methods) to reach practical and robust solutions given today's computer resources and knowledge. We also highlight the need to not only use geophysical and hydrogeological data for parameter estimation purposes, but also to use them to falsify or corroborate alternative geological scenarios.
Subfields: Geophysics, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/dfb98486f6853e1e2b3b4870d3be75e35aac64c9
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Citations in the corpus
- Training‐Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network (2017, background): “Capturing subsurface complexity and connectivity is of utmost importance for reliable groundwater modeling and uncertainty quantification (e.g., Mariethoz and Caers , 2014; Linde et al. , 2015).”