Geological realism in hydrogeological and geophysical inverse modeling: A review

•Geological concepts improve geophysical and hydrogeological inversion results.•Geophysical and hydrogeological data can falsify and corroborate geological scenarios.•General inversion strategies may be infeasible in many realistic settings.•Which assumptions have the least impact on predictions and...

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Bibliographic Details
Published in:Advances in water resources Vol. 86; pp. 86 - 101
Main Authors: Linde, Niklas, Renard, Philippe, Mukerji, Tapan, Caers, Jef
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.12.2015
Subjects:
Algorithms
Computer simulation
computers
Forward problem
Geology
Geophysics
Geostatistics
Hydrogeology
Hydrogeophysics
Inverse problems
Markov chain
Markov chain Monte Carlo
Mathematical models
physical properties
Rock
statistical models
system optimization
water resources
Inverse problems
Hydrogeology
Hydrogeophysics
Markov chain Monte Carlo
Geostatistics
Geophysics
ISSN:0309-1708, 1872-9657
Online Access:Get full text
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Summary:•Geological concepts improve geophysical and hydrogeological inversion results.•Geophysical and hydrogeological data can falsify and corroborate geological scenarios.•General inversion strategies may be infeasible in many realistic settings.•Which assumptions have the least impact on predictions and uncertainty estimates? 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.
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ISSN:0309-1708
1872-9657
DOI:10.1016/j.advwatres.2015.09.019