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.
Deep Dive into Geological realism in hydrogeological and geophysical inverse modeling: a review.
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 con
1
Geological
Realism
in
Hydrogeological
and
Geophysical
Inverse
Modeling:
a
Review
Niklas
Linde1*,
Philippe
Renard2,
Tapan
Mukerji3,
Jef
Caers3
1Applied
and
Environmental
Geophysics
Group,
Institute
of
Earth
Sciences,
University
of
Lausanne,
Switzerland;
2Stochastic
Hydrogeology
Group,
Centre
for
Hydrogeology
and
Geothermics
(CHYN),
University
of
Neuchâtel,
Switzerland;
3Stanford
Center
for
Reservoir
Forecasting,
Department
of
Energy
Resources
Engineering,
School
of
Earth
Sciences,
Stanford
University,
California.
*
Corresponding
author:
Niklas
Linde
University
of
Lausanne
Géopolis
-‐
bureau
3779
CH-‐1015
Lausanne
Email
:
Niklas.Linde@unil.ch
Phone
:
+41
21
692
4401
Fax
:
+41
21
692
44
05
This
work
is
published
in
Advances
in
Water
Resources
(2015),
please
cite
as:
Linde,
N.,
P.
Renard,
T.
Mukerji,
and
J.
Caers,
2015.
Geological
realism
in
hydrogeological
and
geophysical
inverse
modeling:
a
review.
Advances
in
Water
Resources,
86,
86-‐101.
10.1016/j.advwatres.2015.09.019.
2
Abstract.
Scientific
curiosity,
exploration
of
georesources
and
environmental
concerns
are
pushing
the
geoscientific
research
community
towards
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.
3
1.
Introduction
Geophysical
data
help
to
understand
geological
processes
and
to
test
scientific
hypotheses
throughout
the
Earth
Sciences,
while
also
providing
critical
information
and
constraints
for
forecasting
and
management
of
subsurface
formations
(e.g.,
oil
and
gas
reservoirs,
mineral
prospects,
aquifers,
and
the
critical
zone).
The
processing
of
virtually
all
geophysical
surveys
involves
inversion,
a
computational
process
in
which
measurement
responses
(e.g.,
signals
in
time
and
space
for
seismic
and
electromagnetic
data)
are
translated
into
multi-‐dimensional
images
of
physical
properties
(e.g.,
seismic
wavespeed,
density,
electrical
conductivity)
(Menke,
1989;
Tarantola,
2005)
or
into
properties
of
direct
relevance
for
geologica
…(Full text truncated)…
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