Geo-spatial Information Science,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 21
Published: Oct. 18, 2024
Reservoir
characterization
is
a
vital
task
within
the
oil
and
gas
industry,
with
identification
of
lithofacies
in
subsurface
formations
being
fundamental
aspect
this
process.
However,
complex
geological
environments
high
dimensions,
such
as
Lower
Indus
Basin
Pakistan,
poses
notable
challenge,
especially
when
dealing
limited
data.
To
address
issue,
we
propose
four
common
data-driven
machine
learning
approaches:
multi-resolution
graph-based
clustering
(MRGC),
artificial
neural
networks
(ANN),
K-nearest
neighbors
(KNN),
self-organizing
map
(SOM).
We
utilized
these
proposed
approaches
to
assess
their
performance
scenarios
varying
core
sample
availability,
specifically
evaluating
effectiveness
identifying
Goru
formation
middle
Basin.
The
study
reveals
that
number
samples,
MRGC
preferred
choice,
while
KNN
or
more
suitable
for
larger
datasets.
results
demonstrate
superior
specified
environment,
SOM
following
closely
behind,
ANN
exhibiting
comparatively
lower
efficacy.
accurate
from
selected
model
complemented
by
application
truncated
Gaussian
simulation
method
facies
modeling.
Comparative
confirm
excellent
agreement
between
well
logs
electro-facies
obtained
volume.
This
highlights
crucial
role
selecting
right
approach
precise
modeling
environments.
comparative
analysis
provides
practitioners
petroleum
industry
insights
into
strengths
limitations
each
method,
enhancing
existing
knowledge.
In
conclusion,
research
emphasizes
significance
comprehensive
selection
advancing
diverse
areas,
ultimately
benefiting
broader
field
industry.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Jan. 13, 2024
Abstract
Determining
the
degree
of
high
groundwater
arsenic
(As)
and
fluoride
(F
−
)
risk
is
crucial
for
successful
management
protection
public
health,
as
elevated
contamination
in
poses
a
to
environment
human
health.
It
fact
that
several
non-point
sources
pollutants
contaminate
multi-aquifers
Ganges
delta.
This
study
used
logistic
regression
(LR),
random
forest
(RF)
artificial
neural
network
(ANN)
machine
learning
algorithm
evaluate
vulnerability
Holocene
multi-layered
aquifers
delta,
which
part
Indo-Bangladesh
region.
Fifteen
hydro-chemical
data
were
modelling
purposes
sophisticated
statistical
tests
carried
out
check
dataset
regarding
their
dependent
relationships.
ANN
performed
best
with
an
AUC
0.902
validation
prepared
map
accordingly.
The
spatial
distribution
indicates
eastern
some
isolated
south-eastern
central
middle
portions
are
very
vulnerable
terms
As
F
concentration.
overall
prediction
demonstrates
29%
areal
coverage
delta
contents.
Finally,
this
discusses
major
categories,
rising
security
issues,
problems
related
quality
globally.
Henceforth,
monitoring
must
be
significantly
improved
successfully
detect
reduce
hazards
from
past,
present,
future
contamination.
International Journal of Digital Earth,
Journal Year:
2023,
Volume and Issue:
16(1), P. 593 - 619
Published: March 1, 2023
Drainage
pattern
recognition
is
crucial
for
geospatial
understanding
and
hydrologic
modelling.
Currently,
drainage
methods
employ
geometric
measures
of
overall
local
features
river
networks
but
lack
basin
unit
shape
features,
so
that
potential
correlations
between
segments
are
usually
ignored,
resulting
in
poor
results.
In
order
to
overcome
this
problem,
paper
proposes
a
supervised
graph
neural
network
method
considers
the
networks.
First,
based
on
hierarchy
networks,
confluence
angle
units,
multiple
classification
extracted.
Then,
typical
samples
from
multi-scale
NSDI
USGS
databases
used
complete
training,
validation
testing
steps.
Experimental
results
show
indexes
proposed
can
describe
characteristics
different
patterns.
The
effectively
sample
adjacent
segments,
flexibly
transfer
associated
among
segment
neighbours,
aggregate
deeper
thus
improving
accuracy
relative
other
reliably
distinguishing
