Natural Resources Research,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 18, 2024
Abstract
The
surging
demand
for
Ni
and
Co,
driven
by
the
acceleration
of
clean
energy
transitions,
has
sparked
interest
in
Lachlan
Orogen
New
South
Wales
its
potential
lateritic
Ni–Co
resources.
Despite
recent
discoveries,
a
substantial
knowledge
gap
exists
understanding
full
scope
these
critical
metals
this
geological
province.
This
study
employed
machine
learning-based
framework,
integrating
multidimensional
datasets
to
create
prospectivity
maps
deposits
within
specific
segment.
framework
generated
variety
data-driven
models
incorporating
(rock
units,
metamorphic
facies),
structural,
geophysical
(magnetics,
gravity,
radiometrics,
remote
sensing
spectroscopy)
data
layers.
These
ranged
from
comprehensive
that
use
all
available
layers
fine-tuned
restricted
high-ranking
features.
Additionally,
two
hybrid
(knowledge-data-driven)
distinguished
between
hypogene
supergene
components
mineral
systems.
implemented
augmentation
methods
tackled
imbalances
training
samples
using
SMOTE–GAN
method,
addressing
common
learning
challenges
with
sparse
data.
overcame
difficulties
defining
negative
translating
into
proxy
employing
positive
unlabeled
bagging
technique.
revealed
robust
spatial
correlation
high
probabilities
known
occurrences,
projecting
extensions
sites
identifying
greenfield
areas
future
exploration
Orogen.
high-accuracy
developed
utilizing
Random
Forest
classifier
enhanced
mineralization
processes
promising
region.
Mining,
Journal Year:
2025,
Volume and Issue:
5(1), P. 5 - 5
Published: Jan. 6, 2025
Automation
is
increasingly
gaining
attention
as
the
global
industry
moves
toward
intelligent,
unmanned
approaches
to
perform
hazardous
tasks.
Although
integration
of
autonomous
technologies
has
revolutionized
various
industries
for
decades,
mining
sector
only
recently
started
harness
potential
technology.
Lately,
been
transforming
by
implementing
automated
systems
shape
future
and
minimize
human
involvement
in
process.
Automated
such
robotics,
artificial
intelligence
(AI),
Industrial
Internet
Things
(IIOT),
data
analytics
have
contributed
immensely
towards
ensuring
improved
productivity
safety
promoting
sustainable
mineral
industry.
Despite
substantial
benefits
promising
automation
sector,
its
adoption
faces
challenges
due
concerns
about
human–machine
interaction.
This
paper
extensively
reviews
current
trends,
attempts,
trials
converting
traditional
machines
with
no
or
less
involvement.
It
also
delves
into
application
AI
operations
from
exploration
phase
processing
stage.
To
advance
knowledge
base
this
domain,
study
describes
method
used
develop
interface
(HMI)
that
controls
monitors
activity
a
six-degrees-of-freedom
robotic
arm,
roof
bolter
machine,
status
machine.
The
notable
findings
draw
critical
roles
humans
operations.
shows
operators
are
still
relevant
must
control,
operate,
maintain
these
innovative
Thus,
establishing
an
effective
interaction
between
can
promote
acceptability
implementation
extraction
processes.
Science Advances,
Journal Year:
2024,
Volume and Issue:
10(39)
Published: Sept. 27, 2024
Global
demand
for
lithium,
the
primary
component
of
lithium-ion
batteries,
greatly
exceeds
known
supplies,
and
this
imbalance
is
expected
to
increase
as
world
transitions
away
from
fossil
fuel
energy
sources.
High
concentrations
lithium
in
brines
have
been
observed
Smackover
Formation
southern
Arkansas
(>400
milligrams
per
liter).
We
used
published
newly
collected
brine
concentration
data
train
a
random
forest
machine-learning
model
using
geologic,
geochemical,
temperature
explanatory
variables
create
map
predicted
across
Arkansas.
Using
these
maps
with
reservoir
parameters
geologic
information,
we
calculated
that
there
are
5.1
19
million
tons
Arkansas,
which
represents
35
136%
current
US
resource
estimate.
Based
on
calculations,
2022,
5000
dissolved
were
brought
surface
within
waste
streams
oil,
gas,
bromine
industries.
Tectonics,
Journal Year:
2025,
Volume and Issue:
44(3)
Published: March 1, 2025
Abstract
The
discovery
of
new
economic
copper
deposits
is
critical
for
the
development
renewable
energy
infrastructure
and
zero‐emissions
transport.
majority
existing
mines
are
located
within
current
or
extinct
continental
arc
systems,
but
our
understanding
tectonic
geodynamic
conditions
favoring
formation
porphyry
systems
still
incomplete.
Traditionally,
exploration
criteria
based
on
present‐day
geological
geophysical
observations
rather
than
time‐dependent
evolution
subduction
systems.
Addressing
this
knowledge
gap,
study
connects
particularly
enriched
in
copper,
with
zone
evolution,
utilizing
machine
learning
a
spatio‐temporal
mineral
prospectivity
framework.
Incorporating
Cenozoic
intrusion‐related
copper‐gold
New
Guinea
Solomon
Islands
region,
we
develop
model
that
accurately
predicts
known
occurrences
identifies
key
features
potential
mineralization
area.
Key
findings
include
importance
obliquity
angle
subduction,
which
significantly
affects
strain
partitioning,
crustal
fluid
flow,
ore
deposition,
angles
between
10
50°
favored
mineralization.
Furthermore,
rapid
plate
convergence
seafloor
spreading
half‐rates
ranging
from
30
to
45
mm/yr
potentially
enhance
prospects
by
promoting
metasomatism
hydrous
melting.
This
approach,
integrating
motion
models
learning,
provides
criteria,
enhancing
mechanisms
guiding
future
both
active
abandoned
zones.
