Journal of Geophysical Research Earth Surface,
Год журнала:
2024,
Номер
129(11)
Опубликована: Окт. 31, 2024
Abstract
In
recent
years,
Synthetic
Aperture
Radar
Interferometry
(InSAR)
has
become
widely
utilized
for
slow‐moving
landslide
monitoring
due
to
its
high
resolution,
accuracy,
and
extensive
coverage.
By
integrating
data
from
various
orbits/platforms
sources,
one‐dimensional
(1‐D)
line‐of‐sight
(LOS)
InSAR
measurements
can
be
explored
infer
three‐dimensional
(3‐D)
movements.
However,
inconsistencies
in
observation
times
among
different
orbits
sources
pose
challenges
accurately
capturing
dynamic
3‐D
movements
over
time
(referred
as
4‐D).
this
study,
we
propose
a
novel
method,
termed
KFI‐4D
that
incorporates
spatiotemporal
constraints
into
the
traditional
Kalman
filter.
This
enhancement
transforms
underdetermined
problem
of
4‐D
movement
acquisition
parameter
estimation
problem,
enabling
precise
The
method
was
evaluated
using
both
synthetic
sets
real
Hooskanaden
landslide,
demonstrating
an
improvement
exceeding
50%
root
mean
square
errors
(RMSEs)
compared
conventional
methods.
Additionally,
high‐resolution
characteristics
InSAR‐derived
allow
analysis
strain
invariants,
providing
insights
block
interactions
dynamics.
Our
findings
reveal
invariants
effectively
indicate
distribution
activity
blocks
slip
surfaces
well
their
response
triggers.
Notably,
abnormal
signals
identified
prior
catastrophic
event
at
suggest
potential
early
warning
landslides.
future
integration
advanced
satellites,
such
NISAR,
ALOS4
PALSAR3,
Sentinel‐1C,
is
expected
further
enhance
method's
capabilities,
improving
temporal
resolution
monitoring.
Frontiers in Earth Science,
Год журнала:
2025,
Номер
13
Опубликована: Июнь 3, 2025
Landslide
deformation
prediction
is
a
crucial
task
in
geotechnical
engineering
and
disaster
prevention.
Developing
an
accurate
reliable
landslide
displacement
model
vital
for
effective
warning
systems.
This
paper
proposes
TCN-Multihead-Attention
based
on
temporal
convolutional
networks
(TCNs).
We
collected
8
years
of
monitoring
data
from
the
Huangniba
Dengkan
Three
Gorges
Reservoir
area,
including
surface
(horizontal
elevation),
rainfall,
reservoir
levels.
A
comprehensive
analysis
was
conducted
to
assess
effects
levels,
elevation
horizontal
displacement.
Utilizing
multi-input
single-output
characteristics
long-period
time
series
dataset,
we
developed
deformation.
Model
evaluation
demonstrated
that
coefficient
determination
(R
2
)
test
set
reached
0.995,
with
MAPE
RMSE
at
only
0.482
7.180,
respectively,
indicating
high
accuracy.
Additionally,
other
models
single
TCN,
Attention-based
Transformer,
RNN-based
LSTM,
hybrid
CNN-BiLSTM
comparison.
Compared
existing
models,
integrates
dilated
causal
convolutions
TCN
multi-head
attention
effectively
fuse
nonlinear
interactions
multi-source
environmental
factors,
capture
long-term
evolutionary
trends,
accurately
identify
local
mutation
patterns,
demonstrating
superior
reliability
forecasting
regions.
Journal of Geophysical Research Earth Surface,
Год журнала:
2024,
Номер
129(11)
Опубликована: Окт. 31, 2024
Abstract
In
recent
years,
Synthetic
Aperture
Radar
Interferometry
(InSAR)
has
become
widely
utilized
for
slow‐moving
landslide
monitoring
due
to
its
high
resolution,
accuracy,
and
extensive
coverage.
By
integrating
data
from
various
orbits/platforms
sources,
one‐dimensional
(1‐D)
line‐of‐sight
(LOS)
InSAR
measurements
can
be
explored
infer
three‐dimensional
(3‐D)
movements.
However,
inconsistencies
in
observation
times
among
different
orbits
sources
pose
challenges
accurately
capturing
dynamic
3‐D
movements
over
time
(referred
as
4‐D).
this
study,
we
propose
a
novel
method,
termed
KFI‐4D
that
incorporates
spatiotemporal
constraints
into
the
traditional
Kalman
filter.
This
enhancement
transforms
underdetermined
problem
of
4‐D
movement
acquisition
parameter
estimation
problem,
enabling
precise
The
method
was
evaluated
using
both
synthetic
sets
real
Hooskanaden
landslide,
demonstrating
an
improvement
exceeding
50%
root
mean
square
errors
(RMSEs)
compared
conventional
methods.
Additionally,
high‐resolution
characteristics
InSAR‐derived
allow
analysis
strain
invariants,
providing
insights
block
interactions
dynamics.
Our
findings
reveal
invariants
effectively
indicate
distribution
activity
blocks
slip
surfaces
well
their
response
triggers.
Notably,
abnormal
signals
identified
prior
catastrophic
event
at
suggest
potential
early
warning
landslides.
future
integration
advanced
satellites,
such
NISAR,
ALOS4
PALSAR3,
Sentinel‐1C,
is
expected
further
enhance
method's
capabilities,
improving
temporal
resolution
monitoring.