28 research outputs found
Diffusion Models for Interferometric Satellite Aperture Radar
Probabilistic Diffusion Models (PDMs) have recently emerged as a very
promising class of generative models, achieving high performance in natural
image generation. However, their performance relative to non-natural images,
like radar-based satellite data, remains largely unknown. Generating large
amounts of synthetic (and especially labelled) satellite data is crucial to
implement deep-learning approaches for the processing and analysis of
(interferometric) satellite aperture radar data. Here, we leverage PDMs to
generate several radar-based satellite image datasets. We show that PDMs
succeed in generating images with complex and realistic structures, but that
sampling time remains an issue. Indeed, accelerated sampling strategies, which
work well on simple image datasets like MNIST, fail on our radar datasets. We
provide a simple and versatile open-source
https://github.com/thomaskerdreux/PDM_SAR_InSAR_generation to train, sample and
evaluate PDMs using any dataset on a single GPU
Autonomous Detection of Methane Emissions in Multispectral Satellite Data Using Deep Learning
Methane is one of the most potent greenhouse gases, and its short atmospheric
half-life makes it a prime target to rapidly curb global warming. However,
current methane emission monitoring techniques primarily rely on approximate
emission factors or self-reporting, which have been shown to often dramatically
underestimate emissions. Although initially designed to monitor surface
properties, satellite multispectral data has recently emerged as a powerful
method to analyze atmospheric content. However, the spectral resolution of
multispectral instruments is poor, and methane measurements are typically very
noisy. Methane data products are also sensitive to absorption by the surface
and other atmospheric gases (water vapor in particular) and therefore provide
noisy maps of potential methane plumes, that typically require extensive human
analysis. Here, we show that the image recognition capabilities of deep
learning methods can be leveraged to automatize the detection of methane leaks
in Sentinel-2 satellite multispectral data, with dramatically reduced false
positive rates compared with state-of-the-art multispectral methane data
products, and without the need for a priori knowledge of potential leak sites.
Our proposed approach paves the way for the automated, high-definition and
high-frequency monitoring of point-source methane emissions across the world
Autonomous Extraction of Millimeter-scale Deformation in InSAR Time Series Using Deep Learning
Systematic characterization of slip behaviours on active faults is key to
unraveling the physics of tectonic faulting and the interplay between slow and
fast earthquakes. Interferometric Synthetic Aperture Radar (InSAR), by enabling
measurement of ground deformation at a global scale every few days, may hold
the key to those interactions. However, atmospheric propagation delays often
exceed ground deformation of interest despite state-of-the art processing, and
thus InSAR analysis requires expert interpretation and a priori knowledge of
fault systems, precluding global investigations of deformation dynamics. Here
we show that a deep auto-encoder architecture tailored to untangle ground
deformation from noise in InSAR time series autonomously extracts deformation
signals, without prior knowledge of a fault's location or slip behaviour.
Applied to InSAR data over the North Anatolian Fault, our method reaches 2 mm
detection, revealing a slow earthquake twice as extensive as previously
recognized. We further explore the generalization of our approach to
inflation/deflation-induced deformation, applying the same methodology to the
geothermal field of Coso, California
Une longue phase de nucléation identifiée par Machine Learning pour les séismes lents des Cascades
International audienc
An exponential build-up in seismic energy suggests a months-long nucleation of slow slip in Cascadia
International audienceSlow slip events result from the spontaneous weakening of the subduction megathrust and bear strong resemblance to earthquakes, only slower. This resemblance allows us to study fundamental aspects of nucleation that remain elusive for classic, fast earthquakes. We rely on machine learning algorithms to infer slow slip timing from statistics of seismic waveforms. We find that patterns in seismic power follow the 14-month slow slip cycle in Cascadia, arguing in favor of the predictability of slow slip rupture. Here, we show that seismic power exponentially increases as the slowly slipping portion of the subduction zone approaches failure, a behavior that shares a striking similarity with the increase in acoustic power observed prior to laboratory slow slip events. Our results suggest that the nucleation phase of Cascadia slow slip events may last from several weeks up to several months