Machine learning for data-driven discovery in solid Earth geoscience
BACKGROUND The solid Earth, oceans, and atmosphere together form a complex
interacting geosystem. Processes relevant to understanding Earth's geosystem behavior …
interacting geosystem. Processes relevant to understanding Earth's geosystem behavior …
[HTML][HTML] Machine learning in microseismic monitoring
The confluence of our ability to handle big data, significant increases in instrumentation
density and quality, and rapid advances in machine learning (ML) algorithms have placed …
density and quality, and rapid advances in machine learning (ML) algorithms have placed …
The precursory phase of large earthquakes
Q Bletery, JM Nocquet - Science, 2023 - science.org
The existence of an observable precursory phase of slip on the fault before large
earthquakes has been debated for decades. Although observations preceding several large …
earthquakes has been debated for decades. Although observations preceding several large …
Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook
In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
Using a physics-informed neural network and fault zone acoustic monitoring to predict lab earthquakes
Predicting failure in solids has broad applications including earthquake prediction which
remains an unattainable goal. However, recent machine learning work shows that laboratory …
remains an unattainable goal. However, recent machine learning work shows that laboratory …
Investigations and new insights on earthquake mechanics from fault slip experiments
Earthquakes occur mainly on active faults. Fault slip is closely related to seismicity and is
thus widely discussed in Geosciences, Seismology, and Engineering. Slip experiment is a …
thus widely discussed in Geosciences, Seismology, and Engineering. Slip experiment is a …
Improving AI system awareness of geoscience knowledge: Symbiotic integration of physical approaches and deep learning
Modeling dynamic geophysical phenomena is at the core of Earth and environmental
studies. The geoscientific community relying mainly on physical representations may want to …
studies. The geoscientific community relying mainly on physical representations may want to …
Big data seismology
The discipline of seismology is based on observations of ground motion that are inherently
undersampled in space and time. Our basic understanding of earthquake processes and our …
undersampled in space and time. Our basic understanding of earthquake processes and our …
Reconstructing Earth's atmospheric oxygenation history using machine learning
Reconstructing historical atmospheric oxygen (O2) levels at finer temporal resolution is a top
priority for exploring the evolution of life on Earth. This goal, however, is challenged by gaps …
priority for exploring the evolution of life on Earth. This goal, however, is challenged by gaps …
Laboratory earthquake forecasting: A machine learning competition
PA Johnson, B Rouet-Leduc… - Proceedings of the …, 2021 - National Acad Sciences
Earthquake prediction, the long-sought holy grail of earthquake science, continues to
confound Earth scientists. Could we make advances by crowdsourcing, drawing from the …
confound Earth scientists. Could we make advances by crowdsourcing, drawing from the …