Machine learning in volcanology: a review
A volcano is a complex system, and the characterization of its state at any given time is not
an easy task. Monitoring data can be used to estimate the probability of an unrest and/or an …
an easy task. Monitoring data can be used to estimate the probability of an unrest and/or an …
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 …
Predicting future laboratory fault friction through deep learning transformer models
Abstract Machine learning models using seismic emissions as input can predict
instantaneous fault characteristics such as displacement and friction in laboratory …
instantaneous fault characteristics such as displacement and friction in laboratory …
Using template matching to detect hidden fluid release episodes Beneath Crater Lakes in Ruapehu, Copahue, and Kawah Ijen Volcanoes
Volcano crater lakes, while picturesque, can sometimes mask the occurrence of small
eruptions or hydrothermal fluid release events. However, these seemingly hidden events …
eruptions or hydrothermal fluid release events. However, these seemingly hidden events …
Evaluation of short-term probabilistic eruption forecasting at Whakaari, New Zealand
Phreatic explosions at volcanoes are difficult to forecast but can be locally devastating, as
illustrated by the deadly 2019 Whakaari (New Zealand) eruption. Quantifying eruption …
illustrated by the deadly 2019 Whakaari (New Zealand) eruption. Quantifying eruption …
Machine learning predicts the timing and shear stress evolution of lab earthquakes using active seismic monitoring of fault zone processes
Abstract Machine learning (ML) techniques have become increasingly important in
seismology and earthquake science. Lab‐based studies have used acoustic emission data …
seismology and earthquake science. Lab‐based studies have used acoustic emission data …
Tracking volcanic explosions using Shannon entropy at Volcán de Colima
The main objective of this work is to show that Shannon Entropy (SE) calculated on
continuous seismic signals can be used in a volcanic eruption monitoring system. We …
continuous seismic signals can be used in a volcanic eruption monitoring system. We …
[HTML][HTML] Semantic segmentation of explosive volcanic plumes through deep learning
Tracking explosive volcanic phenomena can provide important information for hazard
monitoring and volcano research. Perhaps the simplest forms of monitoring instruments are …
monitoring and volcano research. Perhaps the simplest forms of monitoring instruments are …
Eruption forecasting model for Copahue volcano (southern Andes) using seismic data and machine learning: A joint interpretation with geodetic data (GNSS and …
Anticipating volcanic eruptions remains a challenge despite significant scientific
advancements, leading to substantial human and economic losses. Traditional approaches …
advancements, leading to substantial human and economic losses. Traditional approaches …
Semantically enhanced IoT-oriented seismic event detection: An application to Colima and Vesuvius volcanoes
M Falanga, E De Lauro, S Petrosino… - IEEE Internet of …, 2022 - ieeexplore.ieee.org
Collecting massive seismic signals is a high-priority task in seismic risk evaluation,
especially in densely populated areas, with cases of strong magnitude earthquake …
especially in densely populated areas, with cases of strong magnitude earthquake …