This review gives an overview of the development of machine learning in geoscience. A
thorough analysis of the codevelopments of machine learning applications throughout the …

Environmental monitoring is a topic of increasing interest, especially concerning the matter
of natural hazards prediction. Regarding volcanic unrest, effective methodologies along with …

The continuously growing amount of seismic data collected worldwide is outpacing our
abilities for analysis, since to date, such datasets have been analyzed in a human-expert …

In earthquake early warning (EEW), every sufficiently impulsive signal is potentially the first
evidence for an unfolding large earthquake. More often than not, however, impulsive signals …

Automatic identification of debris flow signals in continuous seismic records remains a
challenge. To tackle this problem, we use machine learning, which can be applied to …

Surficial mass movements, such as debris avalanches, rock falls, lahars, pyroclastic flows,
and outburst floods, are a dominant hazard at many volcanoes worldwide. Understanding …

Accurate seismic phase arrival time picking is the basis for earthquake location and seismic
travel time tomography. With the increase of seismic stations and the improvement of …

Monitoring the endogenous seismicity of volcanoes helps to forecast eruptions and prevent
their related risks, and also provides critical information on the eruptive processes. Due the …

Continuous seismograms contain a wealth of information with a large variety of signals with
different origin. Identifying these signals is a crucial step in understanding physical …

Earthquake catalogs are essential to analyze the evolution of active fault systems. The
background seismicity rate, or rate of earthquakes that are not directly triggered by other …