Machine learning (ML) is a collection of methods used to develop understanding and
predictive capability by learning relationships embedded in data. ML methods are becoming …

The observation of microseismicity has raised the interest of the seismological and
geoengineering communities in the last decades, and a significant effort has been spent to …

Source localization in ocean acoustics is posed as a machine learning problem in which
data-driven methods learn source ranges directly from observed acoustic data. The pressure …

We develop an automated strategy for discriminating deep microseismic events from
shallow ones on the basis of the waveforms recorded on a limited number of surface …

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 …

Modern acquisition of seismic data on receiver networks worldwide produces an increasing
amount of continuous wavefield recordings. In addition to manual data inspection …

Variations in the spectral content of volcano seismicity related to changes in volcanic activity
are commonly identified manually in spectrograms. However, long time series of monitoring …

Each year worldwide, more and more data is collected. In fact, it is estimated that the amount
of data collected and stored at least doubles every 2years. Of this data, a large percentage is …

Nowadays, an increasing amount of seismic data is collected by daily observatory routines.
The basic step for successfully analyzing those data is the correct detection of various event …

Volcanic eruptions are often preceded by seismic activity that can be used to quantify the
volcanic activity. In order to allow consistent inference of the volcanic activity state from the …