In recent years, Earth system sciences are urgently calling for innovation on improving
accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in …

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

Seismology is a data rich and data-driven science. Application of machine learning for
gaining new insights from seismic data is a rapidly evolving sub-field of seismology. The …

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 …

Efficient handling and planning for the urban regions' sustainable development require a
vast range of up-to-date and thematic information. Besides, obtaining an uncontaminated …

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 …

A primary goal of volcanology is forecasting hazardous eruptive activity. Despite much
progress over the last century, however, volcanoes still erupt with no detected precursors …

Robust event detection and picking is a prerequisite for reliable (micro-) seismic
interpretations. Detection of weak events is a common challenge among various available …

Deep neural networks (DNNs) could help to identify the internal sources of volcano-seismic
events. However, direct applications of DNNs are challenging, given the multiple seismic …

This paper introduces recurrent neural networks (RNN), long short-term memory (LSTM),
and gated recurrent unit (GRU) to detect and classify continuous sequences of volcano …