Seismic waves from earthquakes and other sources are used to infer the structure and
properties of Earth's interior. The availability of large-scale seismic datasets and the …

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 …

Time series anomaly detection is important for a wide range of research fields and
applications, including financial markets, economics, earth sciences, manufacturing, and …

Earthquake signal detection and seismic phase picking are challenging tasks in the
processing of noisy data and the monitoring of microearthquakes. Here we present a global …

As natural disasters are induced by geodynamic activities or abnormal changes in the
environment, geological hazards tend to wreak havoc on the environment and human …

Plant diseases affect the growth of their respective species, therefore their early identification
is very important. Many Machine Learning (ML) models have been employed for the …

Natural hazards have the potential to cause catastrophic damage and significant
socioeconomic loss. The actual damage and loss observed in the recent decades has …

BACKGROUND The solid Earth, oceans, and atmosphere together form a complex
interacting geosystem. Processes relevant to understanding Earth's geosystem behavior …

Hydraulic fracturing (HF) is a technique that is used for extracting petroleum resources from
impermeable host rocks. In this process, fluid injected under high pressure causes fractures …

This article provides an overview of current applications of machine learning (ML) in
seismology. ML techniques are becoming increasingly widespread in seismology, with …