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 …

Anthropogenic operations involving underground fluid extraction or injection can cause
unexpectedly large and even damaging earthquakes, despite operational and regulatory …

In this chapter, we go through the fundamentals of artificial neural networks and deep
learning methods. We describe the inspiration for artificial neural networks and how the …

The confluence of our ability to handle big data, significant increases in instrumentation
density and quality, and rapid advances in machine learning (ML) algorithms have placed …

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

In this paper, a scalable end-to-end tree boosting system called XGBoost has been applied
for predicting the magnitude of an earthquake from the early part of earthquake waveform …

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

A new generation of earthquake catalogs developed through supervised machine-learning
illuminates earthquake activity with unprecedented detail. Application of unsupervised …

In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …

The Corona virus outbreak sped up the process of digitalizing healthcare. The ubiquity of IoT
devices in healthcare has thrust the Healthcare Internet of Things (HIoT) to the forefront as a …