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 …

Recently deep learning (DL), as a new data‐driven technique compared to conventional
approaches, has attracted increasing attention in geophysical community, resulting in many …

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 …

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 …

Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise
above certain limits. Over the last years, machine and deep learning techniques have been …

Since the advent of double network (DN) hydrogels nearly 20 years ago, they have
flourished as smart soft materials. Their two unique contrasting interpenetrating network …

The ever‐increasing networks and quantity of seismic data drive the need for seamless and
automatic workflows for rapid and accurate earthquake detection and location. In recent …

An immediate report of the source focal mechanism with full automation after a destructive
earthquake is crucial for timely characterizing the faulting geometry, evaluating the stress …

The Italian earthquake waveform data are collected here in a dataset suited for machine
learning analysis (ML) applications. The dataset consists of nearly 1.2 million three …

In this paper, deep learning models trained with real seismic data are proposed and proven
to detect earthquakes in fiber-optic distributed acoustic sensor (DAS) measurements. The …