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 …

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

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 …

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 …

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 …

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

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 …

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 …