Machine learning for data-driven discovery in solid Earth geoscience
BACKGROUND The solid Earth, oceans, and atmosphere together form a complex
interacting geosystem. Processes relevant to understanding Earth's geosystem behavior …
interacting geosystem. Processes relevant to understanding Earth's geosystem behavior …
Convolutional neural network for earthquake detection and location
The recent evolution of induced seismicity in Central United States calls for exhaustive
catalogs to improve seismic hazard assessment. Over the last decades, the volume of …
catalogs to improve seismic hazard assessment. Over the last decades, the volume of …
Seismic intensity estimation for earthquake early warning using optimized machine learning model
The need for an earthquake early-warning system (EEWS) is unavoidable to save lives. In
terms of managing earthquake disasters and achieving effective risk mitigation, the quick …
terms of managing earthquake disasters and achieving effective risk mitigation, the quick …
A versatile framework to solve the Helmholtz equation using physics-informed neural networks
Solving the wave equation to obtain wavefield solutions is an essential step in illuminating
the subsurface using seismic imaging and waveform inversion methods. Here, we utilize a …
the subsurface using seismic imaging and waveform inversion methods. Here, we utilize a …
A deep learning model for earthquake parameters observation in IoT system-based earthquake early warning
Earthquake early-warning system (EEWS) is inevitable for saving human lives. The fast
determination of the Earthquake's (EQ's) magnitude and its location is significant in disaster …
determination of the Earthquake's (EQ's) magnitude and its location is significant in disaster …
Simulating seismic multifrequency wavefields with the Fourier feature physics-informed neural network
To simulate seismic wavefields with a frequency-domain wave equation, conventional
numerical methods must solve the equation sequentially to obtain the wavefields for different …
numerical methods must solve the equation sequentially to obtain the wavefields for different …
Recognition of geochemical anomalies using a deep autoencoder network
In this paper, we train an autoencoder network to encode and reconstruct a geochemical
sample population with unknown complex multivariate probability distributions. During the …
sample population with unknown complex multivariate probability distributions. During the …
[HTML][HTML] Lithium exploration targeting through robust variable selection and deep anomaly detection: an integrated application of sparse principal component analysis …
Lithium is a strategic metal for high-technology industries that plays a vital role in realizing
electromobility and effective energy storage for smartphones and electric/hybrid vehicles, in …
electromobility and effective energy storage for smartphones and electric/hybrid vehicles, in …
Poststack seismic data denoising based on 3-D convolutional neural network
Deep learning has been successfully applied to image denoising. In this study, we take one
step forward by using deep learning to suppress random noise in poststack seismic data …
step forward by using deep learning to suppress random noise in poststack seismic data …