STanford EArthquake Dataset (STEAD): A global data set of seismic signals for AI
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 …
gaining new insights from seismic data is a rapidly evolving sub-field of seismology. The …
A machine‐learning approach for earthquake magnitude estimation
SM Mousavi, GC Beroza - Geophysical Research Letters, 2020 - Wiley Online Library
In this study, we present a fast and reliable method for end‐to‐end estimation of earthquake
magnitude from raw waveforms recorded at single stations. We design a regressor (MagNet) …
magnitude from raw waveforms recorded at single stations. We design a regressor (MagNet) …
Unsupervised 3-D random noise attenuation using deep skip autoencoder
Effective random noise attenuation is critical for subsequent processing of seismic data,
such as velocity analysis, migration, and inversion. Thus, the removal of seismic random …
such as velocity analysis, migration, and inversion. Thus, the removal of seismic random …
Seismic velocity inversion transformer
Velocity model inversion is one of the most challenging tasks in seismic exploration, and an
accurate velocity model is essential for high-resolution seismic imaging. Recently, velocity …
accurate velocity model is essential for high-resolution seismic imaging. Recently, velocity …
Deep-learning seismic full-waveform inversion for realistic structural models
Velocity model inversion is one of the most important tasks in seismic exploration. Full-
waveform inversion (FWI) can obtain the highest resolution in traditional velocity inversion …
waveform inversion (FWI) can obtain the highest resolution in traditional velocity inversion …
A fully unsupervised and highly generalized deep learning approach for random noise suppression
In this study, we proposed a deep learning algorithm (PATCHUNET) to suppress random
noise and preserve the coherent seismic signal. The input data are divided into several …
noise and preserve the coherent seismic signal. The input data are divided into several …
[HTML][HTML] INSTANCE–the Italian seismic dataset for machine learning
A Michelini, S Cianetti, S Gaviano… - Earth System …, 2021 - essd.copernicus.org
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 …
learning analysis (ML) applications. The dataset consists of nearly 1.2 million three …
StorSeismic: A new paradigm in deep learning for seismic processing
R Harsuko, TA Alkhalifah - IEEE Transactions on Geoscience …, 2022 - ieeexplore.ieee.org
Machine learned tasks on seismic data are often trained sequentially and separately, even
though they utilize the same features (ie, geometrical) of the data. We present StorSeismic …
though they utilize the same features (ie, geometrical) of the data. We present StorSeismic …
Deep learning seismic random noise attenuation via improved residual convolutional neural network
Because a high signal-to-noise ratio (SNR) is beneficial to the subsequent processing
procedures, the noise attenuation is important. We propose an adaptive random noise …
procedures, the noise attenuation is important. We propose an adaptive random noise …
Denoising of distributed acoustic sensing data using supervised deep learning
Distributed acoustic sensing (DAS) is an emerging technology for acquiring seismic data
due to its high-density and low-cost advantages. Because of the harsh acquisition …
due to its high-density and low-cost advantages. Because of the harsh acquisition …