STanford EArthquake Dataset (STEAD): A global data set of seismic signals for AI

SM Mousavi, Y Sheng, W Zhu, GC Beroza - IEEE Access, 2019 - ieeexplore.ieee.org
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 …

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) …

Unsupervised 3-D random noise attenuation using deep skip autoencoder

L Yang, S Wang, X Chen, OM Saad… - … on Geoscience and …, 2021 - ieeexplore.ieee.org
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 …

Seismic velocity inversion transformer

H Wang, J Lin, X Dong, S Lu, Y Li, B Yang - Geophysics, 2023 - library.seg.org
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 …

Deep-learning seismic full-waveform inversion for realistic structural models

B Liu, S Yang, Y Ren, X Xu, P Jiang, Y Chen - Geophysics, 2021 - library.seg.org
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 …

A fully unsupervised and highly generalized deep learning approach for random noise suppression

OM Saad, Y Chen - Geophysical Prospecting, 2021 - earthdoc.org
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 …

[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 …

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 …

Deep learning seismic random noise attenuation via improved residual convolutional neural network

L Yang, W Chen, H Wang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
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 …

Denoising of distributed acoustic sensing data using supervised deep learning

L Yang, S Fomel, S Wang, X Chen, W Chen, OM Saad… - Geophysics, 2023 - library.seg.org
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 …