Deep-learning seismology
SM Mousavi, GC Beroza - Science, 2022 - science.org
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 …
properties of Earth's interior. The availability of large-scale seismic datasets and the …
Deep denoising autoencoder for seismic random noise attenuation
Attenuation of seismic random noise is considered an important processing step to enhance
the signal-to-noise ratio of seismic data. A new approach is proposed to attenuate random …
the signal-to-noise ratio of seismic data. A new approach is proposed to attenuate random …
Applications of deep neural networks in exploration seismology: A technical survey
Exploration seismology uses reflected and refracted seismic waves, emitted from a
controlled (active) source into the ground, and recorded by an array of seismic sensors …
controlled (active) source into the ground, and recorded by an array of seismic sensors …
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 …
Denoising deep learning network based on singular spectrum analysis—DAS seismic data denoising with multichannel SVDDCNN
Q Feng, Y Li - IEEE Transactions on Geoscience and Remote …, 2021 - ieeexplore.ieee.org
Distributed acoustic sensing (DAS) is a new tool with low cost, sensitive signal capture, and
complete coverage for vertical seismic profile (VSP) acquisition. Although DAS has obvious …
complete coverage for vertical seismic profile (VSP) acquisition. Although DAS has obvious …
DeepSeg: Deep segmental denoising neural network for seismic data
N Iqbal - IEEE Transactions on Neural Networks and Learning …, 2022 - ieeexplore.ieee.org
Noise attenuation is a crucial phase in seismic signal processing. Enhancing the signal-to-
noise ratio (SNR) of registered seismic signals improves subsequent processing and …
noise ratio (SNR) of registered seismic signals improves subsequent processing and …
Seismic random noise separation and attenuation based on MVMD and MSSA
Seismic noise separation and attenuation is a fundamental topic in the seismic signal
processing and geological interpretation. Several kinds of algorithms are proposed for …
processing and geological interpretation. Several kinds of algorithms are proposed for …
Unsupervised seismic random noise attenuation based on deep convolutional neural network
Random noise attenuation is one of the most essential steps in seismic signal processing.
We propose a novel approach to attenuate seismic random noise based on deep …
We propose a novel approach to attenuate seismic random noise based on deep …
Random noise attenuation based on residual convolutional neural network in seismic datasets
Seismic random noise attenuation is a key step in seismic data processing. The random
seismic data recorded by the detector tends to have strong noise, and this noisy seismic …
seismic data recorded by the detector tends to have strong noise, and this noisy seismic …
Trace-wise coherent noise suppression via a self-supervised blind-trace deep-learning scheme
Seismic data denoising via supervised deep learning is effective and popular but requires
noise-free labels, which are rarely available. Blind-spot networks circumvent this …
noise-free labels, which are rarely available. Blind-spot networks circumvent this …