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 …

We propose to denoise and reconstruct the 3D seismic data simultaneously using an
unsupervised deep learning (DL) framework, which does not require any prior information …

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 …

Deep learning (DL) has achieved promising results for impedance inversion via seismic
data. Generally, these networks, composed of convolution layers and residual blocks, tend …

The attenuation of coherent noise in land seismic data, specifically ground roll and near-
surface scattered energy, remains a longstanding challenge. Although recent advances in …

We design a fully automated system for real‐time magnitude estimation based on a vision
transformer (ViT) network. ViT is an attention mechanisms, which guides the proposed …

We propose to use unsupervised deep learning (DL) and attention networks to mute the
unwanted components of the single-channel earthquake data. The proposed algorithm is an …

Super-resolution reconstruction is an essential task of seismic inversion due to the low
resolution and strong noise of field data. Popular deep networks derived from U-Net lack the …

Random noise attenuation is a crucial task in seismic data processing, which can not only
improve the signal-to-noise ratio (SNR) of seismic data but also facilitate accurate geological …

Hyperspectral image (HSI) denoising is a fundamental task in remote sensing image
processing, which is helpful for HSI subsequent applications, such as unmixing and …