Results 51 to 60 of about 1,182 (196)
Design &implementation of complex-valued FIR digital filters with application to migration of seismic data [PDF]
, 2006 One-dimensional (I-D) and two-dimensional (2-D) frequency-space seismic migration FIR digital filter coefficients are of complex values when such filters require special space domain as well as wavenumber domain characteristics. In this thesis, such FIR
Mousa, Wail Abdul-Hakim
core
Unsupervised Multi-Stage Deep Learning Network for Seismic Data Denoising [PDF]
Seismic data denoising plays an essential role at various stages of the seismic processing workflow. However, it is always a challenge to find the right balance between preserving the seismic signals and attenuating the seismic noise.
Ravasi, Matteo +2 more
core +1 more source
Gabor-based learnable sparse representation for self-supervised denoising [PDF]
, 2023 Traditional supervised denoising networks learn network weights through “black box” (pixel-oriented) training, which requires clean training labels. The inability of such denoising networks to interpret their behavior and the requirement for clean data ...
Cheng, Shijun +2 more
core +1 more source
Transient Porosity During Fluid‐Mineral Interaction, Part 2: Reconstruction Using Generative AI
Journal of Geophysical Research: Solid Earth, Volume 131, Issue 6, June 2026.Abstract Quantifying fluid–rock interactions within the lithosphere is vital for both geological processes and applications such as CO2 ${\text{CO}}_{2}$ storage and geothermal energy development. Mineral replacement reactions generate transient pore networks that enhance fluid flow, yet many pores become isolated once reactions are completed, reducing
Hamed Amiri +5 more
wiley +1 more source
StorSeismic: A new paradigm in deep learning for seismic processing [PDF]
, 2022 Machine learned tasks on seismic data are often trained sequentially and separately, even though they utilize the same features (i.e. geometrical) of the data.
Alkhalifah, Tariq +2 more
core +1 more source
Journal of Petroleum Exploration and Production TechnologyDenoising micro-seismic signals is paramount for ensuring reliable data for localizing mining-related seismic events and analyzing the state of rock masses during mining operations.
Jianxian Cai +4 more
doaj +1 more source
Water Resources Research, Volume 62, Issue 6, June 2026.Abstract Generative adversarial networks (GANs) have proven effective in simulating complex reservoir environments, such as meandering channels and deltas. In classic GANs, the dimensionality of training data determines that of generated data: a 2D (or 3D) reservoir facies simulator (generator) requires training with corresponding 2D (or 3D) data sets.
Xun Hu +4 more
wiley +1 more source
Self-supervised multi-stage deep learning network for seismic data denoising [PDF]
Seismic data denoising is a critical process usually applied at various stages of the seismic processing workflow, as our ability to mitigate noise in seismic data affects the quality of our subsequent analyses.
Ravasi, Matteo +2 more
core +1 more source
A Novel Approach to Train Self-Supervised Seismic Denoising Dnn Architectures [PDF]
, 2022 Removing noise present in seismic data is of prime importance for seismic processing workflows and a matter of continuous research in the academic community.
Oikonomou, Dimitrios +2 more
core +1 more source
Random Noise Attenuation Based on Residual Convolutional Neural Network in Seismic Datasets
IEEE Access, 2020 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 ratio can be seen as a low signal-to-noise ratio (SNR).
Liuqing Yang +4 more
doaj +1 more source