Results 61 to 70 of about 4,173 (187)

Total variation regularization for manifold-valued data

, 2013
We consider total variation minimization for manifold valued data. We propose a cyclic proximal point algorithm and a parallel proximal point algorithm to minimize TV functionals with $\ell^p$-type data terms in the manifold case.
Demaret, Laurent, Storath, Martin, Weinmann, Andreas   +2 more
core   +1 more source

Distributed Acoustic Sensing Denoising Using a Self‐supervised Conditional Diffusion Model

Geophysical Prospecting, Volume 74, Issue 3, March 2026.
ABSTRACT Distributed acoustic sensing (DAS) data are characterized by a low signal‐to‐noise ratio due to the complex noise present in its challenging operational environment. To enhance the quality of the DAS data, we propose a self‐supervised diffusion model to attenuate the DAS noise.
Omar M. Saad, Tariq Alkhalifah
wiley   +1 more source

Self-adaptive seismic data reconstruction and denoising using dictionary learning based on morphological component analysis

Frontiers in Earth Science, 2023
Data reconstruction and data denoising are two critical preliminary steps in seismic data processing. Compressed Sensing states that a signal can be recovered by a series of solving algorithms if it is sparse in a transform domain, and has been well ...
De-Ying Wang, Xing-Rong Xu, Hua-Hui Zeng, Jia-Qing Sun, Xin Xu, Yi-Kui Zhang   +5 more
doaj   +1 more source

A Continuum of Slow Slip Events in the Cascadia Subduction Zone Illuminated by High‐Resolution Deep‐Learning Denoising

Geophysical Research Letters, Volume 53, Issue 3, 16 February 2026.
Abstract Slow, aseismic fault slip has emerged as a significant contributor to the seismic cycle. However, whether slow and fast slip arise from similar physical processes remains unresolved, due to detection biases affecting noisy surface measurements and the analysis of the source properties of slow slip.
Giuseppe Costantino, Mathilde Radiguet, Zaccaria El Yousfi, Anne Socquet   +3 more
wiley   +1 more source

Multifractional splines: from seismic singularities to geological transitions [PDF]

, 2002
A matching pursuit technique in conjunction with an imaging method is used to obtain quantitative information on geological records from seismic data. The technique is based on a greedy, non-linear search algorithm decomposing data into atoms.
de Hoop, Martijn V., Herrmann, Felix
core  

Synthetic Electrocardiogram Spectrogram Generation Using Generative Adversarial Network‐Based Models: A Comparative Study

Advanced Intelligent Systems, Volume 8, Issue 2, February 2026.
Cardiovascular diseases are leading death causes; electrocardiogram (ECG) analysis is slow, motivating machine learning and deep learning. This study compares deep convolutional generative adversarial network, conditional GAN, and Wasserstein GAN with gradient penalty (WGAN‐GP) for synthetic ECG spectrograms; Fréchet Inception Distance (FID) and ...
Giovanny Barbosa‐Casanova, Norelli Schettini, Winston Percybrooks, Begoña García‐Zapirain   +3 more
wiley   +1 more source

Seismic random noise attenuation using modified wavelet thresholding

Annals of Geophysics, 2017
In seismic exploration, random noise deteriorates the quality of acquired data. This study analyzed existing denoising methods used in seismic exploration from the perspective of random noise. Wavelet thresholding offers a new approach to reducing random
Qi-sheng Zhang, Jin-juan Jiang, Jin-hai Zhai, Xin-yue Zhang, Yi-jun Yuan, Xin-wu Huang   +5 more
doaj   +1 more source

Toward Systematic Modeling of Volcano Deformation Sources Using Automatically‐Generated InSAR Products

Journal of Geophysical Research: Solid Earth, Volume 131, Issue 2, February 2026.
Abstract Volcano deformation measured through Interferometric Synthetic Aperture Radar (InSAR) is ideal for volcano monitoring in many regions due to its global coverage, characteristic spatio‐temporal patterns, and modeling insights. Routinely acquired and processed Sentinel‐1 InSAR datacubes provide the first opportunity to systematically catalog ...
B. Ireland, J. Biggs, N. Anantrasirichai, F. Albino, E. W. Dualeh   +4 more
wiley   +1 more source

Learning Wave Scattering Properties From Seismograms

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract Heterogeneities in the Earth's crust scatter seismic waves at many scales, trapping seismic energy and producing coda waves that encode valuable information on geological structures. In regions such as volcanoes and fault systems, analyzing coda waves is essential for characterizing non‐uniform subsurface heterogeneity, improving ...
Reza Esfahani, Mirko Bracale, Léonard Seydoux, Michel Campillo   +3 more
wiley   +1 more source

Should all Noises Be Treated Equally: Impact of Input Noise Variability on Neural Network Robustness

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract Geophysical data collected from active field sites are often contaminated by complex and heterogeneous noise, obscuring weak seismic events, and complicating automated interpretation. Although deep learning offers promising solutions for seismic processing, its performance is highly sensitive to the nature of training noise, especially under ...
S. Alsinan, M. Makarenko, S. Liu, A. Aldawood, I. Hoteit   +4 more
wiley   +1 more source