Results 71 to 80 of about 4,442 (191)

A Physics-Informed Neural Network Framework for Seismic Signal Denoising Based on Time–Frequency Adaptive Decomposition

Applied Sciences
Seismic signal denoising stands as a vital process that enables precise seismic data analysis because noise interference blocks the detection of weak but valuable seismic signals.
Qinghua Zhang, Miantao Zhang, Houle Zhang, Yongxin Wu, Yanjie Zhang   +4 more
doaj   +1 more source

An Effective Denoising Method Based on Cumulative Distribution Function Thresholding and its Application in the Microseismic Signal of a Metal Mine With High Sampling Rate (6 kHz)

Frontiers in Earth Science, 2022
Microseismic events can be used to analyze the risk of tunnel collapse, rock burst, and other mine hazards in space and time. In practice, the artificial activities and other signals at the mining site can seriously interfere with the microseismic ...
Da Zhang, Da Zhang, Da Zhang, Zhiyi Zeng, Zhiyi Zeng, Zhiyi Zeng, Yaqian Shi, Yaqian Shi, Yaqian Shi, Ying Chang, Ying Chang, Ying Chang, Rui Dai, Rui Dai, Rui Dai, Hu Ji, Hu Ji, Hu Ji, Peng Han, Peng Han, Peng Han   +20 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

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

Cold Diffusion Model for Seismic Denoising

Journal of Geophysical Research: Machine Learning and Computation
AbstractSeismic waves contain information about the earthquake source, the geologic structure they traverse, and many forms of noise. Separating the noise from the earthquake is a difficult task because optimal parameters for filtering noise typically vary with time and, if chosen inappropriately, may strongly alter the original seismic waveform ...
Daniele Trappolini, Laura Laurenti, Giulio Poggiali, Elisa Tinti, Fabio Galasso, Alberto Michelini, Chris Marone   +6 more
openaire   +1 more source

Seismic data denoising and deblending using deep learning

CoRR, 2019
An important step of seismic data processing is removing noise, including interference due to simultaneous and blended sources, from the recorded data. Traditional methods are time-consuming to apply as they often require manual choosing of parameters to obtain good results.
Alan Richardson, Caelen Feller
openaire   +2 more sources

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