Results 91 to 100 of about 1,182 (196)

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

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

Detecting changes between a DSM and a high resolution SAR image with the support of simulation based separation of urban scenes [PDF]

, 2012
As a SAR image is often the only available data in crisis situations, e.g. after an earthquake, a change analysis of the SAR image with previously acquired data may enable a fast analysis of the damage caused by the disas-ter.
Reinartz, Peter, Auer, Stefan, Tao, Junyi   +2 more
core  

StorSeismic Deep Learning Paradigm for Seismic Processing: Attention is All What a Seismic Dataset Needs [PDF]

Seismic datasets exhibit distinct characteristics from subsurface properties, survey parameters, and noise conditions. Capturing these details in a pre-trained neural network forms the foundation for stream-lined applications in seismic processing ...
Harsuko, R., Alkhalifah, Tariq Ali
core   +1 more source

Geophysical data denoising using dictionary learning method with Ramanujan sums for oil and minerals exploration

Artificial Intelligence in Geosciences
Denoising is an important preprocessing step in seismic exploration that improves the signal-to-noise ratio (SNR) and helps identify oil and minerals. Dictionary learning (DL) is a promising method for noise attenuation.
Lakshmi Kuruguntla, Mamatha Bandaru, Dokku Tejaswi, Anup Kumar Mandpura, Sravan Kumar Sikhakoli, Vineela Chandra Dodda   +5 more
doaj   +1 more source

Simultaneous multi-component seismic denoising and reconstruction via K-SVD [PDF]

, 2018
Data denoising and reconstruction play an increasingly significant role in seismic prospecting for their value in enhancing effective signals, dealing with surface obstacles and reducing acquisition costs.
Sian Hou, Zhao, Qiang, Feng Zhang, Qiang Zhao, Hengchang Dai, Xiangyang Li, Dai, Hengchang, Hou, Sian, Zhang, Feng, Li, Xiangyang   +9 more
core   +1 more source

ePoster

European Journal of Neurology, Volume 33, Issue S1, June 2026.
wiley   +1 more source

Collaborative denoising network for blind separation of seismic data: denoising of seismic signals under urban multi-source noise

Measurement Science and Technology
Abstract With the rapid development of technology and the growth of the global population, the development of above ground space is insufficient to meet the needs of modern society. Therefore, the coordinated development of above ground and underground spaces is crucial for future smart cities.
Yuqi Wang, Xintong Dong, Xunqian Tong, Hongyuan Yang, Linhang Zhang, Huaizhu Zhang   +5 more
openaire   +1 more source

Time–Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks

Applied Sciences
Existing deep learning-based seismic signal denoising methods primarily operate in the time domain. Those methods are ineffective when noise overlaps with the seismic signal in the time domain.
Ming Wei, Xinlei Sun, Jianye Zong
doaj   +1 more source