Results 61 to 70 of about 612 (172)

Interpolation and Denoising of Seismic Data using Convolutional Neural Networks

CoRR, 2019
Seismic data processing algorithms greatly benefit from regularly sampled and reliable data. Therefore, interpolation and denoising play a fundamental role as one of the starting steps of most seismic processing workflows. We exploit convolutional neural networks for the joint tasks of interpolation and random noise attenuation of 2D common shot ...
Sara Mandelli, Vincenzo Lipari, Paolo Bestagini, Stefano Tubaro   +3 more
openaire   +2 more sources

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

Self-Attention Generative Adversarial Network Interpolating and Denoising Seismic Signals Simultaneously

Remote Sensing
In light of the challenging conditions of exploration environments coupled with escalating exploration expenses, seismic data acquisition frequently entails the capturing of signals entangled amidst diverse noise interferences and instances of data loss.
Mu Ding, Yatong Zhou, Yue Chi
doaj   +1 more source

Random Noise Reduction in Seismic Data by Using Bidimensional Empirical Mode Decomposition and Shearlet Transform

IEEE Access, 2019
Due to the limitation of the seismic data acquisition environment and instrument, seismic data are often subjected to random noise interference. At the same time, random noise is inevitably introduced in the processing of seismic data.
Wen-Long Hou, Rui-Sheng Jia, Hong-Mei Sun, Xing-Li Zhang, Meng-Di Deng, Yu Tian   +5 more
doaj   +1 more source

Self-Supervised Seismic Random Noise Suppression With Higher-Quality Training Data Based on Similarity Differences

IEEE Access
Suppressing random noise and improving the signal-to-noise ratio of seismic data holds immense significance for subsequent high-precision processing. As one of the most widely used denoising methods, self-learning-based algorithms typically partition the
Jian Gao, Zhenchun Li, Min Zhang, Wanyue Gao, Yixuan Gao   +4 more
doaj   +1 more source

ePoster

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

Nonlinear Seismic Signal Denoising Using Template Matching with Time Difference Detection Method

Remote Sensing
As seismic exploration shifts towards areas with more complex surface and subsurface structures, the complexity of the geological conditions often results in seismic data with low signal-to-noise ratio. It is therefore essential to implement denoising in
Rongwei Xu, Bo Feng, Huazhong Wang, Chengliang Wu, Zhenbo Nie   +4 more
doaj   +1 more source

Deep Learning-Based Blind Denoising for Distributed Acoustic Sensing Seismic Data With Self-Supervised and Transfer Learning

Photonic Sensors
A distributed acoustic sensing (DAS) technology, extensively utilized in the seabed geological exploration, ocean current analysis, and marine seismic monitoring, faces challenges due to the presence of various noise types in sensing signals, which ...
Tianrui LI, Zhengyong LIU, Qi SUI, Chao LU, Jun HAN, Shaoyi CHEN, Zhaohui LI   +6 more
doaj   +1 more source

Research on High-Density Discrete Seismic Signal Denoising Processing Method Based on the SFOA-VMD Algorithm

Geosciences
With the increasing demand for precision in seismic exploration, high-resolution surveys and shallow-layer identification have become essential. This requires higher sampling frequencies during seismic data acquisition, which shortens seismic wavelengths
Xiaoji Wang, Kai Lin, Guangzhao Guo, Xiaotao Wen, Dan Chen   +4 more
doaj   +1 more source