Results 61 to 70 of about 672 (170)

Investigating the Detectability of Body Wave Phases From Tidal Ice Cracking Events on Titan With the Dragonfly Short‐Period Seismometer

Journal of Geophysical Research: Planets, Volume 131, Issue 4, April 2026.
Abstract Detecting seismic activity on Saturn's icy moon Titan during the Dragonfly mission could provide crucial information on its internal structure. The geological complexity of the moon's surface suggests significant cyclic tidal deformation, likely leading to the fracturing of the ice shell.
L. Delaroque, T. Kawamura, A. Lucas, S. Rodriguez, K. Onodera, H. Shiraishi, R. Yamada, S. Tanaka, M. P. Panning, R. D. Lorenz   +9 more
wiley   +1 more source

FocoNet: Transformer‐Based Focal‐Mechanism Determination

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract Traditional focal‐mechanism determination primarily relies on fitting the first‐motion polarities with grid‐search algorithms. We developed a machine‐learning model, FocoNet, to include more seismic information into focal mechanism determination.
Xiaohan Song, Men‐Andrin Meier, William L. Ellsworth, Gregory C. Beroza   +3 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

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   +3 more sources

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

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

ePoster

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