Results 51 to 60 of about 612 (172)

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

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

GeoFWI: A Large Velocity Model Data Set for Benchmarking Full Waveform Inversion Using Deep Learning

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract Full waveform inversion (FWI) plays an increasingly important role in the field of seismic imaging due to its strong ability to estimate subsurface properties. Specifically, data‐driven FWI (DDFWI) establishes a straightforward mapping relationship between seismic data and the corresponding velocity model, yielding promising results.
Chao Li, Yiran Shen, Sergey Fomel, Umair Bin Waheed, Alexandros Savvaidis, Yangkang Chen   +5 more
wiley   +1 more source

Research on Precise Identification of Rock Strength Based on Bolt Drilling Parameters

Energy Science &Engineering, Volume 14, Issue 3, Page 1353-1371, March 2026.
Drilling detection test platform. ABSTRACT During roadway excavation, the presence of weak interlayers and fractured rock masses significantly affects roof stability. To achieve timely and effective roadway support, it is crucial to identify and predict different rock types based on drilling signals from roof bolters.
Qiang Zhu, Sheng Biao Wang, Da Jiang Wen, Chong‐Yu Song, Kang Zhou   +4 more
wiley   +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

Deglitching Martian Seismic Data: Application to Marsquake Detection

Earth and Space Science, Volume 13, Issue 3, March 2026.
Abstract NASA's InSight mission investigates the interior structure of Mars. The data is characterized by multiple non‐seismic signals with varying attributes, including high‐energy instrumental noise, known as glitches, which frequently exhibit large linear polarization.
Jair Zampieri, Juan I. Sabbione, Danilo R. Velis   +2 more
wiley   +1 more source

Suppressing random noise in seismic signals using wavelet thresholding based on improved chaotic fruit fly optimization

EURASIP Journal on Advances in Signal Processing
Suppressing random noise in seismic signals is an important issue in research on processing seismic data. Such data are difficult to interpret because seismic signals usually contain a large amount of random noise.
Feng Yang, Jun Liu, Qingming Hou, Lu Wu
doaj   +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

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