Results 71 to 80 of about 4,173 (187)

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

Earthquake Source Depth Determination Using Single Station Waveforms and Deep Learning

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract In areas with limited station coverage, earthquake depth constraints are much less accurate than their latitude and longitude. Traditional travel‐time‐based location methods struggle to constrain depths due to imperfect station distribution and the strong trade‐off between source depth and origin time.
Wenda Li, Miao Zhang
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

Synthetic Geology: Structural Geology Meets Deep Learning

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract Reconstructing the structural geology and mineral composition of the first few kilometers of the Earth's subsurface from sparse or indirect surface observations remains a long‐standing challenge with critical applications in mineral exploration, geohazard assessment, and geotechnical engineering.
Simon Ghyselincks, Valeriia Okhmak, Stefano Zampini, George Turkiyyah, David Keyes, Eldad Haber   +5 more
wiley   +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   +2 more sources

Revealing Blind Faults Through High‐Resolution Imaging of Shallow Structures: A Case Study on Chenghai Fault, Yunnan, China

Geophysical Research Letters, Volume 53, Issue 1, 16 January 2026.
Abstract Blind faults pose significant seismic hazards because they remain hidden beneath the surface and are often unrecognized until they generate large earthquakes. High‐resolution shallow velocity models are essential for imaging these blind structures.
Lei Qin, Hongrui Qiu, Hongfeng Yang, Fenglin Niu, Nori Nakata, Xiong Xiong   +5 more
wiley   +1 more source

Effect of Land Cover Type on 3D Deformation Recovery From Synthetically Deformed High Resolution Satellite Optical Imagery

Earth and Space Science, Volume 13, Issue 1, January 2026.
Abstract The limits of detection for earthquake surface deformation in the spatial domain have improved with advances in remote sensing imagery data availability, resolution, and analysis. Sub‐pixel correlation and digital elevation model (DEM) differencing from sub‐meter, earthquake‐spanning satellite optical imagery has enhanced surface rupture ...
C. Hanagan, S. B. DeLong, N. G. Reitman
wiley   +1 more source

Blind Curvelet based Denoising of Seismic Surveys in Coherent and Incoherent Noise Environments

, 2018
The localized nature of curvelet functions, together with their frequency and dip characteristics, makes the curvelet transform an excellent choice for processing seismic data.
AlRegib, Ghassan, Deriche, Mohamed, Iqbal, Naveed   +2 more
core  

Analysis of High Frequency Marsquake Swarms Informed by Deep Learning

Journal of Geophysical Research: Planets, Volume 131, Issue 1, January 2026.
Abstract NASA's InSight mission has provided an unprecedented snapshot of Mars' seismicity, despite data analysis challenges arising from low signal‐to‐noise ratios (SNR) and single‐station constraints. High frequency (HF) events—the most common type—were initially assumed to propagate through shallow crustal layers.
Nikolaj L. Dahmen, John F. Clinton, Simon C. Stähler, Savas Ceylan, Domenico Giardini   +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