Results 81 to 90 of about 4,442 (191)

Three-dimensional seismic denoising based on deep convolutional dictionary learning

Results in Applied Mathematics
Dictionary learning (DL) has been widely used for seismic data denoising. However, it is associated with the following challenges. First, learning a dictionary from one dataset cannot be applied to another dataset and requires setting learning and ...
Yuntong Li, Lina Liu
doaj   +1 more source

Seismic Random Noise Attenuation Based on PCC Classification in Transform Domain

IEEE Access, 2020
Random noise attenuation of seismic data is an essential step in the processing of seismic signals. However, as the exploration environment is becoming more and more complicated, the energy of valid signals is weaker and the signal to noise (SNR) is much
Yu Sang, Jinguang Sun, Xiangfu Meng, Haibo Jin, Yanfei Peng, Xinjun Zhang   +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

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

Efficient Seismic Denoising Transformer with Gradient Prediction and Parameter-Free Attention [PDF]

Jisuanji kexue yu tansuo
Suppression of random noise can effectively improve the signal-to-noise ratio (SNR) of seismic data. In recent years, convolutional neural network (CNN)-based deep learning methods have shown significant performance in seismic data denoising.
GAO Lei, QIAO Haowei, LIANG Dongsheng, MIN Fan, YANG Mei
doaj   +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

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

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
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