Results 61 to 70 of about 382,825 (168)

A Bottom‐Up Design Framework for Multifunctional Lattice Metamaterials

Advanced Science, Volume 13, Issue 26, 8 May 2026.
This study introduces a generative AI framework for designing multifunctional lattice metamaterials. The method combines 3D Gaussian voxel generation with deep learning, enabling greater design freedom and structural performance. The optimized lattice metamaterials achieve enhanced energy absorption by 40–200% compared to conventional structures and ...
Zongxin Hu, Quanqing Tao, Junhao Ding, Shuo Qu, Haitao Ye, Jun Wei Chua, Tianxiao Niu, Rui Li, Winston Wai Shing Ma, Haoming Mo, Hui Liu, Wei Zhai, Xinwei Li, Xu Song   +13 more
wiley   +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

Total Variation Regularized GRACE(‐FO) Inversion

Journal of Geophysical Research: Solid Earth, Volume 131, Issue 5, May 2026.
Abstract Gravity estimation from satellite‐satellite tracking missions such as GRACE(‐FO) is an ill‐posed inverse problem. The conventional approach to regularized inversion of GRACE(‐FO) measurements uses L2 ${L}_{2}$‐Tikhonov regularization with a heuristic constraint matrix derived based on knowledge of spatiotemporal distribution of the signal ...
G. Jacob, S. Bettadpur, H. Save, P. Nagel, F. Wang   +4 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

Fault Volume Digital Twin to Reproduce the Full Slip Spectrum, Scaling, and Statistical Laws

Journal of Geophysical Research: Solid Earth, Volume 131, Issue 5, May 2026.
Abstract Seismological and geodetic observations of fault zones reveal diverse slip dynamics, scaling, and statistical laws. Existing mechanisms explain some but not all of these behaviors. We show that incorporating an off‐fault damage zone—characterized by distributed fractures surrounding a main fault—can reproduce many key features observed in ...
M. Almakari, N. Kheirdast, C. Villafuerte, M. Y. Thomas, P. Dubernet, J. Cheng, A. Gupta, P. Romanet, S. Chaillat, H. S. Bhat   +9 more
wiley   +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

Seismic denoising using curvelet analysis [PDF]

, 2012
A curvelet is a new and effective spectral transform, that allows sparse representations of complex data. It has many applications in several fields, including denoising, wave propagation in disordered media and pattern recognition.
Henriques, M.V.C., Corso, G., M.S. Oliveira, Leite, F.E.A., Oliveira, M.S., L.S. Lucena, M.V.C. Henriques, F.E.A. Leite, G. Corso, Lucena, L.S.   +9 more
core   +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

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

Curvelet denoising of 4d seismic

, 2004
With burgeoning world demand and a limited rate of discovery of new reserves, there is increasing impetus upon the industry to optimize recovery from already existing fields.
Bayreuther, Moritz, Cristall, Jamin, Herrmann, Felix J.   +2 more
core   +1 more source