Results 41 to 50 of about 382,825 (168)

Dislocation Bands and Subboundaries in Experimentally Deformed Olivine

Journal of Geophysical Research: Solid Earth, Volume 131, Issue 8, August 2026.
Abstract Transmission electron microscopy imaging of dislocations in olivine indicates heterogeneous structures and diversity of dislocation types. However, the volumes imaged at high resolution are smaller than individual grains even of fine‐grained samples.
Ulrich Faul
wiley   +1 more source

Seismic Insights Into the Role of Rockfall in Rockslide Destruction Processes

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract The dynamic link between rockslide failure and rockfall activity remains elusive, primarily due to the challenge of detecting weak signals amidst high noise. We propose a seismic attribute guided deep learning framework that formulates rockfall detection as a time‐series segmentation task, facilitating the precise extraction of rockfall events
Yaojun Wang, Yuxi Chen, Tuo Zhang, Hai Huang, Qi Zhou   +4 more
wiley   +1 more source

A method of combining coherence-constrained sparse coding and dictionary learning for denoising

, 2017
We have addressed the seismic data denoising problem, in which the noise is random and has an unknown spatiotemporally varying variance. In seismic data processing, random noise is often attenuated using transform-based methods.
Turquais, Pierre, Sollner, Walter, Asgedom, Endrias Getachew   +2 more
core   +1 more source

Denoising Seismic Signal via Resampling Local Applicability Functions

, 2022
We propose a novel seismic signal processing approach to efficiently and effectively attenuate seismic random noises. The proposed approach is a generalized seismic noise attenuation solution that can be applied to typical denoising operators.
Liu, Naihao, Xie, Rui, Li, Fangyu, Sun, Fengyuan   +3 more
core   +1 more source

An adaptive parameter-free seismic data denoising approach by combining general cross-validation thresholding and pixel connectivity in synchrosqueezed domain

Earth, Planets and Space
High signal-to-noise ratio (SNR) seismic waveform data are conductive to various studies in seismology. Seismic denoising aims to enhance SNR by eliminating additive noise through signal processing while preserving important features of the seismic ...
Zhiyi Zeng, Peng Han, Wei Zhang, Yong Zhou, Xubiao Deng, Da Zhang, Yaqian Shi, Ying Chang, Hu Ji, Rui Dai, Jianzhong Zhang   +10 more
doaj   +1 more source

A Convolutional Neural Network to Spiking Neural Network Conversion Framework for Seismic Denoising

IEEE Access
This study investigates the application of Spiking Neural Network (SNN) in seismic signal denoising by developing a Convolutional Neural Network (CNN) to SNN conversion framework. We focus on two challenges: optimal spike encoding strategy adaptation for
Shuna Chen, Zhege Liu, Ziyu Qin, Xinyi Liu, Yajuan Xue, Junxing Cao   +5 more
doaj   +1 more source

A Patch Based Denoising Method Using Deep Convolutional Neural Network for Seismic Image

IEEE Access, 2019
The deep convolutional neural networks (CNNs) have been shown excellent performances for image denoising. However, the denoising CNN model trained with a specific noise level cannot deal with the images which have spatiotemporally variant random noise ...
Yushu Zhang, Hongbo Lin, Yue Li, Haitao Ma   +3 more
doaj   +1 more source

Adaptive 3D Robust Projection Filtering for Erratic Noise Attenuation

Geophysical Prospecting, Volume 74, Issue 6, July 2026.
ABSTRACT The effective processing of seismic data contaminated by high‐amplitude erratic noise presents a problem in geophysical signal processing, particularly within onshore surveys where noise distributions are complex, non‐stationary and largely unknown.
Akash Nair, Mauricio D. Sacchi
wiley   +1 more source

Diffusion Model for DAS-VSP Data Denoising

Sensors, 2023
Distributed acoustic sensing (DAS) has emerged as a transformational technology for seismic data acquisition. However, noise remains a major impediment, necessitating advanced denoising techniques. This study pioneers the application of diffusion models,
Donglin Zhu, Lei Fu, Vladimir Kazei, Weichang Li   +3 more
doaj   +1 more source

Dynamic Reservoir Property Estimation With Uncertainty Quantification From 4D Seismic Data Using Bayesian Neural Networks

Geophysical Prospecting, Volume 74, Issue 6, July 2026.
ABSTRACT Quantifying dynamic reservoir properties, such as pressure and saturation from 4D seismic data, is crucial for improving reservoir management, increasing hydrocarbon recovery and maximizing economic returns. Although data‐driven approaches, particularly deep neural networks (DNNs), have shown promise in mapping seismic attributes to reservoir ...
Boshara Sukar, Gustavo Côrte, Colin MacBeth   +2 more
wiley   +1 more source