Results 41 to 50 of about 672 (170)

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

Outlier Denoising Using a Novel Statistics-Based Mask Strategy for Compressive Sensing

Remote Sensing, 2023
Denoising is always an important step in seismic processing, in order to produce high-quality data for subsequent imaging and inversion. Different types of noise can be suppressed using targeted denoising methods.
Weiqi Wang, Jidong Yang, Jianping Huang, Zhenchun Li, Miaomiao Sun   +4 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

Multiscale dilated denoising convolution with channel attention mechanism for micro-seismic signal denoising

Journal of Petroleum Exploration and Production Technology
Denoising micro-seismic signals is paramount for ensuring reliable data for localizing mining-related seismic events and analyzing the state of rock masses during mining operations.
Jianxian Cai, Zhijun Duan, Li Wang, Juan Meng, Zhenjing Yao   +4 more
doaj   +1 more source

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

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

A Physics-Informed Neural Network Framework for Seismic Signal Denoising Based on Time–Frequency Adaptive Decomposition

Applied Sciences
Seismic signal denoising stands as a vital process that enables precise seismic data analysis because noise interference blocks the detection of weak but valuable seismic signals.
Qinghua Zhang, Miantao Zhang, Houle Zhang, Yongxin Wu, Yanjie Zhang   +4 more
doaj   +1 more source

Physics‐Supervised Autonomous Inverse Fracture Modeling via Generative Artificial Intelligence

Geophysical Research Letters, Volume 53, Issue 12, 28 June 2026.
Abstract Fracture networks act as critical pathways for groundwater flow and transport, yet their characterization remains challenging due to subsurface inaccessibility and stochastic complexity. Traditional inversion methods are computationally expensive and often fail to capture fracture heterogeneity accurately.
Guodong Chen, Jiu Jimmy Jiao, Zhongzheng Wang, Rong Mao, Tao Yang, Nori Nakata   +5 more
wiley   +1 more source

Transient Porosity During Fluid‐Mineral Interaction, Part 2: Reconstruction Using Generative AI

Journal of Geophysical Research: Solid Earth, Volume 131, Issue 6, June 2026.
Abstract Quantifying fluid–rock interactions within the lithosphere is vital for both geological processes and applications such as CO2 ${\text{CO}}_{2}$ storage and geothermal energy development. Mineral replacement reactions generate transient pore networks that enhance fluid flow, yet many pores become isolated once reactions are completed, reducing
Hamed Amiri, Vangelis Dialeismas, Damien Freitas, Roberto Rizzo, Alireza Chogani, Oliver Plümper   +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