Results 31 to 40 of about 612 (172)

A Machine Learning-Based Seismic Data Compression and Interpretation Using a Novel Shifted-Matrix Decomposition Algorithm

Applied Sciences, 2021
Seismic data provides integral information in geophysical exploration, for locating hydrocarbon rich areas as well as for fracture monitoring during well stimulation.
Milan Brankovic, Eduardo Gildin, Richard L. Gibson, Mark E. Everett   +3 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

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

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

Non-Parametric Simultaneous Reconstruction and Denoising via Sparse and Low-Rank Regularization

Frontiers in Earth Science, 2022
Spatial irregular sampling and random noise are two important factors that restrict the accuracy of seismic imaging. Seismic wavefield reconstruction and denoising based on sparse representation are two popular antidotes to these two inevitable issues ...
Lingjun Meng, Zhanzhan Shi, Zhanzhan Shi, Yan Ye, Yuanjun Wang, Yuanjun Wang   +5 more
doaj   +1 more source

Cross‐Dimensional Generative Adversarial Networks (CDGAN) Geomodeling: Bridging 2D Geological Figures and 3D Reservoir Modeling

Water Resources Research, Volume 62, Issue 6, June 2026.
Abstract Generative adversarial networks (GANs) have proven effective in simulating complex reservoir environments, such as meandering channels and deltas. In classic GANs, the dimensionality of training data determines that of generated data: a 2D (or 3D) reservoir facies simulator (generator) requires training with corresponding 2D (or 3D) data sets.
Xun Hu, Yanshu Yin, Changmin Zhang, Pengfei Xie, Lixin Wang   +4 more
wiley   +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

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

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