Subsurface sedimentary structure identification using deep learning: A review

C Zhan, Z Dai, Z Yang, X Zhang, Z Ma, HV Thanh… - Earth-Science …, 2023 - Elsevier
The reliable identification of subsurface sedimentary structures (ie, geologic heterogeneity)
is critical in various earth and environmental sciences, petroleum reservoir engineering, and …

A review of spatial Markov models for predicting pre-asymptotic and anomalous transport in porous and fractured media

T Sherman, NB Engdahl, G Porta, D Bolster - Journal of Contaminant …, 2021 - Elsevier
Heterogeneity across a broad range of scales in geologic porous media often manifests in
observations of non-Fickian or anomalous transport. While traditional anomalous transport …

Stage‐wise stochastic deep learning inversion framework for subsurface sedimentary structure identification

C Zhan, Z Dai, MR Soltanian… - Geophysical research …, 2022 - Wiley Online Library
The stochastic models and deep‐learning models are the two most commonly used
methods for subsurface sedimentary structures identification. The results from the stochastic …

[HTML][HTML] Estimation of the anisotropy of hydraulic conductivity through 3D fracture networks using the directional geological entropy

C Zhou, Z Ye, C Yao, X Fan, F ** tests and numerical groundwater flow …
J Lin, R Ma, Z Sun, L Tang - Journal of Earth Science, 2023 - Springer
Aquifer connectivity could greatly affect groundwater flow and further control the contaminant
transport in fractured medium. However, assessing connectivity of fractured aquifer at …