Results 221 to 230 of about 4,702 (274)

Landslide susceptibility assessment using hybrid geospatial, frequency ratio, and AHP models in Souk Ahras province, Northeastern of Algeria. [PDF]

Sci Rep
Rebouh N, Belkendil A, Tout F, Dinar H, Benzid Y, Zeghmar A, Alliouche A, Farah MI, Najar IA, Khan NA.   +9 more
europepmc   +1 more source

Research on mechanism and control technique of floor heave in deep soft rock roadway at Banji Coal Mine. [PDF]

Sci Rep
Zhang X, Jing L, Dong H, He H, Lv J, Sun M, Fan Y.   +6 more
europepmc   +1 more source

Comment on ‘Near‐Synchronous Cambrian Evolution of the Transbrasiliano‐Kandi‐4°50′ Shear Zone in South America and Africa’

Terra Nova, EarlyView.
Sérgio P. Neves, Alain Vauchez
wiley   +1 more source

Lithological Mapping in High-Vegetation Areas Using Sentinel-2, Sentinel-1, and Digital Elevation Models. [PDF]

Sensors (Basel)
Chen Y, Liu G, Song Z, Li M, Wang M, Wang S.   +5 more
europepmc   +1 more source

An improved method for predicting the permeability of Chang 7 shale oil reservoirs based on lithology and hydraulic flow units. [PDF]

Sci Rep
Gao H, Sun Y, Zhao H, Zhou F, Tian W, Wen Z, Fan Y, Chen X.   +7 more
europepmc   +1 more source

Active Domain Adaptation With Application to Intelligent Logging Lithology Identification [PDF]

IEEE Transactions on Cybernetics, 2022
Lithology identification plays an essential role in formation characterization and reservoir exploration. As an emerging technology, intelligent logging lithology identification has received great attention recently, which aims to infer the lithology type through the well-logging curves using machine-learning methods.
Ji Chang, Yu Kang 0001, Wei Xing Zheng 0001, Yang Cao 0010, Zerui Li, Wenjun Lv, Xing-Mou Wang   +6 more
openaire   +4 more sources

An improved method for lithology identification based on a hidden Markov model and random forests [PDF]

Geophysics, 2020
Subsurface petrophysical properties usually differ between different reservoirs, which affects lithology identification, especially for unconventional reservoirs. Thus, the lithology identification of subsurface reservoirs is a challenging task.
Pu Wang, Jingye Li, Pu Wang
exaly   +2 more sources

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Refined lithology identification: Methodology, challenges and prospects

Geoenergy Science and Engineering, 2023
Z H Xu, Heng Shi
exaly   +2 more sources

Deep learning of rock images for intelligent lithology identification

Computers & Geosciences, 2021
Abstract An intelligent lithology identification method is proposed based on the deep learning of rock images. The lithology information and position information in rock images can be predicted using the Faster R–CNN architecture through the RPN proposal generation algorithm and the Fast R–CNN detector.
Zhenhao Xu, Wen Ma, Peng Lin, Heng Shi 0004, Dongdong Pan, Tonghui Liu   +5 more
openaire   +1 more source

Carbonate Lithology Identification wth Generative Adversarial Networks

International Petroleum Technology Conference, 2020
Carbonate sedimentary rocks form the reservoir rocks of many oil and gas fields. The largest oil and gas fields in the world, such as the Ghawar field in Saudi Arabia and the Zakum field in Abu Dhabi, consist of carbonate reservoirs.
Takashi Nanjo, Satoru Tanaka
openaire   +1 more source