Machine learning classification of Austin Chalk chemofacies from high-resolution x-ray fluorescence core characterization-Reference-Cited by-同舟云学术

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Machine learning classification of Austin Chalk chemofacies from high-resolution x-ray fluorescence core characterization

Published:2023-06 Issue:6 Volume:107 Page:907-927
ISSN:0149-1423
Container-title:AAPG Bulletin
language:en
Short-container-title:Bulletin

Author:

Larson Toti E.,Loucks Robert G.,Sivil J. Evan,Hattori Kelly E.,Zahm Christopher K.

Publisher

American Association of Petroleum Geologists AAPG/Datapages

Subject

Earth and Planetary Sciences (miscellaneous),Geochemistry and Petrology,Geology,Energy Engineering and Power Technology,Fuel Technology

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Chemofacies characterization of lacustrine shale based on machine learning classification: A case study from the Dongying depression, Bohai bay basin, China;Geoenergy Science and Engineering;2024-10

2. X-ray Fluorescence Core Scanning for High-Resolution Geochemical Characterisation of Soils;Soil Systems;2024-05-17

3. Machine learning of the chemical elements for enhanced interpretation of depositional environments: Upper Jurassic strata case study in central Saudi Arabia;Marine and Petroleum Geology;2024-04

4. Hierarchical cluster and principal component analyses of multi-scale pore structure and shale components in the Upper Triassic Chang 7 Member in the Ordos Basin of Northern China;Journal of Asian Earth Sciences;2024-02

5. X-Ray Fluorescence Core Scanning for High Resolution Geochemical Characterisation of Soils;2024

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