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Deep Subsurface Pseudo-Lithostratigraphic Modeling Based on Three-Dimensional Convolutional Neural Network (3D CNN) Using Inversed Geophysical Properties and Shallow Subsurface Geological Model

文献类型：期刊论文

作者Zhang, Baoyi5; Xu, Zhanghao5; Wei, Xiuzong4; Song, Lei3,5; Shah, Syed Yasir Ali5; Khan, Umair2; Du, Linze5; Li, Xuefeng1
刊名LITHOSPHERE
出版日期2024
卷号2024期号:1页码:19
ISSN号1941-8264
DOI10.2113/2024/lithosphere_2023_273
英文摘要Lithostratigraphic modeling holds a vital role in mineral resource exploration and geological studies. In this study, we introduce a novel approach for automating pseudo-lithostratigraphic modeling in the deep subsurface, leveraging inversed geophysical properties. We propose a three-dimensional convolutional neural network with adaptive moment estimation (3D Adam -CNN) to achieve this objective. Our model employs 3D geophysical properties as input features for training, concurrently reconstructing a 3D geological model of the shallow subsurface for lithostratigraphic labeling purposes. To enhance the accuracy of pseudo-lithostratigraphic modeling during the model training phase, we redesign the 3D CNN framework, fine-tuning its parameters using the Adam optimizer. The Adam optimizer ensures controlled parameter updates with minimal memory overhead, rendering it particularly well -suited for convolutional learning involving huge 3D datasets with multi -dimensional features. To validate our proposed 3D Adam -CNN model, we compare the performance of our approach with 1D and 2D CNN models in the Qingniandian area of Heilongjiang Province, Northeastern China. By cross -matching the model's predictions with manually modeled shallow subsurface lithostratigraphic distributions, we substantiate its reliability and accuracy. The 3D Adam -CNN model emerges as a robust and effective solution for lithostratigraphic modeling in the deep subsurface, utilizing geophysical properties.
WOS关键词HEILONGJIANG PROVINCE ; GEOCHRONOLOGY ; GEOCHEMISTRY ; DEPOSIT ; CHINA ; FIELD ; IDENTIFICATION ; RECOGNITION ; PREDICTION ; LITHOLOGY
资助项目National Engineering Research Center for Geographic Information System of China ; National Natural Science Foundation of China[42072326] ; China Geological Survey Project[DD20190156]
WOS研究方向Geochemistry & Geophysics ; Geology
语种英语
WOS记录号WOS:001203015900004
出版者GEOSCIENCEWORLD
资助机构National Engineering Research Center for Geographic Information System of China ; National Natural Science Foundation of China ; China Geological Survey Project
源URL[http://ir.idsse.ac.cn/handle/183446/11005]  
专题深海科学研究部_深海地球物理与资源研究室
研究生部
通讯作者Khan, Umair
作者单位1.Nat Resources Survey Inst Heilongjiang Prov, Harbin 150094, Peoples R China
2.Chinese Acad Sci, Inst Deep Sea Sci & Engn, Sanya 572000, Peoples R China
3.Hunan Inst Geol Disaster Invest & Monitoring, Changsha 410004, Peoples R China
4.Guangxi Commun Design Grp Co Ltd, Geotech Engn Invest & Design Inst, Nanning 530029, Peoples R China
5.Cent South Univ, Sch Geosci & Info Phys, Key Lab Metallogen Predict Nonferrous Met & Geol E, Minist Educ, Changsha 410083, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Baoyi,Xu, Zhanghao,Wei, Xiuzong,et al. Deep Subsurface Pseudo-Lithostratigraphic Modeling Based on Three-Dimensional Convolutional Neural Network (3D CNN) Using Inversed Geophysical Properties and Shallow Subsurface Geological Model[J]. LITHOSPHERE,2024,2024(1):19.
APA Zhang, Baoyi.,Xu, Zhanghao.,Wei, Xiuzong.,Song, Lei.,Shah, Syed Yasir Ali.,...&Li, Xuefeng.(2024).Deep Subsurface Pseudo-Lithostratigraphic Modeling Based on Three-Dimensional Convolutional Neural Network (3D CNN) Using Inversed Geophysical Properties and Shallow Subsurface Geological Model.LITHOSPHERE,2024(1),19.
MLA Zhang, Baoyi,et al."Deep Subsurface Pseudo-Lithostratigraphic Modeling Based on Three-Dimensional Convolutional Neural Network (3D CNN) Using Inversed Geophysical Properties and Shallow Subsurface Geological Model".LITHOSPHERE 2024.1(2024):19.

入库方式： OAI收割

来源：深海科学与工程研究所

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