Enhancing knowledge discovery from unstructured data using a deep learning approach to support subsurface modeling predictions

被引:2
作者
Hoover, Brendan [1 ,2 ,3 ]
Zaengle, Dakota [1 ,2 ]
Mark-Moser, Mackenzie [1 ,2 ]
Wingo, Patrick [1 ,2 ]
Suhag, Anuj [1 ]
Rose, Kelly [1 ]
机构
[1] Natl Energy Technol Lab, Albany, OR 97321 USA
[2] NETL Support Contractor, Albany, OR 97321 USA
[3] US Army Corps Engineers, Geospatial Res Lab, Alexandria, VA 22315 USA
来源
FRONTIERS IN BIG DATA | 2023年 / 6卷
关键词
subsurface; knowledge discovery (data mining); unstructured data; deep learning; artificial intelligence; modeling; NEURAL-NETWORKS;
D O I
10.3389/fdata.2023.1227189
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Subsurface interpretations and models rely on knowledge from subject matter experts who utilize unstructured information from images, maps, cross sections, and other products to provide context to measured data (e. g., cores, well logs, seismic surveys). To enhance such knowledge discovery, we advanced the National Energy Technology Laboratory ' s (NETL) Subsurface Trend Analysis (STA) workflow with an artificial intelligence (AI) deep learning approach for image embedding. NETL ' s STA method offers a validated science-based approach of combining geologic systems knowledge, statistical modeling, and datasets to improve predictions of subsurface properties. The STA image embedding tool quickly extracts images from unstructured knowledge products like publications, maps, websites, and presentations; categorically labels the images; and creates a repository for geologic domain postulation. Via a case study on geographic and subsurface literature of the Gulf of Mexico (GOM), results show the STA image embedding tool extracts images and correctly labels them with similar to 90 to similar to 95% accuracy.
引用
收藏
页数:11
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