Summary of the geology, mineral resources, environmental geochemistry, and engineering geology characteristics of the northern Powder River coal region, Montana: Engineering geologic characteristics

1977 ◽  
Author(s):  
F. W. Osterwald
Keyword(s):  
Engineering Geology ◽  
Mineral Resources ◽  
Environmental Geochemistry ◽  
Coal Region

Summary of the geology, mineral resources, engineering geology characteristics, and environmental geochemistry of east-central Utah

1977 ◽  
Author(s):  
Philip Thayer Hayes ◽  
T.A. Ryer ◽  
L.W. Kiteley ◽  
J.R. Hatch ◽  
F.W. Osterwald ◽  
...  
Keyword(s):  
Engineering Geology ◽  
Mineral Resources ◽  
Environmental Geochemistry ◽  
East Central

Mineral resources and engineering geology

1987 ◽  
Vol 24 (2) ◽  
pp. 145-147
Author(s):  
Th.O.E. Reimer
Keyword(s):  
Engineering Geology ◽  
Mineral Resources

scholarly journals Engineering geology of the world ocean seabed (to the 50th anniversary of the laboratory of engineering geology of the world ocean seabed FSBI “VNIIOkeangeologiya”)

2019 ◽  
pp. 3-18
Author(s):  
A. V. Kondratenko ◽  
S. A. Kozlov ◽  
M. S. Zakharov
Keyword(s):  
Russian Federation ◽  
Engineering Geology ◽  
World Ocean ◽  
50Th Anniversary ◽  
Mineral Resources ◽  
Future Planning ◽  
Geological Engineering ◽  
The World ◽  
Survey Results ◽  
The Russian Federation

This paper reviews the development of engineering geological studies at the Ocean seabed in the Russian Federation for the last 50 years in relation to the works undertaken by Engineering Geology Laboratory of the Ocean seabed the department of FSBI VNIIOkeangeologiya. The potential perspectives of the Ocean mineral resources exploration and extraction attract the attention of experts to the seabed engineering geology. This includes an analysis of the geological, engineering geological and other survey results undertaken so far, as well as the future planning for the engineering geological studies in the Ocean seabed.

scholarly journals The Role of Deep Learning Method Based on Environmental Geochemical Data in Resource

2021 ◽  
Vol 245 ◽  
pp. 02001
Author(s):  
Pei Yao
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Environmental Science ◽  
Mineral Resources ◽  
Environmental Geochemistry ◽  
Geochemical Data ◽  
Environmental Investigation ◽  
Wide Range ◽  
Important Branch

In recent years, with the continuous development of artificial intelligence, deep learning, as an important method of artificial intelligence learning, has made great progress. At present, deep learning has been successfully applied in many engineering fields. Environmental science itself involves a wide range, among which environmental geochemistry is an important branch. The combination of environmental geochemical problems and deep learning can better study the role of geochemical problems in environmental investigation, and also can use the basic situation of regional environmental elements to predict mineral resources.

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