2026-09-18T19:26:05Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1150392022-01-13T07:53:59Zcom_1721.1_7582com_1721.1_7581col_1721.1_131024
Bradford Hager.
Zhakiya, Elezhan
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences.
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
s-bl---
2018-04-27T18:11:10Z
2018-04-27T18:11:10Z
2016
2016
http://hdl.handle.net/1721.1/115039
1031985504
Thesis: S.B., Massachusetts Institute of Technology, Department of Earth, Atmospheric, and Planetary Sciences, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages [28]).
Machine Learning techniques are being widely used in Social Sciences to find connections amongst various variables. Machine Learning connects features across different fields that do not seem to have known mathematical relationships with each other. In natural resource prospecting, machine learning can be applied to connect geochemical, geophysical, and geological variables. However, the biggest challenge in machine learning remains obtaining the data to train the ML algorithms. Here, we have applied machine learning on data extracted from maps via image processing. While the overall accuracy of prediction remains as low as 33% at this stage, we see places where the algorithm can be improved and the accuracy increased.
by Elezhan Zhakiya
S.B.
28 unnumbered pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Earth, Atmospheric, and Planetary Sciences.
Using machine learning for hydrocarbon prospecting in Reconcavo Basin, Brazil
Thesis
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