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Titel:

Demonstration and Mitigation of Spatial Sampling Bias for Machine-Learning Predictions

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Liu, Wendi; Ikonnikova, Svetlana; Scott Hamlin, H.; Sivila, Livia; Pyrcz, Michael J.
Nicht-TUM Koautoren:
ja
Kooperation:
international
Abstract:
Summary Machine learning provides powerful methods for inferential and predictive modeling of complicated multivariate relationships to support decision-making for spatial problems such as optimization of unconventional reservoir development. Current machine-learning methods have been widely used in exhaustive spatial data sets like satellite images. However, geological subsurface characterization is significantly different because it is conditioned by sparse, nonrepresentative sampling. These...     »
Intellectual Contribution:
Contribution to Practice
Zeitschriftentitel:
SPE Reservoir Evaluation & Engineering
Jahr:
2021
Band / Volume:
24
Monat:
February
Heft / Issue:
01
Seitenangaben Beitrag:
262--274
Sprache:
en
Volltext / DOI:
doi:10.2118/203838-PA
WWW:
https://onepetro.org/REE/article/24/01/262/448271/Demonstration-and-Mitigation-of-Spatial-Sampling
Print-ISSN:
1094-6470, 1930-0212
Urteilsbesprechung:
0
Peer reviewed:
Ja
commissioned:
not commissioned
Technology:
Ja
Interdisziplinarität:
Ja
Leitbild:
;
Ethics und Sustainability:
Nein
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