FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2977594178> ?p ?o ?g. }

• W2977594178 endingPage "O15" @default.
• W2977594178 startingPage "O1" @default.
• W2977594178 abstract "Machine-learning techniques allow geoscientists to extract meaningful information from data in an automated fashion, and they are also an efficient alternative to traditional manual interpretation methods. Many geophysical problems have an abundance of unlabeled data and a paucity of labeled data, and the lithology classification of wireline data reflects this situation. Training supervised algorithms on small labeled data sets can lead to overtraining, and subsequent predictions for the numerous unlabeled data may be unstable. However, semisupervised algorithms are designed for classification problems with limited amounts of labeled data, and they are theoretically able to achieve better accuracies than supervised algorithms in these situations. We explore this hypothesis by applying two semisupervised techniques, label propagation (LP) and self-training, to a well-log data set and compare their performance to three popular supervised algorithms. LP is an established method, but our self-training method is a unique adaptation of existing implementations. The well-log data were made public through an SEG competition held in 2016. We simulate a semisupervised scenario with these data by assuming that only one of the 10 wells has labels (i.e., core samples), and our objective is to predict the labels for the remaining nine wells. We generate results from these data in two stages. The first stage is applying all the algorithms in question to the data as is (i.e., the global data), and the results from this motivate the second stage, which is applying all algorithms to the data when they are decomposed into two separate data sets. Overall, our findings suggest that LP does not outperform the supervised methods, but our self-training method coupled with LP can outperform the supervised methods by a notable margin if the assumptions of LP are met." @default.
• W2977594178 created "2019-10-10" @default.
• W2977594178 creator A5010533253 @default.
• W2977594178 creator A5048682944 @default.
• W2977594178 creator A5083212457 @default.
• W2977594178 date "2020-01-01" @default.
• W2977594178 modified "2023-10-11" @default.
• W2977594178 title "Improved well-log classification using semisupervised label propagation and self-training, with comparisons to popular supervised algorithms" @default.
• W2977594178 cites W1479807131 @default.
• W2977594178 cites W1678356000 @default.
• W2977594178 cites W1973785137 @default.
• W2977594178 cites W1988529147 @default.
• W2977594178 cites W1990334093 @default.
• W2977594178 cites W1993192109 @default.
• W2977594178 cites W1996975175 @default.
• W2977594178 cites W2007061024 @default.
• W2977594178 cites W2016451901 @default.
• W2977594178 cites W2042056340 @default.
• W2977594178 cites W2049488676 @default.
• W2977594178 cites W2055017877 @default.
• W2977594178 cites W2055520435 @default.
• W2977594178 cites W2074009903 @default.
• W2977594178 cites W2079057609 @default.
• W2977594178 cites W2097089247 @default.
• W2977594178 cites W2099528333 @default.
• W2977594178 cites W2106092565 @default.
• W2977594178 cites W2107636931 @default.
• W2977594178 cites W2112796928 @default.
• W2977594178 cites W2114327617 @default.
• W2977594178 cites W2119821739 @default.
• W2977594178 cites W2136490963 @default.
• W2977594178 cites W2153409933 @default.
• W2977594178 cites W2169042894 @default.
• W2977594178 cites W2280489085 @default.
• W2977594178 cites W2529108047 @default.
• W2977594178 cites W2586262374 @default.
• W2977594178 cites W2594681445 @default.
• W2977594178 cites W2601307685 @default.
• W2977594178 cites W2747502838 @default.
• W2977594178 cites W2889600526 @default.
• W2977594178 cites W2889679918 @default.
• W2977594178 cites W2889786488 @default.
• W2977594178 cites W3102476541 @default.
• W2977594178 cites W4211024465 @default.
• W2977594178 cites W4244784707 @default.
• W2977594178 cites W1989398372 @default.
• W2977594178 doi "https://doi.org/10.1190/geo2019-0238.1" @default.
• W2977594178 hasPublicationYear "2020" @default.
• W2977594178 type Work @default.
• W2977594178 sameAs 2977594178 @default.
• W2977594178 citedByCount "19" @default.
• W2977594178 countsByYear W29775941782020 @default.
• W2977594178 countsByYear W29775941782021 @default.
• W2977594178 countsByYear W29775941782022 @default.
• W2977594178 countsByYear W29775941782023 @default.
• W2977594178 crossrefType "journal-article" @default.
• W2977594178 hasAuthorship W2977594178A5010533253 @default.
• W2977594178 hasAuthorship W2977594178A5048682944 @default.
• W2977594178 hasAuthorship W2977594178A5083212457 @default.
• W2977594178 hasConcept C11413529 @default.
• W2977594178 hasConcept C119857082 @default.
• W2977594178 hasConcept C124101348 @default.
• W2977594178 hasConcept C153180895 @default.
• W2977594178 hasConcept C154945302 @default.
• W2977594178 hasConcept C177264268 @default.
• W2977594178 hasConcept C199360897 @default.
• W2977594178 hasConcept C2776145971 @default.
• W2977594178 hasConcept C41008148 @default.
• W2977594178 hasConcept C58489278 @default.
• W2977594178 hasConceptScore W2977594178C11413529 @default.
• W2977594178 hasConceptScore W2977594178C119857082 @default.
• W2977594178 hasConceptScore W2977594178C124101348 @default.
• W2977594178 hasConceptScore W2977594178C153180895 @default.
• W2977594178 hasConceptScore W2977594178C154945302 @default.
• W2977594178 hasConceptScore W2977594178C177264268 @default.
• W2977594178 hasConceptScore W2977594178C199360897 @default.
• W2977594178 hasConceptScore W2977594178C2776145971 @default.
• W2977594178 hasConceptScore W2977594178C41008148 @default.
• W2977594178 hasConceptScore W2977594178C58489278 @default.
• W2977594178 hasIssue "1" @default.
• W2977594178 hasLocation W29775941781 @default.
• W2977594178 hasOpenAccess W2977594178 @default.
• W2977594178 hasPrimaryLocation W29775941781 @default.
• W2977594178 hasRelatedWork W1543345676 @default.
• W2977594178 hasRelatedWork W1585083011 @default.
• W2977594178 hasRelatedWork W2078736197 @default.
• W2977594178 hasRelatedWork W2091018730 @default.
• W2977594178 hasRelatedWork W2098669189 @default.
• W2977594178 hasRelatedWork W2250140425 @default.
• W2977594178 hasRelatedWork W2389064843 @default.
• W2977594178 hasRelatedWork W2389272265 @default.
• W2977594178 hasRelatedWork W2734587838 @default.
• W2977594178 hasRelatedWork W3213030203 @default.
• W2977594178 hasVolume "85" @default.
• W2977594178 isParatext "false" @default.
• W2977594178 isRetracted "false" @default.
• W2977594178 magId "2977594178" @default.
• W2977594178 workType "article" @default.