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GEOREF RECORD

Three data analytics party tricks

Matt Hall
Three data analytics party tricks (in Data analytics and machine learning, Mike Davidson (editor))
Leading Edge (Tulsa, OK) (March 2017) 36 (3): 262-266

Abstract

Making little software tools might seem trivial, but "party tricks" are a good way to explore a new field, find useful code libraries, and help build skills. In this spirit, it is fun and instructive to apply machine learning methods to text-mining tasks. It is especially interesting to use bibliographic data from the journal Geophysics because the results actually might be useful to those conducting geophysical research. For example, by vectorizing abstracts - using free and open-source natural-language processing tools in Python - it is possible to use the vector space to find nearby abstracts and interpret those as being similar in content. This forms the basis of a recommendation engine for geophysical papers. If not outright useful, then the party trick still might be interesting. For example, the collaboration network from the journal reveals the most prolific collaborators as George McMechan, Alan Green, and Jerry Harris, and it lets us calculate the collaboration distance between Brian Russell and Sergey Fomel (it is 4). Other party tricks are less useful and strictly silly, for example a recurrent neural network that generates random articles and authors from a parallel universe (e.g., Like-wave beam by D. J. Laniert; one imagines Like waves are a sort of attenuated Love wave).


ISSN: 1070-485X
EISSN: 1938-3789
Serial Title: Leading Edge (Tulsa, OK)
Serial Volume: 36
Serial Issue: 3
Title: Three data analytics party tricks
Title: Data analytics and machine learning
Author(s): Hall, Matt
Author(s): Davidson, Mikeeditor
Affiliation: Agile, International
Affiliation: ConocoPhillips, International
Pages: 262-266
Published: 201703
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 2
Accession Number: 2018-085181
Categories: Miscellaneous
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus.
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2019, American Geosciences Institute. Reference includes data from GeoScienceWorld, Alexandria, VA, United States. Reference includes data supplied by Society of Exploration Geophysicists, Tulsa, OK, United States
Update Code: 201847
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