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Discovery and analysis of topographic features using learning algorithms: A seamount case study
Author(s) -
Valentine Andrew P.,
Kalnins Lara M.,
Trampert Jeannot
Publication year - 2013
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1002/grl.50615
Subject(s) - seamount , computer science , feature (linguistics) , autoencoder , artificial intelligence , artificial neural network , construct (python library) , automation , class (philosophy) , algorithm , data mining , machine learning , geology , mechanical engineering , philosophy , linguistics , oceanography , engineering , programming language
Identifying and cataloging occurrences of particular topographic features are important but time‐consuming tasks. Typically, automation is challenging, as simple models do not fully describe the complexities of natural features. We propose a new approach, where a particular class of neural network (the “autoencoder”) is used to assimilate the characteristics of the feature to be cataloged, and then applied to a systematic search for new examples. To demonstrate the feasibility of this method, we construct a network that may be used to find seamounts in global bathymetric data. We show results for two test regions, which compare favorably with results from traditional algorithms.