DOI: 10.1002/grl.50615
论文题名: Discovery and analysis of topographic features using learning algorithms: A seamount case study
作者: Valentine A.P. ; Kalnins L.M. ; Trampert J.
刊名: Geophysical Research Letters
ISSN: 0094-8837
EISSN: 1944-8568
出版年: 2013
卷: 40, 期: 12 起始页码: 3048
结束页码: 3054
语种: 英语
英文关键词: automatic identification
; geomorphology
; landforms
; neural networks
; seamounts
Scopus关键词: Automatic identification
; Bathymetric data
; Natural features
; New approaches
; seamounts
; Systematic searches
; Time-consuming tasks
; Topographic features
; Automation
; Complex networks
; Geomorphology
; Landforms
; Neural networks
; Submarine geology
; algorithm
; artificial neural network
; automation
; bathymetric survey
; geomorphological mapping
; landform
; learning
; seamount
; topography
英文摘要: 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. Key Points Neural networks can learn complex features in a hand-selected set of landforms They can then be used to systematically search for further examples We demonstrate the method by identifying seamounts in bathymetric data ©2013. American Geophysical Union. All Rights Reserved. URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84880551819&doi=10.1002%2fgrl.50615&partnerID=40&md5=f438c92b77bd9d0dc5f56eb92cb5a974
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/6101
Appears in Collections: 气候减缓与适应
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作者单位: Department of Earth Sciences, Universiteit Utrecht, PO Box 80021, NL-3508 TA Utrecht, Netherlands, Netherlands
Recommended Citation:
Valentine A.P.,Kalnins L.M.,Trampert J.. Discovery and analysis of topographic features using learning algorithms: A seamount case study[J]. Geophysical Research Letters,2013-01-01,40(12).