High-Silica Lava Morphology at Ocean Spreading Ridges: Machine-Learning Seafloor Classification at Alarcon Rise

The oceanic crust consists mostly of basalt, but more evolved compositions may be far more common than previously thought. To aid in distinguishing rhyolite from basaltic lava and help guide sampling and understand spatial distribution, we constructed a classifier using neural networks and fuzzy inf...

Full description

Bibliographic Details
Main Authors:	Christina H. Maschmeyer, Scott M. White, Brian M. Dreyer, David A. Clague
Format:	Article
Language:	English
Published:	MDPI AG 2019-06-01
Series:	Geosciences
Subjects:	seafloor classification lava morphology remote sensing machine learning fuzzy logic oceanic spreading ridge
Online Access:	https://www.mdpi.com/2076-3263/9/6/245

id	doaj-d0c5887306e3435187ebf1cfbc1a3569
record_format	Article
spelling	doaj-d0c5887306e3435187ebf1cfbc1a35692020-11-25T02:10:47ZengMDPI AGGeosciences2076-32632019-06-019624510.3390/geosciences9060245geosciences9060245High-Silica Lava Morphology at Ocean Spreading Ridges: Machine-Learning Seafloor Classification at Alarcon RiseChristina H. Maschmeyer0Scott M. White1Brian M. Dreyer2David A. Clague3School of the Earth, Ocean and Environment, University of South Carolina, Columbia, SC 29208, USASchool of the Earth, Ocean and Environment, University of South Carolina, Columbia, SC 29208, USAInstitute of Marine Sciences, University of California, Santa Cruz, CA 95064, USAMonterey Bay Aquarium Research Institute, Moss Landing, CA 95039, USAThe oceanic crust consists mostly of basalt, but more evolved compositions may be far more common than previously thought. To aid in distinguishing rhyolite from basaltic lava and help guide sampling and understand spatial distribution, we constructed a classifier using neural networks and fuzzy inference to recognize rhyolite from its lava morphology in sonar data. The Alarcon Rise is ideal to study the relationship between lava flow morphology and composition, because it exhibits a full range of lava compositions in a well-mapped ocean ridge segment. This study shows that the most dramatic geomorphic threshold in submarine lava separates rhyolitic lava from lower-silica compositions. Extremely viscous rhyolite erupts as jagged lobes and lava branches in submarine environments. An automated classification of sonar data is a useful first-order tool to differentiate submarine rhyolite flows from widespread basalts, yielding insights into eruption, emplacement, and architecture of the ocean crust.https://www.mdpi.com/2076-3263/9/6/245seafloor classificationlava morphologyremote sensingmachine learningfuzzy logicoceanic spreading ridge
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Christina H. Maschmeyer Scott M. White Brian M. Dreyer David A. Clague
spellingShingle	Christina H. Maschmeyer Scott M. White Brian M. Dreyer David A. Clague High-Silica Lava Morphology at Ocean Spreading Ridges: Machine-Learning Seafloor Classification at Alarcon Rise Geosciences seafloor classification lava morphology remote sensing machine learning fuzzy logic oceanic spreading ridge
author_facet	Christina H. Maschmeyer Scott M. White Brian M. Dreyer David A. Clague
author_sort	Christina H. Maschmeyer
title	High-Silica Lava Morphology at Ocean Spreading Ridges: Machine-Learning Seafloor Classification at Alarcon Rise
title_short	High-Silica Lava Morphology at Ocean Spreading Ridges: Machine-Learning Seafloor Classification at Alarcon Rise
title_full	High-Silica Lava Morphology at Ocean Spreading Ridges: Machine-Learning Seafloor Classification at Alarcon Rise
title_fullStr	High-Silica Lava Morphology at Ocean Spreading Ridges: Machine-Learning Seafloor Classification at Alarcon Rise
title_full_unstemmed	High-Silica Lava Morphology at Ocean Spreading Ridges: Machine-Learning Seafloor Classification at Alarcon Rise
title_sort	high-silica lava morphology at ocean spreading ridges: machine-learning seafloor classification at alarcon rise
publisher	MDPI AG
series	Geosciences
issn	2076-3263
publishDate	2019-06-01
description	The oceanic crust consists mostly of basalt, but more evolved compositions may be far more common than previously thought. To aid in distinguishing rhyolite from basaltic lava and help guide sampling and understand spatial distribution, we constructed a classifier using neural networks and fuzzy inference to recognize rhyolite from its lava morphology in sonar data. The Alarcon Rise is ideal to study the relationship between lava flow morphology and composition, because it exhibits a full range of lava compositions in a well-mapped ocean ridge segment. This study shows that the most dramatic geomorphic threshold in submarine lava separates rhyolitic lava from lower-silica compositions. Extremely viscous rhyolite erupts as jagged lobes and lava branches in submarine environments. An automated classification of sonar data is a useful first-order tool to differentiate submarine rhyolite flows from widespread basalts, yielding insights into eruption, emplacement, and architecture of the ocean crust.
topic	seafloor classification lava morphology remote sensing machine learning fuzzy logic oceanic spreading ridge
url	https://www.mdpi.com/2076-3263/9/6/245
work_keys_str_mv	AT christinahmaschmeyer highsilicalavamorphologyatoceanspreadingridgesmachinelearningseafloorclassificationatalarconrise AT scottmwhite highsilicalavamorphologyatoceanspreadingridgesmachinelearningseafloorclassificationatalarconrise AT brianmdreyer highsilicalavamorphologyatoceanspreadingridgesmachinelearningseafloorclassificationatalarconrise AT davidaclague highsilicalavamorphologyatoceanspreadingridgesmachinelearningseafloorclassificationatalarconrise
_version_	1724917404960555008

High-Silica Lava Morphology at Ocean Spreading Ridges: Machine-Learning Seafloor Classification at Alarcon Rise

Similar Items