MARC details
| 000 -LEADER |
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03865namaa2200397uu 4500 |
| 001 - CONTROL NUMBER |
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oapen49362 |
| 003 - CONTROL NUMBER IDENTIFIER |
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oapen |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20231220171437.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS--GENERAL INFORMATION |
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m o d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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cr|mn|---annan |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
210602s2020 xx |||||o ||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
intechopen.94217 |
| 024 7# - OTHER STANDARD IDENTIFIER |
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| Standard number or code |
10.5772/intechopen.94217 |
| Source of number or code |
doi |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
oapen |
| Transcribing agency |
oapen |
| 041 0# - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
eng |
| 042 ## - AUTHENTICATION CODE |
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| Authentication code |
dc |
| 072 #7 - SUBJECT CATEGORY CODE |
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| Subject category code |
RB |
| Source |
bicssc |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Carniel, Roberto |
| Relator code |
auth |
| 9 (RLIN) |
1599801 |
| 245 10 - TITLE STATEMENT |
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| Title |
Chapter Machine Learning in Volcanology: A Review |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
InTechOpen |
| Date of publication, distribution, etc |
2020 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
1 online resource |
| 336 ## - |
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text |
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txt |
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rdacontent |
| 337 ## - |
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computer |
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c |
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rdamedia |
| 338 ## - |
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online resource |
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cr |
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rdacarrier |
| 506 0# - RESTRICTIONS ON ACCESS NOTE |
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| Terms governing access |
Open Access |
| Standardized terminology for access restriction |
Unrestricted online access |
| Source of term |
star |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
A volcano is a complex system, and the characterization of its state at any given time is not an easy task. Monitoring data can be used to estimate the probability of an unrest and/or an eruption episode. These can include seismic, magnetic, electromagnetic, deformation, infrasonic, thermal, geochemical data or, in an ideal situation, a combination of them. Merging data of different origins is a non-trivial task, and often even extracting few relevant and information-rich parameters from a homogeneous time series is already challenging. The key to the characterization of volcanic regimes is in fact a process of data reduction that should produce a relatively small vector of features. The next step is the interpretation of the resulting features, through the recognition of similar vectors and for example, their association to a given state of the volcano. This can lead in turn to highlight possible precursors of unrests and eruptions. This final step can benefit from the application of machine learning techniques, that are able to process big data in an efficient way. Other applications of machine learning in volcanology include the analysis and classification of geological, geochemical and petrological "static" data to infer for example, the possible source and mechanism of observed deposits, the analysis of satellite imagery to quickly classify vast regions difficult to investigate on the ground or, again, to detect changes that could indicate an unrest. Moreover, the use of machine learning is gaining importance in other areas of volcanology, not only for monitoring purposes but for differentiating particular geochemical patterns, stratigraphic issues, differentiating morphological patterns of volcanic edifices, or to assess spatial distribution of volcanoes. Machine learning is helpful in the discrimination of magmatic complexes, in distinguishing tectonic settings of volcanic rocks, in the evaluation of correlations of volcanic units, being particularly helpful in tephrochronology, etc. In this chapter we will review the relevant methods and results published in the last decades using machine learning in volcanology, both with respect to the choice of the optimal feature vectors and to their subsequent classification, taking into account both the unsupervised and the supervised approaches. |
| 540 ## - TERMS GOVERNING USE AND REPRODUCTION NOTE |
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| Terms governing use and reproduction |
Creative Commons |
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https://creativecommons.org/licenses/by/3.0/ |
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cc |
| Uniform Resource Identifier |
<a href="https://creativecommons.org/licenses/by/3.0/">https://creativecommons.org/licenses/by/3.0/</a> |
| 546 ## - LANGUAGE NOTE |
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| Language note |
English |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Earth sciences |
| Source of heading or term |
bicssc |
| 9 (RLIN) |
72931 |
| 653 ## - INDEX TERM--UNCONTROLLED |
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| Uncontrolled term |
machine learning, volcano seismology, volcano geophysics, volcano geochemistry, volcano geology, data reduction, feature vectors |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Guzmǹ, Silvina |
| Relator code |
auth |
| 9 (RLIN) |
1647688 |
| 773 1# - HOST ITEM ENTRY |
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| Control subfield |
nnaa |
| 793 0# - |
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| -- |
OAPEN Library. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="https://library.oapen.org/bitstream/id/8edc6b88-f47c-40fe-82fa-72b02880cf8b/73667.pdf">https://library.oapen.org/bitstream/id/8edc6b88-f47c-40fe-82fa-72b02880cf8b/73667.pdf</a> |
| -- |
0 |
| Public note |
Open Access: OAPEN Library, download the publication |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
<a href="https://library.oapen.org/handle/20.500.12657/49362">https://library.oapen.org/handle/20.500.12657/49362</a> |
| -- |
0 |
| Public note |
Open Access: OAPEN Library: description of the publication |
