Please use this identifier to cite or link to this item:
http://hdl.handle.net/2122/15658
DC Field | Value | Language |
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dc.date.accessioned | 2022-06-17T08:12:35Z | - |
dc.date.available | 2022-06-17T08:12:35Z | - |
dc.date.issued | 2022-02-17 | - |
dc.identifier.uri | http://hdl.handle.net/2122/15658 | - |
dc.description.abstract | Sea wave monitoring is key in many applications in oceanography such as the validation of weather and wave models. Conventional in situ solutions are based on moored buoys whose measurements are often recognized as a standard. However, being exposed to a harsh environment, they are not reliable, need frequent maintenance, and the datasets feature many gaps. To overcome the previous limitations, we propose a system including a buoy, a micro-seismic measuring station, and a machine learning algorithm. The working principle is based on measuring the micro-seismic signals generated by the sea waves. Thus, the machine learning algorithm will be trained to reconstruct the missing buoy data from the micro-seismic data. As the micro-seismic station can be installed indoor, it assures high reliability while the machine learning algorithm provides accurate reconstruction of the missing buoy data. In this work, we present the methods to process the data, develop and train the machine learning algorithm, and assess the reconstruction accuracy. As a case of study, we used experimental data collected in 2014 from the Northern Tyrrhenian Sea demonstrating that the data reconstruction can be done both for significant wave height and wave period. The proposed approach was inspired from Data Science, whose methods were the foundation for the new solutions presented in this work. For example, estimating the period of the sea waves, often not discussed in previous works, was relatively simple with machine learning. In conclusion, the experimental results demonstrated that the new system can overcome the reliability issues of the buoy keeping the same accuracy. | en_US |
dc.description.sponsorship | Assist in Gravitation and Instrumentation srl Istituto Nazionale di Geofisica e Vulcanologia | en_US |
dc.language.iso | English | en_US |
dc.publisher.name | Frontiers Media S.A. | en_US |
dc.relation.ispartof | Frontiers in Marine Science | en_US |
dc.relation.ispartofseries | /9(2022) | en_US |
dc.rights | Attribution-NonCommercial-ShareAlike 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/us/ | * |
dc.subject | sea swell | en_US |
dc.subject | machine learning | en_US |
dc.subject | ocean waves | en_US |
dc.subject | micro-seismic data | en_US |
dc.subject | sea state | en_US |
dc.subject | sea wave period | en_US |
dc.subject | buoy | en_US |
dc.title | Sea Wave Data Reconstruction Using Micro-Seismic Measurements and Machine Learning Methods | en_US |
dc.type | article | en |
dc.description.status | Published | en_US |
dc.type.QualityControl | Peer-reviewed | en_US |
dc.description.pagenumber | 798167 | en_US |
dc.identifier.URL | https://www.frontiersin.org/articles/10.3389/fmars.2022.798167/full | en_US |
dc.subject.INGV | Marine Science | en_US |
dc.subject.INGV | Oceanography | en_US |
dc.identifier.doi | 10.3389/fmars.2022.798167 | en_US |
dc.description.obiettivoSpecifico | 4A. Oceanografia e clima | en_US |
dc.description.journalType | JCR Journal | en_US |
dc.relation.issn | 2296-7745 | en_US |
dc.contributor.author | Iafolla, Lorenzo | - |
dc.contributor.author | Fiorenza, Emiliano | - |
dc.contributor.author | Chiappini, Massimo | - |
dc.contributor.author | Carmisciano, Cosmo | - |
dc.contributor.author | Iafolla, Valerio Antonio | - |
dc.contributor.department | Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Roma2, Roma, Italia | en_US |
dc.contributor.department | Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Roma2, Roma, Italia | en_US |
item.openairetype | article | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.grantfulltext | open | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
crisitem.author.dept | Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Roma2, Roma, Italia | - |
crisitem.author.dept | Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Roma2, Roma, Italia | - |
crisitem.author.dept | Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Roma2, Roma, Italia | - |
crisitem.author.dept | AGI s.r.l. - Roma | - |
crisitem.author.orcid | 0000-0001-5584-2318 | - |
crisitem.author.orcid | 0000-0001-7634-2152 | - |
crisitem.author.orcid | 0000-0001-7433-9435 | - |
crisitem.author.orcid | 0000-0001-7357-2147 | - |
crisitem.author.orcid | 0000-0001-5297-1157 | - |
crisitem.author.parentorg | Istituto Nazionale di Geofisica e Vulcanologia | - |
crisitem.author.parentorg | Istituto Nazionale di Geofisica e Vulcanologia | - |
crisitem.author.parentorg | Istituto Nazionale di Geofisica e Vulcanologia | - |
crisitem.department.parentorg | Istituto Nazionale di Geofisica e Vulcanologia | - |
crisitem.department.parentorg | Istituto Nazionale di Geofisica e Vulcanologia | - |
Appears in Collections: | Article published / in press |
Files in This Item:
File | Description | Size | Format | |
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2022-Sea Wave Data Reconstruction Using Micro-Seismic Measurements and Machine Learning Methods.pdf | Open Access Published Version | 558.55 kB | Adobe PDF | View/Open |
Data_Sheet_1_Sea Wave Data Reconstruction Using Micro-Seismic Measurements and Machine Learning Methods (1).docx | Supplementary Material | 4.64 MB | Microsoft Word XML | View/Open |
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