[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120969-en":3,"doc-seo-120969-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120969,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Towards a Monitoring System of the Sea State Based on Microseism and Machine Learning","This work develops a microseism-based monitoring approach to infer significant wave height in the Sicily Channel. Seismic and sea-state data (from hindcast maps) spanning 2018–2021 were analyzed using combined statistical methods and machine learning. Spectral and amplitude results show microseism depends on surrounding sea conditions, while correlation analysis indicates dominant origins within 400 km of coastlines. Machine learning enables reconstruction of spatial and temporal wave distributions; Random Forest performs best with R2=0.89 and mean prediction error ~0.21 m.","Environmental Modelling and Software 167 (2023) 105781  \nContents lists available at ScienceDirect  \nEnvironmental Modelling and Software  \njournal [homepage: www.elsevier.com/locate/envsoft](homepage: www.elsevier.com/locate/envsoft)  \n| Towards a monitoring system of the machine learning |  |  | sea state based on microseism and |  |\n| --- | --- | --- | --- | --- |\n| Vittorio Minio a, *, Alfio Marco Borzì a, Susanna Saittab, Salvatore Alparone c, Andrea Cannata a, c, Giuseppe Ciraolo d, Danilo Contrafatto c, Sebastiano D’Amico e, Giuseppe Di Grazia c, Graziano Larocca c, Flavio Cannav`o c\u003Cbr>a Dipartimento di Scienze Biologiche, Geologiche ed Ambientali-Sezione di Scienze della Terra, Universit`a degli Studi di Catania, Corso Italia, 57, 95129, Catania, Italy b Dipartimento di Matematica e Informatica, Universit`a di Catania, Viale Andrea Doria, 6, 95100, Catania, Italy\u003Cbr>c Istituto Nazionale di Geofisica e Vulcanologia-Sezione di Catania, Osservatorio Etneo, Piazza Roma, 2, 95125, Catania, Italy\u003Cbr>d Dipartimento di Ingegneria Civile, Ambientale, Aerospaziale, dei Materiali, Universit`a di Palermo, Viale delle Scienze, Building 8, 90128, Palermo, Italy e Department of Geosciences, University of Malta, Msida, 2080, Malta |  |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |  |\n| Handling Editor: Daniel P Ames |  | In this work, we exploited the ubiquitous seismic noise generated by energy transfer from the sea to the solid Earth (called microseism) to infer the significant wave height data, with the aim of developing a microseismbased monitoring system of the Sicily Channel. We used a combined approach based on statistical analysis and machine learning by using seismic and sea state data (provided by the hindcast maps), recorded between 2018 and 2021.Through spectral and amplitude analysis, we observed that microseism was influenced by the conditions of the seas surrounding Sicily. Correlation analysis demonstrates that microseism mostly originates from sources located up to 400 km from the coastlines. Moreover, employing machine learning algorithms, we successfully reconstruct spatial and temporal sea wave distributions using microseism data. Among the tested methods, the Random Forest algorithm yields the best results, with an R2 value of 0.89 and a mean prediction error of about 0.21 m. |  |  |\n| Keywords:\u003Cbr>Microseism\u003Cbr>Significant wave height\u003Cbr>Correlation coefficient\u003Cbr>Array analysis\u003Cbr>Machine learning |  |  |  |  |\n\nSoftware and data availability  \nThe codes that were used for the analysis of the seismic and sea state data using python language (version 3.10) based on ObsPy and scikitlearn libraries can be found in Github: [https://github.com/VittorioMi](https://github.com/VittorioMi)[nio93/shwpredict. This repository was created by Vittorio Minio](nio93/shwpredict. This repository was created by Vittorio Minio)[ ](nio93/shwpredict. This repository was created by Vittorio Minio)(Email: vittorio.minio@phd.unict.it) in 2023 and contains program codes (40 KB). Development environment and code testing were as follows:  \n● OS: Windows 11 Pro 64-bit  \n● CPU: 2 CPU AMD EPYC 7713 64core 225 W 2.0 GHz  \n● RAM: 640 GB RAM TrueDDR4 3200MHz (optional memory expansion of up to 8 TB)  \n● GPU: 2 x GPU A100 40 GB PCIe Gen 40  \nThe seismic data can be downloaded free of charge from European  \nIntegrated Data Archive (EIDA; [http://www.orfeus-eu.org/data/eida/](http://www.orfeus-eu.org/data/eida/), last access May 2023). Sea state data can be downloaded by using E.U. Copernicus Marine Service Information ([https://doi.org/10.25423/c](https://doi.org/10.25423/c)[mcc/medsea_multiyear_wav_006_012](mcc/medsea_multiyear_wav_006_012), last access May 2023). The earthquake catalogue that was used for the analysis can be downloaded from the United States Geological Survey (USGS; [https://earthquake.us](https://earthquake.us)[gs.gov/fdsnws/event](gs.gov/fdsnws/event), last access May 2023).  \n1. Introduction  \nMicroseism is the ","cbCaigFiW4GvHo1w","https://ap.wps.com/l/cbCaigFiW4GvHo1w","pdf",2817467,1,15,"English","en",105,"# Introduction\n## Classification of microseism\n# Methods and data\n## Statistical analysis and machine learning approach\n# Results\n## Spectral/amplitude dependence\n## Correlation and source localization\n## Wave reconstruction performance","[{\"question\":\"What is the main goal of the proposed monitoring system?\",\"answer\":\"To infer significant wave height using ubiquitous seismic noise (microseism), enabling microseism-based monitoring for the Sicily Channel.\"},{\"question\":\"How do the authors connect microseism to sea-state conditions?\",\"answer\":\"They perform spectral and amplitude analysis to show that microseism is influenced by the sea conditions surrounding Sicily.\"},{\"question\":\"Which machine learning method performs best and what accuracy is reported?\",\"answer\":\"Random Forest yields the best results, with R2=0.89 and a mean prediction error of about 0.21 m.\"}]","Towards a Monitoring System of the Sea State Based on Microseism and Machine Learning | PDF",1785733115,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"towards-a-monitoring-system-of-the-sea-state-based-on-microseism-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/towards-a-monitoring-system-of-the-sea-state-based-on-microseism-and-machine-learning/120969/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the proposed monitoring system?","Question",{"text":75,"@type":76},"To infer significant wave height using ubiquitous seismic noise (microseism), enabling microseism-based monitoring for the Sicily Channel.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the authors connect microseism to sea-state conditions?",{"text":80,"@type":76},"They perform spectral and amplitude analysis to show that microseism is influenced by the sea conditions surrounding Sicily.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning method performs best and what accuracy is reported?",{"text":84,"@type":76},"Random Forest yields the best results, with R2=0.89 and a mean prediction error of about 0.21 m.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]