Statistics and Pattern Recognition Applied to the Spatio-Temporal Properties of Seismicity
Due to the significant increase in the availability of new data in recent years, as a result of the expansion of available seismic stations, laboratory experiments, and the availability of increasingly reliable synthetic catalogs, considerable progress has been made in understanding the spatiotempor...
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| Format: | Online |
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| Jezik: | engleski |
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MDPI - Multidisciplinary Digital Publishing Institute
2022
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| Online pristup: | ONIX_20220706_9783036542638_75 |
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| _version_ | 1869531285725118464 |
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| collection | Directory of Open Access Books |
| description | Due to the significant increase in the availability of new data in recent years, as a result of the expansion of available seismic stations, laboratory experiments, and the availability of increasingly reliable synthetic catalogs, considerable progress has been made in understanding the spatiotemporal properties of earthquakes. The study of the preparatory phase of earthquakes and the analysis of past seismicity has led to the formulation of seismicity models for the forecasting of future earthquakes or to the development of seismic hazard maps. The results are tested and validated by increasingly accurate statistical methods. A relevant part of the development of many models is the correct identification of seismicity clusters and scaling laws of background seismicity. In this collection, we present eight innovative papers that address all the above topics. The occurrence of strong earthquakes (mainshocks) is analyzed from different perspectives in this Special Issue. |
| format | Online |
| id | doab-20.500.12854ir-87480 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-874802024-04-11T15:11:18Z Statistics and Pattern Recognition Applied to the Spatio-Temporal Properties of Seismicity Gentili, Stefania Giovambattista, Rita Di Shcherbakov, Robert Vallianatos, Filippos system-analytical method earthquake-prone areas pattern recognition clustering machine learning earthquake catalogs high seismicity criteria tidal triggering of earthquakes seismic cycle coulomb failure stress preparatory phase seismic prediction earthquake forecasting precursors statistical seismology earthquake likelihood models seismicity patterns New Zealand California smoothed seismicity methods global seismicity foreshocks and aftershocks earthquake forecasting model statistical methods magnitude-frequency distribution corner magnitude tapered Pareto tapered Gutenberg-Richter epidemic type aftershock sequence model extreme value distribution Bayesian predictive distribution seismicity clustering DBSCAN algorithm markovian arrival processes numerical modeling earthquake simulator earthquake clustering northern and central Apennines n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology Due to the significant increase in the availability of new data in recent years, as a result of the expansion of available seismic stations, laboratory experiments, and the availability of increasingly reliable synthetic catalogs, considerable progress has been made in understanding the spatiotemporal properties of earthquakes. The study of the preparatory phase of earthquakes and the analysis of past seismicity has led to the formulation of seismicity models for the forecasting of future earthquakes or to the development of seismic hazard maps. The results are tested and validated by increasingly accurate statistical methods. A relevant part of the development of many models is the correct identification of seismicity clusters and scaling laws of background seismicity. In this collection, we present eight innovative papers that address all the above topics. The occurrence of strong earthquakes (mainshocks) is analyzed from different perspectives in this Special Issue. 2022-07-06T11:51:45Z 2022-07-06T11:51:45Z 2022 book ONIX_20220706_9783036542638_75 9783036542638 9783036542645 https://directory.doabooks.org/handle/20.500.12854/87480 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/5678 https://mdpi.com/books/pdfview/book/5678 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-4264-5 10.3390/books978-3-0365-4264-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036542638 9783036542645 180 Basel open access |
| spellingShingle | system-analytical method earthquake-prone areas pattern recognition clustering machine learning earthquake catalogs high seismicity criteria tidal triggering of earthquakes seismic cycle coulomb failure stress preparatory phase seismic prediction earthquake forecasting precursors statistical seismology earthquake likelihood models seismicity patterns New Zealand California smoothed seismicity methods global seismicity foreshocks and aftershocks earthquake forecasting model statistical methods magnitude-frequency distribution corner magnitude tapered Pareto tapered Gutenberg-Richter epidemic type aftershock sequence model extreme value distribution Bayesian predictive distribution seismicity clustering DBSCAN algorithm markovian arrival processes numerical modeling earthquake simulator earthquake clustering northern and central Apennines n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology Statistics and Pattern Recognition Applied to the Spatio-Temporal Properties of Seismicity |
| title | Statistics and Pattern Recognition Applied to the Spatio-Temporal Properties of Seismicity |
| title_full | Statistics and Pattern Recognition Applied to the Spatio-Temporal Properties of Seismicity |
| title_fullStr | Statistics and Pattern Recognition Applied to the Spatio-Temporal Properties of Seismicity |
| title_full_unstemmed | Statistics and Pattern Recognition Applied to the Spatio-Temporal Properties of Seismicity |
| title_short | Statistics and Pattern Recognition Applied to the Spatio-Temporal Properties of Seismicity |
| title_sort | statistics and pattern recognition applied to the spatio temporal properties of seismicity |
| topic | system-analytical method earthquake-prone areas pattern recognition clustering machine learning earthquake catalogs high seismicity criteria tidal triggering of earthquakes seismic cycle coulomb failure stress preparatory phase seismic prediction earthquake forecasting precursors statistical seismology earthquake likelihood models seismicity patterns New Zealand California smoothed seismicity methods global seismicity foreshocks and aftershocks earthquake forecasting model statistical methods magnitude-frequency distribution corner magnitude tapered Pareto tapered Gutenberg-Richter epidemic type aftershock sequence model extreme value distribution Bayesian predictive distribution seismicity clustering DBSCAN algorithm markovian arrival processes numerical modeling earthquake simulator earthquake clustering northern and central Apennines n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology |
| topic_facet | system-analytical method earthquake-prone areas pattern recognition clustering machine learning earthquake catalogs high seismicity criteria tidal triggering of earthquakes seismic cycle coulomb failure stress preparatory phase seismic prediction earthquake forecasting precursors statistical seismology earthquake likelihood models seismicity patterns New Zealand California smoothed seismicity methods global seismicity foreshocks and aftershocks earthquake forecasting model statistical methods magnitude-frequency distribution corner magnitude tapered Pareto tapered Gutenberg-Richter epidemic type aftershock sequence model extreme value distribution Bayesian predictive distribution seismicity clustering DBSCAN algorithm markovian arrival processes numerical modeling earthquake simulator earthquake clustering northern and central Apennines n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology |
| url | ONIX_20220706_9783036542638_75 |