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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| Year of Publication: | 2022 |
| Language: | English |
| Physical Description: | 1 electronic resource (180 p.) |
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| 041 | 0 | |a eng | |
| 100 | 1 | |a Gentili, Stefania |4 edt | |
| 245 | 1 | 0 | |a Statistics and Pattern Recognition Applied to the Spatio-Temporal Properties of Seismicity |
| 260 | |a Basel |b MDPI - Multidisciplinary Digital Publishing Institute |c 2022 | ||
| 300 | |a 1 electronic resource (180 p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 520 | |a 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. | ||
| 546 | |a English | ||
| 650 | 7 | |a Technology: general issues |2 bicssc | |
| 650 | 7 | |a Environmental science, engineering & technology |2 bicssc | |
| 653 | |a system-analytical method | ||
| 653 | |a earthquake-prone areas | ||
| 653 | |a pattern recognition | ||
| 653 | |a clustering | ||
| 653 | |a machine learning | ||
| 653 | |a earthquake catalogs | ||
| 653 | |a high seismicity criteria | ||
| 653 | |a tidal triggering of earthquakes | ||
| 653 | |a seismic cycle | ||
| 653 | |a coulomb failure stress | ||
| 653 | |a preparatory phase | ||
| 653 | |a seismic prediction | ||
| 653 | |a earthquake forecasting | ||
| 653 | |a precursors | ||
| 653 | |a statistical seismology | ||
| 653 | |a earthquake likelihood models | ||
| 653 | |a seismicity patterns | ||
| 653 | |a New Zealand | ||
| 653 | |a California | ||
| 653 | |a smoothed seismicity methods | ||
| 653 | |a global seismicity | ||
| 653 | |a foreshocks and aftershocks | ||
| 653 | |a earthquake forecasting model | ||
| 653 | |a statistical methods | ||
| 653 | |a magnitude-frequency distribution | ||
| 653 | |a corner magnitude | ||
| 653 | |a tapered Pareto | ||
| 653 | |a tapered Gutenberg-Richter | ||
| 653 | |a epidemic type aftershock sequence model | ||
| 653 | |a extreme value distribution | ||
| 653 | |a Bayesian predictive distribution | ||
| 653 | |a seismicity clustering | ||
| 653 | |a DBSCAN algorithm | ||
| 653 | |a markovian arrival processes | ||
| 653 | |a numerical modeling | ||
| 653 | |a earthquake simulator | ||
| 653 | |a earthquake clustering | ||
| 653 | |a northern and central Apennines | ||
| 653 | |a n/a | ||
| 776 | |z 3-0365-4263-9 | ||
| 776 | |z 3-0365-4264-7 | ||
| 700 | 1 | |a Giovambattista, Rita Di |4 edt | |
| 700 | 1 | |a Shcherbakov, Robert |4 edt | |
| 700 | 1 | |a Vallianatos, Filippos |4 edt | |
| 700 | 1 | |a Gentili, Stefania |4 oth | |
| 700 | 1 | |a Giovambattista, Rita Di |4 oth | |
| 700 | 1 | |a Shcherbakov, Robert |4 oth | |
| 700 | 1 | |a Vallianatos, Filippos |4 oth | |
| 906 | |a BOOK | ||
| ADM | |b 2023-03-03 01:55:17 Europe/Vienna |f system |c marc21 |a 2022-07-14 08:50:39 Europe/Vienna |g false | ||
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