Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/9900
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dc.contributor.authorallMulargia, F.; Dipartimento di Fisica e Astronomia - Università di Bolognaen
dc.contributor.authorallGaperini, P.; Dipartimento di Fisica e Astronomia - Università di Bolognaen
dc.contributor.authorallLolli, B.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Bologna, Bologna, Italiaen
dc.contributor.authorallStark, P. B.; Department of Statistics, Code 3860. University of California, Berkeley CAen
dc.date.accessioned2015-06-12T06:42:36Zen
dc.date.available2015-06-12T06:42:36Zen
dc.date.issued2015en
dc.identifier.urihttp://hdl.handle.net/2122/9900en
dc.description.abstractThe destructive 2009 L’Aquila and 2012 Emilia Romagna earthquakes led the Italian Dipartimento della Protezione Civile (DPC) to fund nine groups studying seismic precursors. Three of the groups produced testable predictions by the DPC deadline of 31 May 2013, using: (1) Radon in a well in Friuli, (2) temperature, flow, CO2 flux, and other variables measured in wells in Emilia Romagna, and (3) an artificial neural network (ANN) algorithm applied to seismicity. We evaluated the geochemical precursors by comparing their success to that of an equal number of predictions at the same locations and with the same individual and total durations as the actual predictions, but at random times. This approach avoids modeling seismicity and thereby precludes concluding that predictions are “good” simply because the model for seismicity is bad. Neither precursor predicts significantly better than chance. ANN was a poor predictor of events large enough to affect public safety.en
dc.description.sponsorshipItalian Presidenza del Consiglio dei Ministri – Dipartimento della Protezione Civile (DPC)en
dc.language.isoEnglishen
dc.publisher.nameIstituto Nazionale di Oceanografia e di Geofisica Sperimentale-OGSen
dc.relation.ispartofBollettino di Geofisica Teorica ed Applicataen
dc.relation.ispartofseries2/56 (2015)en
dc.subjectseismic precursors, statistical analysisen
dc.titlePurported Precursors: Poor Predictorsen
dc.typearticleen
dc.description.statusPublisheden
dc.type.QualityControlPeer-revieweden
dc.description.pagenumber351-356en
dc.identifier.URLhttp://www3.ogs.trieste.it/bgta/provapage.php?id_articolo=662en
dc.subject.INGV05. General::05.01. Computational geophysics::05.01.04. Statistical analysisen
dc.description.obiettivoSpecifico3T. Pericolosità sismica e contributo alla definizione del rischioen
dc.description.journalTypeJCR Journalen
dc.description.fulltextrestricteden
dc.relation.issn0006-6729en
dc.contributor.authorMulargia, F.en
dc.contributor.authorGaperini, P.en
dc.contributor.authorLolli, B.en
dc.contributor.authorStark, P. B.en
dc.contributor.departmentDipartimento di Fisica e Astronomia - Università di Bolognaen
dc.contributor.departmentDipartimento di Fisica e Astronomia - Università di Bolognaen
dc.contributor.departmentIstituto Nazionale di Geofisica e Vulcanologia, Sezione Bologna, Bologna, Italiaen
dc.contributor.departmentDepartment of Statistics, Code 3860. University of California, Berkeley CAen
item.openairetypearticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.grantfulltextrestricted-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
crisitem.author.deptDipartimento di Fisica e Astronomia - Università di Bologna-
crisitem.author.deptDipartimento di Fisica e Astronomia - Università di Bologna-
crisitem.author.deptIstituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Bologna, Bologna, Italia-
crisitem.author.deptDepartment of Statistics, Code 3860. University of California, Berkeley CA-
crisitem.author.orcid0000-0003-4186-9055-
crisitem.author.parentorgIstituto Nazionale di Geofisica e Vulcanologia-
crisitem.classification.parent05. General-
crisitem.department.parentorgIstituto Nazionale di Geofisica e Vulcanologia-
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