{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T12:09:04Z","timestamp":1753358944345,"version":"3.37.3"},"reference-count":90,"publisher":"Seismological Society of America (SSA)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n               <jats:p>Ground-motion models (GMMs) are fundamental for the estimation of ground shaking for probabilistic seismic hazard assessment. Because of the paucity of ground motion recordings in regions of low seismicity, stochastic approaches are often employed to generate synthetic data. In this study, we developed a GMM using a stochastic simulation approach for southwest Iberia\u2014a zone for which seismic hazard is usually assessed using models developed for other regions. We collected geological, tectonic, and ground-motion data for offshore and inland Iberia, and calibrated several parameters for a stochastic simulation. The resulting synthetic response spectra were used to train a machine learning algorithm (artificial neural network) capable of predicting peak ground acceleration, peak ground velocity, and spectral acceleration on rock (VS30=760\u2009\u2009m\/s), along with the associated between-event and within-event terms. The resulting model was compared against other existing models for stable continental regions and ground-motion recordings for Portugal and Spain. The results indicate a good agreement with observations and the model can be used directly in probabilistic seismic hazard analysis for southwest Iberia.<\/jats:p>","DOI":"10.1785\/0120230250","type":"journal-article","created":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T13:11:27Z","timestamp":1720703487000},"page":"2613-2638","source":"Crossref","is-referenced-by-count":1,"title":["An Earthquake Ground-Motion Model for Southwest Iberia"],"prefix":"10.1785","volume":"114","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7012-3319","authenticated-orcid":false,"given":"Amir","family":"Taherian","sequence":"first","affiliation":[{"name":"1RISCO\/University of Aveiro, Aveiro, Portugal"}]},{"given":"Vitor","family":"Silva","sequence":"additional","affiliation":[{"name":"2Global Earthquake Model Foundation (GEM), Pavia, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2868-2328","authenticated-orcid":false,"given":"Petros","family":"Kalakonas","sequence":"additional","affiliation":[{"name":"3Zurich Insurance Group, Zurich, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5456-1642","authenticated-orcid":false,"given":"Romeu","family":"Vicente","sequence":"additional","affiliation":[{"name":"1RISCO\/University of Aveiro, Aveiro, Portugal"}]}],"member":"790","published-online":{"date-parts":[[2024,7,11]]},"reference":[{"key":"2024092319261760900_rf1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1603.04467","article-title":"TensorFlow: Large-scale machine learning on heterogeneous distributed systems","author":"Abadi","year":"2016"},{"key":"2024092319261760900_rf2","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1177\/8755293020942537","article-title":"Seismic risk assessment for the residential buildings of the major three cities in Colombia: Bogot\u00e1, Medell\u00edn, and Cali","author":"Acevedo","year":"2020","journal-title":"Earthq. 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