- 1.6Impact Factor
- 2.2CiteScore
- 18 daysTime to First Decision
GeoHazards, Volume 3, Issue 2
June 2022 - 11 articles
Cover Story: This paper looks at earthquake nowcasting, which is the estimation of hazards in the present, the immediate past, and the near future. It reviews some previous methods and introduces new approaches based on deep learning using three distinct models based on recurrent neural networks and transformers. It discusses different choices for observables and measures presenting promising initial results for a region of Southern California using data from 1950 to 2020. The neural net learns patterns of activity to identify hidden variables holding a clue for future activity. The overall estimation quality is measured by the Nash Sutcliffe efficiency comparing the deviation of nowcast and observation with the variance over time in each spatial region. The software is available as open-source. View this paper
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GeoHazards - ISSN 2624-795X