首页    期刊浏览 2025年01月21日 星期二
登录注册

文章基本信息

  • 标题:Deep Learning and Parallel Processing Spatio-Temporal Clustering Unveil New Ionian Distinct Seismic Zone
  • 本地全文:下载
  • 作者:Antonios Konstantaras
  • 期刊名称:Informatics
  • 电子版ISSN:2227-9709
  • 出版年度:2020
  • 卷号:7
  • 期号:4
  • 页码:39-48
  • DOI:10.3390/informatics7040039
  • 出版社:MDPI Publishing
  • 摘要:This research work employs theoretical and empirical expert knowledge in constructing an agglomerative parallel processing algorithm that performs spatio-temporal clustering upon seismic data. This is made possible by exploiting the spatial and temporal sphere of influence of the main earthquakes solely, clustering seismic events into a number of fuzzy bordered, interactive and yet potentially distinct seismic zones. To evaluate whether the unveiled clusters indeed depict a distinct seismic zone, deep learning neural networks are deployed to map seismic energy release rates with time intervals between consecutive large earthquakes. Such a correlation fails should there be influence by neighboring seismic areas, hence casting the seismic region as non-distinct, or if the extent of the seismic zone has not been captured fully. For the deep learning neural network to depict such a correlation requires a steady seismic energy input flow. To address that the western area of the Hellenic seismic arc has been selected as a test case due to the nearly constant motion of the African plate that sinks beneath the Eurasian plate at a steady yearly rate. This causes a steady flow of strain energy stored in tectonic underground faults, i.e., the seismic energy storage elements; a partial release of which, when propagated all the way to the surface, casts as an earthquake. The results are complementary two-fold with the correlation between the energy release rates and the time interval amongst large earthquakes supporting the presence of a potential distinct seismic zone in the Ionian Sea and vice versa.
  • 关键词:deep learning; neural networks; parallel algorithms; seismic big data; spatio-temporal clustering; heterogeneous programming; distinct seismic zones deep learning ; neural networks ; parallel algorithms ; seismic big data ; spatio-temporal clustering ; heterogeneous programming ; distinct seismic zones
国家哲学社会科学文献中心版权所有