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Open AccessArticle

Deep Learning and Parallel Processing Spatio-Temporal Clustering Unveil New Ionian Distinct Seismic Zone

Department of Electronic Engineering, Hellenic Mediterranean University, 73133 Chania, Greece
Received: 1 September 2020 / Revised: 23 September 2020 / Accepted: 27 September 2020 / Published: 29 September 2020
(This article belongs to the Special Issue Feature Papers in Big Data)
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. View Full-Text
Keywords: 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
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MDPI and ACS Style

Konstantaras, A. Deep Learning and Parallel Processing Spatio-Temporal Clustering Unveil New Ionian Distinct Seismic Zone. Informatics 2020, 7, 39. https://0-doi-org.brum.beds.ac.uk/10.3390/informatics7040039

AMA Style

Konstantaras A. Deep Learning and Parallel Processing Spatio-Temporal Clustering Unveil New Ionian Distinct Seismic Zone. Informatics. 2020; 7(4):39. https://0-doi-org.brum.beds.ac.uk/10.3390/informatics7040039

Chicago/Turabian Style

Konstantaras, Antonios. 2020. "Deep Learning and Parallel Processing Spatio-Temporal Clustering Unveil New Ionian Distinct Seismic Zone" Informatics 7, no. 4: 39. https://0-doi-org.brum.beds.ac.uk/10.3390/informatics7040039

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