{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:19:05Z","timestamp":1760239145982,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T00:00:00Z","timestamp":1601337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/informatics7040039","type":"journal-article","created":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T20:56:22Z","timestamp":1601412982000},"page":"39","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Deep Learning and Parallel Processing Spatio-Temporal Clustering Unveil New Ionian Distinct Seismic Zone"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1052-1948","authenticated-orcid":false,"given":"Antonios","family":"Konstantaras","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Hellenic Mediterranean University, 73133 Chania, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1109\/TNN.2007.915109","article-title":"On a Neural Approximator to ODEs","volume":"19","author":"Filici","year":"2008","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gurumoorthy, R., and Kodiyalam, S. (1996, January 15\u201317). Neural network approximator with a novel learning scheme for design optimization with variable complexity data. Proceedings of the 37th Structure, Structural Dynamics and Materials Conference, Salt Lake City, UT, USA.","DOI":"10.2514\/6.1996-1339"},{"key":"ref_3","unstructured":"Zomaya, A. (2004). Advanced Heterogeneous Parallel Programming in mpC. Parallel Computing on Heterogeneous Networks, Wiley. [1st ed.]."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Michelucci, U. (2018). Applied Deep Learning, Apress. [1st ed.].","DOI":"10.1007\/978-1-4842-3790-8"},{"key":"ref_5","unstructured":"Roy, P.S., Dwivedi, R.S., and Vijayan, D. (2015). Earthquake and Active Faults. Remote Sensing Applications, NRSC."},{"key":"ref_6","unstructured":"Earle, S. (2019). Plate Tectonics. Physical Geology, BCcampus. [2nd ed.]."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s12145-015-0236-0","article-title":"Expert knowledge-based algorithm for the dynamic discrimination of interactive natural clusters","volume":"9","author":"Konstantaras","year":"2016","journal-title":"Earth Sci. Inform."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1109\/JSTARS.2012.2227244","article-title":"Classification of distinct seismic regions and regional temporal modelling of seismicity in the vicinity of the Hellenic seismic arc","volume":"6","author":"Konstantaras","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5539\/esr.v1n2p1","article-title":"Intelligent spatial-clustering of seismicity in the vicinity of the Hellenic seismic arc","volume":"1","author":"Konstantaras","year":"2012","journal-title":"Earth Sci. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/0031-9201(89)90224-0","article-title":"Theory of electrokinetic effects occurring at the final stage in the preparation of a tectonic earthquake","volume":"57","author":"Dobrovolsky","year":"1989","journal-title":"Phys. Earth Planet. Inter."},{"key":"ref_11","first-page":"87","article-title":"The appearance times of earthquake precursors","volume":"5","author":"Zubkov","year":"1987","journal-title":"Izv. Akad. Nauk SSSR Fiz. Zemli (Solid Earth)"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1023\/A:1011473432628","article-title":"A catalog of aftershock sequences in Greece (1971\u20131997): Their spatial and temporal characteristics","volume":"5","author":"Drakatos","year":"2001","journal-title":"J. Seismol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1326","DOI":"10.1080\/13658816.2018.1434889","article-title":"Detecting spatial community structure in movements","volume":"32","author":"Guo","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102809","DOI":"10.1016\/j.cities.2020.102809","article-title":"Measuring megaregional structure in the Pearl River Delta by mobile phone signaling data: A complex network approach","volume":"104","author":"Zhang","year":"2020","journal-title":"Cities"},{"key":"ref_15","unstructured":"(2020, July 13). GI-NOA: Geodynamics Institute\u2014National Observatory of Athens. Available online: http:\/\/www.gein.noa.gr\/en."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0264-3707(01)00039-4","article-title":"A neural-network model for earthquake occurrence","volume":"32","author":"Bodri","year":"2001","journal-title":"Geodynamics"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Aggarwal, C. (2018). Training deep neural networks. Neural Networks and Deep Learning, Springer.","DOI":"10.1007\/978-3-319-94463-0"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1109\/LGRS.2008.916069","article-title":"Soft-computing modelling of seismicity in the southern Hellenic Arc","volume":"5","author":"Konstantaras","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1109\/LGRS.2006.887068","article-title":"Detection of weak seismo-electric signals upon the recordings of the electrotelluric field by means of neuro-fuzzy technology","volume":"4","author":"Konstantaras","year":"2007","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Maravelakis, E., Konstantaras, A., Kabassi, K., Chrysakis, I., Georgis, C., and Axaridou, A. (2014, January 7\u20139). 3DSYSTEK web-based point cloud viewer. Proceedings of the 5th International Conference on Information, Intelligence, Systems & Applications (IISA 2014), Chania, Greece.","DOI":"10.1109\/IISA.2014.6878726"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Axaridou, A., Chrysakis, I., Georgis, C., Theodoridou, M., Doerr, M., Konstantaras, A., and Maravelakis, E. (2014, January 7\u20139). 3D-SYSTEK: Recording and exploiting the production workflow of 3D-models in cultural heritage. Proceedings of the 5th International Conference on Information, Intelligence, Systems & Applications (IISA 2014), Chania, Greece.","DOI":"10.1109\/IISA.2014.6878745"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.compenvurbsys.2017.08.004","article-title":"Detecting and visualizing cohesive activity-travel patterns: A network analysis approach","volume":"66","author":"Zhang","year":"2017","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_23","unstructured":"(2020, July 13). Hellenic Survey of Geology & Mineral Exploration. Available online: www.igme.gr."},{"key":"ref_24","first-page":"80","article-title":"Analysis of intensive learning and supervised learning","volume":"1","author":"Jing","year":"2018","journal-title":"Acad. J. Comput. Inf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1109\/LGRS.2006.875435","article-title":"Neuro-fuzzy prediction-based adaptive filtering applied to severely distorted magnetic field recordings","volume":"3","author":"Konstantaras","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","unstructured":"Manish, J. (2018). Beginning Modern Unix, Apress. [1st ed.]."}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/7\/4\/39\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:15:02Z","timestamp":1760177702000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/7\/4\/39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,29]]},"references-count":26,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["informatics7040039"],"URL":"https:\/\/doi.org\/10.3390\/informatics7040039","relation":{},"ISSN":["2227-9709"],"issn-type":[{"type":"electronic","value":"2227-9709"}],"subject":[],"published":{"date-parts":[[2020,9,29]]}}}