Abstract
The convergence of the Indian, Eurasian, and Burma tectonic plates around the Bengal Basin is one of the most seismically complex regions on Earth. However, this zone is typically studied using conventional seismic zoning, which relies on subjective fault definitions and overlooks the structural heterogeneities in the sediment-filled basin. This study applies unsupervised machine learning, specifically Density-Based Spatial Clustering of Applications with Noise (DBSCAN), to analyse the United States Geological Survey (USGS) earthquake catalogue for this area between 1925 and 2025. By transforming the geographic coordinates into a Cartesian coordinate system and a rheologically weighted depth parameter, we identified more than 43 different seismic clusters. Structural orientation was determined using Principal Component Analysis (PCA), and stress regimes were determined using the Gutenberg-Richter b-value. The findings were categorized into high-stress zones (b 1.0). From the event analogue of the recent 2025 Mw 7.7 rupture in the Sangaing fault, we found a similar prolonged seismic sleep near Sreemangal. The observed combination of prolonged quiescence and low b-values is consistent with a locked fault segment, as defined in classical seismic gap theory. The southern end of Cluster 6 on the northern Shillong Plateau is associated with the Oldham fault system, suggesting a potentially locked fault segment. The result stability was confirmed by statistical analysis, which provides new insights into the division of the megathrust and crustal faults, suggesting comparatively elevated seismic hazard potential that warrants careful monitoring.
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Seismotectonic Clustering and Seismic Gap Identification in the Indo-Eurasian-Burman Triple Junction Region | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 10 February 2026 V1 Latest version Share on Seismotectonic Clustering and Seismic Gap Identification in the Indo-Eurasian-Burman Triple Junction Region Authors : Md. Asif Uz Zaman Antu 0009-0009-8196-0823 , Mohammad Solaiman 0009-0008-2806-5443 , Md. Sajjadul Islam Fahim , and Md Anwar Hossain Bhuiyan 0000-0002-2445-2912 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177075252.22824743/v1 186 views 123 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The convergence of the Indian, Eurasian, and Burma tectonic plates around the Bengal Basin is one of the most seismically complex regions on Earth. However, this zone is typically studied using conventional seismic zoning, which relies on subjective fault definitions and overlooks the structural heterogeneities in the sediment-filled basin. This study applies unsupervised machine learning, specifically Density-Based Spatial Clustering of Applications with Noise (DBSCAN), to analyse the United States Geological Survey (USGS) earthquake catalogue for this area between 1925 and 2025. By transforming the geographic coordinates into a Cartesian coordinate system and a rheologically weighted depth parameter, we identified more than 43 different seismic clusters. Structural orientation was determined using Principal Component Analysis (PCA), and stress regimes were determined using the Gutenberg-Richter b-value. The findings were categorized into high-stress zones (b 1.0). From the event analogue of the recent 2025 Mw 7.7 rupture in the Sangaing fault, we found a similar prolonged seismic sleep near Sreemangal. The observed combination of prolonged quiescence and low b-values is consistent with a locked fault segment, as defined in classical seismic gap theory. The southern end of Cluster 6 on the northern Shillong Plateau is associated with the Oldham fault system, suggesting a potentially locked fault segment. The result stability was confirmed by statistical analysis, which provides new insights into the division of the megathrust and crustal faults, suggesting comparatively elevated seismic hazard potential that warrants careful monitoring. Supplementary Material File (1065510_0_merged_1770555679.pdf) Download 1.90 MB File (seismotectonic_v10.docx) Download 73.80 MB Information & Authors Information Version history V1 Version 1 10 February 2026 Copyright This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License Keywords geophysics machine learning magnitude risk identification seismology Authors Affiliations Md. Asif Uz Zaman Antu 0009-0009-8196-0823 University of Dhaka View all articles by this author Mohammad Solaiman 0009-0008-2806-5443 University of Dhaka View all articles by this author Md. Sajjadul Islam Fahim University of Dhaka View all articles by this author Md Anwar Hossain Bhuiyan 0000-0002-2445-2912 [email protected] University of Dhaka View all articles by this author Metrics & Citations Metrics Article Usage 186 views 123 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Md. Asif Uz Zaman Antu, Mohammad Solaiman, Md. Sajjadul Islam Fahim, et al. Seismotectonic Clustering and Seismic Gap Identification in the Indo-Eurasian-Burman Triple Junction Region. Authorea . 10 February 2026. DOI: https://doi.org/10.22541/au.177075252.22824743/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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