An integrated assessment of seismic hazard exposure and its societal impact in Seven Sister States of North Eastern Region of India for sustainable disaster mitigation planning

The Seven Sister States of the North Eastern Region of India, located on the complex 13 seismotectonic belt, is characterized by high seismicity. A comprehensive seismic hazard 14 exposure assessment is carried out by quantifying hazard using a probabilistic approach, 15 vulnerability by factor analysis, and exposure mapping by integrating seismic hazard and 16 vulnerability. Peak ground acceleration (PGA) values at bedrock are calculated with the help 17 of ground motion prediction equations (GMPE) for 10% probability of exceedance in 50 years 18 (475 years) and 100 years (950 years), and 2% probability of exceedance in 50 years (2475 19 years). The resulting spatial distribution of the PGA values considering return periods of 475, 20 950, and 2475 years are presented through seismic hazard maps. The social vulnerability 21 analysis indicates that 21 districts covering 91.43% area of the state of Assam and the entire 22 state of Tripura are under high vulnerability. With the help of spatial cluster analysis, it is found that 17.14% of the study area are having an average social vulnerability index (SVI) score of 24 0.329 and therefore can be considered as hotspots. Through seismic hazard analysis, it is 25 observed that more than 50% of the area of North East India is under moderate to very high 26 exposure class. The seismic hazard maps developed can help in disaster mitigation planning 27 and execution leading to sustainable development goals and targets. 28


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The North Eastern Region (NER) of India comprises eight states, namely Arunachal Pradesh,32 Assam, Manipur, Meghalaya, Mizoram, Nagaland, Tripura, and Sikkim. The Seven Sister 33 States (SSS) of India is a popular term for the seven contiguous states in NER except for 34 Sikkim. The Himalayan arc ranges, extending from west-northwest to east-southeast of India, 35 lies near the subduction zone of Indian and Eurasian tectonic plates. Due to the collision of the 36 Indian plate with the Tibet plateau towards the northern part and the Burmese landmass towards 37 the east, the formation of seismotectonic features like the Himalayan thrust, Arakan-Yoma, 38 Naga Hills, and Tripura fold have resulted (Verma and Kumar 1987). The Himalayan tectonic 39 feature in north-eastern India is very complex and exhibits high seismicity. Due to its 40 geological, geomorphological, and seismotectonic setting, the NER is highly exposed to 41 seismic hazards. The region has suffered extensive loss of lives and damage to property due to 42 significant earthquakes in the past. On 28 April 2021, an Mw 6.0 earthquake occurred near 43 Dhekiajuli in Assam, India, leading to ground cracking and the collapse of several houses. For 44 the past few decades, The NER has been experiencing high seismic risk, which can be 45 attributed to an increase in population density and unplanned rapid urbanization and 46 infrastructure developments.
Quantifying seismic risk by assessment of hazard and vulnerability at a regional level is a hazard by considering different assessment frameworks like the Hazard of Place Model, HoP 73 (Cutter 1996) seismically active regions in the world. As per IS1893 (2016), it is categorized as the most severe seismic zone in India i.e. zone V. Its tectonic setting is shown in Fig. 2 terms of fault properties, seismic source, geology, and plate tectonics (Fig. 4).  Table 2.

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The completeness study of seismic data in terms of time, as shown in Fig. 5, was performed 186 using the statistical analysis proposed by Stepp (1972). The magnitude ranges of Mw 3.0 -4.0, The seismicity of a region can be described by seismic parameters a and b, which correlate 193 with the rate of occurrence of an event of a particular size. The distribution of event sizes in a 194 given period is best described by a most widely accepted Gutenberg-Richter recurrence law 195 (Kramer 1996) as given by Eq. 1.

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Where N represents the number of cumulative events, per year, greater than an event of given 198 moment magnitude; a and b are constants of regression, known as seismic parameters.

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Based on the completeness study, the earthquake catalogue of the recent 70 years is considered  Table 2.  The largest possible earthquake, Mmax, that a seismic source can produce ever is an important   Table 3.   In the present study, six GMPEs are selected ( plotted, as shown in Fig. 8(a-e). It can be seen that irrespective of the Mw and h combinations,

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ANBA2013 and NATH2012 predicted PGA values are relatively lower than the observed ones.

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Besides, ATBO2003 is found to have overestimated the PGA for shorter distances and 268 underestimated for longer distances (Fig.8). BAHU2020 also underestimates the PGA, making Where PGA in g, R is the shortest source-to-site distance, and SE is the standard error 2 Atkinson and Boore (2003) where Y in cm/s 2 , , ,  In order to evaluate seismic hazard at bedrock level, using a probabilistic approach, the entire 289 study area was divided into a grid size of 0.2° x 0.2°. Each grid centre is considered as the site 290 of interest at which the seismic hazard in terms of PGA is evaluated by considering all the 291 active seismic sources within a radius of 500 km.

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The procedure followed for PSHA assumes that an event within a seismic source follows a 293 stationary Poisson process (Kramer 1996). The probability of GMP, Y, exceeding a specified  The typical PDF of magnitude and distance is shown in Fig. 9. To produce the hazard curve   Table 5. The social vulnerability index (SVI) is evaluated using the steps summarized below.      ArcGIS is employed to identify the spatial clusters within a specific area (Brandt et al. 2020). correction is applied to identify spatial clusters at the local level better.   and SVI were then integrated to prepare the exposure maps for the study region.

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From Table 7, it can be seen that the seismic parameters, a and b, obtained from the present   factors is also confirmed by observing the change in slope of the scree plot (Fig. 11). These 424 three factors cumulatively explain the 90.534 percent variance among the datasets. The 425 descriptive statistic of each factor is given in Table 6.

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The spatial cluster analysis given in Fig. 16a represents hotspot and cold spot analysis. Finally, the seismic hazard map (Fig 17a) is integrated with the social vulnerability map (Fig   477   15), and the exposure map of the study area is prepared and analyzed using the risk matrix in 478 Fig. 12. Fig. 17b  for publication that could have appeared to influence the work reported in this paper.