Earthquake Probability Assessment for the Indian Subcontinent Using Deep Learning.

Earthquake prediction is a popular topic among earth scientists; however, this task is challenging and exhibits uncertainty therefore, probability assessment is indispensable in the current period. During the last decades, the volume of seismic data has increased exponentially, adding scalability issues to probability assessment models. Several machine learning methods, such as deep learning, have been applied to large-scale images, video, and text processing; however, they have been rarely utilized in earthquake probability assessment. Therefore, the present research leveraged advances in deep learning techniques to generate scalable earthquake probability mapping. To achieve this objective, this research used a convolutional neural network (CNN). Nine indicators, namely, proximity to faults, fault density, lithology with an amplification factor value, slope angle, elevation, magnitude density, epicenter density, distance from the epicenter, and peak ground acceleration (PGA) density, served as inputs. Meanwhile, 0 and 1 were used as outputs corresponding to non-earthquake and earthquake parameters, respectively. The proposed classification model was tested at the country level on datasets gathered to update the probability map for the Indian subcontinent using statistical measures, such as overall accuracy (OA), F1 score, recall, and precision. The OA values of the model based on the training and testing datasets were 96% and 92%, respectively. The proposed model also achieved precision, recall, and F1 score values of 0.88, 0.99, and 0.93, respectively, for the positive (earthquake) class based on the testing dataset. The model predicted two classes and observed very-high (712,375 km2) and high probability (591,240.5 km2) areas consisting of 19.8% and 16.43% of the abovementioned zones, respectively. Results indicated that the proposed model is superior to the traditional methods for earthquake probability assessment in terms of accuracy. Aside from facilitating the prediction of the pixel values for probability assessment, the proposed model can also help urban-planners and disaster managers make appropriate decisions regarding future plans and earthquake management

Tectonic geodesy revealing geodynamic complexity of the Indo-Burmese arc region, North East India

The plate boundary between India and Sunda plates across the Indo-Burmese arc (IBA) region is probably the most neglected domain as far as the plate motion, crustal deformation and earthquake occurrence processes are concerned. Because of the limited or no geodetic measurements across the IBA region, debate continues on the most appropriate plate boundary model for the region. Subduction along this boundary occurred in geological past, but whether it is still active is a debatable issue. It is believed that the predominantly northward India–Sunda relative plate motion of about 36 mm/year is partitioned between the Indo-Burmese wedge (IBW) and the Sagaing Fault (SF). However, it is not clear how relative plate motion between India and Sunda plates is accommodated across the IBA region – whether localized, partitioned or distributed, and in particular what is the slip rate and mode of slip accommodation across faults in the region? In such cases, Global Positioning System (GPS) measurements of crustal deformation have proved to be the best and probably the only tool. Our detailed seismo-tectonic study, crustal deformation study using high precision GPS measurements of eight years, strain rate estimates, field studies, analytical and finite element modelling of GPS data from the IBW region in North East India provide evidence for present-day active deformation front (or the plate boundary fault) between the India and Burma plates. On the basis of our extensive studies, it is now suggested that the Churachandpur–Mao Fault (CMF), a geologically older thrust fault, accommodates motion of about 16 mm/year through dextral strike–slip manner. The motion across the CMF constitutes about 43% of the relative plate motion of 36 mm/year between the India and Sunda plates. The remaining motion is accommodated at SF. On the basis of modelling, which suggests low friction along the CMF, absence of low-magnitude seismicity along the CMF, lack of historic and great and major earthquakes on the CMF and regions around it, and field studies, it is proposed that the motion across the CMF occurs predominantly in an aseismic manner. Such behaviour of the CMF significantly lowers the seismic hazard in the region

Principles and Practices of Seismic Microzonation: Case Studies in India

This paper presents an overview of the principles and practices of seismic microzonation with some case studies in India. India has experienced major damages and loss of life due to earthquakes. Macrozonation map in Indian seismic code IS-1893 is frequently revised soon after a major earthquake in the country. New revision which was published in 2002 after Bhuj earthquake in 2001 contains four macro zones. These zones are based on geology and limited seismology input without considering geotechnical aspects such as site effects and liquefaction. In order to understand the earthquake vulnerability of major urban centers and prepare new zonation map, the Govt. of India has initiated microzonation of 63 cities in India after 2001 earthquake. Many microzonation studies are under progress and few of them have been completed. This paper presents an overview of these studies. Seismic microzonation of Jabalpur urban area is the first work in India towards seismic microzonation of Indian cities. Jabalpur study has provided many learning lessons to other studies. Preliminary microzonation of Delhi has been completed and detailed one is under progress. Seismic Hazard and Microzonation Atlas of the Sikkim Himalaya has been published with geological and seismological background. Microzonation of Guwahati was done based on geology, geomorphology, seismotectonics, soil characteristics, pre-dominant frequencies, peak ground acceleration, seismic hazard and demography. Seismic Microzonation of Dehradun has been prepared based on shear wave velocity with site response. First order Microzonation of Haldia has been developed based on peak ground acceleration, predominant frequency and elevation map. Different maps and results were presented for Gujarat microzonation based on noise survey and after shock data. None of these studies included the geotechnical aspects. The geotechnical aspects were fully incorporated in the recently completed Microzonation work of Bangalore and the ongoing study of Chennai microzonation. An overview of seismic microzonation studies in India is presented in this paper

GIS-based Earthquake Disaster Management A case study for Solapur city (Maharashtra, India)

This paper aims to demonstrate a Geographic Information System (GIS)-based study on development of City Disaster Management System for earthquake for Solapur city (India).An approach has been designed to explore the scope for the combination of Disaster Management and GIS. The disaster-prone areas have been identified and their positions are marked using ArcView 9.1. GIS has been exploited to obtain the spatial information for the effective disaster management for earthquake-affected areas. ArcView 9.1 has been used as a tool for storing all types of relevant data for analysis and decision making. The various thematic maps include road network map, drinking water sources map, land use map, population density map, ward boundaries and location of slums. The paper proposes development of a GIS-based early response system, and an emergency preparedness plan for the Solapur city and also analysis of the impact of earthquake disasters in the region and its relationship to infrastructure development with a view to identifying how local governing bodies could be helped in addressing these issues. The proposed GIS-based flood mitigation and management program would improve the current practices of disaster management process. If implemented properly, it would result in proper and quick decisions for the rescue and safetyof the general public, which in turn would help in minimizing loss of life and propert

Shear wave velocity estimation in the Bengal Basin, Bangladesh by HVSR analysis: implications for engineering bedrock depth

The S-wave velocity (VS) in the Bengal Basin, Bangladesh has not been resolved from the ground surface to an intermediate depth in a regional context despite its importance for seismic hazard and risk evaluation. For this reason, we estimated VS profiles beneath 19 seismic stations in Bangladesh to a depth approximately 2800 m by employing full horizontal to vertical spectral ratio (HVSR) curve inversion under the diffuse field theory for the noise wavefield. The seismic stations are concentrated in three tectonic zones within the basin: the Surma basin (SB, Zone 1), Bengal Foredeep (BF, Zone 2), and Chittagong Tripura Fold Belt (CTFB, Zone 3). Full HVSR analysis (from 0.2 to 10 Hz) allowed us to obtain deep profiles with combined insights from shallow geotechnical boreholes and deep P-wave velocity (VP) information from active seismic surveys. From the resultant VS profiles, engineering bedrock (VS > 760 m/s) depths were also identified throughout the study area for the first time. The VS profiles within the Holocene to Miocene sedimentary sequences showed rapid variations from location to location. This is due to the highly variable near-surface geology caused by the dense and complex river network and tectonic deformation in Bangladesh. Except for three stations, the engineering bedrock depth exceeded 30 m at all stations. These results indicate the existence of deep soft soil in the study area, where VS³⁰ based site characterization is inappropriate. Furthermore, seismic site response was estimated at a station (DHAK) by simulating a subduction zone earthquake. The resulting response spectrum (RS) exhibited ground motion amplification over a longer period, suggesting that multistory buildings at the site may be at risk if subjected to large earthquakes. The outcomes of this study can serve as useful guidelines for seismic risk reduction planning in Bangladesh

Earthquake risk assessment in NE India using deep learning and geospatial analysis

Earthquake prediction is currently the most crucial task required for the probability, hazard, risk mapping, and mitigation purposes. Earthquake prediction attracts the researchers' attention from both academia and industries. Traditionally, the risk assessment approaches have used various traditional and machine learning models. However, deep learning techniques have been rarely tested for earthquake probability mapping. Therefore, this study develops a convolutional neural network (CNN) model for earthquake probability assessment in NE India. Then conducts vulnerability using analytical hierarchy process (AHP), Venn's intersection theory for hazard, and integrated model for risk mapping. A prediction of classification task was performed in which the model predicts magnitudes more than 4 Mw that considers nine indicators. Prediction classification results and intensity variation were then used for probability and hazard mapping, respectively. Finally, earthquake risk map was produced by multiplying hazard, vulnerability, and coping capacity. The vulnerability was prepared by using six vulnerable factors, and the coping capacity was estimated by using the number of hospitals and associated variables, including budget available for disaster management. The CNN model for a probability distribution is a robust technique that provides good accuracy. Results show that CNN is superior to the other algorithms, which completed the classification prediction task with an accuracy of 0.94, precision of 0.98, recall of 0.85, and F1 score of 0.91. These indicators were used for probability mapping, and the total area of hazard (21,412.94 Km ), vulnerability (480.98 Km ), and risk (34,586.10 Km ) was estimated. 2 2