Imtiyaz A. Parvez

Estimation of seismic hazard and risks for the Himalayas and surrounding regions based on Unified Scaling Law for Earthquakes

Abstract To estimate seismic hazard, the basic law of seismicity, the Gutenberg–Richter recurrence relation, is applied in a modified form involving a spatial term:

Neo-deterministic seismic hazard scenarios for India???a preventive tool for disaster mitigation

\log N\left( {M,\;L} \right) = A - B\left( {M - 5} \right) + C\log L

Seismic hazard and risk assessment based on Unified Scaling Law for Earthquakes: thirteen principal urban agglomerations of India

logNM,L=A-BM-5+ClogL, where N(M,L) is the expected annual number of earthquakes of a certain magnitude M within an area of linear size L. The parameters A, B, and C of this Unified Scaling Law for Earthquakes (USLE) in the Himalayas and surrounding regions have been studied on the basis of a variable space and time-scale approach. The observed temporal variability of the A, B, and C coefficients indicates significant changes of seismic activity at the time scales of a few decades. At global scale, the value of A ranges mainly between −1.0 and 0.5, which determines the average rate of earthquakes that accordingly differs by a factor of 30 or more. The value of B concentrates about 0.9 ranging from under 0.6 to above 1.1, while the fractal dimension of the local seismic prone setting, C, changes from 0.5 to 1.4 and larger. For Himalayan region, the values of A, B, and C have been estimated mainly ranging from −1.6 to −1.0, from 0.8 to 1.3, and from 1.0 to 1.4, respectively. We have used the deterministic approach to map the local value of the expected peak ground acceleration (PGA) from the USLE estimated maximum magnitude or, if reliable estimation was not possible, from the observed maximum magnitude during 1900–2012. In result, the seismic hazard map of the Himalayas with spatially distributed PGA was prepared. Further, an attempt is made to generate a series of the earthquake risk maps of the region based on the population density exposed to the seismic hazard.

The Role of Microzonation in Estimating Earthquake Risk

The Unified Scaling Law for Earthquakes (USLE), that generalizes the Gutenberg–Richter recurrence relation, has evident implications since any estimate of seismic hazard depends on the size of territory that is used for investigation, averaging, and extrapolation into the future. Therefore, the hazard may differ dramatically when scaled down to the proportion of the area of interest (e.g. a city) from the enveloping area of investigation. In fact, given the observed patterns of distributed seismic activity the results of multi-scale analysis embedded in USLE approach demonstrate that traditional estimations of seismic hazard and risks for cities and urban agglomerations are usually underestimated. Moreover, the USLE approach provides a significant improvement when compared to the results of probabilistic seismic hazard analysis, e.g. the maps resulted from the Global Seismic Hazard Assessment Project (GSHAP). In this paper, we apply the USLE approach to evaluating seismic hazard and risks to population of the three territories of different size representing a sub-continental and two different regional scales of analysis, i.e. the Himalayas and surroundings, Lake Baikal, and Central China regions.

Identifying the Transition Zone Between East and West Dharwar Craton by Seismic Imaging

Current computational resources and physical knowledge of the seismic wave generation and propagation processes allow for reliable numerical and analytical models of waveform generation and propagation. From the simulation of ground motion, it is easy to extract the desired earthquake hazard parameters. Accordingly, a scenario-based approach to seismic hazard assessment has been developed, namely the neo-deterministic seismic hazard assessment (NDSHA), which allows for a wide range of possible seismic sources to be used in the definition of reliable scenarios by means of realistic waveforms modelling. Such reliable and comprehensive characterization of expected earthquake ground motion is essential to improve building codes, particularly for the protection of critical infrastructures and for land use planning. Parvez et al. (Geophys J Int 155:489–508, 2003) published the first ever neo-deterministic seismic hazard map of India by computing synthetic seismograms with input data set consisting of structural models, seismogenic zones, focal mechanisms and earthquake catalogues. As described in Panza et al. (Adv Geophys 53:93–165, 2012), the NDSHA methodology evolved with respect to the original formulation used by Parvez et al. (Geophys J Int 155:489–508, 2003): the computer codes were improved to better fit the need of producing realistic ground shaking maps and ground shaking scenarios, at different scale levels, exploiting the most significant pertinent progresses in data acquisition and modelling. Accordingly, the present study supplies a revised NDSHA map for India. The seismic hazard, expressed in terms of maximum displacement (Dmax), maximum velocity (Vmax) and design ground acceleration (DGA), has been extracted from the synthetic signals and mapped on a regular grid over the studied territory.

Seismic hazard and seismic risk assessment based on the unified scaling law for earthquakes: Himalayas and adjacent regions

Earthquakes constitute among the most feared natural hazards and these occur with no warning which can result in great destruction and loss of lives, particularly in developing countries. One way to mitigate the destructive impact of such earthquakes is to conduct a seismic hazard assessment and take remedial measures. This article aims at demonstrating significant contributions in the field of seismic zonation and microzonation studies in the Indian subcontinent. The contributions in the field of earthquake hazard have been very valuable and beneficial not only for science but also for society. The historical seismicity and seismic zonation studies as well as the present scenario of seismic hazard assessment in the Indian subcontinent, whether through probabilistic or deterministic approaches, are discussed. It has been found that many parts of the Himalayan region have peak acceleration values exceeding 0.6g. The epicentral areas of the great Assam earthquakes of 1897 and 1950 in northeast India represent the maximum hazard with acceleration values reaching 1.2–1.3g. The peak velocity and displacement in the same region is estimated as 120–177 cm s−1 and 60–90 cm, respectively. To mitigate seismic risk, it is necessary to define a correct response in terms of both peak ground acceleration and spectral amplification. These factors are highly dependent on local soil conditions and on the source characteristics of the expected earthquakes. This article will also present the findings of site-specific hazard assessment in megacities.

A deterministic seismic hazard map of India and adjacent areas

The deterministic seismic hazard map of India with spatially distributed peak ground acceleration was used to estimate seismic risk using two data sets of the Indian population—the model population data set and the data set based on India’s Census 2011. Four series of the earthquake risk maps of the region based on these two population density sets were cross-compared. The discrepancy of the population data and seismic risks estimation were illuminated for the thirteen principal urban agglomerations of India. The confirmed fractal nature of earthquakes and their distribution in space implies that traditional probabilistic estimations of seismic hazard and risks of cities and urban agglomerations are usually underestimated. The evident patterns of distributed seismic activity follow the Unified Scaling Law for Earthquakes, USLE, which generalizes Gutenberg–Richter recurrence relation. The results of the systematic global analysis imply that the occurrence of earthquakes in a region is characterized with USLE: log10N (M, L) = A + B × (5 − M) + C × log10L, where N(M, L)—expected annual number of earthquakes of magnitude M within an area of liner size L, A determines seismic static rate, B—balance between magnitude ranges, and C—fractal dimension of seismic loci. We apply the seismic hazard and risk assessment methodology developed recently based on USLE, pattern recognition of earthquake-prone geomorphic nodes, and neo-deterministic scenarios of destructive ground shaking. Objects of risk are individuals (1) as reported in the 2011 National Census data and (2) as predicted for 2010 by Gridded Population of the World (model GPWv3); vulnerability depends nonlinearly on population density. The resulting maps of seismic hazard and different risk estimates based on population density are cross-compared. To avoid misleading interpretations, we emphasize that risk estimates presented here for academic purposes only. In the matter of fact, they confirm that estimations addressing more realistic and practical kinds of seismic risks should involve experts in distribution of objects of risk of different vulnerability, i.e., specialists in earthquake engineering, social sciences, and economics.

Estimation of coda wave attenuation for NW Himalayan region using local earthquakes

Abstract This chapter is dedicated to understanding the role of seismic zonation and microzonation, as well as understanding seismic risk analysis and mitigation strategy. The merits and demerits of various approaches to estimating earthquake hazard are discussed in terms of whether it is probabilistic, deterministic, or neodeterministic. The importance of geotechnical, geomorphological, and geological databases for seismic microzonation has been highlighted along with various techniques available to characterize site conditions. A variety of tools currently in use illustrate the basic principles of microzonation mapping at different scales. The main parameters involved in earthquake loss assessments and evaluating the influence of soil conditions on these estimates are discussed using QLARM, an advanced seismic risk estimation tool, for a few case histories.