BITS Faculty Publications
Permanent URI for this communityhttp://localhost:4000/handle/123456789/1867
Browse
13 results
Search Results
Item Estimation of Current Earthquake Hazard Through Nowcasting Method(Springer, 2022) Pasari, SumantaIn several tectonically active regions of the world, large magnitude earthquakes on fault systems are observed to occur in near-repetitive cycles as a consequence of stress accumulation and moment release. Since absolute measurements of stress–strain is unavailable through direct observations at all regions of interest, the area-based nowcasting method based on earthquake data is a potential alternative to estimate the uncertain current state of earthquake hazard in a defined region. Using the concept of natural-time counts, the nowcasting result comprises time-dependent earthquake potential score—a numerical quantification of earthquake-cycle progression since the last major event in the region. The nowcast score may be linked to the instantaneous risk of large events. This paper summarizes some basic formulation and key concepts of earthquake nowcasting with a demonstration of its applicability in disaster preparation and risk estimation. A case study from Java, Indonesia, is considered for illustration.Item Earthquake Forecasting in the Himalayas Artificial Neural Networks(Springer, 2022) Pasari, SumantaEarthquake is a natural phenomenon that causes huge loss in both life and property. Improvement of seismic hazard assessment requires integrated techniques such as geodetic, stochastic, and machine learning models. Forecasting of the time of the event, magnitude, and location of the epicenter of future events has been the major focus of several efforts in recent years. Many methods have been proposed to forecast the occurrence of earthquakes like statistical methods and other modeling approaches. Such methods are based on either the study of electric or magnetic signals or microseismicity patterns in which changes are experienced due to an upcoming event. In this study, our aim is to forecast earthquakes using neural networks, based on some seismicity indicators which capture the intrinsic information of the earthquake events. For this, an effective neural network architecture is created with different deep learning optimization algorithms and the results showed that the eight seismicity indicators have essentially captured most of the information of earthquake events. It is observed that neural networks are an effective tool for forecasting earthquakes as the neural networks well capture the nonlinearity and heterogeneity of inherent mechanisms with appropriate weights. The proposed network provides 90% accuracy and an F1-score of 0.89. It is hoped that this study shall provide useful information to the industry, academia, and government agencies to develop new standards of monitoring and mitigation measures of earthquake hazard.Item Three-parameter generalized exponential distribution in earthquake recurrence interval estimation(Springer, 2014-03) Pasari, SumantaThe purpose of this article is to study the three-parameter (scale, shape, and location) generalized exponential (GE) distribution and examine its suitability in probabilistic earthquake recurrence modeling. The GE distribution shares many physical properties of the gamma and Weibull distributions. This distribution, unlike the exponential distribution, overcomes the burden of memoryless property. For shape parameter β> 1, the GE distribution offers increasing hazard function, which is in accordance with the elastic rebound theory of earthquake generation. In the present study, we consider a real, complete, and homogeneous earthquake catalog of 20 events with magnitude above 7.0 (Yadav et al. in Pure Appl Geophys 167:1331–1342, 2010) from northeast India and its adjacent regions (20°–32°N and 87°–100°E) to analyze earthquake inter-occurrence time from the GE distribution. We apply the modified maximum likelihood estimation method to estimate model parameters. We then perform a number of goodness-of-fit tests to evaluate the suitability of the GE model to other competitive models, such as the gamma and Weibull models. It is observed that for the present data set, the GE distribution has a better and more economical representation than the gamma and Weibull distributions. Finally, a few conditional probability curves (hazard curves) are presented to demonstrate the significance of the GE distribution in probabilistic assessment of earthquake hazards.Item Distribution of Earthquake Interevent Times in Northeast India and Adjoining Regions(Springer, 2014-02) Pasari, SumantaThis study analyzes earthquake interoccurrence times of northeast India and its vicinity from eleven probability distributions, namely exponential, Frechet, gamma, generalized exponential, inverse Gaussian, Levy, lognormal, Maxwell, Pareto, Rayleigh, and Weibull distributions. Parameters of these distributions are estimated from the method of maximum likelihood estimation, and their respective asymptotic variances as well as confidence bounds are calculated using Fisher information matrices. Three model selection criteria namely the Chi-square criterion, the maximum likelihood criterion, and the Kolmogorov–Smirnov minimum distance criterion are used to compare model suitability for the present earthquake catalog (Yadav et al. in Pure Appl Geophys 167:1331–1342, 2010). It is observed that gamma, generalized exponential, and Weibull distributions provide the best fitting, while exponential, Frechet, inverse Gaussian, and lognormal distributions provide intermediate fitting, and the rest, namely Levy, Maxwell Pareto, and Rayleigh distributions fit poorly to the present data. The conditional probabilities for a future earthquake and related conditional probability curves are presented towards the end of this article.Item Earthquake interevent time distribution in Kachchh, Northwestern India(Springer, 2015-08) Pasari, SumantaStatistical properties of earthquake interevent times have long been the topic of interest to seismologists and earthquake professionals, mainly for hazard-related concerns. In this paper, we present a comprehensive study on the temporal statistics of earthquake interoccurrence times of the seismically active Kachchh peninsula (western India) from thirteen probability distributions. Those distributions are exponential, gamma, lognormal, Weibull, Levy, Maxwell, Pareto, Rayleigh, inverse Gaussian (Brownian passage time), inverse Weibull (Frechet), exponentiated exponential, exponentiated Rayleigh (Burr type X), and exponentiated Weibull distributions. Statistical inferences of the scale and shape parameters of these distributions are discussed from the maximum likelihood estimations and the Fisher information matrices. The latter are used as a surrogate tool to appraise the parametric uncertainty in the estimation process. The results were found on the basis of two goodness-of-fit tests: the maximum likelihood criterion with its modification to Akaike information criterion (AIC) and the Kolmogorov-Smirnov (K-S) minimum distance criterion. These results reveal that (i) the exponential model provides the best fit, (ii) the gamma, lognormal, Weibull, inverse Gaussian, exponentiated exponential, exponentiated Rayleigh, and exponentiated Weibull models provide an intermediate fit, and (iii) the rest, namely Levy, Maxwell, Pareto, Rayleigh, and inverse Weibull, fit poorly to the earthquake catalog of Kachchh and its adjacent regions. This study also analyzes the present-day seismicity in terms of the estimated recurrence interval and conditional probability curves (hazard curves). The estimated cumulative probability and the conditional probability of a magnitude 5.0 or higher event reach 0.8–0.9 by 2027–2036 and 2034–2043, respectively. These values have significant implications in a variety of practical applications including earthquake insurance, seismic zonation, location identification of lifeline structures, and revision of building codes.Item Stochastic earthquake interevent time modeling from exponentiated Weibull distributions(IDEAS is a RePEc, 2018) Pasari, SumantaIn view of the growing importance of stochastic earthquake modeling in disaster preparation, the present study introduces a new family of exponentiated Weibull distribution and examines its performance in earthquake interevent time analysis in a stationary point process. This three-parameter (one scale and two shapes) distribution not only covers the Weibull distribution, exponentiated exponential distribution, Burr-type X distribution, Rayleigh distribution, and exponential distribution as special sub-families, but also offers monotone and non-monotone hazard shapes. Here we first describe some of the exponentiated Weibull distribution properties, such as the survival rate, mode, median, and hazard rate. We then provide statistical inference and goodness-of-fit measures to examine the suitability of exponentiated Weibull model in comparison with other popular models, like exponential, gamma, lognormal, Weibull, and exponentiated exponential. Finally, we conduct real data analysis to assess the usefulness and flexibility of exponentiated Weibull distribution in the context of seismic interevent time modeling and associated applications. Results suggest that the exponentiated Weibull distribution has a comparable performance with other popular distributions of its nature. However, further investigations are necessary to confirm the importance and flexibility of exponentiated Weibull distribution in statistical seismology.Item Inverse Gaussian versus lognormal distribution in earthquake forecasting: keys and clues(Springer, 2019-03) Pasari, SumantaIn earthquake fault systems, active faults release elastic strain energy in a near-repetitive manner. Earthquake forecasting that basically refers to the assessment of earthquake hazards via probability estimates is crucial for many strategic and engineering planning. As the current need across sciences dominantly grows for conceptualization, abstraction, and application, comparison of lifetime probability distributions or understanding their physical significance becomes a fundamental concern in statistical seismology. Using various characteristic measures derived from density function, hazard rate function, and mean residual life function with its asymptotic (limiting) behavior, the present study examines the similitude of the two most versatile inverse Gaussian and lognormal distributions in earthquake forecasting. We consider three homogeneous and complete seismic catalogs from northeast India, northwest Himalaya, and Kachchh (western India) region for illustration. We employ maximum likelihood and moment methods for parameter estimation, and Fisher information for uncertainty valuation. Using three performance tests based on Akaike information criterion, Kolmogorov-Smirnov criterion, and Anderson-Darling test, we show that the heavy-tailed lognormal distribution performs relatively better in terms of its model fit to the observed data. We envisage that the ubiquitous heavy-tailed property of lognormal distribution helps in capturing desired characteristics of seismicity dynamics, providing better insights to the long-term earthquake forecasting in a seismically active regionItem Spatial distribution of earthquake potential along the Himalayan arc(Elsevier, 2020-09) Pasari, SumantaTo determine the spatial distribution of earthquake potential along the active Himalayan arc, we utilize GPS measurements and earthquake data. We derive horizontal velocity field and 2-D strain rates from a new set of 41 regional GPS stations along with 446 published velocities. We convert these strain rate tensors to geodetic moment rate build-up within 24 contiguous segments and compare to the seismic moment rate release derived from a reassessed earthquake catalog of 900 years. The geodetic to seismic moment rate ratio, an indicator of stored strain energy, varies from below unity to more than 50 in different segments. The estimated geodetic moment rate ranges from 1.7 × 1018 Nm/yr to 10.2 × 1018 Nm/yr, whereas the seismic moment rate ranges from 3.7 × 1016 Nm/yr to 5.1 × 1019 Nm/yr. This variation between the geodetic and seismic moment rate corresponds to a moment deficit rate of ~1.15×1017 Nm/yr to 7.97 × 1018 Nm/yr along various segments of the study region. The above moment deficit rate provides an equivalent earthquake potential of magnitude 5.7 − 8.2 in different segments. Specifically, the higher earthquake potential (Mw≥8.0) corresponds to the segments in the central seismic gap and the northeast part of Himalaya, whereas the lower earthquake potential (Mw<7.0) corresponds to the segments encompassing the rupture areas of recent large events. The present findings not only provide input constraints on the contemporary crustal deformation but also contributes to the time-dependent seismic hazard analysis along the Himalaya.Item The Current State of Earthquake Potential on Java Island, Indonesia(Springer, 2021-07) Pasari, SumantaBetween 2006 and 2020, earthquakes and other geohazards on volcano-dotted Java Island have caused about 7000 deaths, and another 1.8 million people were injured, displaced, or left homeless. In this study, we quantify the current state of earthquake hazard for 29 cities of Java, using seismicity statistics of a cumulative number of small events (natural times) between pairs of large earthquakes. This approach, known as earthquake nowcasting (Rundle et al., 2016), rests on the key concepts of elastic rebound and ergodic dynamics in earthquake fault networks. Our analysis of statistical inference shows that the estimated earthquake potential score (EPS) as on February 18, 2021 corresponding to M ≥ 6.5 events in a 300 km circular area ranges from 43 to 94%, with the scores of Jakarta (43), Surabaya (89), Bandung (69), Semarang (48), Serang (47), and Yogyakarta (59). This means, for example, that Surabaya has progressed significantly in the regional cycle of large earthquakes, whereas Yogyakarta is about midway in its seismic cycle. We observe that a change in magnitude threshold or geographic area has a consistent impact on the nowcast scores. These findings not only enable a rapid yet meaningful way to rank several cities based on their current exposure to earthquake hazards, but also empower earthquake scientists and policymakers towards better policymaking, land-use planning, earthquake insurance, disaster risk mitigation, and social awareness with respect to the seismically active island of Java.Item A synoptic view of the natural time distribution and contemporary earthquake hazards in Sumatra, Indonesia(IDEAS is a RePEc, 2021) Pasari, SumantaTectonic plate interactions in Sumatra have caused a range of devastating earthquake events. In this study, we develop an analytical framework, known as earthquake nowcasting (Rundle et al. in Earth and Space Science 3:480–486, 2016. 10.1002/2016EA000185), to assess the current dynamical state of earthquake hazards in Sumatra and adjacent islands from the empirical distribution of natural times, the cumulative counts of “small” events (say, M ≥ 4) between two successive “large” earthquakes (say, M ≥ 6.5). Based on 50 years of instrumental earthquake data, the best fit Weibull distribution assigns earthquake potential score between 29 and 96% to 19 large cities in the study region with the values (%) of Aceh (72), Bengkulu (34), Binjai (81), Jambi (35), Lahat (29), Lampung (45), Lhoksuemawe (54), Medan (75), Mentawai (76), Meulaboh (71), Nias (95), Palembang (80), Padang (74), Pekanbaru (39), Sabang (72), Siantar (82), Sibolga (92), Sinabang (96), and Tanjung Balai (55). These areal-source based nowcast scores, analogous to the tectonic stress buildup since the last major event, essentially provides a unique characterization of the current level of seismic progression of a city through its repetitive cycle of regional earthquakes. Inclusion of dependent events with aftershocks being more common and the concept of natural times are some of the distinctive advantages of the proposed method. The resulting natural time statistics and consequent earthquake potential scores will facilitate seismic risk estimation, multistate decision-making, and community awareness, leading to an efficient seismic risk reduction strategy in the densely populated study region.