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Himalayan earthquakes have deep societal and economic impact. In this article, we implement a surrogate method of nowcasting (Rundle et al., 2016) to determine the current state of seismic hazard from large earthquakes in a dozen populous cities from India and Pakistan that belong to the west-northwest part of Himalayan orogeny. For this, we (1) perform statistical inference of natural times, intersperse counts of small-magnitude events between pairs of succeeding large events, based on a set of eight probability distributions; (2) compute earthquake potential score (EPS) of 14 cities from the best-fit cumulative distribution of natural times; and (3) carry out a sensitivity testing of parameters—threshold magnitude and area of city region. Formulation of natural time (Varostos et al., 2005) based on frequency–magnitude power-law statistics essentially avoids the daunting need of seismicity declustering in hazard estimation. A retrospective analysis of natural time counts corresponding to M≥6 events for the Indian cities provides an EPS (%) as New Delhi (56), Chandigarh (86), Dehradun (83), Jammu (99), Ludhiana (89), Moradabad (84), and Shimla (87), whereas the cities in Pakistan observe an EPS (%) as Islamabad (99), Faisalabad (88), Gujranwala (99), Lahore (89), Multan (98), Peshawar (38), and Rawalpindi (99). The estimated nowcast values that range from 38% to as high as 99% lead to a rapid yet useful ranking of cities in terms of their present progression to the regional earthquake cycle of magnitude ≥6.0 events. The analysis inevitably encourages scientists and engineers from governments and industry to join hands for better policymaking toward land-use planning, insurance, and disaster preparation in the west-northwest part of active Himalayan belt.

<p><strong>Abstract.</strong> With the rapid increase and availability of seismic data, an automatic, transparent and regular way of earthquake hazard estimation strategy is highly desirable in many seismically active large geographical regions. In this paper, we implement a novel method of nowcasting (Rundle et al., 2016) that can indirectly assess the current progression of a region through its earthquake cycle of large events. Nowcasting differs from the method of forecasting in which future earthquake probabilities are calculated. Using statistics of natural times, counts of small earthquakes between large earthquakes in a defined region, nowcasting provides an earthquake potential score (EPS) to enable scientists and city planners a snapshot of the current level of earthquake hazard in the region. Applied to a number of selected major cities in the northwest Himalaya and surrounding regions, we found that the EPS values corresponding to M<span class="thinspace"></span>&amp;geq;<span class="thinspace"></span>6 events in New Delhi, Chandigarh, Dehradun and Shimla reach about 0.56, 0.87, 0.85 and 0.88, respectively. These estimated scores thus indicate that New Delhi is about half-way through its cycle for magnitude 6.0 or higher earthquakes, while Dehradun is about 85 percent of the way through its cycle. Towards the end, we discuss some implications and applications of these nowcast values to improve the present earthquake hazard assessment practice in the study region.</p>

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Large devastating events such as earthquakes often display frequency–magnitude statistics that exhibit power-law distribution. In this study, we implement a recently developed method called earthquake nowcasting (Rundle et al. in Earth Space Sci 3: 480–486, 2016) to evaluate the current state of earthquake hazards in the seismic prone Sulawesi province, Indonesia. The nowcasting technique considers statistical behavior of small event counts between successive large earthquakes, known as natural times, to infer the seismic progression of large earthquake cycles in a defined region. To develop natural-time statistics in the Sulawesi Island, we employ four probability models, namely exponential, exponentiated exponential, gamma, and Weibull distribution. Statistical inference of natural times reveals that (i) exponential distribution has the best representation to the observed data; (ii) estimated nowcast scores (%) corresponding to M ≥ 6.5 events for 21 cities are Bau-bau (41), Bitung (70), Bone (44), Buton (39), Donggala (63), Gorontalo (49), Kendari (27), Kolaka (30), Luwuk (56), Makassar (52), Mamuju (58), Manado (70), Morowali (37), Palopo (34), Palu (62), Pare-pare (82), Polewali (61), Poso (42), Taliabu (55), Toli-toli (58), and Watampone (55); and (iii) the results are broadly stable against the changes of magnitude threshold and area of local regions. The presently revealed stationary Poissonian nature of the underlying natural-time statistics in Sulawesi brings out a key conclusion that the seismic risk is the same for all city regions despite their different levels of cycle progression realized through nowcast scores. In addition, though the earthquake potential scores of the city regions will be updated with the occurrence of each small earthquake in the respective region, the seismic risk remains the same throughout the Sulawesi Island.

Earthquake is one of the most devastating natural calamities that takes thousands of lives and leaves millions more homeless and deprives them of the basic necessities. Earthquake forecasting can minimize the death count and economic loss encountered by the affected region to a great extent. This study presents an earthquake forecasting system by using Artificial Neural Networks (ANN). Two different techniques are used with the first focusing on the accuracy evaluation of multilayer perceptron using different inputs and different set of hyper-parameters. The limitation of earthquake data in the first experiment led us to explore another technique, known as nowcasting of earthquakes. The nowcasting technique determines the current progression of earthquake cycle of higher magnitude earthquakes by taking into account the number of smaller earthquake events in the same region. To implement the nowcasting method, a Long Short Term Memory (LSTM) neural network architecture is considered because such networks are one of the most recent and promising developments in the time-series analysis. Results of different experiments are discussed along with their consequences. * Corresponding author network (Lakshmi and Tiwari, 2006). For this particular task, there are so many factors involved in the process that other model based approaches cannot accommodate as accurately as neural network does (Perol et al., 2017).This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-5-823-2018 | © Authors 2018. CC BY 4.0 License.

This work concentrates on the distribution of earthquake interevent times in northwest Himalaya and its adjacent regions. We consider 12 timedependent probability distributions for analysis. The maximum likelihood estimation and Fisher information matrix-based methods are used to estimate model parameters and their respective uncertainties. Results from three model selection criteria suggest that the best fit arises from exponentiated Weibull, exponentiated exponential, Weibull and gamma distributions. An intermediate fit comes from exponentiated Rayleigh, and lognormal distributions, whereas the remaining distributions exhibit a poor fit to the seismic interoccurrence times of present catalogue. Using exponentiated Weibull model, it is observed that the estimated cumulative probability of magnitude 6.0 or higher event in the northwest Himalaya reaches 0.92-0.95 after about 20-23 (2019-2022) years since the last event in 1999. The conditional probability, for an elapsed time of 19 years (i.e. 2018), reaches 0.90-0.95 after about 20-25 (2038-2043) years from now. A series of conditional probability curves is also presented to understand the recent and future earthquake hazard in the study region. This temporal evolution of seismic interevent times may provide important clues to the underlying physical mechanism of earthquake genesis in the northwest Himalaya region.