NOWCASTING EARTHQUAKES IN THE NORTHWEST HIMALAYA AND SURROUNDING REGIONS

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 ≥ 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

EARTHQUAKE FORECASTING USING ARTIFICIAL NEURAL NETWORKS

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

GPS-BASED MONITORING OF CRUSTAL DEFORMATION IN GARHWAL-KUMAUN HIMALAYA

The Himalayan region has experienced a number of large magnitude earthquakes in the past. Seismicity is mainly due to tectonic activity along the thrust faults that trend parallel to the Himalayan mountain belt. In order to study the ongoing tectonic process, we report Global Positioning System (GPS) measurements of crustal deformation in the Garhwal-Kumaun Himalaya through two continuous and 21 campaign stations. We collect GPS data since 2013 and analyze with the GAMIT/GLOBK suite of postprocessing software. Our estimated surface velocities in ITRF2008, India-fixed, and Eurasia-fixed reference frame lie in the range of 42–52 mm/yr, 1–6 mm/yr, and 31–37 mm/yr, respectively. We observe insignificant slip rate (∼ 1 mm/yr) of HFT that indicates its locking behavior. The slip rates of MBT and MCT, however, are consistent with the seismic activity of the study region

A synthesis is emerging between biodiversity–ecosystem function and ecological resilience research: reply to Mori

Letter