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Detection of seismic quiescences before 1991 Uttarkashi (MW 6.8) and 1999 Chamoli MW (6.6) earthquakes and its implications for stress change sensor

Chingtham Prasanta, Sharma Babita

Acta Geophysica

2022

Vol. 70, no 2

509--523

The statistically point process model known as epidemic-type aftershock sequence (ETAS) model is employed for systematically investigating the seismic quiescence or seismic anomalies around the focal regions of large/strong earthquakes for NW Himalaya. For this propose, the model predicted the expected occurrence rates of earthquakes by estimating the model parameters from the earthquake occurrences times using maximum likelihood method, has been used. Then the exhibited relative quiescence due to decreasing occurrence rates from the modeled ones can be identified by inspecting the abnormally downward deviated plot from the extended cumulative curve of the Residual Point Process (RPP) events. Examination of such RPP events in the whole time interval exhibits significant 1.5 years and 2.0 years of relative seismic quiescence before the strong 1991 Uttarkashi (MW 6.8) and 1999 Chamoli (MW 6.6) earthquakes, respectively. Considering the optimally oriented planes of Uttarkashi earthquake, the Coulomb stress changes (ΔCFS) have been investigated to check the rate of seismicity around the focal region of Chamoli earthquake. It has been found that ΔCFS of Uttarkashi earthquake exhibits stress shadow in or near the source zone of Chamoli earthquake and eventually decreases seismicity rates due to seismic quiescence in the source zone. On the other hand, the detected quiescence and activation relative to the predicted seismicity rate are consistent with the obtained Coulomb stress to depict the associated anomalies being sensitive enough to detect a slight stress change in the study region. Henceforth, the increased or decreased seismic activity due to seismic activation or quiescence is found to be consistent with the patterns of the Coulomb’s stress changes calculated on the ruptured fault planes of Uttarkashi earthquake. Hence, this ETAS model based on statistical technique can thus be incorporated with other sensitive geophysical instruments for identifying seismically quiet period not only in the seismic gaps, but also in its neighborhoods along the Himalayan range for mitigating seismic hazards due to impending great earthquakes.

Forecasting seismic activity rates in northwest Himalaya through multivariate autoregressive forecast of seismicity algorithm

Chingtham Prasanta, Tiwari Anurag, Yadav Arabind Kumar

Acta Geophysica

2019

Vol. 67, no. 2

465--476

In this study, a model based on multivariate autoregressive forecast of seismicity (MARFS) algorithm is adopted to forecast seismic activity rates in northwest Himalaya, using the compiled homogenized moment magnitude (MW) based catalogue. For this purpose, each source zone delineated by Yadav et al. (Pure Appl Geophys 170:283–295, 2012) is divided into a spatial grid interval of 0.5° × 0.5° while the entire catalogue span (1975–2010) is segregated into six time periods/grids to estimate seismic activity rates spatially and temporally. These seismic activity rates which are estimated from spatial density map of hypocenters exhibit high values in Chaman Fault (Zone 1), Hindukush-Pamir region (Zone 3) and the mega thrust systems, i.e., Main Central Thrust, Main Boundary Thrust and Himalayan Frontal Thrust (Zone 4). Then, the seismic activity rates during 2011–2016 could be forecasted by extrapolating (through auto-regression procedure) those observed for previous time periods. The forecast seismic activity rates are estimated within the values of 0 and 7.57 with high values primarily observed in Hindukush-Pamir region of Zone 3 and the gently north-dipping thrust fault systems (Main Central Thrust, Main Boundary Thrust, Himalayan Frontal Thrust) of Zone 4. Finally, the associated area under the curve of receiver operating characteristics graph suggests the superiority of forecasting model with respect to random prediction, whereas results of the data-consistency test, i.e., N test of our model, exhibit consistency in between the observed and simulated likelihoods. Moreover, the hypothetical t test performed in between the spatial grids of forecast seismic activity rates and observed seismic activity rates confirms that the former is consistent with the latter.