No Arabic abstract
We present evidence for an uninterrupted continuation of Indian continental lithospheric mantle into the adjoining Bay of Bengal to a distance of 400-500 km away from the passive margin. The inference is based on the shear wave velocity image of the uppermost mantle beneath the Bay of Bengal, Bangladesh, and the adjoining Indian craton, computed using ambient noise and earthquake waveform data. The Indian lithospheric mantle is characterized by a shear wave velocity of ~ 4.1-4.3 km at the Moho depth of 35-40 km, progressively increasing to ~4.5-4.7 km/s at least up to a depth of 140 km. This velocity structure continues uninterrupted to about 86{deg} E in the Bay of Bengal. Further east, the thickness of the lithospheric lid decreases to ~90 km and is underlain by reduced shear wave velocity (~4.1-4.3 km/s) in the uppermost mantle. We postulate that the Indian craton is embedded in the western Bay of Bengal and the continent-ocean boundary lay around 86{deg} E. The craton possibly submerged soon after the India-Australia-Antractica rifting at around 136 Ma. The significantly reduced shear wave velocity beneath the eastern Bay of Bengal may be due to reheating of the mantle as a consequence of its interaction with the Kergulean hotspot around 90 Ma.
In countries with a moderate seismic hazard, the classical methods developed for strong motion prone countries to estimate the seismic behaviour and subsequent vulnerability of existing buildings are often inadequate and not financially realistic. The main goals of this paper are to show how the modal analysis can contribute to the understanding of the seismic building response and the good relevancy of a modal model based on ambient vibrations for estimating the structural deformation under moderate earthquakes. We describe the application of an enhanced modal analysis technique (Frequency Domain Decomposition) to process ambient vibration recordings taken at the Grenoble City Hall building (France). The frequencies of ambient vibrations are compared with those of weak earthquakes recorded by the French permanent accelerometric network (RAP) that was installed to monitor the building. The frequency variations of the building under moderate earthquakes are shown to be slight (~2%) and therefore ambient vibration frequencies are relevant over the elastic domain of the building. The modal parameters extracted from ambient vibrations are then used to determine the 1D lumped-mass model in order to reproduce the inter-storey drift under weak earthquakes and to fix a 3D numerical model that could be used for strong earthquakes. The correlation coefficients between data and synthetic motion are close to 80% and 90% in horizontal directions, for the 1D and 3D modelling, respectively.
Hazardous tsunamis are known to be generated predominantly at subduction zones by large earthquakes on dip (vertical)-slip faults. However, a moment magnitude ($M_{w}$) 7.5 earthquake on a strike (lateral)-slip fault in Sulawesi (Indonesia) in 2018 generated a tsunami that devastated the city of Palu. The mechanism by which this large tsunami originated from a strike-slip earthquake has been debated. Here we present near-field ground motion data from a GPS station that confirms that the 2018 Palu earthquake attained supershear speed, i.e., a rupture speed greater than the speed of shear waves in the host medium. We study the effect of this supershear rupture on tsunami generation by coupling the ground motion to a 1D non-linear shallow-water wave model that accounts for both the time-dependent bathymetric displacement and velocity. With the local bathymetric profile of the Palu bay around a tidal gauge, we find that these simulations reproduce the tsunami motions measured by the gauge, with only minimal tuning of parameters. We conclude that Mach (shock) fronts, generated by the supershear speed of the earthquake, interacted with the bathymetry and contributed to the tsunami. This suggests that rupture speed should be considered in tsunami hazard assessments.
An article for the Springer Encyclopedia of Complexity and System Science
Earthquake network is known to be of the small-world type. The values of the network characteristics, however, depend not only on the cell size (i.e., the scale of coarse graining needed for constructing the network) but also on the size of a seismic data set. Here, discovery of a scaling law for the clustering coefficient in terms of the data size, which is refereed to here as finite data-size scaling, is reported. Its universality is shown to be supported by the detailed analysis of the data taken from California, Japan and Iran. Effects of setting threshold of magnitude are also discussed.
Forecasting the full distribution of the number of earthquakes is revealed to be inherently superior to forecasting their mean. Forecasting the full distribution of earthquake numbers is also shown to yield robust projections in the presence of surprise large earthquakes, which in the past have strongly deteriorated the scores of existing models. We show this with pseudo-prospective experiments on synthetic as well as real data from the Advanced National Seismic System (ANSS) database for California, with earthquakes with magnitude larger than 2.95 that occurred between the period 1971-2016. Our results call in question the testing methodology of the Collaboratory for the study of earthquake predictability (CSEP), which amounts to assuming a Poisson distribution of earthquake numbers, which is known to be a poor representation of the heavy-tailed distribution of earthquake numbers. Using a spatially varying ETAS model, we demonstrate a remarkable stability of the forecasting performance, when using the full distribution of earthquake numbers for the forecasts, even in the presence of large earthquakes such as Mw 7.1 Hector Mine, Mw 7.2 El Mayor-Cucapah, Mw 6.6 Sam Simeon earthquakes, or in the presence of intense swarm activity in Northwest Nevada in 2014. While our results have been derived for ETAS type models, we propose that all earthquake forecasting models of any type should embrace the full distribution of earthquake numbers, such that their true forecasting potential is revealed.