Adapting Empirical Mmi Formulae Aimed At Developing Early Tsunami Hazard Assessment Charts

Abstract eng:
In recent years, Chile has been exposed to an important number of strong earthquakes that have triggered tsunamis of different amplitudes and extension. Notable examples include the Maule earthquake (Mw 8.8) which took place on February 27th, 2010; the Iquique earthquake (Mw 8.1) on April 1st, 2014; and the Illapel earthquake (Mw 8.3) on September 16th, 2015. These events have permitted to identify some weaknesses of the then existing Chilean Tsunami Warning system, and to devise an improvement strategy that has been implemented recently. Despite these improvements, due to the information flow procedure and time required to process data, there is still some uncertainty regarding the exact area potentially affected by a tsunami immediately after an earthquake occurs, and, more importantly, a finite time before solutions are found. Hence the necessity of a faster, but reliable first assessment of hazard, defined with low level parameters. In this paper, an analysis of the relation between the intensity of an earthquake (via the Modified Mercalli Intensity, MMI) and the occurrence of a tsunami and its expected wave height is performed, taking into account the distance from the point of interest to the hypocenter, and the earthquake magnitude. The objective of this is to test whether the Mercalli Intensity Scale can be used as a tool which adds support to the existing system from a technical point of view, during the period of when no other data driven technical assessments are available. First, a series of empirical formulae relating the intensity with either Peak Ground Acceleration or Peak Ground Velocity, and others that relate PGA or PGV with the parameters that define the earthquake (e.g. depth, magnitude, dip, and strike, among others) are tested and calibrated to the Chilean case. To perform this, data available from earthquakes occurred between 2011 and 2014 are used. In order to verify and validate the results obtained, additional data from 2015 earthquakes are utilized. The result is an empirical relationship between MMI, hypocentral distance and earthquake magnitude. Next, to obtain the corresponding tsunami hazard, numerical simulations of a range of scenarios are performed to ensure data coverage and statistical robustness. Here, a series of points of interest located around nine coastal cities are considered to create city-specific charts which relate tsunami hazard with MMI curves as well. A city-specific approach is used under the premise that a tsunami would vary greatly among different locations, even under the same conditions, due to bathymetric and topographic controls. By completing the aforementioned steps, an additional and fast way to evaluate the risk of a tsunami after a considerably large seismic event will be available for use and, in consequence, the current evaluation system could be reinforced; thereby improving its reliability and the area over which a preventive evacuation alert could be issued.

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