Next-Generation EEW Empowered by NDSHA: From Concept to Implementation

Abstract

In this paper, we discuss a possible combination of Earthquake Early Warning (EEW) and Neo-deterministic Seismic Hazard Assessment (NDSHA), and propose a new warning model, EEW2.0. The aim is to provide a differentiated warning alert to various end-users based on the results of seismic hazard assessment evaluation. The implementation of such a system contains three basic steps: (a) classification of “potential to cause hazard” in terms of magnitude; (b) determination of the source areas and building a hazard database in terms of Modified Mercalli Intensity (MMI) maps, considering all possible earthquake scenarios in the source area, for the whole protected area; (3) equipping unique decision framework for specific end-users. When a damaging earthquake (M ≥ 5.0) is detected, EEW2.0 quickly matches the prepared MMI map by estimated magnitude and epicenter, then directly extracts the MMI value and issues an early warning to the public. With the great attention and resources put into the reduction in seismic and its secondary risk in the 21st century, the proposed EEW2.0 will likely play an active role in protecting lives and reducing economic losses.

Earthquake Early Warning System (EEWS) empowered by Time‐Dependent Neo‐Deterministic Seismic Hazard Assessment (TD‐NDSHA)

“…Earthquake early warning system makes use of the message carried by the P‐wave arrival to issue warnings about the later coming, and potentially destructive, S and surface waves, to field emergency countermeasures, which are scaled to the different objects to be protected (Allen et al, 2009; Allen & Melgar, 2019; Cremen et al, 2022; Kanamori, 2005; Satriano et al, 2011). If the detection network does not cover the seismic source, it has been shown that SHA may help to improve their performance (Cremen & Galasso, 2020; Zhang, Wu et al, 2021). In this paper we focus on the case that the network covers both the seismic source and the target to be protected, and explore how TD‐NDSHA, (see Panza et al, 2022) may help.…”