Two Novel Approaches to Reduce False Alarm Due to Non‐Earthquake Events for On‐Site Earthquake Early Warning System

Abstract

An on‐site earthquake early warning system (EEWS) can provide more lead‐time at regions that are close to the epicenter of an earthquake because only seismic information of a target site is required. Instead of leveraging the information of several stations, the on‐site system extracts some P‐wave features from the first few seconds of vertical ground acceleration of a single station. It then predicts the intensity of the forthcoming earthquake at the same station according to these features. However, the system may be triggered by some vibration signals that are not caused by an earthquake or by interference from electronic signals, which may consequently result in a false alarm at the station. Thus, this study proposes two approaches to distinguish the vibration signals caused by non‐earthquake events from those caused by earthquake events based on support vector classification (SVC) and singular spectrum analysis (SSA). In the first approach (Approach I), the fast Fourier transform algorithm and the established SVC model are employed to classify the vibration signals. In the second approach (Approach II), a SSA criterion is added to Approach I for the purpose of identifying earthquake events that are classified as non‐earthquake events by the SVC model with increased accuracy. Both approaches are verified by using data collected from the Taiwan Strong Motion Instrumentation Program and EEW stations of the National Center for Research on Earthquake Engineering. The results indicate that both of the proposed approaches effectively reduce the possibility of false alarms caused by an unknown vibration event.

A decision‐making methodology for risk‐informed earthquake early warning

“…Earthquake early warning (EEW) is undergoing a growth in popularity worldwide as an attractive tool for enhancing and promoting seismic resilience in urban areas (e.g., Cauzzi et al., 2016; Gasparini et al, 2011). Specifically, EEW systems rely on real‐time data telemetry to provide information about ongoing earthquakes, enabling various stakeholders (end‐users) to take effective steps for reducing potential harmful impacts of an event before strong shaking occurs at a target site (e.g., Allen & Melgar, 2019; Hsu et al., 2016; Panakkat & Adeli, 2008; Rafiei & Adeli, 2017; Satriano, Wu et al, 2011). This type of technology is currently operating in nine countries, and is being tested for feasibility in many more (Cremen & Galasso, 2020; Cremen et al, 2021).…”

Earthquake early warning: Recent advances and perspectives

“…Recent work has focused on improving the event detection capabilities of EEW systems, so that they are less likely to cause a false alert by misinterpreting local impulsive noise from natural or anthropogenic sources (Li et al, 2018). Hsu et al (2016) and Meier et al (2019) demonstrate that various machine learning algorithms (e.g. support vector classification, general adversarial network, random forest, convolutional neural network) may effectively reduce the probability of false alarms caused by non-earthquake vibration events.…”

Optimization of Temporary Debris Management Site Selection and Site Service Regions for Enhancing Postdisaster Debris Removal Operations

“…Many of these studies have focused on either predisaster planning or logistic operations in postdisaster relief missions. Recent predisaster studies, for instance, have developed models for resources preparation (Chang et al., ), debris prediction (Hsu et al., ; Miao and Ding, ), and prepositioning of supplies (Rawls and Turnquist, ). Previous studies have investigated routing assessment (Hobeika et al., ; Huang et al., ; Brooks et al., ), rescue team deployment (Zverovich et al., ), evacuation planning (Guo et al., ; Zverovich et al., ), ambulance and resources distribution (Sheu, ; Fetter and Rakes, ; Özdamar and Demir, ), relief demand management (Sheu, ), and human behaviors (Nejat and Damnjanovic, ) in postdisaster scenarios.…”