Designing a Network-Based Earthquake Early Warning Algorithm for California: ElarmS-2

Abstract

The California Integrated Seismic Network (CISN) is developing an earthquake early warning (EEW) demonstration system for the state of California. Within this CISN ShakeAlert project, three algorithms are being tested, one of which is the network-based Earthquake Alarm Systems (ElarmS) EEW system. Over the last three years, the ElarmS algorithms have undergone a large-scale reassessment and have been recoded to solve technological and methodological challenges. The improved algorithms in the new production-grade version of the ElarmS version 2 (referred to as ElarmS-2 or E2) code maximize the current seismic network's configuration, hardware, and software performance capabilities, improving both the speed of the early warning processing and the accuracy of the warnings. E2 is designed as a modular code and consists of a new event monitor module with an improved associator that allows for more rapid association with fewer triggers, while also adding several new alert filter checks that help minimize false alarms. Here, we outline the methodology and summarize the performance of this new online real-time system. The online performance from 2 October 2012 to 15 February 2013 shows, on average, ElarmS currently issues an alert 8:68 3:73 s after the first P-wave detection for all events across California. This time is reduced by 2 s in regions with dense station instrumentation. Standard deviations of magnitude, origin time are 0.4 magnitude units, 1.2 s, and the median location errors is 3.8 km. E2 successfully detected 26 of 29 earthquakes (M ANSS > 3:5) across California, while issuing two false alarms. E2 is now delivering alerts to ShakeAlert, which in turn distributes warnings to test users.

Backpropagation neural network as earthquake early warning tool using a new modified elementary Levenberg–Marquardt Algorithm to minimise backpropagation errors

“…The traditional Levenberg-Marquardt Algorithm (LMA; Levenberg, 1944) determines the desired minimum error by locating the minimum of a multivariate function of an independent variable, expressed as the sum of the squares of nonlinear real-valued functions. While the traditional LMA serves as a backpropagation correction to train a BPNN, it cannot update two independent variables simultaneously, i.e.…”

“…The processing was also complicated. Kuyuk et al (2014) designed a network based on the EEW algorithm for California called ElarmS-2. This algorithm had complicated processes and the P-wave parameters must be triggered.…”

“…Therefore, this method could be used as part of the EEW when the EEW is not validated for proximal receiver stations, e.g. some mistaken wave phases using other methods, and the installation of additional seismic monitoring network is unnecessary (Wu and Teng, 2002;Allen and Kanamori, 2003;Gentili and Michelini, 2006;Böse et al, 2008;Wu and Kanamori, 2008;Kuyuk et al, 2014). The seismic receiver stations belong to an existing seismic monitoring network called the Free Field Strong Earthquake Observation Network, and the P-, S-, and surface-wave phases in their records may be identified (Central Weather Bureau, Taiwan, CWB; Shin et al, 2002).…”

Finite-Fault Rupture Detector (FinDer): Going Real-Time in CalifornianShakeAlertWarning System

“…Designed as a hybrid system, ShakeAlert combines the estimates from one single-sensor and two network-based EEW algorithms that run in parallel, including τ c P d Onsite (Wu et al, 2007;Böse, Hauksson, Solanki, Kanamori, Wu, et al, 2009;, ElarmS Kuyuk et al, 2014), and the Virtual Seismologist Behr et al, 2015). Once an earthquake has been detected, each algorithm starts independently sending reports to the ShakeAlert Decision Module, which-based on these reports-determines the most probable event parameters (location, magnitude, and origin time) and uncertainties, which are then continuously updated with progressing time as new data arrive.…”

Backpropagation Neural Network as Earthquake Early Warning Tool using a new Elementary Modified Levenberg–Marquardt Algorithm to minimise Backpropagation Errors

“…The processing was also complicated. Kuyuk et al, (2014) designed a network based on Geosci. Instrum.…”