The future strong motion national seismic networks in Central America designed for earthquake early warning.

2020 ◽  
Author(s):  
Frederick Massin ◽  
John Clinton ◽  
Roman Racine ◽  
Maren Bose ◽  
Yara Rossi ◽  
...  
Keyword(s):  
Central America ◽  
Early Warning ◽  
Earthquake Early Warning ◽  
Force Balance ◽  
Maximum Magnitude ◽  
Real Time Processing ◽  
National Network ◽  
Finite Fault ◽  
Seismic Networks ◽  
Network Upgrade

<p>The national seismic networks in Central America have been developing network-based early warning since 2016 for Nicaragua, 2018 for El Salvador and 2019 for Costa Rica. This effort is part of a project with the Swiss Seismological Service (ETH Zurich) including funds for accelerograph deployment. At each network, delay for first earthquake parameter estimations have been significantly reduced by optimizing data acquisition, metadata quality, and configuration of the EEW algorithms implemented in SeisComP3, i.e. Virtual Seismologist and the Finite fault rupture Detector. Issues remain with significant numbers of deployed instrumentation that for a variety of reasons, do not optimally contribute to the EEW systems. Building on our experience so far, we design national network upgrades that will optimize the earthquake early warning performance in the Central America region, mitigating the current issues with velocimeter clipping during large events, datalogger delays, and incomplete network coverage. The new instruments have been selected after testing all available EEW-capable accelerographs natively compatible with SeisComP3 including class A force balance accelerometers as well as MEMs. To justify our instrument selection, we summarize the performance of these different instruments. We model and discuss reference maps for performance expectations, and present planned instrument vaults. Our primary focus is on minimizing first alert times but we also wish to accentuate the broad value of the network upgrade for seismological monitoring showing changes in the magnitude of completeness in the region. We demonstrate the value of the network upgrade for earthquake early warning with real-time processing simulation using synthetic data for the maximum magnitude earthquake expected for the Central America subduction zone.</p>

The Extended Integrated Particle Filter Method (IPFx) as a High-Performance Earthquake Early Warning System

2021 ◽  
Author(s):  
Masumi Yamada ◽  
Koji Tamaribuchi ◽  
Stephen Wu
Keyword(s):  
Particle Filter ◽  
Early Warning ◽  
Seismic Intensity ◽  
Earthquake Early Warning ◽  
Japan Meteorological Agency ◽  
Estimation Accuracy ◽  
Filter Method ◽  
Source Estimation ◽  
Single Station ◽  
Seismic Networks

ABSTRACT An earthquake early warning (EEW) system rapidly analyzes seismic data to report the occurrence of an earthquake before strong shaking is felt at a site. In Japan, the integrated particle filter (IPF) method, a new source-estimation algorithm, was recently incorporated into the EEW system to improve the source-estimation accuracy during active seismicity. The problem of the current IPF method is that it uses the trigger information computed at each station in a specific format as the input and is therefore applicable to only limited seismic networks. This study proposes the extended IPF (IPFx) method to deal with continuous waveforms and merge all Japanese real-time seismic networks into a single framework. The new source determination algorithm processes seismic waveforms in two stages. The first stage (single-station processing) extracts trigger and amplitude information from continuous waveforms. The second stage (network processing) accumulates information from multiple stations and estimates the location and magnitude of ongoing earthquakes based on Bayesian inference. In 10 months of continuous online experiments, the IPFx method showed good performance in detecting earthquakes with maximum seismic intensity ≥3 in the Japan Meteorological Agency (JMA) catalog. By merging multiple seismic networks into a single EEW system, the warning time of the current EEW system can be improved further. The IPFx method provides accurate shaking estimation even at the beginning of event detection and achieves seismic intensity error <0.25  s after detecting an event. This method correctly avoided two major false alarms on 5 January 2018 and 30 July 2020. The IPFx method offers the potential of expanding the JMA IPF method to global seismic networks.

Source Process-Related Delays in Earthquake Early Warning for Example Cases in Greece

2021 ◽  
Author(s):  
Nikolaos Vavlas ◽  
Anastasia A. Kiratzi ◽  
Zafeiria Roumelioti
Keyword(s):  
Early Warning ◽  
Monitoring Network ◽  
Earthquake Early Warning ◽  
Source Process ◽  
Spatial Density ◽  
Intermediate Depth ◽  
National Monitoring ◽  
Gulf Of Corinth ◽  
Crete Island ◽  
Finite Fault

ABSTRACT We explore a hypothetical zero-latency earthquake early warning (EEW) system in Greece, aiming to provide alerts before warning thresholds of the intensity of ground motion are exceeded. Within the seismotectonic context of Greece, both shallow- and intermediate-depth earthquakes (along the Hellenic subduction zone) are plausible and, thus, examined. Using regionally applicable attenuation relations, we combine and adjust the methodologies of Minson et al. (2018) and Hoshiba (2020) to examine what are the minimum magnitudes required to invoke the warning thresholds at the user site. With simple modeling, we examine how fast an alert can be issued and what is the available warning time when taking into account delays due to finite-fault rupture propagation, alongside other delays. These computations are merged with delays introduced due to the present-day configuration of the Greek national monitoring network (varying spatial density of permanent monitoring stations). This approach serves as a tool to assess the feasibility of an EEW system at specific sites and to redesign the national permanent monitoring network to serve such a system more effectively (we provide results for four sites.). Warning times for on-land crustal earthquakes are found to be shorter, whereas for intermediate-depth earthquakes in Greece an EEW system is feasible (provides warning times of several tens of seconds at large cities, e.g., on Crete Island) even with the current configuration of the national monitoring network, which is quite sparse in the southern part of the country. The current network configuration also provides sufficient early warning (e.g., of the order of 10 s for a warning threshold of 0.05g) at the center of Athens from earthquakes of the eastern Gulf of Corinth—a zone posing elevated hazard in the broader area of the Greek capital. Several additional assumptions and factors affecting the operability of an EEW system in Greece (i.e., source process complexity and uncertainty in attenuation laws) are also discussed.

scholarly journals One minute after: Strong-motion map, effective epicenter, and effective magnitude

1997 ◽  
Vol 87 (5) ◽  
pp. 1209-1219 ◽  
Author(s):  
Ta-liang Teng ◽  
Ludan Wu ◽  
Tzay-Chyn Shin ◽  
Yi-Ben Tsai ◽  
William H. K. Lee
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Initial Point ◽  
Strong Motion ◽  
Large Earthquake ◽  
Seismic Monitoring ◽  
Earthquake Early Warning ◽  
Force Balance ◽  
Total Energy Release ◽  
Intensity Map

Abstract This article reports the recent progress on real-time seismic monitoring in Taiwan, particularly the real-time strong-motion monitoring by the Taiwan Central Weather Bureau's telemetered seismic network (CWBSN), which is presently aiming at rapid reporting immediately after a large earthquake occurrence. If rapid reporting can be achieved before the arrival of the strong shaking, earthquake early warning will become possible. CWBSN has achieved the generation of the intensity map, epicenter, and magnitude within 1 min of the occurrence of a large earthquake. Both rapid reporting and early warning are principally applied to large (M ≫ 5) events; the requirement of on-scale waveform recording prompted CWBSN in 1995 to integrate strong-motion sensors (e.g., force-balance accelerometers) into its telemetered seismic monitoring system. Time-domain recursive processing is applied to the multi-channel incoming seismic signals by a group of networked personal computers to generate the intensity map. From the isoseismal contours, an effective epicenter is immediately identified that resides in the middle of the largest (usually the 100-gal) contour curve of the intensity map. An effective magnitude is also defined that can be derived immediately from the surface area covered by the largest (usually the 100-gal) contour curve. For a large event with a finite rupture surface, the epicenter and magnitude so derived are more adequate estimates of the source location and of the strength of destruction. The effective epicenter gives the center of the damage area; it stands in contrast with the conventional epicenter location, which only gives the initial point of rupture nucleation. The effective magnitude reflects more closely the earthquake damage potential, instead of the classical magnitude definition that emphasizes the total energy release. The CWBSN has achieved in obtaining the above crucial source information well within 1 min. This time can further be reduced to better than 30 sec, as illustrated by the example in this article, showing that earthquake early warning is indeed an achievable goal. The rapid reporting and early warning information is electronically transmitted to users to allow rapid response actions, with or without further human intervention.

scholarly journals Preliminary Results of an Earthquake Early Warning System in Costa Rica

2021 ◽  
Vol 9 ◽  
Author(s):  
Juan Porras ◽  
Frédérick Massin ◽  
Mario Arroyo-Solórzano ◽  
Ivonne Arroyo ◽  
Lepolt Linkimer ◽  
...  
Keyword(s):  
Costa Rica ◽  
Early Warning ◽  
Early Warning System ◽  
Source Parameters ◽  
Warning System ◽  
Earthquake Early Warning ◽  
Capital City ◽  
San Jose ◽  
Earthquake Early Warning System ◽  
Finite Fault

We analyze the performance of a prototype earthquake early warning system deployed at the National Seismological Network of Costa Rica in collaboration with the Swiss Seismological Service by presenting the real-time performance during six earthquakes (Mw 5.1-6.4) that took place during 2018 and 2019. We observe that, despite only limited efforts to optimize the existing network of 158 stations, for EEW purposes, the network density allows fast determination of source parameters using both the Virtual Seismologist and the Finite Fault Rupture Detector algorithms. Shallow earthquakes on or near-shore are routinely identified within 11–20 s of their occurrence. The warning times for the capital city of San Jose are of 43 s for epicenters located at 220 km, like for the Mw 6.4 Armuelles earthquake. On the other hand, during the time analyzed, the EEW system did not provide positive warning times for earthquakes at distances less than 40 km from San Jose. Even though large (Mw > 7) distant historical earthquakes have not caused heavy damage in San Jose, there is potential for developing an EEW system for Costa Rica, especially for the purposes of rapid earthquake notifications, disaster response management, and seismic risk mitigation.

scholarly journals Feasibility Study of an Earthquake Early Warning System in Eastern Central Italy

2021 ◽  
Vol 9 ◽  
Author(s):  
Chiara Ladina ◽  
Simone Marzorati ◽  
Alessandro Amato ◽  
Marco Cattaneo
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Central Italy ◽  
Warning System ◽  
Earthquake Early Warning ◽  
Seismic Sources ◽  
Lead Times ◽  
Arrival Times ◽  
Earthquake Early Warning System ◽  
Seismic Networks

An earthquake early warning system (EEWS) is a monitoring infrastructure that allows alerting strategic points (targets) before the arrival of strong shaking waves during an earthquake. In a region like Central Italy, struck by recent and historical destructive earthquakes, the assessment of implementation of an EEWS is a significant challenge due to the proximity of seismic sources to many potential targets, such as historical towns, industrial plants, and hospitals. In order to understand the feasibility of an EEWS in such an area, we developed an original method of event declaration simulation (EDS), a tool for assessing the effectiveness of an EEWS for existing seismic networks, improving them with new stations, and designing new networks for EEW applications. Values of the time first alert (TFA), blind zone radius (BZ), and lead time (LT) have been estimated with respect to selected targets for different network configurations in the study region. Starting from virtual sources homogeneously arranged on regular mesh grids, the alert response was evaluated for actual and improved seismic networks operating in the area, taking into account the effects of the transmission and acquisition systems. In the procedure, the arrival times of the P wave picks, the association binder, the transmission latencies, and the computation times were used to simulate the configuration of PRESTo EEWS, simulating both real-time and playback elaborations of real earthquakes. The NLLOC software was used to estimate P and S arrival times, with a local velocity model also implemented in the PRESTo EEWS. Our results show that, although Italy’s main seismic sources are located close to urban areas, the lead times calculated with the EDS procedure, applied to actual and to improved seismic networks, encourage the implementation of EEWS in the study area. Considering actual delays due to data transmission and computation time, lead times of 5–10 s were obtained simulating real historical events striking some important targets of the region. We conclude that EEWSs are useful tools that can contribute to protecting people from the harmful effects of earthquakes in Italy.

Earthquake Early Warning: Advances, Scientific Challenges, and Societal Needs

2019 ◽  
Vol 47 (1) ◽  
pp. 361-388 ◽  
Author(s):  
Richard M. Allen ◽  
Diego Melgar
Keyword(s):  
Ground Motion ◽  
Early Warning ◽  
The United States ◽  
Earthquake Early Warning ◽  
Ground Shaking ◽  
Motion Models ◽  
Societal Needs ◽  
Finite Fault ◽  
User Groups ◽  
Ground Motion Models

Earthquake early warning (EEW) is the delivery of ground shaking alerts or warnings. It is distinguished from earthquake prediction in that the earthquake has nucleated to provide detectable ground motion when an EEW is issued. Here we review progress in the field in the last 10 years. We begin with EEW users, synthesizing what we now know about who uses EEW and what information they need and can digest. We summarize the approaches to EEW and gather information about currently existing EEW systems implemented in various countries while providing the context and stimulus for their creation and development. We survey important advances in methods, instrumentation, and algorithms that improve the quality and timeliness of EEW alerts. We also discuss the development of new, potentially transformative ideas and methodologies that could change how we provide alerts in the future. ▪ Earthquake early warning (EEW) is the rapid detection and characterization of earthquakes and delivery of an alert so that protective actions can be taken. ▪ EEW systems now provide public alerts in Mexico, Japan, South Korea, and Taiwan and alerts to select user groups in India, Turkey, Romania, and the United States. ▪ EEW methodologies fall into three categories, point source, finite fault, and ground motion models, and we review the advantages of each of these approaches. ▪ The wealth of information about EEW uses and user needs must be employed to focus future developments and improvements in EEW systems.

Generating Phone-quality Records to Train Machine Learning Models for Smartphone-based Earthquake Early Warning

2020 ◽  
Author(s):  
Zengwei Zheng ◽  
Lifei Shi ◽  
Sha Zhao ◽  
Jianmin Hou ◽  
Lin Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Transformation Method ◽  
Earthquake Early Warning ◽  
Sensor Data ◽  
Superior Performance ◽  
Quality Data ◽  
Learning Models ◽  
Seismic Networks ◽  
Machine Learning Models

Abstract Earthquake Early Warning (EEW) system detects earthquakes and sends an early warning to areas likely to be affected, which plays a significant role in reducing earthquake damage. In recent years, as with the widespread distribution of smartphones, as well as their powerful computing ability and advanced built-in sensors, a new interdisciplinary research method of smartphone-based earthquake early warning has emerged. Smartphones-based earthquake early warning system applies signal processing techniques and machine learning algorithms to the sensor data recorded by smartphones for better monitoring earthquakes. But it is challenging to collect abundant phone-recorded seismic data for training related machine learning models and selecting appropriate features for these models. One alternative way to solve this problem is to transform the data recorded by seismic networks into phone-quality data. In this paper, we propose such a transformation method by learning the differences between the data recorded by seismic networks and smartphones, in two scenarios: phone fixed and free located on tables, respectively. By doing this, we can easily generate abundant phone-quality earthquake data to train machine learning models used in EEW systems. We evaluate our transformation method by conducting various experiments, and our method performs much better than existing methods. Furthermore, we set up a case study where we use the transformed records to train machine learning models for earthquake intensity prediction. The results show that the model trained by using our transformed data produces superior performance, suggesting that our transformation method is useful for smartphone-based earthquake early warning.

The Impact of 3D Finite-Fault Information on Ground-Motion Forecasting for Earthquake Early Warning

2021 ◽  
Author(s):  
Jessica R. Murray ◽  
Eric M. Thompson ◽  
Annemarie S. Baltay ◽  
Sarah E. Minson
Keyword(s):  
Ground Motion ◽  
Early Warning ◽  
Line Source ◽  
Earthquake Early Warning ◽  
Source Characterization ◽  
Origin Time ◽  
Finite Fault ◽  
Alert Threshold ◽  
The Impact ◽  
Fault Information

ABSTRACT We identify aspects of finite-source parameterization that strongly affect the accuracy of estimated ground motion for earthquake early warning (EEW). EEW systems aim to alert users to impending shaking before it reaches them. The U.S. West Coast EEW system, ShakeAlert, currently uses two algorithms based on seismic data to characterize the earthquake’s location, magnitude, and origin time, treating it as a point or line source. From this information, ShakeAlert calculates shaking intensity and alerts locations where shaking estimates exceed a threshold. Several geodetic EEW algorithms under development would provide 3D finite-fault information. We investigate conditions under which this information produces sufficiently better intensity estimates to potentially improve alerting. Using scenario crustal and subduction interface sources, we (1) identify the most influential source geometry parameters for an EEW algorithm’s shaking forecast, and (2) assess the intensity alert thresholds and magnitude ranges for which more detailed source characterization affects alert accuracy. We find that alert regions determined using 3D-source representations of correct magnitude and faulting mechanism are generally more accurate than those obtained using line sources. If a line-source representation is used and magnitude is calculated from the estimated length, then incorrect length estimates significantly degrade alert region accuracy. In detail, the value of 3D-source characterization depends on the user’s chosen alert threshold, tectonic regime, and faulting style. For the suite of source models we tested, the error in shaking intensity introduced by incorrect geometry could reach levels comparable to the intrinsic uncertainty in ground-motion calculations (e.g., 0.5–1.3 modified Mercalli intensity [MMI] units for MMI 4.5) but, especially for crustal sources, was often less. For subduction interface sources, 3D representations substantially improved alert area accuracy compared to line sources, and incorrect geometry parameters were more likely to cause error in calculated shaking intensity that exceeded uncertainties.

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