CISN ShakeAlert: An Earthquake Early Warning Demonstration System for California

Böse, Maren; Allen, R. M.; Brown, Holly M.; Gua, G.; Fischer, Michael D.; Hauksson, Egill; Heaten, T.; Hellweg, M.; Liukis, M.; Neuhauser, D. S.; Maechling, P. J.; Solanki, K.; Vinci, Matteo; Henson, I. H.; Khainovski, O.; Kuyuk, S.; Carpio, M.; Meier, Men‐Andrin; Jordan, Thomas H.

doi:10.1007/978-3-642-12233-0_3

Cited by 68 publications

(54 citation statements)

References 25 publications

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“…Ideally, a testing and comparison platform would be established for EEW algorithms, similar to the Collaboratory for the Study of Earthquake Predictability [ Jordan , ] where algorithms could be compared with identical input data sets and performance tests. Such a platform could potentially build on an already existing framework initially built for the ShakeAlert system [ Böse et al ., ], which, unfortunately, was discontinued. A new testing platform is currently being developed in Europe as part of the EPOS project, and it is a goal to include ground motion‐based performance metrics such as the ones proposed here (J. Clinton, personal communication, 2017).…”

Section: Discussionmentioning

confidence: 99%

How “good” are real‐time ground motion predictions from Earthquake Early Warning systems?

Meier

2017

JGR Solid Earth

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Real‐time ground motion alerts, as can be provided by Earthquake Early Warning (EEW) systems, need to be both timely and sufficiently accurate to be useful. Yet how timely and how accurate the alerts of existing EEW algorithms are is often poorly understood. In part, this is because EEW algorithm performance is usually evaluated not in terms of ground motion prediction accuracy and timeliness but in terms of other metrics (e.g., magnitude and location estimation errors), which do not directly reflect the usefulness of the alerts from an end user perspective. Here we attempt to identify a suite of metrics for EEW algorithm performance evaluation that directly quantify an algorithm's ability to identify target sites that will experience ground motion above a critical (user‐defined) ground motion threshold. We process 15,553 recordings from 238 earthquakes with M > 5 (mostly from Japan and southern California) in a pseudo‐real‐time environment and investigate two end‐member EEW methods. We use the metrics to highlight both the potential and limitations of the two algorithms and to show under which circumstances useful alerts can be provided. Such metrics could be used by EEW algorithm developers to convincingly demonstrate the added value of new algorithms or algorithm components. They can complement existing performance metrics that quantify other relevant aspects of EEW algorithms (e.g., false event detection rates) for a comprehensive and meaningful EEW performance analysis.

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Section: Discussionmentioning

confidence: 99%

How “good” are real‐time ground motion predictions from Earthquake Early Warning systems?

Meier

2017

JGR Solid Earth

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“…The use of real-time data for earthquake severity and propagation prediction has been an undertaking in recent decades in Japan, Taiwan, Mexico, Romania, Turkey, and more recently California (Böse et al, 2009). The United States Geologic Survey (USGS) is responsible for issuing earthquake alerts in California, and has led the development of an Earthquake Early Warning (EEW) system for the state (Böse et al, 2012). The system makes use of the existing site monitoring infrastructure of the California Integrated Seismic Network (CISN), monitoring and processing real-time data with the introduction of three parallel detection algorithms (Böse et al, 2012).…”

Section: Us Geological Survey (Usgs)mentioning

confidence: 99%

Improved Regulatory Oversight Using Real-Time Data Monitoring Technologies in the Wake of Macondo

Carter

Oort

Barendrecht

2014

Day 1 Wed, September 10, 2014

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As shown by the Macondo blowout, an uncontrolled deepwater well control event can result in loss of life, damage to the environment, and significant damage to company and industry reputation. Consistent adherence to safety regulations is a recurring issue in deepwater well construction. The two federal entities responsible for offshore U.S. safety regulation are the Department of the Interior's Bureau of Safety and Environmental Enforcement (BSEE) and the U.S. Coast Guard (USCG). The regulatory authority of these two bodies spans well planning, drilling, completions, emergency evacuation, environmental response, etc. The wide range of rules these agencies are responsible for cannot be comprehensively verified with the current infrequency of on-site inspections. Offshore regulation and operational safety could be greatly improved through continuous remote real-time data monitoring. Many government agencies have adopted monitoring regimes dependent on real-time data for improved oversight (e.g. NASA Mission Control, USGS Earthquake Early Warning System, USCG Vessel Traffic Services, etc.). Appropriately, real-time data monitoring was either re-developed or introduced in the wake of catastrophic events within those sectors (e.g. Challenger, tsunamis, Exxon Valdez, etc.). Over recent decades, oil and gas operators have developed Real-Time Operations Centers (RTOCs) for continuous, pro-active operations oversight and remote interaction with on-site personnel. Commonly seen as collaborative hubs, RTOCs provide a central conduit for shared knowledge, experience, and improved decision-making, thus optimizing performance, reducing operational risk, and improving safety. In particular, RTOC's have been useful in identifying and mitigating potential well construction incidents that could have resulted in significant non-productive time and trouble cost. In this paper, a comprehensive set of recommendations is made to BSEE and USCG to expand and improve their regulatory oversight activities through remote real-time data monitoring and application of emerging real-time technologies that aid in data acquisition and performance optimization for improved safety. Data sets and tools necessary for regulators to effectively monitor and regulate deepwater operations (Gulf of Mexico, Arctic, etc.) on a continuous basis are identified. Data from actual GOM field cases are used to support the recommendations. In addition, the case is made for the regulator to build a collaborative foundation with deepwater operators, academia and other stakeholders, through the employment of state-of-the-art knowledge management tools and techniques. This will allow the regulator to do "more with less", in order to address the fast pace of activity expansion and technology adoption in deepwater well construction, while maximizing corporate knowledge and retention. Knowledge management provides a connection that can foster a truly collaborative relationship between regulators, industry, and non-governmental organizations with a common goal of safety assurance and without confusing lines of authority or responsibility. This solves several key issues for regulators with respect to having access to experience and technical know-how, by leveraging industry experts who would not normally have been inaccessible. On implementation of the proposed real-time and knowledge management technologies and workflows, a phased approach is advocated to be carried out under the auspices of the Center for Offshore Safety (COS) and/or the Offshore Energy Safety Institute (OESI). Academia can play an important role, particularly in early phases of the program, as a neutral playing ground where tools, techniques and workflows can be tried and tested before wider adoption takes place.

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“…Impulsive natural or anthropogenic noise (e.g., traffic industry activities, lightning, and device malfunction) near the seismometers may accidentally trigger the system and eventually lead to false alerts (Böse et al, 2014;Wurman et al, 2007). Impulsive natural or anthropogenic noise (e.g., traffic industry activities, lightning, and device malfunction) near the seismometers may accidentally trigger the system and eventually lead to false alerts (Böse et al, 2014;Wurman et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Seismic Wave Discrimination: Application to Earthquake Early Warning

Meier

Hauksson

et al. 2018

Geophysical Research Letters

251

105

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Performance of earthquake early warning systems suffers from false alerts caused by local impulsive noise from natural or anthropogenic sources. To mitigate this problem, we train a generative adversarial network (GAN) to learn the characteristics of first‐arrival earthquake P waves, using 300,000 waveforms recorded in southern California and Japan. We apply the GAN critic as an automatic feature extractor and train a Random Forest classifier with about 700,000 earthquake and noise waveforms. We show that the discriminator can recognize 99.2% of the earthquake P waves and 98.4% of the noise signals. This state‐of‐the‐art performance is expected to reduce significantly the number of false triggers from local impulsive noise. Our study demonstrates that GANs can discover a compact and effective representation of seismic waves, which has the potential for wide applications in seismology.

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CISN ShakeAlert: An Earthquake Early Warning Demonstration System for California

Cited by 68 publications

References 25 publications

How “good” are real‐time ground motion predictions from Earthquake Early Warning systems?

How “good” are real‐time ground motion predictions from Earthquake Early Warning systems?

Improved Regulatory Oversight Using Real-Time Data Monitoring Technologies in the Wake of Macondo

Machine Learning Seismic Wave Discrimination: Application to Earthquake Early Warning

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