Multiscale Seismicity Analysis and Forecasting Examples from the Western Pacific and Iceland
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1 Research Collection Doctoral Thesis Multiscale Seismicity Analysis and Forecasting Examples from the Western Pacific and Iceland Author(s): Eberhard, David A. J. Publication Date: 2014 Permanent Link: Rights / License: In Copyright - Non-Commercial Use Permitted This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library
2 Diss.-No. ETH Multiscale Seismicity Analysis and Forecasting: Examples from the Western Pacific and Iceland Athesissubmittedtoattainthedegreeof DOCTOR OF SCIENCES of ETH ZURICH (Dr. sc. ETH Zurich) presented by David A.J. Eberhard MSc. ETH Earth Sc, ETH Zurich born on citizen of Steinen Schwyz SZ accepted on the recommendation of Prof. Dr. Stefan Wiemer Prof. Dr. Frederic Paik Schoenberg Dr. Kristín Vogfjörð Dr. Jeremy Zechar 2014
3 Abstract Seismicity forecasting has three major challenges: (1) the development of models or algorithms to forecast of seismicity, (2) the evaluation and testing of the forecasts and (3) the need for an appropriate software package. The goal of my thesis is to improve seismicity forecasting. I do this by contributing to a solution for all of the three challenges, using data from the West Pacific and from Iceland. The thesis is split into three chapters, each focusing on one of the challenges and each chapter will be (or already is) published in a peer-reviewed scientific journal. This first chapter (chapter 2 in this thesis) is about testing of seismic forecasts. The Collaboratory for the Study of Earthquake Predictability (CSEP, Jordan, 2006) has been conducting an earthquake forecast experiment in the western Pacific. The data and forecasts of this experiment serve as example for this chapter. For the three participating statistical models, I analyze the first four years of this experiment. I use likelihood-based metrics to evaluate the consistency of the forecasts with the observed target earthquakes, and I apply measures based on Student s t-test and the Wilcoxon signed-rank test to compare the forecasts. I estimate also the uncertainties of the test results resulting from uncertainties in earthquake location and seismic moment. The estimated uncertainties are relatively small and suggest that the evaluation metrics are relatively robust. In chapter 3 I focus on the forecast itself with an Operational Earthquake Forecasting (OEF) experiment forecasting seismicity in near real-time for Iceland. The goal is to estimate the feasibility of OEF with the state-of-the-art models. I use the period between 2007 and 2010 and a sparsely processed catalogue as basis for a retrospective forecasting experiment with next-day forecasts. To better understand the effect of updating cycle on the probability gain I also use scheme in which the forecasts are updated after every earthquake M 3. Three different models (ETAS, STEP and TripleS) and an ensemble model consisting of three different models are used for this experiment. I found that while there is still room for improvement, the forecasts model are capable of producing usable forecasts even with the sparsely processed earthquake catalogue. The ensemble forecast has the highest information gain, but the difference to the best non-ensemble model is not significant. Chapter 5 is about the technical aspects of seismicity analysis and forecasting. I introduce in this a new software framework called MapSeis. MapSeis is an open-source software framework with focus on statistical seismology and seismicity forecasting and testing. It has been written by myself and is used in every single step in this thesis;, i
4 0. Abstract because of the rather long development times and diverse tasks for which it has been used, it provides a large variety of handy tools and functions and should address the needs of many scientist working in this field. MapSeis has been built with expandability and maintainability in mind and features a PlugIn system and clearly defined application interfaces (API). My contribution to the three challenges will help to improve seismicity forecasting, but there is still room for improvement and especially the application of Operation Earthquake Forecasting needs to be discussed, not only by scientists but also by the public. ii
5 Zusammenfassung Es gibt drei Hauptherausforderungen bei der Seismizitäts Vorhersage, (1) die eigentliche Vorhersage selbst, (2) das Testen der Vorhersage Modellen auf ihre Tauglichkeit und (3) die technischen Aspekte, heisst das es eine geeignete Software Lösung benötigt wird um Vorhersagen zu erstellen und zu testen. Das Ziel dieser Arbeit ist es Seismizitäts Vorhersagen zu verbessern. Ich hoffe zur Verbesserung beizutragen in dem ich zu allen drei Herausforderungen einen Verbesserung präsentiere. Die verwendeten Daten dafür stammen aus dem West Pazifik und aus Island. Die Arbeit ist in drei Hauptkapitel aufgeteilt, wobei jedes Kapitel sich auf eine der Herausforderung fokussiert und jedes davon wird oder ist bereits publiziert in peer-reviewed scientific Journal. Im erstem Kapitel (Kapitel 2 in dieser Arbeit) geht es um die Analyse und das Testen von Seismizitäts Vorhersagen. Die Collaboratory for the Study of Earthquake Predictability (CSEP, Jordan, 2006) betreibt ein Erdbeben Vorhersage Experiment im West Pazifik. Diese Daten und Vorhersagen bilden die Grundlage für dieses Kapitel. Ich habe für jedes der drei teilnehmenden Modelle die ersten vier Jahre in diesem Experiment analysiert. Ich benutze ein auf Likelihood basierendes Mass um die Vereinbarkeit der Vorhersagen mit den beobachteten Erdbeben zu bestimmen. Zusätzlich wurde der Informationsgehalt der drei Modelle untereinander verglichen mittels eines Student s T-test und eines Wilcoxon Signed-Rank Tests. Ausserdem habe ich den Fehlers dieser Testresultate hervorgerufen durch Ungenauigkeiten in der Lokalisierung und Magnitude der Erdbeben abgeschätzt. Der abgeschätzte Fehler lässt darauf schliessen das die verwendeten Tests relative stabil sind. Im Kapitel 3 liegt der Fokus auf der Seismizitäts Vorhersage selbst. Genauer führe ich in diesem Kapitel ein Operation Earthquake Forecasting (OEF) Experiment, ein Seismizitäts Vorhersage Experiment das in nahezu Echtzeit abläuft, durch. Das Ziel ist es zu bestimmen ob es sich lohnt OEF zu betreiben mit den momentan erhältlichen state-of-the-art Vorhersage Modellen. Ich verwende einen Zeitraum zwischen 2007 und 2010 sowie ein kaum bearbeiteter Erdbebenkatalog herausgegeben vom IMO als Grundlage für ein retrospektives Vorhersage Experiment mit täglich herausgegebenen Vorhersagen. Um auch den Effekt des Vorhersage Zyklus auf den Informationsgehalt der Modelle zu bestimmen habe ich zusätzlich auch Vorhersagen berechnet die nach jedem Erdbeben M 3 erstellt werden. Drei verschiedenen Modelle (ETAS, STEP und Tripels) sowie ein Ensemble Modell, bestehen aus den drei anderen Modellen werden für dieses Experiment verwendet. Obwohl es immer noch Raum für Verbesserungen gibt, war es selbst mit dem kaum prozessierten Katalog mögliche brauchbare Vorhersagen zu produzieren. Das Ensemble hatte dabei den höchsten Informationsgehalt, allerdings iii
6 0. Zusammenfassung war die Differenz zu nächstem Nicht-Ensemble Modell nicht signifikant. Im Kapitel 5 geht es um die technischen Aspekte von Seismizitäts Analysen und Vorhersagen. Ich stelle hier das neue Software Framework mit dem Namen MapSeis vor. MapSeis ist ein Open-Source Software Framework mit dem Fokus auf Statistical Seismologie, Seimizitäts Vorhersagen und dem Testen von Vorhersagen. Die Software wurde von mir selbst geschrieben und in jedem einzelnem Schritt in dieser Arbeit verwendet. Durch die relativ lange Entwicklungszeit und die Vielzahl diverser Arbeit die erledigt werden musste beinhaltet MapSeis eine Vielzahl praktischer Werkzeuge und sollte die Bedürfnisse vieler Wissenschaftler die in diesem Bereich arbeiten befriedigen. MapSeis wurde Entwickelt mit dem Fokus auf Erweiterbarkeit und Wartbarkeit. Dazu beinhaltet es ein PlugIn System sowie klar definierte Programmschnittstellen (API). Mein Beitrag zur Verbesserungen in der Seismizitäts Vorhersage wird dabei helfen zukünftig bessere Vorhersagen zu machen, allerdings gibt es noch viel Platz für Verbesserungen. Vor allem die Anwendung von OEF muss noch stark diskutiert werden, eine Diskussion die nicht nur alleine von Wissenschaftlern geführt werden sollte. iv
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