Machine learning for fast and accurate assessment of earthquake source parameters

Erdbeben gehören zu den zerstörerischsten Naturgefahren auf diesem Planeten. Obwohl Erdbeben seit Jahrtausenden dokumentiert sing, bleiben viele Fragen zu Erdbeben unbeantwortet. Eine Frage ist die Vorhersagbarkeit von Brüchen: Inwieweit ist es möglich, die endgültige Größe eines Bebens zu bestimmen, bevor der zugrundeliegende Bruchprozess endet? Diese Frage ist zentral für Frühwarnsysteme. Die bisherigen Forschungsergebnisse zur Vorhersagbarkeit von Brüchen sind widersprüchlich. Die Menge an verfügbaren Daten für Erdbebenforschung wächst exponentiell und hat den Tera- bis Petabyte-Bereich erreicht. Während viele klassische Methoden, basierend auf manuellen Datenauswertungen, hier ihre Grenzen erreichen, ermöglichen diese Datenmengen den Einsatz hochparametrischer Modelle und datengetriebener Analysen. Insbesondere ermöglichen sie den Einsatz von maschinellem Lernen und deep learning. Diese Doktorarbeit befasst sich mit der Entwicklung von Methoden des maschinellen Lernens zur Untersuchung zur Erbebenanalyse. Wir untersuchen zuerst die Kalibrierung einer hochpräzisen Magnitudenskala in einem post hoc Scenario. Nachfolgend befassen wir uns mit Echtzeitanalyse von Erdbeben mittels deep learning. Wir präsentieren TEAM, eine Methode zur Frühwarnung. Auf TEAM aufbauend entwickeln wir TEAM-LM zur Echtzeitschätzung von Lokation und Magnitude eines Erdbebens. Im letzten Schritt untersuchen wir die Vorhersagbarkeit von Brüchen mittels TEAM-LM anhand eines Datensatzes von teleseismischen P-Wellen-Ankünften. Dieser Analyse stellen wir eine Untersuchung von Quellfunktionen großer Erdbeben gegenüber. Unsere Untersuchung zeigt, dass die Brüche großer Beben erst vorhersagbar sind, nachdem die Hälfte des Bebens vergangen ist. Selbst dann können weitere Subbrüche nicht vorhergesagt werden. Nichtsdestotrotz zeigen die hier entwickelten Methoden, dass deep learning die Echtzeitanalyse von Erdbeben wesentlich verbessert.Earthquakes are among the largest and most destructive natural hazards known to humankind. While records of earthquakes date back millennia, many questions about their nature remain open. One question is termed rupture predictability: to what extent is it possible to foresee the final size of an earthquake while it is still ongoing? This question is integral to earthquake early warning systems. Still, research on this question so far has reached contradictory conclusions. The amount of data available for earthquake research has grown exponentially during the last decades reaching now tera- to petabyte scale. This wealth of data, while making manual inspection infeasible, allows for data-driven analysis and complex models with high numbers of parameters, including machine and deep learning techniques. In seismology, deep learning already led to considerable improvements upon previous methods for many analysis tasks, but the application is still in its infancy. In this thesis, we develop machine learning methods for the study of rupture predictability and earthquake early warning. We first study the calibration of a high-confidence magnitude scale in a post hoc scenario. Subsequently, we focus on real-time estimation models based on deep learning and build the TEAM model for early warning. Based on TEAM, we develop TEAM-LM, a model for real-time location and magnitude estimation. In the last step, we use TEAM-LM to study rupture predictability. We complement this analysis with results obtained from a deep learning model based on moment rate functions. Our analysis shows that earthquake ruptures are not predictable early on, but only after their peak moment release, after approximately half of their duration. Even then, potential further asperities can not be foreseen. While this thesis finds no rupture predictability, the methods developed within this work demonstrate how deep learning methods make a high-quality real-time assessment of earthquakes practically feasible

Seismology - Responsibilities and requirements of a growing science. Part 2 - problems and prospects

Theoretical and applied seismology, earthquake engineering, earth structure, industrial uses, facilities, and underground nuclear explosion detectio

Seismic hazard assessment in terms of macroseismic intensity for the Italian area

A seismic hazard map, in terms of macro seismic intensity with 10% probability of exceedance in 50 years, is proposed for the Italian territory. The input elements used to evaluate the seismic hazard are: the seismogenic zoning ZS9 (Meletti et al., 2007), the earthquake catalogue CPTI04 (Gruppo di lavoro CPTI04, 2004) and intensity attenuation relationships. The first two elements and the historical and statistical completeness of the catalogue are those used in the national seismic hazard map for Italy MPS04 (Gruppo di Lavoro MPS, 2004). Two intensity attenuation models are used: 1) one national relationship obtained with a new approach by Pasolini et al. (2006) and a relationship for the Etna volcanic zone proposed by Azzaro et al. (2006) 2) a set of regional relationships derived from a previous cubic model (Berardi et al., 1993) which is recalibrated in the present study using the macro seismic intensity database DBMI04 (Stucchi et al., 2007), which was used for compiling CPTI04. The computer code adopted to evaluate the seismic hazard, with the elements cited above, is SeisRisk III (Bender and Perkins, 1987), which has been modified within this study to incorporate the aleatory variability of the ground motion (macroseismic intensity). A logic-tree framework allowed to explore some possible alternatives of epistemic character. The seismic hazard map obtained in terms of intensity was subsequently transformed into PGA by means of a linear relation between intensity and PGA, in order to compare it with the recently national seismic hazard map MPS04

Using Physical and Social Sensors in Real-Time Data Streaming for Natural Hazard Monitoring and Response

Technological breakthroughs in computing over the last few decades have resulted in important advances in natural hazards analysis. In particular, integration of a wide variety of information sources, including observations from spatially-referenced physical sensors and new social media sources, enables better estimates of real-time hazard. The main goal of this work is to utilize innovative streaming algorithms for improved real-time seismic hazard analysis by integrating different data sources and processing tools into cloud applications. In streaming algorithms, a sequence of items from physical and social sensors can be processed in as little as one pass with no need to store the data locally. Massive data volumes can be analyzed in near-real time with reasonable limits on storage space, an important advantage for natural hazard analysis. Seismic hazard maps are used by policymakers to set earthquake resistant construction standards, by insurance companies to set insurance rates and by civil engineers to estimate stability and damage potential. This research first focuses on improving probabilistic seismic hazard map production. The result is a series of maps for different frequency bands at significantly increased resolution with much lower latency time that includes a range of high-resolution sensitivity tests. Second, a method is developed for real-time earthquake intensity estimation using joint streaming analysis from physical and social sensors. Automatically calculated intensity estimates from physical sensors such as seismometers use empirical relationships between ground motion and intensity, while those from social sensors employ questionaries that evaluate ground shaking levels based on personal observations. Neither is always sufficiently precise and/or timely. Results demonstrate that joint processing can significantly reduce the response time to a damaging earthquake and estimate preliminary intensity levels during the first ten minutes after an event. The combination of social media and network sensor data, in conjunction with innovative computing algorithms, provides a new paradigm for real-time earthquake detection, facilitating rapid and inexpensive risk reduction. In particular, streaming algorithms are an efficient method that addresses three major problems in hazard estimation by improving resolution, decreasing processing latency to near real-time standards and providing more accurate results through the integration of multiple data sets

Earthquake hazard and risk analysis for natural and induced seismicity: towards objective assessments in the face of uncertainty.

The fundamental objective of earthquake engineering is to protect lives and livelihoods through the reduction of seismic risk. Directly or indirectly, this generally requires quantification of the risk, for which quantification of the seismic hazard is required as a basic input. Over the last several decades, the practice of seismic hazard analysis has evolved enormously, firstly with the introduction of a rational framework for handling the apparent randomness in earthquake processes, which also enabled risk assessments to consider both the severity and likelihood of earthquake effects. The next major evolutionary step was the identification of epistemic uncertainties related to incomplete knowledge, and the formulation of frameworks for both their quantification and their incorporation into hazard assessments. Despite these advances in the practice of seismic hazard analysis, it is not uncommon for the acceptance of seismic hazard estimates to be hindered by invalid comparisons, resistance to new information that challenges prevailing views, and attachment to previous estimates of the hazard. The challenge of achieving impartial acceptance of seismic hazard and risk estimates becomes even more acute in the case of earthquakes attributed to human activities. A more rational evaluation of seismic hazard and risk due to induced earthquakes may be facilitated by adopting, with appropriate adaptations, the advances in risk quantification and risk mitigation developed for natural seismicity. While such practices may provide an impartial starting point for decision making regarding risk mitigation measures, the most promising avenue to achieve broad societal acceptance of the risks associated with induced earthquakes is through effective regulation, which needs to be transparent, independent, and informed by risk considerations based on both sound seismological science and reliable earthquake engineering

Behaviour of buried water supply pipelines in earthquake zones

Imperial Users onl

Innovations in earthquake risk reduction for resilience: Recent advances and challenges

The Sendai Framework for Disaster Risk Reduction 2015-2030 (SFDRR) highlights the importance of scientific research, supporting the ‘availability and application of science and technology to decision making’ in disaster risk reduction (DRR). Science and technology can play a crucial role in the world’s ability to reduce casualties, physical damage, and interruption to critical infrastructure due to natural hazards and their complex interactions. The SFDRR encourages better access to technological innovations combined with increased DRR investments in developing cost-effective approaches and tackling global challenges. To this aim, it is essential to link multi- and interdisciplinary research and technological innovations with policy and engineering/DRR practice. To share knowledge and promote discussion on recent advances, challenges, and future directions on ‘Innovations in Earthquake Risk Reduction for Resilience’, a group of experts from academia and industry met in London, UK, in July 2019. The workshop focused on both cutting-edge ‘soft’ (e.g., novel modelling methods/frameworks, early warning systems, disaster financing and parametric insurance) and ‘hard’ (e.g., novel structural systems/devices for new structures and retrofitting of existing structures, sensors) risk-reduction strategies for the enhancement of structural and infrastructural earthquake safety and resilience. The workshop highlighted emerging trends and lessons from recent earthquake events and pinpointed critical issues for future research and policy interventions. This paper summarises some of the key aspects identified and discussed during the workshop to inform other researchers worldwide and extend the conversation to a broader audience, with the ultimate aim of driving change in how seismic risk is quantified and mitigated