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The present dataset is a comprehensive earthquake catalogue for the Northern Chile subduction zone forearc covering the period 2007-2021, determined from IPOC seismic station data (GFZ and CNRS-INSU 2006; https://doi.org/10.14470/pk615318) plus some auxiliary stations (IPOC = Integrated Plate Boundary Observatory Chile; http://www.ipoc-network.org). The method of automatized earthquake catalogue retrieval, the different relocation steps as well as the different earthquake class labels, and the structures outlined by the seismicity are described in detail in Sippl et al. (2023). The catalogue builds on the one from Sippl et al. (2018; https://doi.org/10.5880/GFZ.4.1.2018.001), but uses a slightly deviating parameter set and a new event category. The columns of the data files are: year, month, day, hour, minute, second, latitude [dec. degrees], longitude [dec. degrees], depth [km], magnitude [ML], identifier The identifier term provides a first-order spatial classification of the seismicity, an explanation is given in Sippl et al. (2023).

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.

In this review article, we compile seismological observations from the different constituent parts of the Northern Chile forearc: the downgoing Nazca Plate, the plate interface, the upper South American Plate as well as the mantle wedge beneath it. As Northern Chile has been monitored by a network of permanent seismic stations since late 2006, there is a wealth of observations that enables us to characterize the structure as well as ongoing processes in the forearc throughout the last 15 years. We put an emphasis on the analysis of seismicity, for which we have extended a massive earthquake catalog that now contains 180,000 events for the years 2007–2021. Moreover, we draw on published results for earthquake mechanisms, source properties, seismic velocity structure, statistical seismology and others, and discuss them in context of results from neighboring disciplines. We thus attempt to provide a comprehensive overview on the seismological knowledge about the structure and ongoing processes in the Northern Chile forearc, a breviary of which is found in the following: The Northern Chile megathrust hosted two major earthquake sequences during the analyzed time period. The 2007 7.8 Tocopilla earthquake broke the deep part of the megathrust just north of Mejillones Peninsula, whereas the 2014 8.1 Iquique earthquake ruptured the central segment in the north of the study region. The latter event has a highly interesting preparatory phase, including a significant foreshock sequence as well as aseismic slip transients. Besides these large events, background seismicity elsewhere on the megathrust may be helpful for characterizing the earthquake potential and locking state in the remaining seismic gap. The downgoing Nazca Plate in Northern Chile exhibits very high seismicity rates, with the vast majority of earthquakes occurring at depths of 80-140 km with downdip extensive mechanisms. While seismic tomography shows no sudden changes in slab geometry along strike, seismicity describes peculiar offsets that may be linked to subducted features on the oceanic plate. Upper plate seismicity likewise shows strong variations along strike, with the north and south of the study area showing only weak activity, whereas the central segment shows pervasive microseismicity throughout the upper plate, all the way to the plate interface. These earthquakes have thrust and strike-slip mechanisms with P-axes striking roughly N-S, indicating margin-parallel compression that may be connected to the concavity of the margin.