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The power of distributed acoustic sensing (DAS) lies in its ability to sample deformation signals along an optical fiber at hundreds of locations with only one interrogation unit (IU). While the IU is calibrated to record 'fiber strain', the properties of the cable and its coupling to the rock control the 'strain transfer rate' and hence how much of 'rock strain' is represented in the recorded signal. We use DAS recordings in an underground installation near an array of strainmeters in order to calibrate the 'strain transfer rate' in situ, using earthquake signals between 0.05 Hz and 0.1 Hz. A tight-buffered cable and a standard loose-tube telecommunication cable (running in parallel) are used, where a section of both cables loaded down by loose sand and sand bags is compared to a section, where cables are just unreeled on the floor. The 'strain transfer rate' varies between 0.13 and 0.53 depending on cable and installation type. The sandbags show no obvious effect and the tight-buffered cable generally provides a larger 'strain transfer rate'. Calibration of the 'strain transfer rate' with respect to the strainmeter does not depend on wave propagation parameters. Hence it is applicable to the large amplitude surface wave signal in a strain component almost perpendicular to the great-circle direction for which a waveform comparison with seismometer data does not work. The noise background for 'rock strain' in the investigated band is found at about an rms-amplitude of 0.1 nstrain in 1/6 decade for the tight-buffered cable. This allows a detection of marine microseisms at times of high microseism amplitude.

Detecting phase arrivals and pinpointing the arrival times of seismic phases in seismograms is crucial for many seismological analysis workflows. For land station data machine learning methods have already found widespread adoption. However, deep learning approaches are not yet commonly applied to ocean bottom data due to a lack of appropriate training data and models. Here, we compiled an extensive and labeled ocean bottom seismometer dataset from 15 deployments in different tectonic settings, comprising ~90,000 P and ~63,000 S manual picks from 13,190 events and 355 stations. We propose PickBlue, an adaptation ot the two popular deep learning networks EQTransformer and PhaseNet. PickBlue joint processes three seismometer recordings in conjunction with a hydrophone component and is trained with the waveforms in the new database. The performance is enhanced by employing transfer learning, where initial weights are derived from models trained with land earthquake data. PickBlue significantly outperforms neural networks trained with land stations and models trained without hydrophone data. The model achieves a mean absolute deviation (MAD) of 0.05 s for P waves and 0.12 s for S waves. We integrate our dataset and trained models into SeisBench to enable an easy and direct application in future deployments.

In this paper, we perform full-waveform regional moment tensor (RMT) inversions, to gain insight into the stress distribution along the Lesser Antilles arc. We developed a novel inversion approach, AmPhiB - Amphibious Bayesian, taking into account uncertainties associated with OBS deployments like the orientation of horizontal components and the high noise level. The inversion is conducted using a direct, uniform importance sampling of the fault parameters within a tree structure. We show that the alignment of the horizontal OBS components influences the obtained source mechanism when using standard least-squares (L2) RMT inversion schemes, resulting in systematic errors in the recovered focal mechanisms including high artificial CLVD contributions. AmPhiB reduces these CLVD components by nearly 60% and the aberration of the focal geometry as measured by the Kagan angle by around 40% relative to a standard L2 inversion. Subsequently, we obtain 45 (Mw > 3.8) regional MT solutions. Combining our new results with existing solutions, we subsequently analyze a total of 151 solutions in a FMC diagram and map them to the regional tectonic setting. We perform stress tensor inversions along the arc. On the plate interface, we observe a compressional stress regime and find evidence for upper-plate strike slip and normal fault behaviour in the north and a near arc-perpendicular extensional stress regime towards the south. A dominant slab perpendicular extensional stress regime is found in the slab at 100-200 km at the arcs center. We interpret this stress condition to be a result of slab pull varying along the arc due to partial slab detachment along previously hypothesized lateral slab tear near Grenada, at the southern end of the LA arc, leading to reactivation of preexisting structures around the subducted Proto-Caribbean ridge., Comment: 27 pages of text, 13 figures, 2 tables; submitted for review to GJI

Machine Learning (ML) methods have demonstrated exceptional performance in recent years when applied to the task of seismic event detection. With numerous ML techniques now available for detecting seismicity, applying these methods in practice can help further highlight their advantages over more traditional approaches. Constructing such workflows also enables benchmarking comparisons of the latest algorithms on practical data. We combine the latest methods in seismic event detection to analyse an 18-day period of aftershock seismicity for the $M_{w}$ 6.4 2019 Durr\"es earthquake in Albania. We test two phase association-based event detection methods, the EarthQuake Transformer (EQT; Mousavi et al., 2020) end-to-end seismic detection workflow, and the PhaseNet (Zhu & Beroza, 2019) picker with the Hyperbolic Event eXtractor (Woollam et al., 2020) associator. Both ML approaches are benchmarked against a data set compiled by two independently operating seismic experts who processed a subset of events of this 18-day period. In total, PhaseNet & HEX identifies 3,551 events, and EQT detects 1,110 events with the larger catalog (PhaseNet & HEX) achieving a magnitude of completeness of ~1. By relocating the derived catalogs with the same minimum 1D velocity model, we calculate statistics on the resulting hypocentral locations and phase picks. We find that the ML-methods yield results consistent with manual pickers, with bias that is no larger than that between different pickers. The achieved fit after relocation is comparable to that of the manual picks but the increased number of picks per event for the ML pickers, especially PhaseNet, yields smaller hypocentral errors. The number of associated events per hour increases for seismically quiet times of the day, and the smallest magnitude events are detected throughout these periods, which we interpret to be indicative of true event associations.

Machine Learning (ML) methods have seen widespread adoption in seismology in recent years. The ability of these techniques to efficiently infer the statistical properties of large datasets often provides significant improvements over traditional techniques. With the entire spectrum of seismological tasks, e.g., seismic picking, source property estimation, ground motion prediction, hypocentre determination; among others, now incorporating ML approaches, numerous models are emerging as these techniques are further adopted within seismology. To evaluate these algorithms, quality controlled benchmark datasets that contain representative class distributions are vital. In addition to this, models require implementation through a common framework to facilitate comparison. Accessing these various benchmark datasets for training and implementing the standardization of models is currently a time-consuming process, hindering further advancement of ML techniques within seismology. These development bottlenecks also affect "practitioners" seeking to deploy the latest models on seismic data, without having to necessarily learn entirely new ML frameworks to perform this task. We present SeisBench as a software package to tackle these issues. SeisBench is an open-source framework for deploying ML in seismology. SeisBench standardises access to both models and datasets, whilst also providing a range of common processing and data augmentation operations through the API. Through SeisBench, users can access several seismological ML models and benchmark datasets available in the literature via a single interface. SeisBench is built to be extensible, with community involvement encouraged to expand the package. Having such frameworks available for accessing leading ML models forms an essential tool for seismologists seeking to iterate and apply the next generation of ML techniques to seismic data.

Seismic event detection and phase picking are the base of many seismological workflows. In recent years, several publications demonstrated that deep learning approaches significantly outperform classical approaches and even achieve human-like performance under certain circumstances. However, as most studies differ in the datasets and exact evaluation tasks studied, it is yet unclear how the different approaches compare to each other. Furthermore, there are no systematic studies how the models perform in a cross-domain scenario, i.e., when applied to data with different characteristics. Here, we address these questions by conducting a large-scale benchmark study. We compare six previously published deep learning models on eight datasets covering local to teleseismic distances and on three tasks: event detection, phase identification and onset time picking. Furthermore, we compare the results to a classical Baer-Kradolfer picker. Overall, we observe the best performance for EQTransformer, GPD and PhaseNet, with EQTransformer having a small advantage for teleseismic data. Furthermore, we conduct a cross-domain study, in which we analyze model performance on datasets they were not trained on. We show that trained models can be transferred between regions with only mild performance degradation, but not from regional to teleseismic data or vice versa. As deep learning for detection and picking is a rapidly evolving field, we ensured extensibility of our benchmark by building our code on standardized frameworks and making it openly accessible. This allows model developers to easily compare new models or evaluate performance on new datasets, beyond those presented here. Furthermore, we make all trained models available through the SeisBench framework, giving end-users an easy way to apply these models in seismological analysis., Comment: 17 pages main text, 24 pages supplement

The VERCE project has pioneered an e-Infrastructure to support researchers using established simulation codes on high-performance computers in conjunction with multiple sources of observational data. This is accessed and organised via the VERCE science gateway that makes it convenient for seismologists to use these resources from any location via the Internet. Their data handling is made flexible and scalable by two Python libraries, ObsPy and dispel4py and by data services delivered by ORFEUS and EUDAT. Provenance driven tools enable rapid exploration of results and of the relationships between data, which accelerates understanding and method improvement. These powerful facilities are integrated and draw on many other e-Infrastructures. This paper presents the motivation for building such systems, it reviews how solid-Earth scientists can make significant research progress using them and explains the architecture and mechanisms that make their construction and operation achievable. We conclude with a summary of the achievements to date and identify the crucial steps needed to extend the capabilities for seismologists, for solid-Earth scientists and for similar disciplines., Comment: 14 pages, 3 figures. Pre-publication version of paper accepted and published at the IEEE eScience 2015 conference in Munich with substantial additions, particularly in the analysis of issues