TY - JOUR A1 - Viltres, Renier A1 - Nobile, Adriano A1 - Vasyura-Bathke, Hannes A1 - Trippanera, Daniele A1 - Xu, Wenbin A1 - Jónsson, Sigurjón T1 - Transtensional rupture within a diffuse plate boundary zone during the 2020 M-w 6.4 Puerto Rico earthquake JF - Seismological research letters N2 - On 7 January 2020, an M-w 6.4 earthquake occurred in the northeastern Caribbean, a few kilometers offshore of the island of Puerto Rico. It was the mainshock of a complex seismic sequence, characterized by a large number of energetic earthquakes illuminating an east-west elongated area along the southwestern coast of Puerto Rico. Deformation fields constrained by Interferometric Synthetic Aperture Radar and Global Navigation Satellite System data indicate that the coseismic movements affected only the western part of the island. To assess the mainshock's source fault parameters, we combined the geodetically derived coseismic deformation with teleseismic waveforms using Bayesian inference. The results indicate a roughly east-west oriented fault, dipping northward and accommodating similar to 1.4 m of transtensional motion. Besides, the determined location and orientation parameters suggest an offshore continuation of the recently mapped North Boqueron Bay-Punta Montalva fault in southwest Puerto Rico. This highlights the existence of unmapped faults with moderate-to-large earthquake potential within the Puerto Rico region. Y1 - 2021 U6 - https://doi.org/10.1785/0220210261 SN - 0895-0695 SN - 1938-2057 VL - 93 IS - 2A SP - 567 EP - 583 PB - Seismological Society of America CY - Boulder, Colo. ER - TY - JOUR A1 - Vasyura-Bathke, Hannes A1 - Dettmer, Jan A1 - Dutta, Rishabh A1 - Mai, Paul Martin A1 - Jónsson, Sigurjón T1 - Accounting for theory errors with empirical Bayesian noise models in nonlinear centroid moment tensor estimation JF - Geophysical journal international / the Royal Astronomical Society, the Deutsche Geophysikalische Gesellschaft and the European Geophysical Society N2 - Centroid moment tensor (CMT) parameters can be estimated from seismic waveforms. Since these data indirectly observe the deformation process, CMTs are inferred as solutions to inverse problems which are generally underdetermined and require significant assumptions, including assumptions about data noise. Broadly speaking, we consider noise to include both theory and measurement errors, where theory errors are due to assumptions in the inverse problem and measurement errors are caused by the measurement process. While data errors are routinely included in parameter estimation for full CMTs, less attention has been paid to theory errors related to velocity-model uncertainties and how these affect the resulting moment-tensor (MT) uncertainties. Therefore, rigorous uncertainty quantification for CMTs may require theory-error estimation which becomes a problem of specifying noise models. Various noise models have been proposed, and these rely on several assumptions. All approaches quantify theory errors by estimating the covariance matrix of data residuals. However, this estimation can be based on explicit modelling, empirical estimation and/or ignore or include covariances. We quantitatively compare several approaches by presenting parameter and uncertainty estimates in nonlinear full CMT estimation for several simulated data sets and regional field data of the M-1 4.4, 2015 June 13 Fox Creek, Canada, event. While our main focus is at regional distances, the tested approaches are general and implemented for arbitrary source model choice. These include known or unknown centroid locations, full MTs, deviatoric MTs and double-couple MTs. We demonstrate that velocity-model uncertainties can profoundly affect parameter estimation and that their inclusion leads to more realistic parameter uncertainty quantification. However, not all approaches perform equally well. Including theory errors by estimating non-stationary (non-Toeplitz) error covariance matrices via iterative schemes during Monte Carlo sampling performs best and is computationally most efficient. In general, including velocity-model uncertainties is most important in cases where velocity structure is poorly known. KW - Inverse theory KW - Probability distributions KW - Waveform inversion KW - Earthquake source observations KW - Seismic noise Y1 - 2021 U6 - https://doi.org/10.1093/gji/ggab034 SN - 0956-540X SN - 1365-246X VL - 225 IS - 2 SP - 1412 EP - 1431 PB - Oxford University Press CY - Oxford ER - TY - JOUR A1 - Steinberg, Andreas A1 - Vasyura-Bathke, Hannes A1 - Gaebler, Peter Jost A1 - Ohrnberger, Matthias A1 - Ceranna, Lars T1 - Estimation of seismic moment tensors using variational inference machine learning JF - Journal of geophysical research : Solid earth N2 - We present an approach for rapidly estimating full moment tensors of earthquakes and their parameter uncertainties based on short time windows of recorded seismic waveform data by considering deep learning of Bayesian Neural Networks (BNNs). The individual neural networks are trained on synthetic seismic waveform data and corresponding known earthquake moment-tensor parameters. A monitoring volume has been predefined to form a three-dimensional grid of locations and to train a BNN for each grid point. Variational inference on several of these networks allows us to consider several sources of error and how they affect the estimated full moment-tensor parameters and their uncertainties. In particular, we demonstrate how estimated parameter distributions are affected by uncertainties in the earthquake centroid location in space and time as well as in the assumed Earth structure model. We apply our approach as a proof of concept on seismic waveform recordings of aftershocks of the Ridgecrest 2019 earthquake with moment magnitudes ranging from Mw 2.7 to Mw 5.5. Overall, good agreement has been achieved between inferred parameter ensembles and independently estimated parameters using classical methods. Our developed approach is fast and robust, and therefore, suitable for down-stream analyses that need rapid estimates of the source mechanism for a large number of earthquakes. KW - seismology KW - machine learning KW - earthquake source KW - moment tensor KW - full KW - waveform Y1 - 2021 U6 - https://doi.org/10.1029/2021JB022685 SN - 2169-9313 SN - 2169-9356 VL - 126 IS - 10 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Liu, Yuan-Kai A1 - Ruch, Joël A1 - Vasyura-Bathke, Hannes A1 - Jónsson, Sigurjón T1 - Influence of ring faulting in localizing surface deformation at subsiding calderas JF - Earth & planetary science letters N2 - Caldera unrest can lead to major volcanic eruptions. Analysis of subtle subsidence or inflation at calderas helps understanding of their subsurface volcanic processes and related hazards. Several subsiding calderas have shown similar patterns of ground deformation composed of broad subsidence affecting the entire volcanic edifice and stronger localized subsidence focused inside the caldera. Physical models of internal deformation sources used to explain these observations typically consist of two magma reservoirs at different depths in an elastic half-space. However, such models ignore important subsurface structures, such as ring faults, that may influence the deformation pattern. Here we use both analog subsidence experiments and boundary element modeling to study the three-dimensional geometry and kinematics of caldera subsidence processes, evolving from an initial downsag to a later collapse stage. We propose that broad subsidence is mainly caused by volume decrease within a single magma reservoir, whereas buried ring-fault activity localizes the deformation within the caldera. Omitting ring faulting in physical models of subsiding calderas and using multiple point/sill-like sources instead can result in erroneous estimates of magma reservoir depths and volume changes. (C) 2019 Elsevier B.V. All rights reserved. KW - volcanic deformation KW - ring faulting KW - caldera collapse KW - analog models KW - boundary element modeling KW - InSAR Y1 - 2019 U6 - https://doi.org/10.1016/j.epsl.2019.115784 SN - 0012-821X SN - 1385-013X VL - 526 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Kriegerowski, Marius A1 - Petersen, Gesa Maria A1 - Vasyura-Bathke, Hannes A1 - Ohrnberger, Matthias T1 - A Deep Convolutional Neural Network for Localization of Clustered Earthquakes Based on Multistation Full Waveforms JF - Seismological research letters N2 - Earthquake localization is both a necessity within the field of seismology, and a prerequisite for further analysis such as source studies and hazard assessment. Traditional localization methods often rely on manually picked phases. We present an alternative approach using deep learning that once trained can predict hypocenter locations efficiently. In seismology, neural networks have typically been trained with either single-station records or based on features that have been extracted previously from the waveforms. We use three-component full-waveform records of multiple stations directly. This means no information is lost during preprocessing and preparation of the data does not require expert knowledge. The first convolutional layer of our deep convolutional neural network (CNN) becomes sensitive to features that characterize the waveforms it is trained on. We show that this layer can therefore additionally be used as an event detector. As a test case, we trained our CNN using more than 2000 earthquake swarm events from West Bohemia, recorded by nine local three-component stations. The CNN successfully located 908 validation events with standard deviations of 56.4 m in east-west, 123.8 m in north-south, and 136.3 m in vertical direction compared to a double-difference relocated reference catalog. The detector is sensitive to events with magnitudes down to M-L = -0.8 with 3.5% false positive detections. Y1 - 2018 U6 - https://doi.org/10.1785/0220180320 SN - 0895-0695 SN - 1938-2057 VL - 90 IS - 2 SP - 510 EP - 516 PB - Seismological Society of America CY - Albany ER - TY - JOUR A1 - Isken, Marius Paul A1 - Vasyura-Bathke, Hannes A1 - Dahm, Torsten A1 - Heimann, Sebastian T1 - De-noising distributed acoustic sensing data using an adaptive frequency-wavenumber filter JF - Geophysical journal international N2 - Data recorded by distributed acoustic sensing (DAS) along an optical fibre sample the spatial and temporal properties of seismic wavefields at high spatial density. Often leading to massive amount of data when collected for seismic monitoring along many kilometre long cables. The spatially coherent signals from weak seismic arrivals within the data are often obscured by incoherent noise. We present a flexible and computationally efficient filtering technique, which makes use of the dense spatial and temporal sampling of the data and that can handle the large amount of data. The presented adaptive frequency-wavenumber filter suppresses the incoherent seismic noise while amplifying the coherent wavefield. We analyse the response of the filter in time and spectral domain, and we demonstrate its performance on a noisy data set that was recorded in a vertical borehole observatory showing active and passive seismic phase arrivals. Lastly, we present a performant open-source software implementation enabling real-time filtering of large DAS data sets. KW - Fourier analysis KW - Image processing KW - Time-series analysis KW - Seismic noise KW - Distributed acoustic sensing Y1 - 2022 U6 - https://doi.org/10.1093/gji/ggac229 SN - 0956-540X SN - 1365-246X VL - 231 IS - 2 SP - 944 EP - 949 PB - Oxford University Press CY - Oxford ER - TY - JOUR A1 - Heimann, Sebastian A1 - Vasyura-Bathke, Hannes A1 - Sudhaus, Henriette A1 - Isken, Marius Paul A1 - Kriegerowski, Marius A1 - Steinberg, Andreas A1 - Dahm, Torsten T1 - A Python framework for efficient use of pre-computed Green's functions in seismological and other physical forward and inverse source problems JF - Solid earth N2 - The computation of such synthetic GFs is computationally and operationally demanding. As a consequence, the onthe-fly recalculation of synthetic GFs in each iteration of an optimisation is time-consuming and impractical. Therefore, the pre-calculation and efficient storage of synthetic GFs on a dense grid of source to receiver combinations enables the efficient lookup and utilisation of GFs in time-critical scenarios. We present a Python-based framework and toolkit - Pyrocko-GF - that enables the pre-calculation of synthetic GF stores, which are independent of their numerical calculation method and GF transfer function. The framework aids in the creation of such GF stores by interfacing a suite of established numerical forward modelling codes in seismology (computational back ends). So far, interfaces to back ends for layered Earth model cases have been provided; however, the architecture of Pyrocko-GF is designed to cover back ends for other geometries (e.g. full 3-D heterogeneous media) and other physical quantities (e.g. gravity, pressure, tilt). Therefore, Pyrocko-GF defines an extensible GF storage format suitable for a wide range of GF types, especially handling elasticity and wave propagation problems. The framework assists with visualisations, quality control, and the exchange of GF stores, which is supported through an online platform that provides many pre-calculated GF stores for local, regional, and global studies. The Pyrocko-GF toolkit comes with a well-documented application programming interface (API) for the Python programming language to efficiently facilitate forward modelling of geophysical processes, e.g. synthetic waveforms or static displacements for a wide range of source models. Y1 - 2019 U6 - https://doi.org/10.5194/se-10-1921-2019 SN - 1869-9510 SN - 1869-9529 VL - 10 IS - 6 SP - 1921 EP - 1935 PB - Copernicus CY - Göttingen ER -