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In September 2022 we launched a large-scale seismic experiment in Germany with more than 350 seismological stations (Eifel large-N), to study the Eifel volcanic system. The temporary network is complementing the permanent seismic networks in the region. The deployed instruments used include both 4.5 Hz 3C, 1 Hz short-period, and broadband instruments. DIEGOS Cube3 digitizers are used for data sampling and 9 V electrical fence batteries provide autarch energy at each site. Most of the instruments were borrowed from the GFZ GIPP Instrument Pool for 1 year. In a second phase of the project, half of the installed stations will be re-deployed along linear profiles with a station spacing of 1 km. In 2023, continuous distributed acoustic sensing measurements along a 100 km dark telecommunication fiber optical cable will begin, for a period of three months. These measurements will complete the campaign. We report on preliminary studies on the design of the Large-N experiment, the logistical and technical approach to handling the network and the data, and show first examples for selected local earthquakes, local noise conditions and noise correlations.The Eifel region in Germany is characterized both as a recreational area with villages, agriculture, forestry and parks, as well as with cities, industrial centers, motorways, railway lines, windmill energy parcs and quarries. The site selection phase was carried out with up to three groups, and began half a year before the network deployment. At the same time, the district administrators and mayors of cities and municipalities who provide valuable support for the project were contacted.Site selection information was organized in a geo information system (GIS). The fieldwork was orchestrated using the mobile QField App. In September 2022, we started to install the large-N network, which covers an area of ​​approximately 150 x 110 km2. Around the scientific target, the Laacher See, is a high station density with inter-station distance of less than 1 km, the inter-station distances increase with distance from the Laacher See. During the installation phases in September and October 2022, the international project partners formed up to 8 groups that installed the nodal stations in the sectors. Each group was equipped with a smartphone or tablet running the QField app also, updating the station information database independently. The QField app provided instant information about the network status via online synchronization.

The Bayesian earthquake analysis tool (BEAT) is an open-source Python software to conduct source-parameter estimation studies for crustal deformation events, such as earthquakes and magma intrusions, by employing a Bayesian framework with a flexible problem definition. The software features functionality to calculate Green’s functions for a homogeneous or a layered elastic half-space. Furthermore, algorithm(s) that explore the solution space may be selected from a suite of implemented samplers. If desired, BEAT’s modular architecture allows for easy implementation of additional features, for example, alternative sampling algorithms. We demonstrate the functionality and performance of the package using five earthquake source estimation examples: a full moment-tensor estimation; a double-couple moment-tensor estimation; an estimation for a rectangular finite source; a static finite-fault estimation with variable slip; and a full kinematic finite-fault estimation with variable hypocenter location, rupture velocity, and rupture duration. This software integrates many aspects of source studies and provides an extensive framework for joint use of geodetic and seismic data for nonlinear source- and noise-covariance estimation within layered elastic half-spaces. Furthermore, the software also provides an open platform for further methodological development and for reproducible source studies in the geophysical community.

One year of deformation and seismicity prior to a volcanic eruption in March 2021 at an oblique plate boundary in Iceland created a unique opportunity to study the interaction between upwelling magma and geothermal processes. We apply poroelastic modelling to explain satellite geodetic data showing three uplift and subsidence cycles at the Svartsengi geothermal field and use gravity data to constrain the density of intruded material. We use recordings on optical cable to generate a high-resolution earthquake catalogue and developed new waveform stacking and migration methods to detect and locate 39,500 earthquakes. The resulting model explains the geodetic, gravity, and seismic data by magmatic derived gas intruded into a horizontal sealed aquifer at 4 km depth in the roots of the geothermal field at the top of up-doming brittle-ductile boundary. The total injected volume is estimated 9.5·107m3 with optimal density of 840 kg/m3. Our results suggest upward migration of three packages of volcanic gas along the brittle-ductile boundary from a subcrustal magmatic source 8–10 km east of the geothermal field, with important implications for the dynamics leading to the eruption.

The fast inversion of reliable centroid moment tensor and kinematic rupture parameters of earthquakes occurring near coastal margins is a key for the assessment of the tsunamigenic potential and early tsunami warning (TEW). In recent years, more and more multi-channel seismic and geodetic online station networks have been built-up to improve the TEW, for instance the GNSS and strong motion networks in Italy, Greece, and Turkey, additionally to the broadband seismological monitoring. Inclusion of such data for the fast kinematic source inversion can improve the resolution and robustness of its’ solutions. However, methods have to be further developed and tested to fully exploit the potential of such rich joint dataset.In this frame, we compare and test two in-house developed, kinematic / dynamic rupture inversion methods which are based on completely different approaches. The IDS (Iterative Deconvolution and Stacking, Zhang et al., 2014) combines an iterative seismic network inversion with back projection techniques to retrieve subfault source time functions. The pseudo dynamic rupture model (Dahm et al., in review) links the rupture front propagation estimate based on the Eikonal equation with the dislocation derived from a boundary element method to model dislocation snapshots. We used the latter in both a fast rupture estimate and a fully probabilistic source inversion.We use some Mw > 6.3 earthquakes that occurred in the coastal range of the Aegean Sea as an example for comparison: the Mw 6.3 Lesbos earthquake (12 June 2017), the Mw 6.6 Bodrum earthquake (20 July 2017), and the recent Mw 7.0 earthquake which occurred at Samos on 30 October 2020. The latter earthquake and the resulting tsunami caused fatalities and severe damage at the shorelines of Samos and around the city of Izmir, Turkey.The fast estimates are based on only little data and/or prior information obtained from the regional seismicity catalogue and available active fault information. The large number of seismic (broadband, strong motion) and geodetic (high-rate GNSS) stations in local and regional distance from the earthquake with good azimuthal coverage jointly inverted with InSAR data allows for robust inversion results. These, and other solutions, are used as a reference for the comparison of our fast source estimates.Preliminary results of the slip distribution and the source time function are in good agreement with modelling results from other authors.We present our insights into the kinematics of the chosen earthquakes investigated by means of joint inversions. Finally, the accuracy of our first fast source estimates, which could be of potential use in tsunami early warning, will be discussed.

We present an analysis and qualitative comparison between acoustic data recorded on a distributed acoustic sensing (DAS) instrument (Silixa iDAS, version 2) and a three-component geophone chain colocated in a 400 m deep ICDP borehole in the magmatically active Vogtland area, Germany. A tight buffer single-mode fiber optic cable with a structured surface was installed and cemented behind casing down to total depth of the well. Additionally, a vertical array of 10 Hz geophones is suspended within the borehole.At the surface, further geophones were installed to shape a permanent three-dimensional seismic array. For this experiment the DAS system sampled strain-rate data at 10 m gauge length and 1 m spacing, yielding a high-resolution image of the wave field. Both seismic systems recorded data for 24 hours at 1 kHz sampling rate.Within these 24 hours of recording, we shot a vertical seismic profile (VSP) with a 300 kg heavy and 2.4 m tall drop weight source moving up to a distance of 400 m away from the wellhead. Furthermore, passive seismic events at local and regional distances were recorded.We compare the signal quality between the DAS system and the calibrated three-component geophones using the active and passive signals, to determine the sensitivity, signal-to-noise ratios and frequency response. Further we investigate the noise characteristics of both systems in this natural and remote environment, and evaluate the feasibility of borehole DAS behind casing for micro-earthquake monitoring. We give an outlook how dense DAS data can be utilized for VSP experiments with the aim to develop methods for fault detection and characterisation for application in DAS data recorded at the surface.

The highly productive high temperature geothermal fields in Iceland are located within active volcanic systems on the plate boundaries. When an earthquake swarm or an unusual surface uplift or subsidence occur, it is important to assess the hazards and whether the unrest is triggered or controlled by volcanic or anthropogenic processes, or a combination of both.On January 22nd, 2020, a rapid, large-scale uplift (14 km x 12 km) started at the Svartsengi geothermal field on the plate boundary of the Reykjanes Peninsula, along with an intense earthquake swarm that began simultaneously about 3 km east of the centre of uplift. The centre of uplift was located about 1 km west of Mt. Thorbjörn, in the middle of the Svartsengi geothermal field, close to the reinjection wells. Over a period of 6 months, three such uplift cycles occurred, each lasting for several weeks and followed by periods of relatively rapid subsidence. The duration and timing of the uplift-subsidence cycles appears to follow a clear trend where the successive inflation episodes lasted longer but with lower inflation rate.The centres of uplift and the deflation cycles are the same and remained stationary. The accompanied intense earthquake swarms migrated along the 40 km long oblique plate boundary of the Reykjanes Peninsula, demonstrating a major plate tectonic event. The maximum depth of earthquakes was close to 4.5 km directly above the centre of uplift but extending to 6-7 km in the surroundings where the maximum magnitudes reached MW 4.8.A few weeks after the onset of the unrest, nine additional seismic stations were deployed to densify the local seismic network in place. In addition, complimentary data from an existing 21 km long fibre optics cable were used to monitor high-frequency linear strain rates. Both measures led to a significant improvement in the earthquake detection and location which predominantly occurred in swarms. Likewise, InSAR data analysis of temporal uplift cycles was performed, repeated gravity measurements at permanent sites were performed, and resistivity was remeasured at chosen sites. Multiple different elementary models were developed and tested to explain the cyclic excitation of the uplift, subsidence, and seismicity. While the individual unrest episodes might be controlled by possible magma intrusions into the lower crust, our favoured model explains the spatio-temporal pattern of ground uplift by the rise and diffusion of pore pressure in a 4-5 km deep geothermal aquifer. To distinguish between different models, we use multi-disciplinary geophysical datasets, such as deformation, seismicity, and gravity.

Within the ICDP project “Drilling the Eger Rift”, we focus on the German-Czech border region West Bohemia/ Vogtland which is known for its earthquake swarms. These swarms are clusters of small magnitude (MLfrequencies above 100 Hz by installing three dense small aperture 3D arrays. Each single 3D array will consist of a 400 m deep vertical array borehole installation and a small aperture (400 m) surface array.The drill site S1 in Landwüst and its surroundings serve as pilot site for the first installation. The borehole chain consists of eight 3-component 10 Hz geophones and the continous recordings are sampled with 1000 Hz. In parallel, twelve surface stations are installed which are equiped with 4.5 Hz geophones. The data were recorded with 400 Hz sampling rate at most locations, but at some selected stations we additionally record data with 1000 Hz sampling rate being the desired sampling rate for the final array configuration. Due to the high sampling rates and the high frequency content of the recorded earthquake signals, local site conditions may lead to non-coherent recordings for different parts of the array which have a major influence on the overall array performance. However, preliminary results from broad band frequency wave number analysis (5-180 Hz) in a moving time window (0.2 s) with first test installation data also indicate that the coherency across the array site is still high enough to clearly identify P and S waves from local earthquakes. In the period December 2020 – January 2021, an earthquake swarm took place with two activity clusters in Nový Kostel (Czech Republic) and Obertriebel/ Oelsnitz (Vogtland, Germany) about 20 km apart. This swarm was recorded by both borehole stations and surface stations in Landwüst. Preliminary results show that more than 14000 events can be identified at the borehole stations and that about 70-80% of these events are also observed at the surface stations. For small earthquakes, mainly the S wave can be identified, but also impulsive P waves are clearly visible at the surface stations. These high frequency waves (up to 230 Hz at the surface) show a good coherency across the surface array. At the borehole stations, we observe an even higher frequency content up to 300 Hz and more. We present recordings from selected events to analyse frequency content and coherency across the 3D array.

SUMMARYThe investigation of stresses, faults, structure and seismic hazards requires a good understanding and mapping of earthquake rupture and slip. Constraining the finite source of earthquakes from seismic and geodetic waveforms is challenging because the directional effects of the rupture itself are small and dynamic numerical solutions often include a large number of free parameters. The computational effort is large and therefore difficult to use in an exploratory forward modelling or inversion approach. Here, we use a simplified self-similar fracture model with only a few parameters, where the propagation of the fracture front is decoupled from the calculation of the slip. The approximative method is flexible and computationally efficient. We discuss the strengths and limitations of the model with real-case examples of well-studied earthquakes. These include the Mw 8.3 2015 Illapel, Chile, megathrust earthquake at the plate interface of a subduction zone and examples of continental intraplate strike-slip earthquakes like the Mw 7.1 2016 Kumamoto, Japan, multisegment variable slip event or the Mw 7.5 2018 Palu, Indonesia, supershear earthquake. Despite the simplicity of the model, a large number of observational features ranging from different rupture-front isochrones and slip distributions to directional waveform effects or high slip patches are easy to model. The temporal evolution of slip rate and rise time are derived from the incremental growth of the rupture and the stress drop without imposing other constraints. The new model is fast and implemented in the open-source Python seismology toolbox Pyrocko, ready to study the physics of rupture and to be used in finite source inversions.

Inferring the geometry and evolution of an earthquake sequence is crucial to understand how fault systems are segmented and interact. However, structural geological models are often poorly constrained in remote areas and fault inference is an ill-posed problem with a reliability that depends on many factors. Here, we investigate the geometry of the Mw 6.3 2008 and 2009 Qaidam earthquakes, in northeast Tibet, by combining InSAR time series and teleseismic data. We conduct a multi-array back-projection analysis from broadband teleseismic data and process three overlapping Envisat tracks covering the two earthquakes to extract the spatio-temporal evolution of seismic ruptures. We then integrate both geodetic and seismological data into a self-consistent kinematic model of the earthquake sequence. Our results constrain the depth and along-strike segmentation of the thrust-faulting sequence. The 2008 earthquake ruptured a ∼32∘ north-dipping fault that roots under the Olongbulak pop-up structure at ∼12 km depth and fault slip evolved post-seismically in a downdip direction. The 2009 earthquake ruptured three south-dipping high-angle thrusts and propagated from ∼9 km depth to the surface and bilaterally along the south-dipping segmented 55–75∘ high-angle faults of the Olonbulak pop-up structure that displace basin deformed sedimentary sequences above Paleozoic bedrock. Our analysis reveals that the inclusion of the post-seismic afterslip into modelling is beneficial in the determination of fault geometry, while teleseismic back-projection appears to be a robust tool for identifying rupture segmentation for moderate-sized earthquakes. These findings support the hypothesis that the Qilian Shan is expanding southward along a low-angle décollement that partitions the oblique convergence along multiple flower and pop-up structures.

The 24 January 2020 Mw 6.77 Elazığ-Sivrice earthquake (Turkey), responsible for 42 casualties and ~ 1600 injured people, is the largest earthquake affecting the East Anatolian Fault (EAF) since 1971. The earthquake partially ruptured a seismic gap. The mainshock was preceded by two foreshocks with Mw ≥ 4.9 and small seismicity clusters occurring in the previous months close to the nucleation point of the main rupture. The significant aftershock sequence comprises twelve earthquakes with Mw ≥ 4.5 within 60 days. We jointly model quasi co-seismic static surface displacements from Interferometric Synthetic Aperture Radar (InSAR) and high-frequency co-seismic data from seismological networks at local, regional and teleseismic distances to retrieve source parameters of the mainshock. We reconstruct the rupture process using a Bayesian bootstrap based probabilistic joint inversion scheme to obtain source parameters and their uncertainties. Full moment tensor for 18 fore-/after-shocks with Mw ≥ 4.3 are obtained based on the modeling of regional broadband data. The posterior mean model for the 2020 Elazığ-Sivrice mainshock shows that the earthquake, with a magnitude Mw 6.77, ruptured at shallow depth (5 ± 2 km) with a left-lateral strike-slip focal mechanism, with a dip angle of 74° ± 2° and a causative fault plane strike of 242° ± 1°, which is compatible with the orientation of the EAF at the centroid location. The rupture nucleated in the vicinity of small foreshock clusters and slowly propagated towards WSW, with a rupture velocity of ~ 2100 ± 130 m s−1 and ~ 27 s rupture duration. The main rupture area, with a length of ~ 26 ± 5 km, only covered 70 % of the former seismic gap, leaving a smaller, unbroken segment of ~ 30 km length to the SE with positive stress change. The subsequent aftershock sequence extended over a broader region of ~ 70 km in length, spreading to both sides of the mainshock rupture patch into the regions experiencing a stress increase according to our Coulomb stress modeling. Our results support the hypothesis of a shallow locking depth of the Anatolian micro-plate, which has a possible implication to the seismic bursts along the EAF and alternating seismic activity on the North Anatolian and the East Anatolian faults.

We present a modular open-source software framework - Kite (http://pyrocko.org) - for rapid post-processing of spaceborne InSAR-derived surface displacement maps. The software enables swift parametrisation, post-processing and sub-sampling of the displacement measurements that are compatible with common InSAR processors (e.g. SNAP, GAMMA, ISCE, etc.) and online processing centers delivering unrwapped InSAR data products, such as NASA ARIA or LiCSAR. The post-processing capabilities include removal of first-order atmospheric phase delays through elevation correlation estimations and regional atmospheric phase screen (APS) estimations based on atmospheric models (GACOS), masking of displacement data, adaptive data sub-sampling using quadtree decomposition and data error covariance estimation.Kite datasets integrate into forward modelling and optimisation frameworks Grond (Heiman et al., 2019) and BEAT (Vasyura-Bathke et al., 2019), both software packages aim to ease and streamline the joint optimisation of earthquake parameters from InSAR and GPS data together with seismological waveforms. These data combinations will improve the estimation of earthquake rupture parameters. Establishing this data processing software framework we want to bridge the gap between InSAR processing software and seismological modelling frameworks, to contribute to a timely and better understanding of earthquake kinematics. This approach paves the way to automated inversion of earthquake models incorporating space-borne InSAR data.Under development is the processing of InSAR displacement time series data to link simultaneous modelling of co- and post-seismic transient deformation processes from InSAR observations to physical earthquake cycle models.We demonstrate the framework’s capabilities with an analysis of the 2019 Ridgecrest earthquakes from InSAR surface displacements (provided by NASA ARIA) combined with GNSS displacements using the Bayesian bootstrapping strategy from the Grond inverse modelling tool.

Southwestern Anatolia is part of a N-S extensional regime mainly driven by rollback along the Hellenic subduction zone beneath the Aegean Sea. This seismically active area is controlled primarily by normal fault systems. The fault structures in the region are segmented and in many cases seismic interaction between these segments can be observed.2017 seismic activity along the Eastern and Western edges of Gökova Bay. Within the same year, three separate moderate sized (Mw~5) events took place near the town of Ula (Muğla) on the eastern edge of Gökova Bay. One of these earthquakes occurred in April before the Bodrum-Kos earthquake while the other pair occurred in November within two days.We relocated all the events that occurred in Ula region in 2017 and remodeled the source mechanisms from regional seismic waveforms by using Bayesian Earthquake Analysis Tool (BEAT). The surface deformations can also be clearly observed from InSAR tracks of both ascending and descending orbits. Because of the large noise margins of the interferograms, atmospheric noise corrections and high resolution DEM data were used.Due to temporal and spatial proximity of the two Mw~5 events during the November sequence, InSAR yields only cumulative deformation of the earthquakes. Therefore, we determined the contribution of each event to the cumulative static displacements observed by InSAR data, using the source models from seismic waveforms. The locations and the source mechanisms of the two Mw~5 earthquake are consistent and explain the observed surface deformation.Our results imply that these earthquakes occurred on a previously unknown normal fault rather than the southeastern branches of the nearby Muğla Fault as proposed earlier. The results are consistent with the recently mapped fault structure by Akyüz et al. (2018). The November activity implies EW trending, south dipping normal faulting system and the change in the strike direction of the fault on the eastern edge can be clearly seen both InSAR and waveform modelling results of April activity.Co-seismic and post-seismic InSAR analysis shows that the seismic activity following the 2017 Mw6.6 Bodrum-Kos propagated from western Gökova Bay where rupture occurred toward east including the Ula region. A long term comparison of seismicity beneath Gökova Bay and Ula region shows that the seismicity in these two regions are temporally correlated. Hence, while the aforementioned moderate sized earthquakes are not directly triggered by the Bodrum-Kos earthquake, increased seismic activity following Bodrum-Kos earthquake shows that the stress changes in these two regions affect each other. The location errors of the events especially the depth errors in the catalogs and the active fault structure in the area cannot be realized without any geodetic or seismic data analysis. This study claims that the interpretations of the moderate size earthquakes should be studied by using multidisciplinary data sets.ACKNOWLEDGEMENTSThis work is supported by the Turkish Directorate of Strategy and Budget under the TAM Project number DPT2007K120610.

The Mw 7.3 Sarpol-Zahab earthquake occurred on 12 November 2017 in the Lurestan arc of the Zagros Simply Folded Belt (ZSFB). It is estimated that 600 people were killed and 8000 were injured in this earthquake. This earthquake has been the largest instrumentally recorded earthquake in the ZSFB and its moment, as well as its mechanism, were unexpected. We present an earthquake source study on the Mw 7.3 Sarpol-Zahab earthquake, two large following earthquakes in the region in 2018 and their corresponding aftershock sequences to gain insight of seismotectonic of the Lurestan arc fold-thrust belt.In this study, we complement previous studies on this earthquake, by non-linear probabilistic optimization of joined geodetic and seismic data using a new, efficient Bayesian bootstrap-based optimization scheme to infer the finite fault geometry and fault slip together with meaningful uncertainty estimates of the model parameters. Our optimization is based on the modeling of ascending and descending Sentinel-1 satellite data, seismological waveform from global seismic networks and the strong motion network of Iran. The posterior mean model of the Sarpol-Zahab earthquake shows that the causative fault plane is centered at is 14±2 km depth and has a low dip angle of 17°±2° and a strike of 350°±10°. The rake angle of 144°±4° points to an oblique thrust mechanism. The rupture area of the uniform-slip, rectangular model is 40±2 km long and 16±2 km width and shows 4.0±0.5 m fault slip, which results in a magnitude estimate of Mw 7.3±0.1.Later, in August and November 2018, two large earthquakes with Mw 6.0 and Mw 6.4 occurred about 40 km east and 60 km south of the Sarpol-Zahab epicenter, respectively. These earthquakes could have been triggered by the 2017 Sarpol-Zahab earthquake. We apply the same joint inversion modeling to derive the corresponding fault plane solutions. We found strike-slip mechanisms for both events but centroid depths at 10±2 km and 16±2 km for Mw 6.0 and Mw 6.4, respectively.The 2017 Sarpol-Zahab earthquake and the following studied 2018 earthquakes were followed by a sustained aftershock sequence, with more than 133 aftershocks exceeding Ml 4.0 until December 30, 2019. We rely on the local and regional seismic broad-band stations of Iran and Iraq permanent networks to estimate full-waveform moment tensor solutions of 70 aftershocks down to Ml 4. Most of these aftershocks have shallow centroid depths between 5 and 12 km, so that they occurred in the uppermost part of the basement and/or in the lower sedimentary cover, which is ~8 km thick in this area.Our results suggest that the Sarpol-Zahab earthquakes activated low-angle thrust faults and shallower strike-slip structures, highlighting that both thin- and thick-skin deformation take place in the fold-thrust belts in the Lurestan arc of the Zagros. Such information on the deformation characteristics is important for the hazard and risk assessment of future large earthquakes in this region.Additionally, we demonstrate how the joint inversion of different geophysical data can help to better resolve the fault geometry and the earthquake source parameters.

Earthquakes have been observed to initiate and terminate near geometrical irregularities (bends, step-overs, branching of secondary faults). Rupture segmentationinfluences the seismic radiation and therefore, the related seismic hazard. Good imaging of rupture segmentation helps to characterize fault geometries at depth for follow-up tectonic, stress-field or other analyses. From reported earthquake source models it appears that large earthquakes with magnitudes above 7 are most often segmented, while earthquakes with magnitudes below 6.5 most often are not. If this observationreflects nature or if it is rather an artifact of our abilities to well observe and infer earthquake sources can not be answered without an objective strategy to constrain rupture complexity. However, data-driven analyses of rupture segmentation are not often conducted in source modeling as it is mostly pre-defined through a given and fixed number of sources. We, here, propose a segmentation-sensitive source analysis by combining a model-independent teleseismic back-projection and image segmentation methods with a kinematic fault inversion. Our approach is twofold. We first develop a time-domain multi-array back-projection of teleseismic data with robust estimations of uncertainties based on bootstrapping of the travel-time models and array weights (Palantiri software, https://braunfuss.github.io/Palantiri/). Backprojection has proven to be a powerful tool to infer rupture propagation from teleseismic data and identify irregularities of the rupture process over time.We then model the earthquake sources with the results obtained from the backprojection and additional information obtained from the application of image segmentation methods to the InSAR displacement maps. For this second step, we use a combination of different observations (teleseismic waveforms and surface displacement maps based on InSAR) to increase the resolution on the spatio-temporal evolution of fault slip. We develop a novel Informational criterion based transdimensional optimization scheme to model an adequate representation of the source complexity. We present our method on two cases study: the 2016 Muji Mw 6.7 earthquake (Pamir) and the 2008-2009 Qaidam (Tibet) sequence of earthquakes. We find that the 2008 Qaidam earthquake ruptures one segment, the 2016 Muji earthquake on two segments and the Qaidam 2009 earthquake on two or three segments.This work is based on the open-source, python-based Pyrocko toolbox and is conducted within the project “Bridging Geodesy and Seismology” (www.bridges.uni-kiel.de) funded by the DFG through an Emmy-Noether grant.

Deep volcanic processes and magma intrusion episodes through the crust are typically accompanied by a variety of seismic signals, including volcano-tectonic (VT) seismicity, very long period (VLP) signals and deep low-frequency (DLF) events. These signals can reveal the migration of magma batches and the resonance of magma reservoirs and dikes. The recent 2018-2019 unrest offshore the island of Mayotte, Comoros archipelago, represents the first case of a geophysically monitored magmatic intrusion from a deep sub-Moho reservoir through the whole crust reaching the surface. At Mayotte, a huge magma movement and the following drainage of a deep reservoir were accompanied by a complex seismic sequence, including a massive VT swarm and energetic long-duration very long period (VLP) signals recorded globally. The identification and characterization of ~7000 VTs and ~400 VLPs by applying waveforms-based seismological methods allowed us to reconst the unrest phases: early VTs, migrating upward, were driven by the ascent of a magmatic dike, and tracked its propagating from Moho depth to the seafloor, while later VTs marked the progressive failure of the reservoir’s roof, triggering its resonance and the generation of long-duration VLPs. At the Eifel, Germany, weak DLFs earthquakes have been recorded over the last decades and located along a deep channel-like structure, extending from sub-Moho depth (~40-45 km) to the upper crust (~5-10 km). While not showing any clear migration, they reveal a different way of fluid transfer from depth towards the surface, possibly marking intermediate small reservoirs along a feeding channel. Here, brittle failure occurring in the vicinity of the reservoirs may cause their resonance. The Mayotte and Eifel observations are example of end member models for deep fluid transfer processes through the crust. These examples show that, by listening to seismic signals at different distances and by analysing them with modern waveform based methods, we can provide a detailed picture of deep magmatic processes and enable future eruption early warning.

Pyrocko is an open source seismology toolbox and library, written in the Python programming language. It can be utilized flexibly for a variety of geophysical tasks, like seismological data processing and analysis, modelling of waveforms, InSAR or GPS displacement data, or for seismic source characterization. At its core, Pyrocko is a library and framework providing building blocks for researchers and students wishing to develop their own applications. Pyrocko contains a few standalone applications for everyday seismological practice. These include the Snuffler program, an extensible seismogram browser and workbench, the Cake tool, providing travel-time and ray-path computations for 1D layered earthmodels, Fomosto, a tool to manage pre-calculated Green’s function stores, Jackseis, a command-line tool for common waveform archive data manipulations, Colosseo, a tool to create synthetic earthquake scenarios, serving waveforms and static displacements, and new, Sparrow, a 3D geophysical data visualization tool. This poster gives a glimpse of Pyrocko’s features, for more examples and tutorials visit https://pyrocko.org/.

Seismic source and moment tensor waveform inversion is often ill-posed or non-unique if station coverage is poor or signals are weak. Three key ingredients can help in these situations: (1) probabilistic inference and global search of the full model space, (2) joint optimisation with datasets yielding complementary information, and (3) robust source parameterisation or additional source constraints. These demands lead to vast technical challenges, on the performance of forward modelling, on the optimisation algorithms, as well as on visualisation, optimisation configuration, and management of the datasets. Implementing a high amount of automation is inevitable.To tackle all these challenges, we are developing a sophisticated new seismic source optimisation framework, Grond. With its innovative Bayesian bootstrap optimiser, it is able to efficiently explore large model spaces, the trade-offs and the uncertainties of source parameters. The program is highly flexible with respect to the adaption to specific source problems, the design of objective functions, and the diversity of empirical datasets.It uses an integrated, robust waveform data processing, and allows for interactive visual inspection of many aspects of the optimisation problem, including visualisation of the result uncertainties. Grond has been applied to CMT moment tensor and finite-fault optimisations at all scales, to nuclear explosions, to a meteorite atmospheric explosion, and to volcano-tectonic processes during caldera collapse and magma ascent. Hundreds of seismic events can be handled in parallel given a single optimisation setup.Grond can be used to optimise simultaneously seismic waveforms, amplitude spectra, waveform features, phase picks, static displacements from InSAR and GNSS, and gravitational signals.Grond is developed as an open-source package and community effort. It builds on and integrates with other established open-source packages, like Kite (for InSAR) and Pyrocko (for seismology).

The dynamics of magma deep in the Earth’s crust are difficult to capture by geophysical monitoring. Since May 2018, a seismically quiet area offshore of Mayotte in the western Indian Ocean has been affected by complex seismic activity, including long-duration, very-long-period signals detected globally. Global Navigation Satellite System stations on Mayotte have also recorded a large surface deflation offshore. Here we analyse regional and global seismic and deformation data to provide a one-year-long detailed picture of a deep, rare magmatic process. We identify about 7,000 volcano-tectonic earthquakes and 407 very-long-period seismic signals. Early earthquakes migrated upward in response to a magmatic dyke propagating from Moho depth to the surface, whereas later events marked the progressive failure of the roof of a magma reservoir, triggering its resonance. An analysis of the very-long-period seismicity and deformation suggests that at least 1.3 km3 of magma drained from a reservoir of 10 to 15 km diameter at 25 to 35 km depth. We demonstrate that such deep offshore magmatic activity can be captured without any on-site monitoring. Recent seismicity near Mayotte in the Indian Ocean is due to dyke propagation from and drainage of a 25–35 km deep magma reservoir, according to an analysis of earthquake and deformation data.

We present an analysis of aftershocks with M L >3 from the 2011 Prague, Oklahoma, earthquake sequence, an intraplate sequence of moderate‐size earthquakes within the North American craton. We apply waveform analysis to seismograms from temporary local seismograph networks to relocate the aftershocks’ hypocenters. The relocations show that the hypocenters are confined to the Precambrian basement. The character of the recorded seismograms waveforms are reconstructed by finite‐difference forward modeling of seismic sources buried at different depths in 2D crustal models. The numerical modeling indicates that the observed complex waveforms, and particularly the coda, are created by waves scattering off numerous shallow crustal velocity anomalies. We infer that these anomalies correspond to paleoriver channels that are embedded in the Pennsylvanian limestone formation. The modeling indicates that the amount of energy within the coda depends on the depth of the source. Our waveform modeling of the observed seismograms is consistent with our relocations of the aftershocks to depths of 5–10 km, within the Precambrian basement. Electronic Supplement:Empirical and synthetic data, model parameters, and geological figures.

This study contributes to the development of a new ground‐motion model for Central and Eastern North America (CENA). We evaluate the impact of the seismic‐velocity structure for different crustal regions on ground‐motion measures. We present an overview of the regionalization of the crustal structure of CENA, the physical parameters used for the simulation codes, and the statistical method applied for the comparison of pseudoabsolute response spectral accelerations (PSAs) of four crustal regions defined within CENA. The four crustal regions are (1) the Atlantic Coastal Plain (ACP), (2) the Appalachians (APP), (3) Central North America (CNA), and (4) the Mississippi Embayment/Gulf Coast (MEM). For each region and its statistically representative velocity structure, PSA matrices were computed, covering response oscillator frequencies from 0.5 to 20 Hz and hypocentral distances up to 500 km. PSA matrices for earthquakes at four different focal depths (5, 10, 20, and 30 km) were calculated. The developed method of the normed mean differences gives a meaningful measure for inter‐regional differences of ground motions in comparison with intra‐regional variability. In this study, we compare the ln(PSA) of the APP, ACP, and MEM region with the reference region CNA, respectively. The percentage of ln(PSA) mean difference values that are smaller than the ln(PSA) standard deviation of the reference region CNA lies between 73% and 91% for the APP region, dependent on the focal depth. For the ACP region, it is about 70% for all focal depths, except for the 20 km source depth, which was excluded because a layer boundary at 20.5 km causes unrealistically strong reflections. The percentage for the MEM region is less than 50% for each of the focal depths. This analysis demonstrates that there are two distinct ground‐motion groups for CENA: Group 1: CNA, APP, and ACP Group 2: MEM. Electronic Supplement: Figures of crustal velocity structures, table of Q studies, and Q studies illustrated for one region.