The design of an array configuration is an important task in array seismology during experiment planning. Often the array response function (ARF), which depends on the relative position of array stations and frequency content of the incoming signals, is used as the array design criterion. In practice, additional constraints and parameters have to be taken into account, for example, land ownership, site-specific noise levels or characteristics of the seismic sources under investigation. In this study, a flexible array design framework is introduced that implements a customizable scenario modelling and optimization scheme by making use of synthetic seismograms. Using synthetic seismograms to evaluate array performance makes it possible to consider additional constraints. We suggest to use synthetic array beamforming as an array design criterion instead of the ARF. The objective function of the optimization scheme is defined according to the monitoring goals, and may consist of a number of subfunctions. The array design framework is exemplified by designing a seven-station small-scale array to monitor earthquake swarm activity in Northwest Bohemia/Vogtland in central Europe. Two subfunctions are introduced to verify the accuracy of horizontal slowness estimation; one to suppress aliasing effects due to possible secondary lobes of synthetic array beamforming calculated in horizontal slowness space and the other to reduce the event’s mislocation caused by miscalculation of the horizontal slowness vector. Subsequently, a weighting technique is applied to combine the subfunctions into one single scalar objective function to use in the optimization process.

Earthquakes often rupture across more than one fault segment. If such rupture segmentation occurs on a significant scale, a simple point-source or one-fault model may not represent the rupture process well. As a consequence earthquake characteristics inferred, based on one-source assumptions, may become systematically wrong. This might have effects on follow-up analyses, for example regional stress field inversions and seismic hazard assessments. While rupture segmentation is evident for most M-w > 7 earthquakes, also smaller ones with 5.5 < M-w < 7 can be segmented. We investigate the sensitivity of globally available data sets to rupture segmentation and their resolution to reliably estimate the mechanisms in presence of segmentation. We focus on the sensitivity of InSAR (Interferometric Synthetic Aperture Radar) data in the static near-field and seismic waveforms in the far-field of the rupture and carry out non-linear and Bayesian optimizations of single-source and two-sources kinematic models (double-couple point sources and finite, rectangular sources) using InSAR and teleseismic waveforms separately. Our case studies comprises of four M-w 6-7 earthquakes: the 2009 L'Aquila and 2016 Amatrice (Italy) and the 2005 and 2008 Zhongba (Tibet) earthquakes. We contrast the data misfits of different source complexity by using the Akaike informational criterion (AIC). We find that the AIC method is well suited for data-driven inferences on significant rupture segmentation for the given data sets. This is based on our observation that an AIC-stated significant improvement of data fit for two-segment models over one-segment models correlates with significantly different mechanisms of the two source segments and their average compared to the single-segment mechanism. We attribute these modelled differences to a sufficient sensitivity of the data to resolve rupture segmentation. Our results show that near-field data are generally more sensitive to rupture segmentation of shallow earthquakes than far-field data but that also teleseismic data can resolve rupture segmentation in the studied magnitude range. We further conclude that a significant difference in the modelled source mechanisms for different segmentations shows that an appropriate choice of model segmentation matters for a robust estimation of source mechanisms. It reduces systematic biases and trade-off and thereby improves the knowledge on the rupture. Our study presents a strategy and method to detect significant rupture segmentation such that an appropriate model complexity can be used in the source mechanism inference. A similar, systematic investigation of earthquakes in the range of M-w 5.5-7 could provide important hazard-relevant statistics on rupture segmentation. In these cases single-source models introduce a systematic bias. Consideration of rupture segmentation therefore matters for a robust estimation of source mechanisms of the studied earthquakes.

Two nuclear explosions were carried out by the Democratic People’s Republic of North Korea in January and September 2016. Epicenters were located close to those of the 2006, 2009, and 2013 previous explosions. We perform a seismological analysis of the 2016 events combining the analysis of full waveforms at regional distances and seismic array beams at teleseismic distances. We estimate the most relevant source parameters, such as source depth, moment release, and full moment tensor (MT). The best MT solution can be decomposed into an isotropic source, directly related with the explosion and an additional deviatoric term, likely due to near‐source interactions with topographic and/or underground facilities features. We additionally perform an accurate resolution test to assess source parameters uncertainties and trade‐offs. This analysis sheds light on source parameters inconsistencies among studies on previous shallow explosive sources. The resolution of the true MT is hindered by strong source parameters trade‐offs, so that a broad range of well‐fitting MT solutions can be found, spanning from a dominant positive isotropic term to a dominant negative vertical compensated linear vector dipole. The true mechanism can be discriminated by additionally modeling first‐motion polarities at seismic arrays at teleseismic distances. A comparative assessment of the 2016 explosion with earlier nuclear tests documents similar vertical waveforms but a significant increase of amplitude for the 2016 explosions, which proves that the 9 September 2016 was the largest nuclear explosion ever performed in North Korea with a magnitude Mw 4.9 and a shallow depth of less than 2 km, although there are no proofs of a fusion explosion. Modeling transversal component waveforms suggests variable size and orientation of the double‐couple components of the 2009, 2013, and 2016 sources.

On April 29, 2017 at 0:56 UTC (2:56 local time), an M (W) = 2.8 earthquake struck the metropolitan area between Leipzig and Halle, Germany, near the small town of Markranstadt. The earthquake was felt within 50 km from the epicenter and reached a local intensity of I (0) = IV. Already in 2015 and only 15 km northwest of the epicenter, a M (W) = 3.2 earthquake struck the area with a similar large felt radius and I (0) = IV. More than 1.1 million people live in the region, and the unusual occurrence of the two earthquakes led to public attention, because the tectonic activity is unclear and induced earthquakes have occurred in neighboring regions. Historical earthquakes south of Leipzig had estimated magnitudes up to M (W) ae 5 and coincide with NW-SE striking crustal basement faults. We use different seismological methods to analyze the two recent earthquakes and discuss them in the context of the known tectonic structures and historical seismicity. Novel stochastic full waveform simulation and inversion approaches are adapted for the application to weak, local earthquakes, to analyze mechanisms and ground motions and their relation to observed intensities. We find NW-SE striking normal faulting mechanisms for both earthquakes and centroid depths of 26 and 29 km. The earthquakes are located where faults with large vertical offsets of several hundred meters and Hercynian strike have developed since the Mesozoic. We use a stochastic full waveform simulation to explain the local peak ground velocities and calibrate the method to simulate intensities. Since the area is densely populated and has sensitive infrastructure, we simulate scenarios assuming that a 12-km long fault segment between the two recent earthquakes is ruptured and study the impact of rupture parameters on ground motions and expected damage.

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.