ALBERT

Description: The geometry, kinematics, and mode of back‐arc extension along the Andaman Sea plate boundary are refined using a new set of significantly improved hypocenters, global centroid moment tensor (CMT) solutions, and high‐resolution bathymetry. By applying cross‐correlation and double‐difference (DD) algorithms to regional and teleseismic waveforms and arrival times from International Seismological Centre and National Earthquake Information Center bulletins (1964–2009), we resolve the fine‐scale structure and spatiotemporal behavior of active faults in the Andaman Sea. The new data reveal that back‐arc extension is primarily accommodated at the Andaman Back‐Arc Spreading Center (ABSC) at ~10°, which hosted three major earthquake swarms in 1984, 2006, and 2009. Short‐term spreading rates estimated from extensional moment tensors account for less than 10% of the long‐term 3.0–3.8 cm/yr spreading rate, indicating that spreading by intrusion and the formation of new crust make up for the difference. A spatiotemporal analysis of the swarms and Coulomb‐stress modeling show that dike intrusions are the primary driver for brittle failure in the ABSC. While spreading direction is close to ridge normal, it is oblique to the adjacent transforms. The resulting component of E‐W extension across the transforms is expressed by deep basins on either side of the rift and a change to extensional faulting along the West Andaman fault system after the Mw = 9.2 Sumatra‐Andaman earthquake of 2004. A possible skew in slip vectors of earthquakes in the eastern part of the ABSC indicates an en‐echelon arrangement of extensional structures, suggesting that the present segment geometry is not in equilibrium with current plate‐motion demands, and thus the ridge experiences ongoing re‐adjustment.

Description: Due to vigorous Neogene geodynamic processes, including oceanic subduction, slab break-off and mountain building in the Carpathian Arc, the architecture of the lithosphere in Romania is quite complicated. To improve the knowledge about the lithosphere–asthenosphere system in this region the passive seismological CALIXTO99 experiment was conducted in 1999 in the SE part of Romania. Here we present crustal models derived from the analysis of teleseismic recordings with the receiver function (RF) method of the 120 temporarily installed stations and of the permanent GEOFON stations MLR and TIRR. The RF results extend the known crustal models which are based mainly on seismic refraction work and analysis of regional earthquakes. We apply a grid-search inversion at 30 stations and use two different error estimation methods to determine the Moho depth and the average crustal vp/vs ratio. The complex 3-D intracrustal structure, especially the deep sedimentary basins, distorts significantly the RF waveforms within the whole station network. This leads to ambiguous results at some stations. Our model of the Moho depth has a maximum crustal thickness in the SE Carpathian Mountains at station MLR with a depth of about 45 km and an average crustal vp/vs ratio of 1.79. The surrounding crust in SE Romania has a thickness of mainly 35–40 km. The RFs at MLR are characterized by clear azimuthal effects that can be correlated with the variation of the sediment thickness in the foredeep of the Carpathian Mountains. A RF waveform inversion verifies these results and gains improved 1-D S-wave velocity models at several stations in SE Romania.

Description: We study the local seismicity in East Java around the Arjuno-Welirang volcanic complex that is connected via the Watukosek Fault System, to the spectacular Lusi eruption site. Lusi is a sediment-hosted hydrothermal system which has been erupting since 2006. It is fed by both mantellic and hydrothermal fluids, rising and mixing with the thermogenic gases and other fluids from shallower sedimentary formations. During a period of 24 months, we observe 156 micro-seismic earthquakes with local magnitudes ranging from ML0.5 to ML1.9, within our network. The events predominantly nucleate at depths of 8–13 km below the Arjuno-Welirang volcanic complex. Despite the geological evidence of active tectonic deformation and faulting observed at the surface, practically no seismicity is observed in the sedimentary basin hosting Lusi. Although we cannot entirely rule out artifacts due to an increased detection threshold in the sedimentary basin, the deficit in significant seismicity suggests aseismic deformation beneath Lusi due to the large amount of fluids that may lubricate the fault system. An analysis of focal mechanisms of nine selected events around the Arjuno-Welirang volcanic complex indicates predominantly strike-slip faulting activity in the region SW of Lusi. This type of activity is consistent with observable features such as fault escarpment, river deviation and railroad deformation; suggesting that the Watukosek fault system extends from the volcanic complex towards the NE of Java. Our results point out that the tectonic deformation of the region is characterized by a segmented fault system being part of a broader damage zone, rather than localized along a distinct fault plane.

Description: 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 when the number of data are large (millions of examples). With the entire spectrum of seismological tasks, for example, seismic picking and detection, magnitude and source property estimation, ground-motion prediction, hypocenter 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—available via GitHub. SeisBench standardizes access to both models and datasets, while 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.

Description: Many Swiss microearthquake sequences have been analyzed using relative location techniques, which often allowed constraining active fault planes and tectonic processes that drive seismicity. Yet, often the number of located earthquakes was too small to infer details of the space-time evolution of the sequences or their statistical properties, and thus resolve clear seismicity patterns and their driving mechanisms. We present a nearly automatic workflow that combines well-established seismological analysis techniques to improve the completeness of detected and located earthquakes of a sequence. Starting from a manual catalog (magnitude of completeness, Mc ≈ 1.0−1.5), we assemble a template set and perform a matched filter analysis on a single station with highest SNR. This allows us to detect events of local magnitude ML 〈 0.0. The waveform similarity is further exploited to derive detection magnitudes. The enhanced catalog is then statistically analyzed to derive high-resolution temporal evolutions of the Gutenberg−Richter a- and b-values, and consequently the occurrence probability of larger events. Strong events are located using relative double-difference, which usually improves the number of well-relocated events by a factor of 2−5. This workflow allows us to significantly enhance the analysis of spatiotemporal behavior of natural and induced microearthquake sequences, which we use to monitor commercial and scientific fluid injections in near real-time. We implemented this workflow in the open-source Python/PostgreSQL toolbox QuakeMatch. We discuss the capabilities of QuakeMatch with examples of induced microearthquake sequences associated with various geothermal projects monitored by the Swiss Seismological Service in the framework of the GEOBEST2020+ project.