ALBERT

Description: The Sumatran subduction zone exhibits strong seismic and tsunamogenic potential with the prominent examples of the 2004, 2005 and 2007 earthquakes. Here, we invert travel-time data of local earthquakes for vp and vp∕vs velocity models of the central Sumatran forearc. Data were acquired by an amphibious seismometer network consisting of 52 land stations and 10 ocean-bottom seismometers located on a segment of the Sumatran subduction zone that had not ruptured in a great earthquake since 1797 but witnessed recent ruptures to the north in 2005 (Nias earthquake, Mw = 8.7) and to the south in 2007 (Bengkulu earthquake, Mw = 8.5). The 2-D and 3-D vp velocity anomalies reveal the downgoing slab and the sedimentary basins. Although the seismicity pattern in the study area appears to be strongly influenced by the obliquely subducting Investigator Fracture Zone to at least 200 km depth, the 3-D velocity model shows prevailing trench-parallel structures at depths of the plate interface. The tomographic model suggests a thinned crust below the basin east of the forearc islands (Nias, Pulau Batu, Siberut) at  ∼ 180 km distance to the trench. vp velocities beneath the magmatic arc and the Sumatran fault zone (SFZ) are around 5 km s−1 at 10 km depth and the vp∕vs ratios in the uppermost 10 km are low, indicating the presence of felsic lithologies typical for continental crust. We find moderately elevated vp∕vs values of 1.85 at  ∼ 150 km distance to the trench in the region of the Mentawai Fault. vp∕vs ratios suggest an absence of large-scale alteration of the mantle wedge and might explain why the seismogenic plate interface (observed as a locked zone from geodetic data) extends below the continental forearc Moho in Sumatra. Reduced vp velocities beneath the forearc basin covering the region between the Mentawai Islands and the Sumatra mainland possibly reflect a reduced thickness of the overriding crust.

Description: The Sumatran subduction zone exhibits strong seismic and tsunamogenic potential with the prominent examples of the 2004, 2005 and 2007 earthquakes. Here, we invert travel time data of local earthquakes for vp and vp/vs velocity models of the central Sumatran forearc. Data were acquired by an amphibious seismometer network consisting of 52 land stations and 10 ocean bottom seismometers located on a segment of the Sumatran subduction zone that had not ruptured in a great earthquake since 1797 but witnessed recent ruptures to the north in 2005 (Nias earthquake, Mw = 8.7) and to the south in 2007 (Bengkulu earthquake, Mw = 8.5). 2D and 3D vp velocity anomalies reveal the downgoing slab and the sedimentary basins. Although the seismicity pattern in the study area appears to be strongly influenced by the obliquely subducting Investigator Fracture Zone to at least 200 km depth, the 3D velocity model shows prevailing trench parallel structures at depths of the plate interface. The tomographic model suggests a thinned crust below the basin east of the forearc islands (Nias, Pulau Batu, Siberut) at ~ 180 km distance to the trench. Vp velocities beneath the magmatic arc and the Sumatran fault zone SFZ are around 5 km/s at 10 km depth and the vp/vs ratios in the uppermost 10 km are low, indicating the presence of felsic lithologies typical for continental crust. We find moderately elevated vp/vs values of 1.85 at ~ 150 km distance to the trench in the region of the Mentawai fault. Vp/vs ratios suggest absence of large scale alteration of the mantle wedge and might explain why the seismogenic plate interface (observed as a locked zone from geodetic data) extends below the continental forearc Moho in Sumatra. Reduced vp velocities beneath the forearc basin covering the region between Mentawai Islands and the Sumatra mainland possibly reflect a reduced thickness of the overriding crust.

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: The ScanArray international collaborative program acquired broadband seismological data at 192 locations in the Baltic Shield during the period between 2012 and 2017. The main objective of the program is to provide seismological constraints on the structure of the lithospheric crust and mantle as well as the sublithospheric upper mantle. The new information will be applied to studies of how the lithospheric and deep structure affect observed fast topographic change and geological-tectonic evolution of the region. The program also provides new information on local seismicity, focal mechanisms, and seismic noise. The recordings are generally of very high quality and are used for analysis by various seismological methods, including P- and S-wave receiver functions for the crust and upper mantle, surface wave and ambient noise inversion for seismic velocity, body-wave P- and S-wave tomography for upper mantle velocity structure using ray and finite frequency methods, and shear-wave splitting measurements for obtaining bulk anisotropy of the upper and lowermost mantle. Here, we provide a short overview of the data acquisition and initial analysis of the new data, together with an example of integrated seismological results obtained by the project group along a representative ∼1800-km-long profile across most of the tectonic provinces in the Baltic Shield between Denmark and the North Cape. The first models support a subdivision of the Paleoproterozoic Svecofennian province into three domains, where the highest topography of the Scandes mountain range in Norway along the Atlantic Coast has developed solely in the southern and northern domains, whereas the topography is more subdued in the central domain.