3D crustal structure of the Eastern Alpine region from ambient noise tomography

The tectonic evolution of the European Eastern Alps within the Alpine orogeny is still under debate. Open questions include: the link between surface, crustal and mantle structures; the nature of the Moho gap between the two plates; the relationship between the Alps, the adjacent foreland basin and the Bohemian Massif lithospheric blocks. We collected one year of continuous data recorded by ~250 broadband seismic stations –55 of which installed within the EASI AlpArray complementary experiment– in the Eastern Alpine region. Exploiting surface wave group velocity from seismic ambient noise, we obtained an high-resolution 3D S-wave crustal model of the area.The Rayleigh-wave group-velocity from 3 s to 35 s are inverted to obtain 2-D group velocity maps with a resolution of ~15 km. From these maps, we determine a set of 1D velocity models via a Neighborhood Algorithm, resulting in a new 3D model of S-wave velocity with associated uncertainties. The vertical parameterization is a 3-layer crust with the velocity properties in each layer described by a gradient. Our final model finds high correlation with specific geological features in the Eastern Alps up to 20 km depth, the deep structure of the Molasse basin and important variations of crustal thickness and velocities as a result of the Alpine orogeny post-collisional evolution. The strength of our new information relies on the absolute S-wave crustal velocity and the velocity gradient unambiguously sampled along the Moho, only limited by the amount and quality distribution of the data available

A multibranch, multitarget neural network for rapid point-source inversion in a microseismic environment: examples from the Hengill Geothermal Field, Iceland

Despite advanced seismological techniques, automatic source characterization for microseismic earthquakes remains difficult and challenging since current inversion and modelling of high-frequency signals are complex and time consuming. For real-time applications such as induced seismicity monitoring, the application of standard methods is often not fast enough for true complete real-time information on seismic sources. In this paper, we present an alternative approach based on recent advances in deep learning for rapid source-parameter estimation of microseismic earthquakes. The seismic inversion is represented in compact form by two convolutional neural networks, with individual feature extraction, and a fully connected neural network, for feature aggregation, to simultaneously obtain full moment tensor and spatial location of microseismic sources. Specifically, a multibranch neural network algorithm is trained to encapsulate the information about the relationship between seismic waveforms and underlying point-source mechanisms and locations. The learning-based model allows rapid inversion (within a fraction of second) once input data are available. A key advantage of the algorithm is that it can be trained using synthetic seismic data only, so it is directly applicable to scenarios where there are insufficient real data for training. Moreover, we find that the method is robust with respect to perturbations such as observational noise and data incompleteness (missing stations). We apply the new approach on synthesized and example recorded small magnitude (M <= 1.6) earthquakes at the Hellisheioi geothermal field in the Hengill area, Iceland. For the examined events, the model achieves excellent performance and shows very good agreement with the inverted solutions determined through standard methodology. In this study, we seek to demonstrate that this approach is viable for microseismicity real-time estimation of source parameters and can be integrated into advanced decision-support tools for controlling induced seismicity

Constraining the Moho Depth Below Bhutan With Global-Phase Seismic Interferometry

We use a novel technique named global-phase seismic interferometry (GloPSI) to image the lithospheric structure, and in particular the Moho, below two parallel north-south transects belonging to the GANSSER network (2013–2014). The profiles cross the Himalayan orogenic wedge in Bhutan, a tectonically important area within the largest continent-continent collision zone on Earth that is still undergoing crustal thickening and represents a challenging imaging target for the GloPSI approach. GloPSI makes use of direct waves from distant earthquakes and receiver-side reverberations with near vertical incidence. Reflections are isolated from earthquake recordings by solving a correlation integral and are turned into a reflectivity image of the lithosphere below the arrays. Our results compare favorably with first-order features observed from a previous receiver function (RF) study. We show that a combined interpretation of GloPSI and RF results allows for a more in-depth understanding of the lithospheric structure across the orogenic wedge in Bhutan