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17 results on '"Keil, Sabrina"'

5. The NEPOS Project: Near-Surface Seismic Exploration of Planetary Bodies with Adaptive Networks

Author

Keil, Sabrina, Igel, Heiner, Bernauer, Felix, Shutin, Dmitriy, Shin, Ban-Sok, Nierula, Kai, Reiss, Philipp, Sesko, Rok, Lindner, Fabian, Keil, Sabrina, Igel, Heiner, Bernauer, Felix, Shutin, Dmitriy, Shin, Ban-Sok, Nierula, Kai, Reiss, Philipp, Sesko, Rok, and Lindner, Fabian

Abstract

Ground motion observations on planetary objects are a prerequisite for a detailed understanding of their interior structure and evolution. The imaging of the near surface structure - in particular on the Moon - has strong practical implications. First, the race is on to detect ice-bearing rocks near the surface from which water could be extracted and used as a resource for crewed missions. Second, due to the substantial bombardment of the lunar surface with meteorites and the lack of an atmosphere, observatories or habitats may have to be built underground. It has been proposed that cavities from ancient lava flows below the lunar surface could be used to place infrastructure. Current mission plans for geophysical exploration focus on static seismic sensors/arrays that would be restricted to the area they can explore. With the NEPOS project we want to go beyond these restrictions and develop concepts for mobile seismic arrays that work in an autonomous way using robotic technology. The scientific challenges include the understanding of wavefield effects of icy rocks and caves in a strongly scattering environment, the provision of optimal source-receiver configurations to detect them, as well as an integrated data-processing workflow from observation to subsurface image including the quantification of uncertainties. In order to solve these challenges, we first developed a Digital Twin for wave propagation in the strongly heterogeneous lunar crust to generate synthetic seismic data using the spectral element code SALVUS. We compared the synthetic seismograms to data from the Apollo 17 Lunar Seismic Profiling Experiment (LPSE) and find that their main characteristics coincide. We further generated synthetic seismograms for a variety of network configurations and subsurface heterogeneities, which will be used to test appropriate imaging methods for the lunar subsurface structure. Due to the presence of strongly scattering media ambient noise tomography seems to be a pro

Published
2024

8. Understanding Dynamic Environments with Fuzzy Perception

Author

Bahrmann, Frank, Hellbach, Sven, Keil, Sabrina, Böhme, Hans-Joachim, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Loo, Chu Kiong, editor, Yap, Keem Siah, editor, Wong, Kok Wai, editor, Beng Jin, Andrew Teoh, editor, and Huang, Kaizhu, editor

Published
2014
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9. Surface wave dispersion curve inversion using mixture density networks.

Author

Keil, Sabrina and Wassermann, Joachim

Subjects
*MARKOV chain Monte Carlo, *DISTRIBUTION (Probability theory), *RAYLEIGH waves, *DISPERSION (Chemistry), *SHALLOW-water equations
Abstract

In many seismological, environmental and engineering applications a detailed S -wave velocity model of the shallow subsurface is required. This is generally achieved by the inversion of surface wave dispersion curves using various inversion methods. The classical inversion approaches suffer from several shortcomings, such as inaccurate solutions due to local minima or large computation times in case of a wide parameter space. A number of machine learning (ML) approaches have been suggested to tackle these problems, which however do not provide probabilistic solutions and/or constrain layer number and layer thickness to a fixed value. In this study, we develop a novel neural network (NN) approach in order to characterize the shallow velocity structure from Love and Rayleigh wave dispersion curves. The novelty of our method lies in the simultaneous estimation of layer numbers, layer depth and a complete probability distribution of the S -wave velocity structure. This is achieved by a two-step ML approach, where (1) a regular NN classifies the number of layers within the upper 100 m of the subsurface and (2) a mixture density network outputs the depth estimates together with a fully probabilistic solution of the S -wave velocity structure. We show the advantages of our ML approach compared to a conventional neighbourhood inversion and a Markov chain Monte Carlo algorithm. Our ML approach is then applied to dispersion curves extracted from recorded noise data in Munich, Germany. The resulting velocity profile is in accordance with lithologic information at the site, which highlights the potential of our approach. [ABSTRACT FROM AUTHOR]

Published
2023
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10. Gradient measurements at volcanoes: Options, questions, pitfalls

Author

Wassermann, Joachim, Braun, Thomas, Bernauer, Felix, Keil, Sabrina, Mattio, Laurent, Guattari, Frederic, and Igel, Heiner

Abstract

The recent advances in the hardware development of instruments that record gradients of the seismic wavefield made several novel measurement strategies at active volcanoes possible. Especially the recording of rotational motions is a prominent candidate to solve some of the problems that are eminent in volcano monitoring. Near field recordings of volcano-induced events often suffer from unaccounted effects of local tilt and unknown shallow velocity structure. In addition, if the station number is limited moment tensor inversions (MTI) are very often unconstrained. However, possible advantages of gradient measurements in these fields come also with a price: the wavefield gradient is very sensitive to local site conditions/heterogeneities. To investigate the advantages and drawbacks of the combined recording of translational and rotational motion measurements (6C), we designed an experiment using six blueSeis-3A rotational motion sensors together with six TrilliumCompact seismometers at Stromboli volcano, Italy. In order to estimate the responses of different sites as well as to evaluate the performance of 6C recordings for location and moment tensor inversion, the stations were distributed along two rings at different height levels. Standard estimates of backazimuth and 1D-velocity profiles using 6C are performed. The results are compared to synthetic data which involve growing complexity of the modelled structure. The results of this tests confirm that local heterogeneities play a key role in the complexity of the recorded gradient wavefield. This complexity needs to be accounted for in order to be able to perform a MTI which should reduce the ambiguity using sparse seismic networks., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Published
2023

11. Using single-station six-component measurements and numerical simulations for site response estimation in Munich, Germany

Author

Keil, Sabrina and Wassermann, Joachim

Abstract

Recent progress in seismic instrumentation has opened new opportunities for using wavefield gradients (strain and rotation) for several seismic applications. We show that single-station six-component (6C) measurements, combining three translational and three rotational motions, can be used to investigate the shallow velocity structure. A remarkable advantage of this single-station 6C approach is its simpler way of deployment compared to array measurements, which especially eases the application in urban areas. We performed ambient noise measurements in Munich, Germany, using an iXblue blueSeis-3A rotational motion sensor together with a Nanometrics Trillium Compact seismometer. From the 6C data we were able to compute Love and Rayleigh dispersion curves, which are inverted together with H/V spectral ratios to obtain 1D P- and S-wave velocity profiles of the upper 100 m. The resulting velocity structure is implemented with additional geological and geophysical data into a 3D subsurface model. This model is used to perform 3D seismic simulations of induced earthquakes using the spectral element code SALVUS. The simulations help to quantify the amplification effect of the shallow low velocity sediments in the Munich area. In addition, the maximum ground motion and its spatial distribution can be estimated, which is important to evaluate the seismic risk in this densely populated area., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Published
2023

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