Finite-frequency tomography with complex body waves

Seismische Tomographie ist die eindrücklichste und intuitivste Methode, Informationen über das tiefe Erdinnere, von der Kruste bis an die Kern-Mantel-Grenze zu erlangen. Die von entfernten Erdbeben aufgezeichneten Bodenbewegungen werden mit den für ein einfaches Erdmodell vorhergesagten verglichen, um ein verbessertes Modell zu erhalten. Dieses dreidimensionale Modell kann dann geodynamisch oder tektonisch interpretiert werden. Durch die Entwicklung leistungsfähiger Computersysteme kann die Ausbreitung seismischer Wellen mittlerweile im gesamten messbaren Frequenzbereich simuliert werden, sodass dieses gesamte Spektrum der Tomographie zur Verfügung steht. Die vorliegende Arbeit beschäftigt sich mit der Verbesserung der Wellenformtomographie. Zum einen wird die Nutzbarkeit eines komplexen Typs seismischer Wellen, der in der Mantelübergangszone zwischen 410 und 660 km Tiefe gestreuten triplizierten Wellen ge-zeigt. Diese Wellen versprechen eine erheblich bessere Auflösung der geodynamisch wichtigen Diskontinuitäten zwischen oberem und unterem Mantel als bisher verwendete teleseismische Wellen. Zum anderen wird der nichtlineare Einfluss des Erdbebenmodells auf die Wellenformtomographie untersucht. Mittels Bayesianischer Inferenz werden Wahrscheinlichkeitsdichten für die Herdparameter des Erdbebens, wie Tiefe, Momententensor und Quellfunktion bestimmt. Dazu wird zuvor ein Modell der Messunsicherheit und des Modellierungsfehlers in der Herdinversion bestimmt, das bis dato nicht vorlag. Dabei zeigt sich im Weiteren, dass der Effekt der Unsicherheit im Herdmodell eine nichtlineare und bisher weitgehend ignorierte Feh-lerquelle in der seismischen Tomographie ist. Dieses Ergebnis ermöglicht es, die Varianz seismischer Laufzeit- und Wellenformmessungen sowie die Kovarianz zwischen einzelnen Messstationen zu bestimmen. Die Ergebnisse dieser Arbeit können in Zukunft erheblich dazu beitragen, die Unsicherheiten der seismischen Tomographie quantitativ zu bestimmen, um eventuell vorhandene Artefakte zu zeigen und damit geologischen Fehlinterpretationen tomographischer Ergebnisse vorzubeugen.Seismic tomography is the most impressive method of inferring a picture of the deep interiour of the Earth, from the lower crust to the core mantle boundary. Recordings of ground motions caused by distant earthquakes are used to refine an existing earth model, employing difference between measured and predicted data. The resulting three-dimensional models and images can be interpreted in terms of tectonics and large-scale geodynamics. The increase in computing power in the last decade has lead to an enormous progress in tomographic methods, which can now simulate and therefore exploit the whole frequency range of seismographic measurements. This thesis refines waveform tomography in its flavour of finite-frequency tomography. It first shows that complex wave types, like the those perturbed by the discontinuities in the mantle transition zone can be used for waveform tomography. Using these waves promise an improved resolution of the geodynamically important transition zone compared to the hitherto used teleseismic waves. A second part checks the nonlinear influence of the source model on waveform tomography. By the method of Bayesian inference, probability density functions of the source parameters depth, moment tensor, and the source time function are determined. For that, a model of the measurement uncertainties is necessary, which was hitherto not available and is derived from a large catalogue of source solutions. The results of the probabilistic source inversion allow to quantify the effect of source uncertainty on seismic tomography. This allows to estimate the variance of seismic travel-times and waveforms and also the covariance between different seismographic stations. The results of this work could improve uncertainty estimation in seismic tomography, show potential artifacts in the result and therefore avoid misinterpretation of tomographic images by geologists and others

Fully probabilistic seismic source inversion - Part 1: Efficient parameterisation

Seismic source inversion is a non-linear problem in seismology where not just the earthquake parameters themselves but also estimates of their uncertainties are of great practical importance. Probabilistic source inversion (Bayesian inference) is very adapted to this challenge, provided that the parameter space can be chosen small enough to make Bayesian sampling computationally feasible. We propose a framework for PRobabilistic Inference of Seismic source Mechanisms (PRISM) that parameterises and samples earthquake depth, moment tensor, and source time function efficiently by using information from previous non-Bayesian inversions. The source time function is expressed as a weighted sum of a small number of empirical orthogonal functions, which were derived from a catalogue of > 1000 source time functions (STFs) by a principal component analysis. We use a likelihood model based on the cross-correlation misfit between observed and predicted waveforms. The resulting ensemble of solutions provides full uncertainty and covariance information for the source parameters, and permits propagating these source uncertainties into travel time estimates used for seismic tomography. The computational effort is such that routine, global estimation of earthquake mechanisms and source time functions from teleseismic broadband waveforms is feasible

New data on direct ion storage dosemeters

The DIS-1 dosemeter from the Finnish company RADOS is an innovative kind of passive electronic dosemeter for photon and beta radiation. This study examines the ‘long-term' linear response behaviour, the calibration and readout accuracy with large samples of ‘used' DIS-1 dosemeters especially in the low-dose region, which is of special interest for radiation protection issues. Our measurements prove the adequacy of the DIS-1 dosemeter for long-term-personal dosimetry. The fast and precise readout seems to make the DIS-1 dosemeter an ideal choice for personal dosimetry in low-dose environment

Finite-frequency tomography with complex body waves

Seismische Tomographie ist die eindrücklichste und intuitivste Methode, Informationen über das tiefe Erdinnere, von der Kruste bis an die Kern-Mantel-Grenze zu erlangen. Die von entfernten Erdbeben aufgezeichneten Bodenbewegungen werden mit den für ein einfaches Erdmodell vorhergesagten verglichen, um ein verbessertes Modell zu erhalten. Dieses dreidimensionale Modell kann dann geodynamisch oder tektonisch interpretiert werden. Durch die Entwicklung leistungsfähiger Computersysteme kann die Ausbreitung seismischer Wellen mittlerweile im gesamten messbaren Frequenzbereich simuliert werden, sodass dieses gesamte Spektrum der Tomographie zur Verfügung steht. Die vorliegende Arbeit beschäftigt sich mit der Verbesserung der Wellenformtomographie. Zum einen wird die Nutzbarkeit eines komplexen Typs seismischer Wellen, der in der Mantelübergangszone zwischen 410 und 660 km Tiefe gestreuten triplizierten Wellen ge-zeigt. Diese Wellen versprechen eine erheblich bessere Auflösung der geodynamisch wichtigen Diskontinuitäten zwischen oberem und unterem Mantel als bisher verwendete teleseismische Wellen. Zum anderen wird der nichtlineare Einfluss des Erdbebenmodells auf die Wellenformtomographie untersucht. Mittels Bayesianischer Inferenz werden Wahrscheinlichkeitsdichten für die Herdparameter des Erdbebens, wie Tiefe, Momententensor und Quellfunktion bestimmt. Dazu wird zuvor ein Modell der Messunsicherheit und des Modellierungsfehlers in der Herdinversion bestimmt, das bis dato nicht vorlag. Dabei zeigt sich im Weiteren, dass der Effekt der Unsicherheit im Herdmodell eine nichtlineare und bisher weitgehend ignorierte Feh-lerquelle in der seismischen Tomographie ist. Dieses Ergebnis ermöglicht es, die Varianz seismischer Laufzeit- und Wellenformmessungen sowie die Kovarianz zwischen einzelnen Messstationen zu bestimmen. Die Ergebnisse dieser Arbeit können in Zukunft erheblich dazu beitragen, die Unsicherheiten der seismischen Tomographie quantitativ zu bestimmen, um eventuell vorhandene Artefakte zu zeigen und damit geologischen Fehlinterpretationen tomographischer Ergebnisse vorzubeugen.Seismic tomography is the most impressive method of inferring a picture of the deep interiour of the Earth, from the lower crust to the core mantle boundary. Recordings of ground motions caused by distant earthquakes are used to refine an existing earth model, employing difference between measured and predicted data. The resulting three-dimensional models and images can be interpreted in terms of tectonics and large-scale geodynamics. The increase in computing power in the last decade has lead to an enormous progress in tomographic methods, which can now simulate and therefore exploit the whole frequency range of seismographic measurements. This thesis refines waveform tomography in its flavour of finite-frequency tomography. It first shows that complex wave types, like the those perturbed by the discontinuities in the mantle transition zone can be used for waveform tomography. Using these waves promise an improved resolution of the geodynamically important transition zone compared to the hitherto used teleseismic waves. A second part checks the nonlinear influence of the source model on waveform tomography. By the method of Bayesian inference, probability density functions of the source parameters depth, moment tensor, and the source time function are determined. For that, a model of the measurement uncertainties is necessary, which was hitherto not available and is derived from a large catalogue of source solutions. The results of the probabilistic source inversion allow to quantify the effect of source uncertainty on seismic tomography. This allows to estimate the variance of seismic travel-times and waveforms and also the covariance between different seismographic stations. The results of this work could improve uncertainty estimation in seismic tomography, show potential artifacts in the result and therefore avoid misinterpretation of tomographic images by geologists and others

Locating the Nordstream explosions without a velocity model using polarization analysis

The seismic events that preceded the leaks in the Nordstream pipelines in the Baltic Sea have been interpreted as explosions on the seabed, most likely man-made. We use a polarization-based location method initially developed for marsquakes to locate the source region without a subsurface velocity model. We show that the 2 largest seismic events can be unambiguously attributed to the methane plumes observed on the sea surface. The two largest events can be located with this method, using 4 and 5 stations located around the source, with location uncertainties of 30km and 10x60km. We can further show that both events emitted seismic energy for at least ten minutes after the initial explosion, indicative of resonances in the water column or the depressurizing pipeline.Comment: 6 pages, 2 figures, submitted as fast report to Seismic

Writ(h)ing Images: Imagination, the Human Form, and the Divine in William Blake, Salman Rushdie, and Simon Louvish

In this paper, we address issues in segmentation Of remotely sensed LIDAR (LIght Detection And Ranging) data. The LIDAR data, which were captured by airborne laser scanner, contain 2.5 dimensional (2.5D) terrain surface height information, e.g. houses, vegetation, flat field, river, basin, etc. Our aim in this paper is to segment ground (flat field)from non-ground (houses and high vegetation) in hilly urban areas. By projecting the 2.5D data onto a surface, we obtain a texture map as a grey-level image. Based on the image, Gabor wavelet filters are applied to generate Gabor wavelet features. These features are then grouped into various windows. Among these windows, a combination of their first and second order of statistics is used as a measure to determine the surface properties. The test results have shown that ground areas can successfully be segmented from LIDAR data. Most buildings and high vegetation can be detected. In addition, Gabor wavelet transform can partially remove hill or slope effects in the original data by tuning Gabor parameters

Bayesian parameter-estimation of Galactic binaries in LISA data with Gaussian Process Regression

The Laser Interferometer Space Antenna (LISA), which is currently under construction, is designed to measure gravitational wave signals in the milli-Hertz frequency band. It is expected that tens of millions of Galactic binaries will be the dominant sources of observed gravitational waves. The Galactic binaries producing signals at mHz frequency range emit quasi monochromatic gravitational waves, which will be constantly measured by LISA. To resolve as many Galactic binaries as possible is a central challenge of the upcoming LISA data set analysis. Although it is estimated that tens of thousands of these overlapping gravitational wave signals are resolvable, and the rest blurs into a galactic foreground noise; extracting tens of thousands of signals using Bayesian approaches is still computationally expensive. We developed a new end-to-end pipeline using Gaussian Process Regression to model the log-likelihood function in order to rapidly compute Bayesian posterior distributions. Using the pipeline we are able to solve the Lisa Data Challange (LDC) 1-3 consisting of noisy data as well as additional challenges with overlapping signals and particularly faint signals.Comment: 12 pages, 10 figure

Accelerating global parameter estimation of gravitational waves from Galactic binaries using a genetic algorithm and GPUs

The Laser Interferometer Space Antenna (LISA) is a planned space-based gravitational wave telescope with the goal of measuring gravitational waves in the milli-Hertz frequency band, which is dominated by millions of Galactic binaries. While some of these binaries produce signals that are loud enough to stand out and be extracted, most of them blur into a confusion foreground. Current methods for analyzing the full frequency band recorded by LISA to extract as many Galactic binaries as possible and to obtain Bayesian posterior distributions for each of the signals are computationally expensive. We introduce a new approach to accelerate the extraction of the best fitting solutions for Galactic binaries across the entire frequency band from data with multiple overlapping signals. Furthermore, we use these best fitting solutions to omit the burn-in stage of a Markov chain Monte Carlo method and to take full advantage of GPU-accelerated signal simulation, allowing us to compute posterior distributions in 2 seconds per signal on a laptop-grade GPU.Comment: 13 pages, 11 figure