Lars Krieger

Automated microseismic event location using Master-Event Waveform Stacking.

Accurate and automated locations of microseismic events are desirable for many seismological and industrial applications. The analysis of microseismicity is particularly challenging because of weak seismic signals with low signal-to-noise ratio. Traditional location approaches rely on automated picking, based on individual seismograms, and make no use of the coherency information between signals at different stations. This strong limitation has been overcome by full-waveform location methods, which exploit the coherency of waveforms at different stations and improve the location robustness even in presence of noise. However, the performance of these methods strongly depend on the accuracy of the adopted velocity model, which is often quite rough; inaccurate models result in large location errors. We present an improved waveform stacking location method based on source-specific station corrections. Our method inherits the advantages of full-waveform location methods while strongly mitigating the dependency on the accuracy of the velocity model. With this approach the influence of an inaccurate velocity model on the results is restricted to the estimation of travel times solely within the seismogenic volume, but not for the entire source-receiver path. We finally successfully applied our new method to a realistic synthetic dataset as well as real data.

First local helioseismic experiments with CO5BOLD

With numerical experiments we explore the feasibility of using high frequency waves for probing the magnetic fields in the photosphere and the chromosphere of the Sun. We track a plane-parallel, monochromatic wave that propagates through a non-stationary, realistic atmosphere, from the convection-zone through the photosphere into the magnetically dominated chromosphere, where it gets refracted and reflected. We compare the wave travel time between two fixed geometrical height levels in the atmosphere (representing the formation height of two spectral lines) with the topography of the surface of equal magnetic and thermal energy density (the magnetic canopy or β =1 contour) and find good correspondence between the two. We conclude that high frequency waves indeed bear information on the topography of the ‘magnetic canopy’.

A new code for Fourier‐Legendre analysis of large datasets: First results and a comparison with ring‐diagram analysis

School of Mathematics & Statistics, University of Sheffield , Sheffield S3 7RH, UKReceived 2010 Sep 9, accepted 2010 Sep 13Published online 2010 Nov 11Key words Sun: helioseismology – Sun: oscillations – methods: data an alysisFourier-Legendre decomposition (FLD) of solar Doppler imaging data is a promising method to estimate the sub-surfacesolar meridional flow. FLD is sensible to low-degree oscillation modes and thus has the potential to probe the deepmeridional flow. We present a newly developed code to be used f or large scale FLD analysis of helioseismic data asprovided by the Global Oscillation Network Group (GONG), the Michelson Doppler Imager (MDI) instrument, and theupcoming Helioseismic and Magnetic Imager (HMI) instrument. First results obtained with the new code are qualitativelycomparable to those obtained from ring-diagram analyis of the same time series.

Pareto-Optimal Multi-objective Inversion of Geophysical Data

In the process of modelling geophysical properties, jointly inverting different data sets can greatly improve model results, provided that the data sets are compatible, i.e., sensitive to similar features. Such a joint inversion requires a relationship between the different data sets, which can either be analytic or structural. Classically, the joint problem is expressed as a scalar objective function that combines the misfit functions of multiple data sets and a joint term which accounts for the assumed connection between the data sets. This approach suffers from two major disadvantages: first, it can be difficult to assess the compatibility of the data sets and second, the aggregation of misfit terms introduces a weighting of the data sets. We present a pareto-optimal multi-objective joint inversion approach based on an existing genetic algorithm. The algorithm treats each data set as a separate objective, avoiding forced weighting and generating curves of the trade-off between the different objectives. These curves are analysed by their shape and evolution to evaluate data set compatibility. Furthermore, the statistical analysis of the generated solution population provides valuable estimates of model uncertainty.