The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: Optimization of the Spectral Line Inversion Code

The Very Fast Inversion of the Stokes Vector (VFISV) is a Milne-Eddington spectral line inversion code used to determine the magnetic and thermodynamic parameters of the solar photosphere from observations of the Stokes vector in the 6173 A Fe I line by the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). We report on the modifications made to the original VFISV inversion code in order to optimize its operation within the HMI data pipeline and provide the smoothest solution in active regions. The changes either sped up the computation or reduced the frequency with which the algorithm failed to converge to a satisfactory solution. Additionally, coding bugs which were detected and fixed in the original VFISV release, are reported here.Comment: Accepted for publication in Solar Physic

Selection of earthquake ground motions for multiple objectives using genetic algorithms

Existing earthquake ground motion (GM) selection methods for the seismic assessment of structural systems focus on spectral compatibility in terms of either only central values or both central values and variability. In this way, important selection criteria related to the seismology of the region, local soil conditions, strong GM intensity and duration as well as the magnitude of scale factors are considered only indirectly by setting them as constraints in the pre-processing phase in the form of permissible ranges. In this study, a novel framework for the optimum selection of earthquake GMs is presented, where the aforementioned criteria are treated explicitly as selection objectives. The framework is based on the principles of multi-objective optimization that is addressed with the aid of the Weighted Sum Method, which supports decision making both in the pre-processing and post-processing phase of the GM selection procedure. The solution of the derived equivalent single-objective optimization problem is performed by the application of a mixed-integer Genetic Algorithm and the effects of its parameters on the efficiency of the selection procedure are investigated. Application of the proposed framework shows that it is able to track GM sets that not only provide excellent spectral matching but they are also able to simultaneously consider more explicitly a set of additional criteria