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"Terrestrial tomography, appraise the capabilities of geostatistical methods to compute uncertainties for the solar case."
PhD project title:
Integrative Inference and Assessment of Subsurface Information derived from Terrestrial and Solar Seismic Measurements.
Project description:
The application of seismology to investigate the internal structure of the Earth and of the Sun has grown considerably over the last few decades. The development of the Reference Earth Model (REM) and the Standard Solar Model (SSM) are notable examples of how seismic measurements have uncovered compelling processes within our planet and neighbouring star. As the internal structures are mostly occluded from view, their existence are derived indirectly by using measurements obtained only at the surface. For this reason, any predictions made, require a detailed assessment of possible inaccuracies if they are to be accepted by a large community.
Terrestrial seismology has established the use of statistical techniques as reliably means to investigate the uncertainties associated with subsurface structures. However, statistical computations can be prohibitively intensive, hard to get right or to establish reliable approaches over realistic size problems. Moreover, it is imperative to determine the applicability of geostatistical techniques to closely relateddisciplines, particularly helioseismology, which has not yet yielded uncertainties based on statistical principles.
I therefore propose more experimentation to derive probabilistic distributions of errors at higher dimensions for terrestrial tomography, appraise the capabilities of geostatistical methods to compute uncertainties for the solar case, and create an interdisciplinary functional pipeline for the analysis of uncertainty that emphasises human readable formats and functionalities that allow improved integration, expandibility and reusablility of software components under the GNU system.