Suomen maankuoren kivilajikoostumuksen tulkinta seismisistä aineistoista

Aim of this study is to investigate composition of the crust in Finland using seismic wide-angle velocity models and laboratory measurements on P- and S-wave velocities of different rock types. The velocities adopted from wide-angle velocity models were compared with laboratory velocities of different rock types corrected for the crustal PT conditions in the study area. The wide-angle velocity models indicate that the P-wave velocity does not only increase step-wise at boundaries of major crustal layers, but there is also gradual increase of velocity within the layers. On the other hand, the laboratory measurements of velocities indicate that no single rock type is able to provide the gradual downward increasing trends. Thus, there must be gradual vertical changes in rock composition. The downward increase of velocities indicates that the composition of the crust becomes gradually more mafic with increasing depth. Even though single rock types cannot simulate the wide-angle model velocities, it can be done with a mixture of rock types. There are a large number of rock type mixtures giving the correct P-wave velocities. Therefore, the inverse solution of rock types and their proportions from velocities is a non-unique problem if only P-wave velocities is available. Amount of the possible rock type mixtures can be limitted using S-wave velocities, reflection seismic results and other geological and geophysical results of the study area. Crustal model FINMIX-2 is presented in this study and it suggest that the crustal velocity profiles can be simulated with rock type mixtures, where the upper crust consists of felsic gneisses and granitic-granodioritic rocks with a minor contribution of quartzite, amphibolite and diabase. In the middle crust the amphibolite proportion increases. The lower crust consists of tonalitic gneiss, mafic garnet granulite, hornblendite, pyroxenite and minor mafic eclogite. This composition model is in agreement with deep crustal kimberlite-hosted xenolith data in eastern Finland and reflectivity of the FIRE (Finnish Reflection Experiment). According to FINMIX-2 model the Moho is deeper and the crustal composition is a more mafic than an average global continental model would suggest. Composition models of southern Finland are quite similar than FINMIX-2 model. However, there are minor differencies between the models, which indicates areal differences of composition. Models of northern Finland shows that the crustal thickness is smaller than southern Finland and composition of the upper crust is different. Density profiles calculated from the lithological models suggest that there is practically no density contrast at Moho in areas of the high-velocity lower crust. This implies that crustal thickness in the central Fennoscandian Shield may have been controlled by the densities of the lower crustal and upper mantle rocks.

Accelerator Mass Spectrometry and Bayesian Data Analysis

Accelerator mass spectrometry (AMS) is an ultrasensitive technique for measuring the concentration of a single isotope. The electric and magnetic fields of an electrostatic accelerator system are used to filter out other isotopes from the ion beam. The high velocity means that molecules can be destroyed and removed from the measurement background. As a result, concentrations down to one atom in 10^16 atoms are measurable. This thesis describes the construction of the new AMS system in the Accelerator Laboratory of the University of Helsinki. The system is described in detail along with the relevant ion optics. System performance and some of the 14C measurements done with the system are described. In a second part of the thesis, a novel statistical model for the analysis of AMS data is presented. Bayesian methods are used in order to make the best use of the available information. In the new model, instrumental drift is modelled with a continuous first-order autoregressive process. This enables rigorous normalization to standards measured at different times. The Poisson statistical nature of a 14C measurement is also taken into account properly, so that uncertainty estimates are much more stable. It is shown that, overall, the new model improves both the accuracy and the precision of AMS measurements. In particular, the results can be improved for samples with very low 14C concentrations or measured only a few times.