Genesis and Evolution of Brittle Structures In Southwestern Finland and Western South Africa. Insights into Fault Reactivation, Fluid Flow and Structural Maturity in Precambrian Cratons

The bedrock of old crystalline cratons is characteristically saturated with brittle structures formed during successive superimposed episodes of deformation and under varying stress regimes. As a result, the crust effectively deforms through the reactivation of pre-existing structures rather than by through the activation, or generation, of new ones, and is said to be in a state of 'structural maturity'. By combining data from Olkiluoto Island, southwestern Finland, which has been investigated as the potential site of a deep geological repository for high-level nuclear waste, with observations from southern Sweden, it can be concluded that the southern part of the Svecofennian shield had already attained structural maturity during the Mesoproterozoic era. This indicates that the phase of activation of the crust, i.e. the time interval during which new fractures were generated, was brief in comparison to the subsequent reactivation phase. Structural maturity of the bedrock was also attained relatively rapidly in Namaqualand, western South Africa, after the formation of first brittle structures during Neoproterozoic time. Subsequent brittle deformation in Namaqualand was controlled by the reactivation of pre-existing strike-slip faults.In such settings, seismic events are likely to occur through reactivation of pre-existing zones that are favourably oriented with respect to prevailing stresses. In Namaqualand, this is shown for present day seismicity by slip tendency analysis, and at Olkiluoto, for a Neoproterozoic earthquake reactivating a Mesoproterozoic fault. By combining detailed field observations with the results of paleostress inversions and relative and absolute time constraints, seven distinctm superimposed paleostress regimes have been recognized in the Olkiluoto region. From oldest to youngest these are: (1) NW-SE to NNW-SSE transpression, which prevailed soon after 1.75 Ga, when the crust had sufficiently cooled down to allow brittle deformation to occur. During this phase conjugate NNW-SSE and NE-SW striking strike-slip faults were active simultaneous with reactivation of SE-dipping low-angle shear zones and foliation planes. This was followed by (2) N-S to NE-SW transpression, which caused partial reactivation of structures formed in the first event; (3) NW-SE extension during the Gothian orogeny and at the time of rapakivi magmatism and intrusion of diabase dikes; (4) NE-SW transtension that occurred between 1.60 and 1.30 Ga and which also formed the NW-SE-trending Satakunta graben located some 20 km north of Olkiluoto. Greisen-type veins also formed during this phase. (5) NE-SW compression that postdates both the formation of the 1.56 Ga rapakivi granites and 1.27 Ga olivine diabases of the region; (6) E-W transpression during the early stages of the Mesoproterozoic Sveconorwegian orogeny and which also predated (7) almost coaxial E-W extension attributed to the collapse of the Sveconorwegian orogeny. The kinematic analysis of fracture systems in crystalline bedrock also provides a robust framework for evaluating fluid-rock interaction in the brittle regime; this is essential in assessment of bedrock integrity for numerous geo-engineering applications, including groundwater management, transient or permanent CO2 storage and site investigations for permanent waste disposal. Investigations at Olkiluoto revealed that fluid flow along fractures is coupled with low normal tractions due to in-situ stresses and thus deviates from the generally accepted critically stressed fracture concept, where fluid flow is concentrated on fractures on the verge of failure. The difference is linked to the shallow conditions of Olkiluoto - due to the low differential stresses inherent at shallow depths, fracture activation and fluid flow is controlled by dilation due to low normal tractions. At deeper settings, however, fluid flow is controlled by fracture criticality caused by large differential stress, which drives shear deformation instead of dilation.

Differential evolution based classification with pool of distances and aggregation operators

The objective of this thesis is to develop and generalize further the differential evolution based data classification method. For many years, evolutionary algorithms have been successfully applied to many classification tasks. Evolution algorithms are population based, stochastic search algorithms that mimic natural selection and genetics. Differential evolution is an evolutionary algorithm that has gained popularity because of its simplicity and good observed performance. In this thesis a differential evolution classifier with pool of distances is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, to determine the optimal values for all free parameters of the classifier model during the training phase of the classifier. The differential evolution classifier applies the individually optimized distance measure for each new data set to be classified is generalized to cover a pool of distances. Instead of optimizing a single distance measure for the given data set, the selection of the optimal distance measure from a predefined pool of alternative measures is attempted systematically and automatically. Furthermore, instead of only selecting the optimal distance measure from a set of alternatives, an attempt is made to optimize the values of the possible control parameters related with the selected distance measure. Specifically, a pool of alternative distance measures is first created and then the differential evolution algorithm is applied to select the optimal distance measure that yields the highest classification accuracy with the current data. After determining the optimal distance measures for the given data set together with their optimal parameters, all determined distance measures are aggregated to form a single total distance measure. The total distance measure is applied to the final classification decisions. The actual classification process is still based on the nearest prototype vector principle; a sample belongs to the class represented by the nearest prototype vector when measured with the optimized total distance measure. During the training process the differential evolution algorithm determines the optimal class vectors, selects optimal distance metrics, and determines the optimal values for the free parameters of each selected distance measure. The results obtained with the above method confirm that the choice of distance measure is one of the most crucial factors for obtaining higher classification accuracy. The results also demonstrate that it is possible to build a classifier that is able to select the optimal distance measure for the given data set automatically and systematically. After finding optimal distance measures together with optimal parameters from the particular distance measure results are then aggregated to form a total distance, which will be used to form the deviation between the class vectors and samples and thus classify the samples. This thesis also discusses two types of aggregation operators, namely, ordered weighted averaging (OWA) based multi-distances and generalized ordered weighted averaging (GOWA). These aggregation operators were applied in this work to the aggregation of the normalized distance values. The results demonstrate that a proper combination of aggregation operator and weight generation scheme play an important role in obtaining good classification accuracy. The main outcomes of the work are the six new generalized versions of previous method called differential evolution classifier. All these DE classifier demonstrated good results in the classification tasks.