Anàlisi geomètrica del Diaclasat de les Guilleries en el sector de la Vall del Ter entre els embassaments del Pasteral i Susqueda (Girona)

The Guilleries are a small and mountainous area located in the north-westem part of the Catalonian Coastal Ranges where metamorphic and igneous Paleozoic rocks are exposed. After the main hercynian folding this area was affected by a brittle deformation that is mainly manifested by the intrusion of a very large number of dykes of granodiorite and the development of a complex joint system. Trends of dykes indicate that their intrusion was related to a SE-NW extension, whose estimated value is 40% on an average. This extension seems to stand, although without any associated igneous event, with the development of NE-SW directed joints which make the main set. Five families more were developed later, one gently-dipping and fou upright; the latter trending roughly SE-NW, ENE-WSW, ESE-WNW and N-S. AU the joint sets appear in the metasedimentary Paleozoic rocks and in the hercynian intrusive bodies. Concerning the ages, joints that belong to the NE-SW and SE-NW directed sets and also those slightly dipping have been attributed to the late-hercynian times and all the other are considered to be later

Modelling structural zeros in compositional data

This analysis was stimulated by the real data analysis problem of household expenditure data. The full dataset contains expenditure data for a sample of 1224 households. The expenditure is broken down at 2 hierarchical levels: 9 major levels (e.g. housing, food, utilities etc.) and 92 minor levels. There are also 5 factors and 5 covariates at the household level. Not surprisingly, there are a small number of zeros at the major level, but many zeros at the minor level. The question is how best to model the zeros. Clearly, models that try to add a small amount to the zero terms are not appropriate in general as at least some of the zeros are clearly structural, e.g. alcohol/tobacco for households that are teetotal. The key question then is how to build suitable conditional models. For example, is the sub-composition of spending excluding alcohol/tobacco similar for teetotal and non-teetotal households? In other words, we are looking for sub-compositional independence. Also, what determines whether a household is teetotal? Can we assume that it is independent of the composition? In general, whether teetotal will clearly depend on the household level variables, so we need to be able to model this dependence. The other tricky question is that with zeros on more than one component, we need to be able to model dependence and independence of zeros on the different components. Lastly, while some zeros are structural, others may not be, for example, for expenditure on durables, it may be chance as to whether a particular household spends money on durables within the sample period. This would clearly be distinguishable if we had longitudinal data, but may still be distinguishable by looking at the distribution, on the assumption that random zeros will usually be for situations where any non-zero expenditure is not small. While this analysis is based on around economic data, the ideas carry over to many other situations, including geological data, where minerals may be missing for structural reasons (similar to alcohol), or missing because they occur only in random regions which may be missed in a sample (similar to the durables)