2 resultados para temporal sequence
em AMS Tesi di Dottorato - Alm@DL - Università di Bologna
Resumo:
Thrust fault-related folds in carbonate rocks are characterized by deformation accommodated by different structures, such as joints, faults, pressure solution seams, and deformation bands. Defining the development of fracture systems related to the folding process is significant both for theoretical and practical purposes. Fracture systems are useful constrains in order to understand the kinematical evolution of the fold. Furthermore, understanding the relationships between folding and fracturing provides a noteworthy contribution for reconstructing the geodynamic and the structural evolution of the studied area. Moreover, as fold-related fractures influence fluid flow through rocks, fracture systems are relevant for energy production (geothermal studies, methane and CO2 , storage and hydrocarbon exploration), environmental and social issues (pollutant distribution, aquifer characterization). The PhD project shows results of a study carried out in a multilayer carbonate anticline characterized by different mechanical properties. The aim of this study is to understand the factors which influence the fracture formation and to define their temporal sequence during the folding process. The studied are is located in the Cingoli anticline (Northern Apennines), which is characterized by a pelagic multilayer characterized by sequences with different mechanical stratigraphies. A multi-scale analysis has been made in several outcrops located in different structural positions. This project shows that the conceptual sketches proposed in literature and the strain distribution models outline well the geometrical orientation of most of the set of fractures observed in the Cingoli anticline. On the other hand, the present work suggests the relevance of the mechanical stratigraphy in particular controlling the type of fractures formed (e.g. pressure solution seams, joints or shear fractures) and their subsequent evolution. Through a multi-scale analysis, and on the basis of the temporal relationship between fracture sets and their orientation respect layering, I also suggest a conceptual model for fracture systems formation.
Resumo:
A critical point in the analysis of ground displacements time series is the development of data driven methods that allow the different sources that generate the observed displacements to be discerned and characterised. A widely used multivariate statistical technique is the Principal Component Analysis (PCA), which allows reducing the dimensionality of the data space maintaining most of the variance of the dataset explained. Anyway, PCA does not perform well in finding the solution to the so-called Blind Source Separation (BSS) problem, i.e. in recovering and separating the original sources that generated the observed data. This is mainly due to the assumptions on which PCA relies: it looks for a new Euclidean space where the projected data are uncorrelated. The Independent Component Analysis (ICA) is a popular technique adopted to approach this problem. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, I use a variational bayesian ICA (vbICA) method, which models the probability density function (pdf) of each source signal using a mix of Gaussian distributions. This technique allows for more flexibility in the description of the pdf of the sources, giving a more reliable estimate of them. Here I present the application of the vbICA technique to GPS position time series. First, I use vbICA on synthetic data that simulate a seismic cycle (interseismic + coseismic + postseismic + seasonal + noise) and a volcanic source, and I study the ability of the algorithm to recover the original (known) sources of deformation. Secondly, I apply vbICA to different tectonically active scenarios, such as the 2009 L'Aquila (central Italy) earthquake, the 2012 Emilia (northern Italy) seismic sequence, and the 2006 Guerrero (Mexico) Slow Slip Event (SSE).