2 resultados para location analysis
em AMS Tesi di Dottorato - Alm@DL - Università di Bologna
Resumo:
Forecasting the time, location, nature, and scale of volcanic eruptions is one of the most urgent aspects of modern applied volcanology. The reliability of probabilistic forecasting procedures is strongly related to the reliability of the input information provided, implying objective criteria for interpreting the historical and monitoring data. For this reason both, detailed analysis of past data and more basic research into the processes of volcanism, are fundamental tasks of a continuous information-gain process; in this way the precursor events of eruptions can be better interpreted in terms of their physical meanings with correlated uncertainties. This should lead to better predictions of the nature of eruptive events. In this work we have studied different problems associated with the long- and short-term eruption forecasting assessment. First, we discuss different approaches for the analysis of the eruptive history of a volcano, most of them generally applied for long-term eruption forecasting purposes; furthermore, we present a model based on the characteristics of a Brownian passage-time process to describe recurrent eruptive activity, and apply it for long-term, time-dependent, eruption forecasting (Chapter 1). Conversely, in an effort to define further monitoring parameters as input data for short-term eruption forecasting in probabilistic models (as for example, the Bayesian Event Tree for eruption forecasting -BET_EF-), we analyze some characteristics of typical seismic activity recorded in active volcanoes; in particular, we use some methodologies that may be applied to analyze long-period (LP) events (Chapter 2) and volcano-tectonic (VT) seismic swarms (Chapter 3); our analysis in general are oriented toward the tracking of phenomena that can provide information about magmatic processes. Finally, we discuss some possible ways to integrate the results presented in Chapters 1 (for long-term EF), 2 and 3 (for short-term EF) in the BET_EF model (Chapter 4).
Resumo:
The aim of this work was to show that refined analyses of background, low magnitude seismicity allow to delineate the main active faults and to accurately estimate the directions of the regional tectonic stress that characterize the Southern Apennines (Italy), a structurally complex area with high seismic potential. Thanks the presence in the area of an integrated dense and wide dynamic network, was possible to analyzed an high quality microearthquake data-set consisting of 1312 events that occurred from August 2005 to April 2011 by integrating the data recorded at 42 seismic stations of various networks. The refined seismicity location and focal mechanisms well delineate a system of NW-SE striking normal faults along the Apenninic chain and an approximately E-W oriented, strike-slip fault, transversely cutting the belt. The seismicity along the chain does not occur on a single fault but in a volume, delimited by the faults activated during the 1980 Irpinia M 6.9 earthquake, on sub-parallel predominant normal faults. Results show that the recent low magnitude earthquakes belongs to the background seismicity and they are likely generated along the major fault segments activated during the most recent earthquakes, suggesting that they are still active today thirty years after the mainshock occurrences. In this sense, this study gives a new perspective to the application of the high quality records of low magnitude background seismicity for the identification and characterization of active fault systems. The analysis of the stress tensor inversion provides two equivalent models to explain the microearthquake generation along both the NW-SE striking normal faults and the E- W oriented fault with a dominant dextral strike-slip motion, but having different geological interpretations. We suggest that the NW-SE-striking Africa-Eurasia convergence acts in the background of all these structures, playing a primary and unifying role in the seismotectonics of the whole region.