Earthquake-induced sliding displacements are commonly used to assess the seismic performance of slopes. These displacements represent the cumulative, downslope movement of a sliding block due to earthquake shaking. While the sliding block model is a simplified representation of the field conditions, the displacements predicted from this model have been shown to be a useful index of seismic performance of slopes. Current evaluation procedures that use sliding block displacements to evaluate the potential for slope instability typically are based on a deterministic approach or a pseudo-probabilistic approach, in which the variabilities in the expected ground motion and predicted displacement are either ignored or not treated rigorously. Thus, there is no concept of the actual hazard (i.e., the annual probability of exceedance) associated with the computed displacement. This dissertation focuses on quantifying the risk for earthquake-induced landslides. The basic approach involves a probabilistic framework for computing the annual rate of exceedance of different levels of sliding displacement for a slope such that a hazard curve for sliding displacement can be developed. The framework incorporates the uncertainties in the prediction of earthquake ground shaking, in the prediction of sliding displacement, and in the assessment of soil properties. The framework considers two procedures that will yield a displacement hazard curve: the scalar hazard approach that utilizes a single ground motion parameter and its associated hazard curve to compute permanent sliding displacements; and a vector hazard approach that predicts displacements based on two (or more) ground motion parameters and the correlation between these parameters. Current predictive models for sliding displacement provide the expected level of displacement as a function of the characteristics of the slope (e.g., geometry, strength, yield acceleration) and the characteristics of earthquake shaking (e.g., peak ground acceleration, peak ground velocity). However, current models contain significant aleatory variability such that the range of predicted displacements is large. To reduce the variability in the sliding displacement prediction and to provide models appropriate for the presented probabilistic framework, sliding displacement predictive equations are developed that utilize single and multiple ground motion parameters. The developed framework is implemented to the Mint Canyon 7.5-minute quadrangle in California to generate a map of earthquake-induced landslide hazard. Application of the probabilistic procedure to a 7-1/2 minute quadrangle of California is an important exercise to identify potential difficulties in California Geological Survey’s (CGS) current application for hazard mapping, and for the eventual adoption by CGS and USGS. / text

Thesis (Ph.D.) - Carleton University, 2002. / Includes bibliographical references (pt. 8, p. 1-7). Also available in electronic format on the Internet.

Thesis (Ph. D.)--Carleton University, 2004. / Includes bibliographical references (p. 122-127). Also available in electronic format on the Internet.

Thesis (M.S.)--Missouri University of Science and Technology, 2008. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed June 5, 2009) Includes bibliographical references (p. 126-132).

Thesis (Ph. D.)--University of Canterbury, 2006. / Typescript (photocopy). Four maps in pocket. Includes bibliographical references (leaves 224-243). Also available via the World Wide Web.

Thesis (M. Sc.)--University of Canterbury, 2006. / Typescript (photocopy). Includes bibliographical references (leaves 140-147). Also available via the World Wide Web.

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Volcanic eruptions and unrest have the potential to have large impacts on society causing social, economic and environmental losses. One of the primary goals of volcanological studies is to understand a volcano’s behaviour so that future instances of unrest or impending eruptions can be predicted. Despite this, our ability to predict the onset, location and size of future periods of unrest remains inadequate and one of the main problems in forecasting is associated with the inherent complexity of volcanoes. In practice, most reliable forecasts have employed a probabilistic approach where knowledge of volcanic activity triggers have been incorporated into scenarios to indicate the probability of unrest. The proposed relationship between large earthquakes and volcanic activity may, therefore, indicate an important precursory signal for volcanic activity forecasting. There have been numerous reports of a spatial and temporal link between volcanic activity and high magnitude seismic events and it has been suggested that significantly more periods of volcanic unrest occur in the months and years following an earthquake than expected by chance. Disparities between earthquake-volcano assessments and variability between responding volcanoes, however, has meant that the conditions that influence a volcano’s response to earthquakes have not been determined. Using data from the MODVOLC algorithm, a proxy for volcanic activity, this research examined a globally comparable database of satellite-derived volcanic radiant flux to identify significant changes in volcanic activity following an earthquake. Cases of potentially triggered volcanic activity were then analysed to identify the earthquake and volcano parameters that influence the relationship and evaluate the mechansisms proposed to trigger volcanic activity following an earthquake. At a global scale, this research identified that 57% [8 out of 14] of all large magnitude earthquakes were followed by increases in global volcanic activity. The most significant change in volcanic radiant flux, which demonstrates the potential of large earthquakes to influence volcanic activity at a global scale, occurred between December 2004 and April 2005. During this time, new thermal activity was detected at 10 volcanoes and the total daily volcanic radiant flux doubled within 52 days. Within a regional setting, this research also identified that instances of potentially triggered volcanic activity were statistically different to instances where no triggering was observed. In addition, assessments of earthquake and volcano parameters identified that earthquake fault characteristics increase the probability of triggered volcanic activity and variable response proportions at individual volcanoes and regionally demonstrated the critical role of the state of the volcanic system in determining if a volcano will respond. Despite the identification of these factors, this research was not able to define a model for the prediction of volcanic activity following earthquakes and, alternatively, proposed a process for response. In doing so, this thesis confirmed the potential use of earthquakes as a precursory indicator to volcanic activity and identified the most likely mechanisms that lead to seismically triggered volcanic unrest.

No description available.

Several seismic precursors and earthquake-causing variables have been proposed in the last decades based on physical considerations and case observations, however none has been confirmed on long datasets using linear analysis. This work adopts an information-theoretical approach to investigate the occurrence of causal flow between these precursors and causing variables and seismicity. It starts by introducing the key concepts in seismology and presenting the current main precursor candidates. Four variables will be considered as possible precursors or anomalies leading to earthquakes: large tidal amplitudes, temporal fluctuations in the Gutenberg-Richter's b-value, surface gravity changes, and preceding anomalous seismicity patterns. To perform the causality test between these variables and their effects, it is developed a method which allows the fast calculation of Transfer Entropy for any two time-series, detecting the direction of the flow of information between the variables of interest. The method is tested to coupled logistic maps and networks with different topologies before application to geophysical events. The analysis shows mutual information relating to coupling strength and also allows inference of the causal direction from data using the Transfer Entropy, both in bivariate systems and in networks. The method was then applied to the earthquake analysis for an interval of 4018 days on an area comprising the Japan trench. Within a conservative margin of confidence, the results could not at this point confirm any of the four precursor options considered, but future work can clarify initial suggestions regarding tidal amplitudes link to seismicity, and pre-seismic gravity changes and cumulative daily magnitude anomalies. The Matlab/Octavecodes for our method are open-source and available at https://github.com/artvalencio/causality-toolbox We hope the method is able to support the quest for other precursor candidates, and to assist other fields of knowledge.