It is being increasingly realized that there exists an interrelationship between seismicity and rock slope failures. Possible chronological clustering of rock avalanches in the Fraser River corridor was investigated to determine if a common seismic trigger existed. It was determined that the events occurred throughout the Holocene indicating that either these slides were not seismically triggered or that seismic triggers were chronologically unrelated. Cascadia Subduction Zone earthquakes are believed to have occurred throughout the Holocene (Adams, 1989; Atwater, 1987; Hull, 1987). The ages of the earthquakes were compared to the ages of rock avalanches in the Fraser River corridor, but no distinct correlation could be made. The lack of distinct correlation between large rock avalanches in Fraser Corridor and paleoseismicity, and the absence of event clustering, indicated either seismicity was not a factor, or that these rock avalanches may not have been susceptible to seismic triggering. A stability study of Cheam Slide was performed to investigate the susceptibility of large rock avalanches to earthquake triggering. The results suggested that the seismic susceptibility of a slope is closely linked to the displacement the slope must undergo for failure to take place. A large critical displacement may render the slope relatively insensitive to seismic triggering, while a low critical displacement may result in high seismic susceptibility. A comparison was made between the effects of seismic and pore pressure related triggering. The results indicated that a high critical displacement slope, which is close to failure, may be more likely to fail by high pore pressures than by seismic loading. Low critical displacement slopes which are stable enough to surviving hydrodynamic loading may, because of their susceptibility to seismic triggering, pose the greatest hazard. / Science, Faculty of / Earth, Ocean and Atmospheric Sciences, Department of / Graduate

A study of the dispersion of long-period Rayleigh waves was proposed in order to acquire additional knowledge about the lithospheric structure of the Vancouver Island region. Three portable, long-period seismographs were designed and built to operate in field conditions. An array was established with stations at Victoria, Vancouver and Quadra Island; during six weeks of operation, several earthquakes were recorded, of which two were aligned conveniently with the array. Another network with stations at Ucluelet, Quadra Island and Victoria was set up later; no usable data were recorded. the earthquake record was chosen for analysis, and group-velocity dispersion calculations were begun. Echo resolution on these data and a lack of ether usable records prevented the continuation of the data processing. No conclusions are drawn about the lithospheric structure; however, recommendations are made regarding possible future experimentation with the existing apparatus. / Science, Faculty of / Earth, Ocean and Atmospheric Sciences, Department of / Graduate

as one of three explanations of a prominenttine delay in the 6.5 km/s branch of their seismic refraction survey in the Rocky Mountain Trench, Eennett et al (1975) suggested a high-angle crustal fault crossing the trench near Radium. If the density contrast between basement and cover rocks is 0.1 g/cm3, a gravity anomaly of approximately 18 mgal should be observed. To test the fault hypothesis, a gravity survey has been carried out in and adjacent to the trench in the Radium area. The resultant data are not consistent with the proposed fault model. The principal feature of the data is a pronounced low which coincides with the trench throughout the survey area. The low is due to Cenozoic fill and interpretation by two-dimensional modeling indicates the thickness of fill is about 550 m to the north and 420 m to the south of Radium. An analysis has also been performed of the shear-wave data recorded during the seismic survey of Bennett et al (1975). Although the quality of the S save data is poor, they show consistent behavior with the P save data. There is weak evidence suggesting a basement refractor velocity of 3.5 km/s and a Moho refractor velocity of 4.2-4.5 km/s. The corresponding Poisscn's ratios are 0.30 and 0.28-0.32. To determine maximum and minimum depth limits to the Hcho allowed by the seismic data, an extremal analysis was performed on both the P and S wave record sections. From the P wave data, the limits on crustal thickness beneath the Rocky Mountain Trench are 52 km and 60 km; from the S wave data, the limits are 47 km and 59 km. Is a result of these additional studies, the tao alternative hypotheses of Bennett et al (1975) to explain the seismic data must be reconsidered. These are (1) the existence of a crustal low velocity zone and (2) a major deformation of the basement and overlying rocks due to the trench being an ancient zone of weakness which coincides with the western limit of the continental Precambrian craton. As reflections from the top of the low velocity zone are not observed by Bennett et al (1975), the second alternative is preferred. / Science, Faculty of / Earth, Ocean and Atmospheric Sciences, Department of / Graduate

Earthquake detection is an important task, focusing on detecting seismic events in past data or in real time from seismic time series. In the past few decades, due to the increasing amount of available seismic data, research in seismic event detection shows remarkable success using neural networks and other machine learning techniques. However, creating high quality labeled data sets is still a manual process that demands tremendous amount of time and expert knowledge, and is stifling big data innovation. When compiling a data set, it is unclear how many earthquakes and noise are mislabeled. Another challenge is how to promote the general applicability of the machine learning based models to different geographical regions. The models trained by data sets from one location should be applicable to the detection at other locations. This thesis explores the most popular deep learning model, convolutional neural networks (CNN), to build a single location detection model. In addition, we build more robust generalized earthquake detection models using transfer learning and meta learning. We also introduce a process for generating high quality labeled datasets. Our technique achieves high detection accuracy even on low signal to noise ratio events. The AI techniques explored in this research have potential to be transferred to other domains that utilize signal processing. There are a myriad of potential applications, with audio processing probably being one of the most directly relevant. Any field that deals with waveforms (e.g. seismic, audio, light) can utilize the developed techniques.

In this thesis I combine data from 29 volcanic earthquake swarms that follow the pattern predicted by the Generic Volcanic Earthquake Swarm Model (GVESM; Benoit and McNutt, 1996) to investigate whether the relative timing of various parameters of pre-eruptive volcanic earthquake swarms could be used to forecast the time of an impending eruption. Based on the analysis of seismic unrest preceding many eruptions, the GVESM suggests that it is common to see an increase first in high-frequency earthquakes, then low-frequency earthquakes, then the onset of volcanic tremor. While this pattern is useful to volcano-seismologists, the relative timing and durations of these three different types of volcanic seismicity, is explored here for the first time. The parameters investigated are the onset times of (i) low-frequency (LF) events and of (ii) tremor, and the time at which (iii) the peak rate (PR) of volcano-tectonic (VT) events and (iv) the maximum magnitude (MM) earthquake occur in relation to normalized time defined by swarm onset and end (i.e., eruption). The normalized time starts at the swarm onset (0%) and ends with the eruption (100%) allowing a comparison and joint consideration of parameter occurrences across swarms of different actual duration. We identify the normalized onset time of for each parameter (LF, tremor, PR, MM) with respect to the duration of each swarm. Each swarm has onset time uncertainties of the swarm itself and of its parameters. A swarm with large onset uncertainty could bias the normalized onset time of each parameter and we use weighted means to decrease the influence of swarms with large uncertainties on overall results. The weighted means of LF onset, tremor onset, MM and PR occurrence are 79% ± 23%, 96% ± 10%, 78% ± 29% and 75% ± 34%, respectively. Errors are the standard deviation of each parameter. The uncertainties for LF, MM and PR are large because their normalized onset times have the characteristics of a uniform distribution and therefore seem to have no predictive value. In contrast, tremor onset has a narrow distribution towards the end of swarms. A possible tremor mechanism consistent with this observation could be boiling of groundwater as magma nears the surface. LF onset always seems to precede tremor onset. LF and tremor start early (at less than 80% of normalized time) at five volcanoes with high SiO2 content possibly related to lower density and higher gas content of the resulting magma.

Neural networks are extremely versatile tools, as evidenced by their widespread adoption into many fields in the sciences and beyond, including the geosciences. In seismology neural networks have been primarily used to automatically detect and discriminate seismic signals within time-series data, as well as provide location estimates for their sources. However, as neural network research has significantly progressed over the past three decades, so too have its applications in seismology. Such applications now include earthquake early warning systems based on smartphone data collected from large numbers of users, the prediction of peak ground acceleration from earthquake source parameters, the efficient computation of synthetic seismograms, providing probabilistic estimates of solutions to geophysical inverse problems, and many others. This thesis contains three components, each of which explore novel uses of neural networks in seismology. In the first component, a previously established earthquake detection and location method is supplemented with a neural network in order to automate the detection process. The detection procedure is then applied to a large volume of seismic data. In addition to automating the detection process, the neural network removes the need for several user-defined thresholds, subjective criteria otherwise necessary for the method. In the second component, a novel approach is developed for inverting seafloor compliance data recorded by ocean-bottom seismometers for the shallow shear-wave velocity structure of oceanic tectonic plates. The approach makes use of mixture density networks, a type of neural network designed to provide probabilistic estimates of solutions to inverse problems, something that standard neural networks are incapable of. In the final component of this thesis, the mixture density network approach to compliance inversion is applied to a group of ocean-bottom seismometers deployed along the continental shelf of the Cascadia Subduction Zone in order to investigate shelf sediment properties.

Four geophysical methods, along with well logs and outcrop data, were used in determining the location of a fault situated on the campus of Oregon Institute of Technology, just north of Klamath Falls, Oregon. The fault displaces rocks of the Yonna Formation, of Pliocene age. Wells located northeast of the fault (on the upthrown side) produce cold water, and wells located southwest of the fault (on the downthrown side) produce hot water. The purpose of this investigation was to define the characteristics of the fault exposed behind a large water tank southeast of the OIT campus.

The geologic setting of the Portland Basin is ideal for gravity surveys because of the large density contrasts between geologic units. The Portland Basin consists of a north-northwest-trending syncline in the Columbia River basalt overlain by Pliocene to Recent alluvium. This study was undertaken to define structures in the Portland Basin which are obscured by the alluvium. An areal gravity survey of the Portland Basin covering approximately 450 square kilometers was conducted for this study.

A dissertation submitted to the Geophysics Department, School of Geosciences, Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Masters of Science. Johannesburg, February 2012 / Impala Platinum Mine (Impala), situated north of the town of Rustenburg in the North West Province of South Africa, has experienced an increase in seismicity from ~841 seismic events in the year 2005 to ~1588 seismic events in 2008. The seismologists and rock engineers need to understand the underlying mechanisms and driving forces responsible for seismicity to develop and design mining layouts and support strategies to lessen the risks posed by rockburts. However, most previous studies of seismicity conducted on Impala and other Bushveld Complex mines in the Rustenburg area provided limited information regarding the source parameters and mechanism due to insufficient data. The study is designed to investigate the seismic hazard on Impala Platinum Mine by means of two approaches: an investigation of seismic source parameters and the mechanism of potentially damaging seismic events, and mapping of the weathered layer of the near surface within the Impala mine lease area. A number of detailed investigations of rockbursts were conducted whereby damage was mapped and photographed. The investigations includes reviews of the seismic history, short-, medium- and long-term seismic hazard assessment methods, and an analysis of the source parameters of the seismic event and associated ground motions. The study has revealed that most of the seismic events occur close to the reef plane, and are the result of the failure of a volume of rock that includes the pillar and the host rock that forms the foundation of the pillar.

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px 'Times New Roman'} A methodology is presented for evaluating the potential surface fault displacement on reverse faults in a probabilistic manner. This methodology follows the procedures put forth for Probabilistic Fault Displacement Hazard Analysis (PFDHA). Empirical probability distributions that are central to performing a PFDHA are derived from field investigations of reverse faulting events. Statistical analyses are used to test previously assumed properties of scale invariance with respect to magnitude for normalized displacement. It is found that normalized displacement is statistically invariant with respect to magnitude and focal mechanism, allowing for the combination of a large number of events into a single dataset for regression purposes. An empirical relationship is developed using this single dataset to be used as a fault displacement prediction equation. A PFDHA is conducted on the Los Osos fault zone in central California and a hazard curve for fault displacement is produced. A full sensitivity analysis is done using this fault as a reference, to test for the sources of variability in the PFDHA methodology. The influence of the major primary variables is quantified to provide a future direction for PFDHA.