<p> In this dissertation, we describe our research contributions for a novel approach to the application of machine learning for the automatic detection of anomalous events. We work in two different domains to ensure a robust data-driven workflow that could be generalized for monitoring other systems. Specifically, in our first domain, we begin with the identification of internal erosion events in earth dams and levees (EDLs) using geophysical data collected from sensors located on the surface of the levee. As EDLs across the globe reach the end of their design lives, effectively monitoring their structural integrity is of critical importance. The second domain of interest is related to mobile telecommunications, where we investigate a system for automatically detecting non-commercial base station routers (BSRs) operating in protected frequency space. The presence of non-commercial BSRs can disrupt the connectivity of end users, cause service issues for the commercial providers, and introduce significant security concerns. We provide our motivation, experimentation, and results from investigating a generalized novel data-driven workflow using several machine learning techniques. </p><p> In Chapter 2, we present results from our performance study that uses popular unsupervised clustering algorithms to gain insights to our real-world problems, and evaluate our results using internal and external validation techniques. Using EDL passive seismic data from an experimental laboratory earth embankment, results consistently show a clear separation of events from non-events in four of the five clustering algorithms applied. </p><p> Chapter 3 uses a multivariate Gaussian machine learning model to identify anomalies in our experimental data sets. For the EDL work, we used experimental data from two different laboratory earth embankments. Additionally, we explore five wavelet transform methods for signal denoising. The best performance is achieved with the Haar wavelets. We achieve up to 97.3% overall accuracy and less than 1.4% false negatives in anomaly detection. </p><p> In Chapter 4, we research using two-class and one-class support vector machines (SVMs) for an effective anomaly detection system. We again use the two different EDL data sets from experimental laboratory earth embankments (each having approximately 80% normal and 20% anomalies) to ensure our workflow is robust enough to work with multiple data sets and different types of anomalous events (e.g., cracks and piping). We apply Haar wavelet-denoising techniques and extract nine spectral features from decomposed segments of the time series data. The two-class SVM with 10-fold cross validation achieved over 94% overall accuracy and 96% F1-score. </p><p> Our approach provides a means for automatically identifying anomalous events using various machine learning techniques. Detecting internal erosion events in aging EDLs, earlier than is currently possible, can allow more time to prevent or mitigate catastrophic failures. Results show that we can successfully separate normal from anomalous data observations in passive seismic data, and provide a step towards techniques for continuous real-time monitoring of EDL health. Our lightweight non-commercial BSR detection system also has promise in separating commercial from non-commercial BSR scans without the need for prior geographic location information, extensive time-lapse surveys, or a database of known commercial carriers. (Abstract shortened by ProQuest.) </p>

<p>In this dissertation, we present our research contributions geared towards creating an automated and efficient wireless sensor network (WSN) for geohazard monitoring. Specifically, this dissertation addresses three overall technical research problems inherent in implementing and deploying such a WSN, i.e., 1) automated event detection from geophysical data, 2) efficient wireless transmission, and 3) low-cost wireless hardware. In addition, after presenting algorithms, experimentation, and results from these three overall problems, we take a step back and discuss how, when, and why such scientific work matters in a geohazardous risk scenario. First, in Chapter 2, we discuss automated geohazard event detection within geophysical data. In particular, we present our pattern recognition workflow that can automatically detect snow avalanche events in seismic (geophone sensor) data. This workflow includes customized signal preprocessing for feature extraction, cluster-based stratified sub-sampling for majority class reduction, and experimentation with 12 different machine learning algorithms; results show that a decision stump classifier achieved 99.8% accuracy, 88.8% recall, and 13.2% precision in detecting avalanches within seismic data collected in the mountains above Davos, Switzerland, an improvement on previous work in the field. To address the second overall research problem (i.e., efficient wireless transmission), we present and evaluate our on-mote compressive sampling algorithm called Randomized Timing Vector (RTV) in Chapter 3 and compare our approach to four other on-mote, lossy compression algorithms in Chapter 4. Results from our work show that our RTV algorithm outperforms current on-mote compressive sampling algorithms and performs comparably to (and in many cases better than) the four state-of-the-art, on-mote lossy compression techniques. The main benefit of RTV is that it can guarantee a desired level of compression performance (and thus, radio usage and power consumption) without subjugating recovered signal quality. Another benefit of RTV is its simplicity and low computational overhead; by sampling directly in compressed form, RTV vastly decreases the amount of memory space and computation time required for on-mote compression. Third, in Chapter 5, we present and evaluate our custom, low-cost, Arduino-based wireless hardware (i.e., GeoMoteShield) developed for wireless seismic data acquisition. In particular, we first provide details regarding the motivation, design, and implementation of our custom GeoMoteShield and then compare our custom hardware against two much more expensive systems, i.e., a traditional wired seismograph and a "from-the-ground-up" wireless mote developed by SmartGeo colleagues. We validate our custom WSN of nine GeoMoteShields using controlled lab tests and then further evaluate the WSN's performance during two seismic field tests, i.e., a "walk-away" test and a seismic refraction survey. Results show that our low-cost, Arduino-based GeoMoteShield performs comparably to a much more expensive wired system and a "from the ground up" wireless mote in terms of signal precision, accuracy, and time synchronization. Finally, in Chapter 6, we provide a broad literature review and discussion of how, when, and why scientific work matters in geohazardous risk scenarios. This work is geared towards scientists conducting research within fields involving geohazard risk assessment and mitigation. In particular, this chapter reviews three topics from Science, Technology, Engineering, and Policy (STEP): 1) risk, scientific uncertainty, and policy, 2) society's perceptions of risk, and 3) the effectiveness of risk communication. Though this chapter is not intended to be a comprehensive STEP literature survey, it addresses many pertinent questions and provides guidance to scientists and engineers operating in such fields. In short, this chapter aims to answer three main questions, i.e., 1) "when does scientific work influence policy decisions?", 2) "how does scientific work impact people's perception of risk?", and 3) "how is technical scientific work communicated to the non-scientific community?".

<p> Imaging the interior structure of comets and asteroids in 3D holds the key for understand- ing early Solar System and planetary processes, aids mitigation of collisional hazards, and enables future space investigation. 3D wavefield extrapolation of time-domain finite differ- ences, which is referred to as reverse-time migration (RTM), is a tool to provide high-quality images of the complex 3D-internal structure of the target. Instead of a type of acquisition that separately deploys one orbiting and one landing satellite, I discuss dual orbiter systems, where transmitter and receiver satellites orbit around the asteroid target at different speeds. The dual orbiter acquisition can provide multi-offset data that improve the image quality by illuminating the target from different directions and by attenuating coherent noise caused by wavefield multi-pathing. Shot-record imaging requires dense and evenly distributed receiver coordinates to fully image the interior structure at every source-location. </p><p> I illustrate a 3D imaging method on a complex asteroid model based on the asteroid 433 Eros using realistic data generated from different acquisition designs for the dual orbiter system. In realistic 3D acquisition, the distribution and number of receivers are limited by the acquisition time, revolving speed and direction of both the transmitter and receiver satellites, and the rotation of the asteroid. The migrated image quality depends on different acquisition parameters (i.e., source frequency bandwidth, acquisition time, the spinning rate of the asteroid) and the intrinsic asteroid medium parameters (i.e., the asteroid attenuation factor and an accurate velocity model). </p><p> A critical element in reconstructing the interior of an asteroid is to have different ac- quisition designs, where the transmitter and receivers revolve quasi-continuously in different inclinational and latitudinal directions and offer evenly distributed receiver coordinates in the shot-record domain. Among different acquisition designs, the simplest orbit (where the transmitter satellite is fixed in the longitudinal plane and the receiver plane gradually shifts in the latitudinal direction around the asteroid target) offers the best data coverage and requires the least energy to shift the satellite. To obtain reasonable coverage for successfully imaging the asteroid interior, the selected acquisition takes up to eight months. However, this mission is attainable because the propulsion requirements are small due to the slow (&lt; 10 cm/s) orbital velocities around a kilometer-sized asteroid.</p>