Real Field Deployment of a Smart Fiber Optic Surveillance System for Pipeline Integrity Threat Detection: Architectural Issues and Blind Field Test Results

This paper presents an on-line augmented surveillance system that aims to real time monitoring of activities along a pipeline. The system is deployed in a fully realistic scenario and exposed to real activities carried out in unknown places at unknown times within a given test time interval (socalled blind field tests). We describe the system architecture that includes specific modules to deal with the fact that continuous on-line monitoring needs to be carried out, while addressing the need of limiting the false alarms at reasonable rates. To the best or our knowledge, this is the first published work in which a pipeline integrity threat detection system is deployed in a realistic scenario (using a fiber optic along an active gas pipeline) and is thoroughly and objectively evaluated in realistic blind conditions. The system integrates two operation modes: The machine+activity identification mode identifies the machine that is carrying out a certain activity along the pipeline, and the threat detection mode directly identifies if the activity along the pipeline is a threat or not. The blind field tests are carried out in two different pipeline sections: The first section corresponds to the case where the sensor is close to the sensed area, while the second one places the sensed area about 35 km far from the sensor. Results of the machine+activity identification mode showed an average machine+activity classification rate of 46:6%. For the threat detection mode, 8 out of 10 threats were correctly detected, with only 1 false alarm appearing in a 55:5-hour sensed period.European CommissionMinisterio de Economía y CompetitividadComunidad de Madri

Data-Driven Distributed Optical Vibration Sensors: A Review

Distributed optical vibration sensors (DOVS) have attracted much attention recently since it can be used to monitor mechanical vibrations or acoustic waves with long reach and high sensitivity. Phase-sensitive optical time domain reflectometry (Φ-OTDR) is one of the most commonly used DOVS schemes. For Φ-OTDR, the whole length of fiber under test (FUT) works as the sensing instrument and continuously generates sensing data during measurement. Researchers have made great efforts to try to extract external intrusions from the redundant data. High signal-to-noise ratio (SNR) is necessary in order to accurately locate and identify external intrusions in Φ-OTDR systems. Improvement in SNR is normally limited by the properties of light source, photodetector and FUT. But this limitation can also be overcome by post-processing of the received optical signals. In this context, detailed methodologies of SNR enhancement post-processing algorithms in Φ-OTDR systems have been described in this paper. Furthermore, after successfully locating the external vibrations, it is also important to identify the types of source of the vibrations. Pattern classification is a powerful tool in recognizing the intrusion types from the vibration signals in practical applications. Recent reports of Φ-OTDR systems employed with pattern classification algorithms are subsequently reviewed and discussed. This thorough review will provide a design pathway for improving the performance of Φ-OTDR while maintaining the cost of the system as no additional hardware is required

A Contextual GMM-HMM Smart Fiber Optic Surveillance System for Pipeline Integrity Threat Detection

This paper presents a novel pipeline integrity surveillance system aimed to the detection and classification of threats in the vicinity of a long gas pipeline. The sensing system is based on phase-sensitive optical time domain reflectometry ( $\phi$ -OTDR) technology for signal acquisition and pattern recognition strategies for threat identification. The proposal incorporates contextual information at the feature level in a Gaussian Mixture Model-Hidden Markov Model (GMM-HMM)-based pattern classification system and applies a system combination strategy for acoustic trace decision. System combination relies on majority voting of the decisions given by the individual contextual information sources and the number of states used for HMM modelling. The system runs in two different modes: (1) machine+activity identification, which recognizes the activity being carried out by a certain machine, and (2) threat detection, aimed to detect threats no matter what the real activity being conducted is. In comparison with the previous systems based on the same rigorous experimental setup, the results show that the system combination from the contextual feature information and the GMM-HMM approach improves the results for both machine+activity identification (7.6% of relative improvement with respect to the best published result in the literature on this task) and threat detection (26.6% of relative improvement in the false alarm rate with 2.1% relative reduction in the threat detection rate).European CommissionMinisterio de Economía y CompetitividadComunidad de Madri

Distributed Fiber Ultrasonic Sensor and Pattern Recognition Analytics

Ultrasound interrogation and structural health monitoring technologies have found a wide array of applications in the health care, aerospace, automobile, and energy sectors. To achieve high spatial resolution, large array electrical transducers have been used in these applications to harness sufficient data for both monitoring and diagnoses. Electronic-based sensors have been the standard technology for ultrasonic detection, which are often expensive and cumbersome for use in large scale deployments. Fiber optical sensors have advantageous characteristics of smaller cross-sectional area, humidity-resistance, immunity to electromagnetic interference, as well as compatibility with telemetry and telecommunications applications, which make them attractive alternatives for use as ultrasonic sensors. A unique trait of fiber sensors is its ability to perform distributed acoustic measurements to achieve high spatial resolution detection using a single fiber. Using ultrafast laser direct-writing techniques, nano-reflectors can be induced inside fiber cores to drastically improve the signal-to-noise ratio of distributed fiber sensors. This dissertation explores the applications of laser-fabricated nano-reflectors in optical fiber cores for both multi-point intrinsic Fabry–Perot (FP) interferometer sensors and a distributed phase-sensitive optical time-domain reflectometry (φ-OTDR) to be used in ultrasound detection. Multi-point intrinsic FP interferometer was based on swept-frequency interferometry with optoelectronic phase-locked loop that interrogated cascaded FP cavities to obtain ultrasound patterns. The ultrasound was demodulated through reassigned short time Fourier transform incorporating with maximum-energy ridges tracking. With tens of centimeters cavity length, this approach achieved 20kHz ultrasound detection that was finesse-insensitive, noise-free, high-sensitivity and multiplex-scalability. The use of φ-OTDR with enhanced Rayleigh backscattering compensated the deficiencies of low inherent signal-to-noise ratio (SNR). The dynamic strain between two adjacent nano-reflectors was extracted by using 3×3 coupler demodulation within Michelson interferometer. With an improvement of over 35 dB SNR, this was adequate for the recognition of the subtle differences in signals, such as footstep of human locomotion and abnormal acoustic echoes from pipeline corrosion. With the help of artificial intelligence in pattern recognition, high accuracy of events’ identification can be achieved in perimeter security and structural health monitoring, with further potential that can be harnessed using unsurprised learning

Aplicações De Métodos De Sensoriamento De Vibração Baseados Em Técnicas

Orientadores: Fabiano Fruett, Claudio FloridiaTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Sensores à fibra óptica distribuídos têm sido empregados para monitorar vários parâmetros, tais como temperatura, vibração, tensão mecânica, campo magnético e corrente elétrica. Quando comparados a outras técnicas convencionais, tais sensores são vantajosos devido a suas pequenas dimensões, imunidade a interferências eletromagnéticas, alta adaptabilidade, robustez a ambientes nocivos, dentre outros. Sensores acústicos distribuídos em particular são interessantes devido a sua capacidade em serem usados em aplicações tais como monitoração de saúde de estruturas e vigilância de perímetros. Através da análise em frequência da estrutura, por exemplo uma aeronave, uma ponte, um edifício ou mesmo máquinas em uma fábrica, é possível avaliar sua condição e detectar danos e falhas em um estágio primário. Tais soluções podem cobrir ambas as aplicações de detecção de intrusão e monitoração estrutural com mínimas adaptações no sistema sensor. Desta forma, vibrações e distúrbios pequenas estruturas com resolução de dezenas de centímetros e em grandes estruturas ou perímetros com alguns metros de resolução espacial e centenas de quilômetros de alcance podem ser detectadas. Outra característica útil desta solução baseada em fibra óptica é a possibilidade de ser combinada com técnicas de processamento digital de sinais, permitindo a detecção e localização de perturbações rápidas, reconhecimento de padrões de intrusão em tempo real e multiplexação de dados de superfícies estruturais para aplicações SHM. O principal objetivo desta tese é fazer uso desses recursos para empregar técnicas de DAS como soluções de tecnologias- chave para várias aplicações. Neste trabalho, as técnicas de phase-OTDR foram estudadas e as principais contribuições da tese focaram em trazer soluções inovadoras e validações para aplicações de vigilância e vigilância. Este doutorado teve um período sanduíche nas instalações da RISE Acreo AB, Estocolmo, Suécia, onde experimentos foram realizados e foi parte da 42ª Chamada CISB/Saab/CNPqAbstract: Distributed optical fiber sensors have been increasingly employed for monitoring several parameters, such as temperature, vibration, strain, magnetic field and current. When compared to other conventional techniques, these sensors are advantageous due to their small dimensions, lightweight, immunity to electromagnetic interference, high adaptability, robustness to hazardous environments, less complex data multiplexing, the feasibility to be embedded into structures with minimum invasion, the capability to extract data with high resolution from long perimeters using a single optical fiber and detect multiple events along the fiber. In particular, distributed acoustic sensors (DAS) based on optical time domain reflectometry (OTDR), are of high interest, due to their capability to be used in applications such as structural health monitoring (SHM) and perimeter surveillance. Through the frequency analysis of a structure, for instance an aircraft, a bridge, a building or even machines in a workshop, it is possible to evaluate its condition and detect damages and failures at an early stage. Also, OTDR based solutions for vibration monitoring can be easily adapted with minimum setup modifications to detect intrusion in a perimeter, a useful tool for surveillance of military facilities, laboratories, power plants and homeland security. The same OTDR technique can be used as a non-destructive diagnostic tool to evaluate vibrations and disturbances on both small structures with some dozens of centimeters¿ resolution and in big structures or perimeters with some meters of spatial resolution and hundreds of kilometers of reach. Another useful feature of this optical fiber based solution is the possibility to be combined with high-performance digital signal processing techniques, enabling fast disturbance detection and location, real-time intrusion pattern recognition and fast data multiplexing of structure surfaces for SHM applications. The main goal of this thesis is to make use of these features to employ DAS techniques as key enabling technologies solutions for several applications. In this work, OTDR based techniques were studied and the thesis main contributions were focused on bringing innovative solutions and validations for SHM and surveillance applications. This PhD had a sandwich period at Acreo AB, Stockholm, Sweden, where experimental tests were performed and it was part of the 42ª CISB/Saab/CNPq CalDoutoradoEletrônica, Microeletrônica e OptoeletrônicaDoutora em Engenharia Elétrica202816/2015-0CAPESCNP

A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation

Real-time monitoring of multiphase fluid flows with distributed fibre optic sensing has the potential to play a major role in industrial flow measurement applications. One such application is the optimization of hydrocarbon production to maximize short-term income, and prolong the operational lifetime of production wells and the reservoir. While the measurement technology itself is well understood and developed, a key remaining challenge is the establishment of robust data analysis tools that are capable of providing real-time conversion of enormous data quantities into actionable process indicators. This paper provides a comprehensive technical review of the data analysis techniques for distributed fibre optic technologies, with a particular focus on characterizing fluid flow in pipes. The review encompasses classical methods, such as the speed of sound estimation and Joule-Thomson coefficient, as well as their data-driven machine learning counterparts, such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Ensemble Kalman Filter (EnKF) algorithms. The study aims to help end-users establish reliable, robust, and accurate solutions that can be deployed in a timely and effective way, and pave the wave for future developments in the field.publishedVersio

Fiber Optic Sensors and Fiber Lasers

The optical fiber industry is emerging from the market for selling simple accessories using optical fiber to the new optical-IT convergence sensor market combined with high value-added smart industries such as the bio industry. Among them, fiber optic sensors and fiber lasers are growing faster and more accurately by utilizing fiber optics in various fields such as shipbuilding, construction, energy, military, railway, security, and medical.This Special Issue aims to present novel and innovative applications of sensors and devices based on fiber optic sensors and fiber lasers, and covers a wide range of applications of optical sensors. In this Special Issue, original research articles, as well as reviews, have been published

Grasp-sensitive surfaces

Grasping objects with our hands allows us to skillfully move and manipulate them. Hand-held tools further extend our capabilities by adapting precision, power, and shape of our hands to the task at hand. Some of these tools, such as mobile phones or computer mice, already incorporate information processing capabilities. Many other tools may be augmented with small, energy-efficient digital sensors and processors. This allows for graspable objects to learn about the user grasping them - and supporting the user's goals. For example, the way we grasp a mobile phone might indicate whether we want to take a photo or call a friend with it - and thus serve as a shortcut to that action. A power drill might sense whether the user is grasping it firmly enough and refuse to turn on if this is not the case. And a computer mouse could distinguish between intentional and unintentional movement and ignore the latter. This dissertation gives an overview of grasp sensing for human-computer interaction, focusing on technologies for building grasp-sensitive surfaces and challenges in designing grasp-sensitive user interfaces. It comprises three major contributions: a comprehensive review of existing research on human grasping and grasp sensing, a detailed description of three novel prototyping tools for grasp-sensitive surfaces, and a framework for analyzing and designing grasp interaction: For nearly a century, scientists have analyzed human grasping. My literature review gives an overview of definitions, classifications, and models of human grasping. A small number of studies have investigated grasping in everyday situations. They found a much greater diversity of grasps than described by existing taxonomies. This diversity makes it difficult to directly associate certain grasps with users' goals. In order to structure related work and own research, I formalize a generic workflow for grasp sensing. It comprises *capturing* of sensor values, *identifying* the associated grasp, and *interpreting* the meaning of the grasp. A comprehensive overview of related work shows that implementation of grasp-sensitive surfaces is still hard, researchers often are not aware of related work from other disciplines, and intuitive grasp interaction has not yet received much attention. In order to address the first issue, I developed three novel sensor technologies designed for grasp-sensitive surfaces. These mitigate one or more limitations of traditional sensing techniques: **HandSense** uses four strategically positioned capacitive sensors for detecting and classifying grasp patterns on mobile phones. The use of custom-built high-resolution sensors allows detecting proximity and avoids the need to cover the whole device surface with sensors. User tests showed a recognition rate of 81%, comparable to that of a system with 72 binary sensors. **FlyEye** uses optical fiber bundles connected to a camera for detecting touch and proximity on arbitrarily shaped surfaces. It allows rapid prototyping of touch- and grasp-sensitive objects and requires only very limited electronics knowledge. For FlyEye I developed a *relative calibration* algorithm that allows determining the locations of groups of sensors whose arrangement is not known. **TDRtouch** extends Time Domain Reflectometry (TDR), a technique traditionally used for inspecting cable faults, for touch and grasp sensing. TDRtouch is able to locate touches along a wire, allowing designers to rapidly prototype and implement modular, extremely thin, and flexible grasp-sensitive surfaces. I summarize how these technologies cater to different requirements and significantly expand the design space for grasp-sensitive objects. Furthermore, I discuss challenges for making sense of raw grasp information and categorize interactions. Traditional application scenarios for grasp sensing use only the grasp sensor's data, and only for mode-switching. I argue that data from grasp sensors is part of the general usage context and should be only used in combination with other context information. For analyzing and discussing the possible meanings of grasp types, I created the GRASP model. It describes five categories of influencing factors that determine how we grasp an object: *Goal* -- what we want to do with the object, *Relationship* -- what we know and feel about the object we want to grasp, *Anatomy* -- hand shape and learned movement patterns, *Setting* -- surrounding and environmental conditions, and *Properties* -- texture, shape, weight, and other intrinsics of the object I conclude the dissertation with a discussion of upcoming challenges in grasp sensing and grasp interaction, and provide suggestions for implementing robust and usable grasp interaction.Die Fähigkeit, Gegenstände mit unseren Händen zu greifen, erlaubt uns, diese vielfältig zu manipulieren. Werkzeuge erweitern unsere Fähigkeiten noch, indem sie Genauigkeit, Kraft und Form unserer Hände an die Aufgabe anpassen. Digitale Werkzeuge, beispielsweise Mobiltelefone oder Computermäuse, erlauben uns auch, die Fähigkeiten unseres Gehirns und unserer Sinnesorgane zu erweitern. Diese Geräte verfügen bereits über Sensoren und Recheneinheiten. Aber auch viele andere Werkzeuge und Objekte lassen sich mit winzigen, effizienten Sensoren und Recheneinheiten erweitern. Dies erlaubt greifbaren Objekten, mehr über den Benutzer zu erfahren, der sie greift - und ermöglicht es, ihn bei der Erreichung seines Ziels zu unterstützen. Zum Beispiel könnte die Art und Weise, in der wir ein Mobiltelefon halten, verraten, ob wir ein Foto aufnehmen oder einen Freund anrufen wollen - und damit als Shortcut für diese Aktionen dienen. Eine Bohrmaschine könnte erkennen, ob der Benutzer sie auch wirklich sicher hält und den Dienst verweigern, falls dem nicht so ist. Und eine Computermaus könnte zwischen absichtlichen und unabsichtlichen Mausbewegungen unterscheiden und letztere ignorieren. Diese Dissertation gibt einen Überblick über Grifferkennung (*grasp sensing*) für die Mensch-Maschine-Interaktion, mit einem Fokus auf Technologien zur Implementierung griffempfindlicher Oberflächen und auf Herausforderungen beim Design griffempfindlicher Benutzerschnittstellen. Sie umfasst drei primäre Beiträge zum wissenschaftlichen Forschungsstand: einen umfassenden Überblick über die bisherige Forschung zu menschlichem Greifen und Grifferkennung, eine detaillierte Beschreibung dreier neuer Prototyping-Werkzeuge für griffempfindliche Oberflächen und ein Framework für Analyse und Design von griff-basierter Interaktion (*grasp interaction*). Seit nahezu einem Jahrhundert erforschen Wissenschaftler menschliches Greifen. Mein Überblick über den Forschungsstand beschreibt Definitionen, Klassifikationen und Modelle menschlichen Greifens. In einigen wenigen Studien wurde bisher Greifen in alltäglichen Situationen untersucht. Diese fanden eine deutlich größere Diversität in den Griffmuster als in existierenden Taxonomien beschreibbar. Diese Diversität erschwert es, bestimmten Griffmustern eine Absicht des Benutzers zuzuordnen. Um verwandte Arbeiten und eigene Forschungsergebnisse zu strukturieren, formalisiere ich einen allgemeinen Ablauf der Grifferkennung. Dieser besteht aus dem *Erfassen* von Sensorwerten, der *Identifizierung* der damit verknüpften Griffe und der *Interpretation* der Bedeutung des Griffes. In einem umfassenden Überblick über verwandte Arbeiten zeige ich, dass die Implementierung von griffempfindlichen Oberflächen immer noch ein herausforderndes Problem ist, dass Forscher regelmäßig keine Ahnung von verwandten Arbeiten in benachbarten Forschungsfeldern haben, und dass intuitive Griffinteraktion bislang wenig Aufmerksamkeit erhalten hat. Um das erstgenannte Problem zu lösen, habe ich drei neuartige Sensortechniken für griffempfindliche Oberflächen entwickelt. Diese mindern jeweils eine oder mehrere Schwächen traditioneller Sensortechniken: **HandSense** verwendet vier strategisch positionierte kapazitive Sensoren um Griffmuster zu erkennen. Durch die Verwendung von selbst entwickelten, hochauflösenden Sensoren ist es möglich, schon die Annäherung an das Objekt zu erkennen. Außerdem muss nicht die komplette Oberfläche des Objekts mit Sensoren bedeckt werden. Benutzertests ergaben eine Erkennungsrate, die vergleichbar mit einem System mit 72 binären Sensoren ist. **FlyEye** verwendet Lichtwellenleiterbündel, die an eine Kamera angeschlossen werden, um Annäherung und Berührung auf beliebig geformten Oberflächen zu erkennen. Es ermöglicht auch Designern mit begrenzter Elektronikerfahrung das Rapid Prototyping von berührungs- und griffempfindlichen Objekten. Für FlyEye entwickelte ich einen *relative-calibration*-Algorithmus, der verwendet werden kann um Gruppen von Sensoren, deren Anordnung unbekannt ist, semi-automatisch anzuordnen. **TDRtouch** erweitert Time Domain Reflectometry (TDR), eine Technik die üblicherweise zur Analyse von Kabelbeschädigungen eingesetzt wird. TDRtouch erlaubt es, Berührungen entlang eines Drahtes zu lokalisieren. Dies ermöglicht es, schnell modulare, extrem dünne und flexible griffempfindliche Oberflächen zu entwickeln. Ich beschreibe, wie diese Techniken verschiedene Anforderungen erfüllen und den *design space* für griffempfindliche Objekte deutlich erweitern. Desweiteren bespreche ich die Herausforderungen beim Verstehen von Griffinformationen und stelle eine Einteilung von Interaktionsmöglichkeiten vor. Bisherige Anwendungsbeispiele für die Grifferkennung nutzen nur Daten der Griffsensoren und beschränken sich auf Moduswechsel. Ich argumentiere, dass diese Sensordaten Teil des allgemeinen Benutzungskontexts sind und nur in Kombination mit anderer Kontextinformation verwendet werden sollten. Um die möglichen Bedeutungen von Griffarten analysieren und diskutieren zu können, entwickelte ich das GRASP-Modell. Dieses beschreibt fünf Kategorien von Einflussfaktoren, die bestimmen wie wir ein Objekt greifen: *Goal* -- das Ziel, das wir mit dem Griff erreichen wollen, *Relationship* -- das Verhältnis zum Objekt, *Anatomy* -- Handform und Bewegungsmuster, *Setting* -- Umgebungsfaktoren und *Properties* -- Eigenschaften des Objekts, wie Oberflächenbeschaffenheit, Form oder Gewicht. Ich schließe mit einer Besprechung neuer Herausforderungen bei der Grifferkennung und Griffinteraktion und mache Vorschläge zur Entwicklung von zuverlässiger und benutzbarer Griffinteraktion

Development of a distributed optical fiber sensor for geological applications

The purpose of the study was to develop a distributed optical fiber acoustic sensor for monitoring ground subsidence before collapse sinkholes form causing costly damage on infrastructure. Costs in excess of R1.3 billion have been incurred while dealing with sinkhole related measures in South Africa. Monitoring sinkholes and the presence of an early warning alert system can drastically reduce the impact, risk and cost caused by sudden ground collapse. A related goal was to construct a reliable collapse alert early warning system to facilitate disaster preparedness and avoid further damage from accidents. This was achieved by developing a spectroscopic shift monitoring algorithm which analysed changes in the subsurface vibration modes using ambient noise signals. For the first time to our knowledge, an optic fiber sensor with an early warning alarm, using ambient noise vibrations to detect and monitor sinkholes was developed at NMU. A polarisation-based, interferometric optical fiber seismic sensor was developed and compared to a commercial geophone. The fiber sensor exhibited superior performance in sensitivity, bandwidth, signal response and recovery times. The sensitivity of the optical fiber sensor was 0.47 rad/Pa surpassing the geophone sensitivity by 9.32%, and the bandwidth of 3.349kHz was 20 times greater for the optical fiber sensor. The fiber sensor was used to measure millisecond events as the impact duration of a bouncing ball was successfully obtained. It was used to detect sinkhole formation in the simulator model, designed. Ground collapse precursors were identified, and early warning alert was achieved using the spectral analysis algorithm, developed. The collapse precursor condition was identified as a functional combination of variations in the peak frequency, bandwidth and peak intensity. A distributed acoustic sensor was built to detect ambient noise induced subsurface signals. Vibrations were located along the 28km length of optical fiber with a relative error of 9.6%. The sensor demonstrated a frequency response range of 212.25Hz, an event distance precision of 224m with time resolution of 1.12µs, and a spatial resolution of 1km. The position of disturbance was measured within 300m of its actual point of 3.21km along the optical fiber. The results showed that distributed optical fiber sensing allows real-time monitoring of the subsurface over extended distances, using ambient noise signals.Thesis (PhD) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 202

Development of a distributed optical fiber sensor for geological applications

The purpose of the study was to develop a distributed optical fiber acoustic sensor for monitoring ground subsidence before collapse sinkholes form causing costly damage on infrastructure. Costs in excess of R1.3 billion have been incurred while dealing with sinkhole related measures in South Africa. Monitoring sinkholes and the presence of an early warning alert system can drastically reduce the impact, risk and cost caused by sudden ground collapse. A related goal was to construct a reliable collapse alert early warning system to facilitate disaster preparedness and avoid further damage from accidents. This was achieved by developing a spectroscopic shift monitoring algorithm which analysed changes in the subsurface vibration modes using ambient noise signals. For the first time to our knowledge, an optic fiber sensor with an early warning alarm, using ambient noise vibrations to detect and monitor sinkholes was developed at NMU. A polarisation-based, interferometric optical fiber seismic sensor was developed and compared to a commercial geophone. The fiber sensor exhibited superior performance in sensitivity, bandwidth, signal response and recovery times. The sensitivity of the optical fiber sensor was 0.47 rad/Pa surpassing the geophone sensitivity by 9.32%, and the bandwidth of 3.349kHz was 20 times greater for the optical fiber sensor. The fiber sensor was used to measure millisecond events as the impact duration of a bouncing ball was successfully obtained. It was used to detect sinkhole formation in the simulator model, designed. Ground collapse precursors were identified, and early warning alert was achieved using the spectral analysis algorithm, developed. The collapse precursor condition was identified as a functional combination of variations in the peak frequency, bandwidth and peak intensity. A distributed acoustic sensor was built to detect ambient noise induced subsurface signals. Vibrations were located along the 28km length of optical fiber with a relative error of 9.6%. The sensor demonstrated a frequency response range of 212.25Hz, an event distance precision of 224m with time resolution of 1.12µs, and a spatial resolution of 1km. The position of disturbance was measured within 300m of its actual point of 3.21km along the optical fiber. The results showed that distributed optical fiber sensing allows real-time monitoring of the subsurface over extended distances, using ambient noise signals.Thesis (PhD) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 202