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Durham e-Theses
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Unsupervised Automatic Detection Of Transient Phenomena In InSAR Time-Series using Machine Learning

SHAKEEL, ANZA (2022) Unsupervised Automatic Detection Of Transient Phenomena In InSAR Time-Series using Machine Learning. Doctoral thesis, Durham University.

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The detection and measurement of transient episodes of crustal deformation from global InSAR datasets are crucial for a wide range of solid earth and natural hazard applications. But the large volumes of unlabelled data captured by satellites preclude manual systematic analysis, and the small signal-to-noise ratio makes the task difficult. In this thesis, I present a state-of-the-art, unsupervised and event-agnostic deep-learning based approach for the automatic identification of transient deformation events in noisy time-series of unwrapped InSAR images. I adopt an anomaly detection framework that learns the ‘normal’ spatio-temporal pattern of noise in the data, and which therefore identifies any transient deformation phenomena that deviate from this pattern as ‘anomalies’. The deep-learning model is built around a bespoke autoencoder that includes convolutional and LSTM layers, as well as a neural network which acts as a bridge between the encoder and decoder. I train our model on real InSAR data from northern Turkey and find it has an overall accuracy and true positive rate of around 85% when trying to detect synthetic deformation signals of length-scale > 350 m and magnitude > 4 cm. Furthermore, I also show the method can detect (1) a real Mw 5.7 earthquake in InSAR data from an entirely different region- SW Turkey, (2) a volcanic deformation in Domuyo, Argentina, (3) a synthetic slow-slip event and (4) an interseismic deformation around NAF in a descending frame in northern Turkey. Overall I show that my method is suitable for automated analysis of large, global InSAR datasets, and for robust detection and separation of deformation signals from nuisance signals in InSAR data.

Item Type:Thesis (Doctoral)
Award:Doctor of Philosophy
Keywords:Deep Learning, Transient Analysis, Anomaly Detection, InSAR, Tectonics, Earthquakes, Volcano
Faculty and Department:Faculty of Science > Earth Sciences, Department of
Thesis Date:2022
Copyright:Copyright of this thesis is held by the author
Deposited On:08 Dec 2022 10:48

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