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

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

ALADDIn: Autoencoder-LSTM based Anomaly Detector of Deformation in InSAR

In this study, we address the challenging problem of automatic detection of transient deformation of the Earth’s crust in time series of differential satellite radar [interferometric synthetic aperture radar (InSAR)] images. The detection of these events is important for a wide range of natural hazard and solid earth applications, and InSAR is an ideal data source for this purpose due to its frequent and global observational coverage. However, the size of this dataset precludes a systematic manual analysis, and a low signal-to-noise ratio makes this task difficult. We present a novel method to address this problem. This approach requires the development of a novel network architecture to take advantage of the unique structure of the InSAR dataset. Our unsupervised deep learning model learns the “normal” unlabeled spatiotemporal patterns of background noise signals in 3-D InSAR datasets and learns the relationship between the input difference images and the underlying unknown set of individual 2-D fields of noise from which the InSAR images are constructed. The detection head of our pipeline consists of two complementary methods, semivariogram analysis and density-based clustering. To evaluate, we test and compare three increasingly complex network architectures: compact, deep, and bi-deep. The analysis demonstrates that the bi-deep architecture is the most accurate, and so it is used in the final detection pipeline [autoencoder long short-term memory-based anomaly detector of deformation in InSAR (ALADDIn)]. The analysis of experimental results is based on the detection of a synthetic deformation test case, achieving a 91.25% overall performance accuracy. Furthermore, we show that the ALADDIn can detect a real earthquake of magnitude 5.7 that occurred in 2019 in southwest Turkey