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.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)