Sinkhole Scanner: A New Method to Detect Sinkhole-Related Spatio-Temporal Patterns in InSAR Deformation Time Series

Abstract

Sinkholes are sudden disasters that are usually small in size and occur at unexpected locations. They may cause serious damage to life and property. Sinkhole-prone areas can be monitored using Interferometric Synthetic Aperture Radar (InSAR) time series. Defining a pattern using InSAR-derived spatio-temporal deformations, this study presents a sinkhole pattern detector, called the Sinkhole Scanner. The Sinkhole Scanner includes a spatio-temporal mathematical model such as a 2-dimensional time evolving Gaussian function as a kernel, which moves over the study area using a sliding window approach. The scanner attempts to fit the model over deformation time series of Constantly Coherent Scatterers (CCS) intersected by the window and returns the posterior variance as a measure of goodness of fit. In this way, the scanner searches for subsiding regions resembling sinkhole shapes over a sinkhole prone area. It is designed to detect large sinkholes with a high efficiency, and small sinkholes with a lower efficiency. It is tested at four different spatial scales, and on a simulated and real set of deformation data. Real data were obtained from Sentinel-1A SLC data in IW mode, over Ireland where a large sinkhole occurred on 24 September 2018. The Sinkhole Scanner was able to identify a pattern of low posterior variance zones consistent with the simulated set. In case of the real data, it is able to identify significantly low posterior variance zones near the sinkhole area with the lowest value being 51.1% of the maximum value. The results from Sinkhole Scanner over the real sinkhole site were compared with Multiple Hypothesis Testing (MHT), which identifies Breakpoint and Heaviside temporal anomalies in the deformation time series of CCS. MHT was able to identify high likelihood for Heaviside anomalies in deformation time series of CCS near the sinkhole site about 10 epochs before the sinkhole occurrence. We show that the Sinkhole Scanner is efficient in monitoring a large area and search for sinkholes and that MHT can be used successively to identify temporal anomalies in the vicinity of areas detected by the Sinkhole Scanner. Future research may address other Sinkhole shapes whereas the underlying stochastic model may be adjusted. We conclude that the Sinkhole Scanner is important to be applied at different levels of scale to converge on potential sinkhole centers.

Use of LSTM for Sinkhole-Related Anomaly Detection and Classification of InSAR Deformation Time Series

“…The estimated deformation velocity showed clusters of significantly high subsidence velocities, ∼ −20 to −25 mm•yr −1 over the mining area near the sinkhole site. This is also similar to what was observed in [18]. Over the study area, the CCS spatial density was found to be low because of nature of the study area which is predominantly rural.…”

“…Some of the applications of TSInSAR include monitoring of urban infrastructure [10]- [12], dam infrastructure [13], [14], geohazards such as volcanoes [15], [16] and sinkholes [17], [18]. TSInSAR is by now well established method to study spatio-temporal ground deformation patterns.…”

InSAR Time Series Analysis for Sinkhole Detection using Deep Learning

“…The estimated deformation velocity showed clusters of significantly high subsidence velocities, ∼ −20 to −25 mm•yr −1 over the mining area near the sinkhole site. This is also similar to what was observed in [78]. Over the study area, the CCS spatial density was found to be low because of nature of the study area which is predominantly rural.…”

“…Persistent Scatterers [15], or Distributed Scatterers [14,18]. Some of the applications of TSInSAR include monitoring of urban infrastructure [72,73,74], dam infrastructure [75,76], geohazards such as volcanoes [48,77] and sinkholes [7,78]. TSInSAR is by now well established method to study spatio-temporal ground deformation patterns.…”

“…These anomalies can be recognized in both space and time. Spatially, sinkhole-related deformations mostly follow a Gaussian-shaped pattern [78], while in time, the anomalies seldomly manifest as Heaviside or Breakpoint anomalies in the deformation time series [101]. These anomalies can be detected using deep learning networks (DNN) which are trained using both real SAR datasets and simulated samples.…”