Stress–strain analysis by genetic algorithm-based integration of long-term subsidence time series from different synthetic aperture radar platforms in Darab, Iran

Abstract

The potential of synthetic aperture radar (SAR) interferometry was shown to study the compaction of the aquifer system in Darab plain, Iran. In so doing, two different datasets, including Envisat advanced SAR (ASAR) spanning 2010 and Sentinel-1A spanning 2016 to 2017, were applied in small baseline subset time series analysis. To estimate the subsidence in the time period for which there is no SAR data available, i.e., 2010 to 2016, the time series analysis results separately obtained from the two datasets were to be integrated using an appropriate model, which should have been fitted to both sets of results. However, as both deformation time series results were calculated taking into account a distinct temporal reference, fitting the model was not a straightforward task. Accordingly, the main attempt was to find the subsidence value corresponding to the temporal reference of Sentinel-1A time series with respect to that of Envisat ASAR. This shift value was optimally determined using a genetic algorithm so as to minimize the misfit between the model and the deformation time series corresponding to the entire period. The average value of the root mean square error estimated as the misfit between the model and the calculated time series at all pixels is 0.011 m, which is an indication of the high performance of the proposed method for modeling the deformation time series. The integration results were further used to derive the stress–strain relationships to study the storage properties of the aquifer system. The fact that the strain linearly increases along with the decrease in water level in most piezometric wells indicates that the subsidence is highly correlated with groundwater exploitation.