Small-Sample InSAR Time-Series Data Prediction Method Based on Generative Models

Abstract

In surface deformation monitoring for mining areas, interferometric synthetic aperture radar (InSAR) technology has become a popular research topic due to its efficiency and high accuracy. However, transforming temporal monitoring data into surface deformation predictions remains challenging. In practical applications, InSAR data often face limitations like low acquisition frequency and insufficient data volume, leading to prediction models being prone to overfitting and having poor accuracy. Therefore, this paper proposes an improved temporal convolutional network (TCN) time-series generative adversarial network (GAN) with an attention mechanism, called the Attention–TCN–TimeGAN, to enhance InSAR surface deformation data for better prediction results. By combining the embedding, recovery, generator, and discriminator networks, we used the TCN to expand the receptive field and capture long-term temporal features. Additionally, we integrated the self-attention mechanism into the generator and discriminator to adapt to random vectors, achieving better data generation results. The loss function uses the Wasserstein distance to measure the original data distribution and adds a gradient penalty term with adaptive weights to achieve effective feature extraction from time-series data. Experimental results show that the data generated by our model more comprehensively cover the original data distribution. The prediction results at four test points showed the lowest mean absolute error and mean-squared error and the highest coefficient of determination (R²). These results demonstrate the effectiveness of our generative model in predicting small-sample InSAR time-series data, providing a new method for surface deformation monitoring.