Multi-task neural network in hydrological tomography to map the transmissivity and storativity simultaneously: HT-XNET

Abstract

• Mapping simultaneously transmissivity and storativity from temporal hydraulic head. • Directly approximate the inversion function using a convolutional neural network. • Adopt a multi-task architecture to enhance the prediction accuracy. • Provide an end-to-end operator which interprets inference instantly. • Effectiveness relies on data coverage but being resistant to data noise. In this paper, we present a novel method to simultaneously map transmissivity and storativity in a heterogenous aquifer using a multitask convolutional neural network. This coupled inversion algorithm translates transient hydraulic head data from pumping tests into two independent tomography of transmissivity and storativity in a two-dimensional problem. Based on the SegNet architecture, the multitask neural network provides an effective solution with minimal weights and biases, works as an end-to-end operator that directly approximates the inverse function. Multiple sharing mechanisms enable the multitasking approach to outperform single-task models by 10% accuracy. Application to synthetic experiments shows that the quality of inverted maps relies on the complexity level of heterogeneity in the hydraulic fields. Mapping accuracy also depends on the data coverage, but being resistant to data noise due to the featuring mechanism in the convolutional network adopted. Comparison with conventional inversion method reveals that the deep learning approach provides better generalization in the inversion while performing each inference on the order of milliseconds without further calibration from users.