Training Image Selection using Recurrent Neural Networks: An Application in Hydrogeology

TL;DR

This paper introduces a Recurrent Neural Network-based method for selecting training images in geostatistics, demonstrating high accuracy in hydrogeological applications with various model configurations.

Contribution

It proposes a novel RNN-based approach for training image selection in geostatistics, specifically applied to hydrogeology, with extensive testing of different architectures and scenarios.

Findings

GRU architecture achieves 97.63% accuracy in TI selection

RNN-based method outperforms traditional selection techniques

Model performance varies with observation noise and dataset size

Abstract

Multiple-point geostatistics plays an important role in characterizing complex subsurface aquifer systems such as channelized structures. However, only a few studies have paid attention to how to choose an applicable training image. In this paper, a TI selection method based on Recurrent Neural Networks is proposed. A synthetic case is tested using two channelized training images given the hydraulic head time series. Three different RNNs architectures are tested for the selection performance. Various scenarios of the model input are also tested including the number of observation wells, the observation time steps, the influence of observation noise, and the training dataset size. In this TI selection task, the GRU has the best performance among all three architectures and can reach to a 97.63% accuracy.