  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.
