Inversion-Free Prediction of Contaminant Plume Fronts in Fractured Media from Hydraulic Tests: A Georesponse-Driven, Dual-Head Multitask Mixture Density Network

Accurate prediction of contaminant transport in fractured geological systems remains a formidable challenge due to the complex spatial distribution and connectivity of fracture networks, which often induce abrupt plume front shifts and preferential pathways. Conventional predictive workflows typically rely on a two-step inversion-simulation paradigm. However, these approaches often face persistent challenges in fractured media, including computational intensity, structural underrepresentation, and inherent non-uniqueness where disparate geological configurations yield similar hydraulic responses (Ringel et al., 2024).

In this study, we propose an inversion-free georesponse mapping framework that bypasses explicit structural reconstruction by learning a direct statistical mapping from hydraulic test (HT) fingerprints to transport outcomes (Hermans et al., 2016). The framework is implemented via a dual-head multitask mixture density network (MDN). This architecture jointly predicts the contaminant plume front, represented by a signed distance field (SDF), and the latent structural features of the fracture network, encoded by a convolutional variational autoencoder (CVAE). By integrating these tasks, the shared encoder is forced to extract a geologically consistent representation of the subsurface from sparse pumping test data.

We evaluated the framework’s performance using two stochastic fracture networks. Results demonstrate that the proposed multitask MDN yields statistically reliable probabilistic forecasts and successfully identifies secondary plume branches controlled by individual fractures compared to hydraulic tomography inversion (Figure 1). This study highlights the potential of georesponse-driven deep learning as a robust and computationally efficient alternative for risk assessment and remediation management in highly heterogeneous fractured aquifers.

Figure 1. Performance of the multitask MDN for contaminant plume front prediction in two test cases. (A) Reference discrete fracture network (DFN) geometries for Test 1 and Test 2. (B) Ensembles of predicted contaminant plume fronts. The blue lines represent the prior training ensemble (5000 realizations), while the red lines represent the posterior prediction ensemble (200 realizations) generated by the dual-head multitask MDN. Yellow dots indicate the true plume front for each test case. Green markers represent the plume fronts obtained via forward solute transport simulation using the hydraulic conductivity fields inverted through hydraulic tomography. (C) Contaminant spatial arrival probability maps derived from the MDN posterior distribution.

 

References:

Hermans, T., Oware, E., & Caers, J. (2016). Direct prediction of spatially and temporally varying physical properties from time-lapse electrical resistance data. Water Resources Research, 52, 7262-7283. https://doi.org/10.1002/2016WR019126
Ringel, L. M., Illman, W. A., & Bayer, P. (2024). Recent developments, challenges, and future research directions in tomographic characterization of fractured aquifers. Journal of Hydrology, 631, 130709. https://doi.org/10.1016/j.jhydrol.2024.130709