Data driven deep learning method for quantifying groundwater flux in deep fractured aquifers with the fractured rock passive flux meter

Qasim Khan, Mohamed M. Mohamed, Harald Klammler, Beth L. Parker, Kirk Hatfield,
Data driven deep learning method for quantifying groundwater flux in deep fractured aquifers with the fractured rock passive flux meter,
Advances in Water Resources,
Volume 204,
2025,
105058,
ISSN 0309-1708,
https://doi.org/10.1016/j.advwatres.2025.105058.
(https://www.sciencedirect.com/science/article/pii/S0309170825001721)
Abstract: Movement of groundwater in fractured aquifers is highly variable and depends on many factors besides fracture apertures. Hence, downhole techniques that directly map fracture locations, orientations, apertures, and measure groundwater fluxes are valuable tools. Here, we explored the possibility of using the Fractured Rock Passive Flux Meter (FRPFM) with visible dye component to measure groundwater fluxes and identify geometric fracture parameters through laboratory experiments. For this purpose, we used the deep learning model YOLOv8 to accurately identify the dye marks and to measure their areas Adye and widths Δzdye from images of the dyed fabric. Results showed that groundwater fluxes were measured with relative errors of ±23 % and ±16 % based on Δzdye and Adye, respectively, with an overall relative error of ±20 %. The YOLOv8 model showed very good accuracy by achieving high precision P = 0.99 and recall R = 0.75 for both object detection and mask predictions. The P-R-curve showed that accuracy can be improved by using more images to train the model.
Keywords: Groundwater flux; Fractured rock passive flux meter; G360 MultiPort System; Deep learning; YOLOv8; Instance segmentation