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DarSIA (Darcy Scale Image Analysis) is an open-source Python library designed for advanced image analysis in porous media research and beyond. Built by researchers for researchers, DarSIA bridges the gap between raw experimental imagery and actionable scientific data.

Key Features

• Specialized for Porous Media Research. Purpose-built tools for analyzing flow experiments, tracer transport, and multiphase systems at the Darcy scale.
• Advanced Color Analysis. Color path regression, spectrum analysis, and label-based color mapping for precise concentration measurements in pH-based flow experiments in heterogeneous media.
• Geometric Corrections. Comprehensive image correction tools including perspective transforms, drift correction, curvature compensation, and coordinate system alignment.
• Optimized Performance. Numba-accelerated computations and memory-efficient algorithms for processing high-resolution experimental data.
• Wasserstein Distance & Optimal Transport. State-of-the-art implementations for comparing distributions and tracking mass transport in images.

Highlight Use Cases and Research Projects

• : Official image analysis toolkit for the FluidFlower international benchmark study
• : Official analysis toolkit for data comparison of submissions using optimal transport
• PoroTwin: Real-time image analysis for a digital twin of a FluidFlower
• TIME4CO2: Calibrated detection and analysis of multiphase flow (CO2 storage in FluidFlower)
• : Parameter estimation through optimal transport metrics
• Education: Analysis tool for MSc and PhD thesis on experimental CCS (Reservoir Physics) and optimal transport research (PMG).

Documentation

• Code and installation instructtions can be found on .
• The basic principles behind DarSIA are described in the associated journal publication: 
  Nordbotten, J. M, Benali, B., Both, J. W., Brattekås, B., Storvik, E., & Fernø, M. A., DarSIA: An open-source Python toolbox for two-scale image processing of dynamics in porous media,