This study presents a method to determine surface relaxivity in soft sediments by combining one-dimensional Nuclear Magnetic Resonance (NMR) imaging with particle size and shape estimates. In order to determine the surface relaxivity up to now often methods like Mercury Intrusion Porosimetry or Brunauer–Emmett–Teller (BET) are used which where drying steps are involved which can alter material properties during analysis, particularly in highly deformable materials, making these techniques unreliable for soft soils. By combining NMR relaxometry and estimates of particle sizes and shapes of a soft soil, this new approach provides accurate, non-invasive surface relaxivity measurements. This method is demonstrated on kaolinite, glass beads, and natural soils, showing that this method supports detailed assessment of pore size distributions in soft sediments, benefiting geotechnical and environmental research where soil stability is critical.

This paper proposes a new technique for the 3D identification of clay particle orientations using images obtained from FIB-SEM observations. The method is based on a three-dimensional reconstruction that combines the Focused Ion Beam abrasion technique and Scanning Electron Microscopy, applied to kaolinitic clay selected for this study. The clay was first subjected to one-dimensional compression up to a given stress level, after which microstructural observations were performed using a post-mortem approach. A novel methodology using appropriate image processing was established for this purpose, allowing for a precise treatment of the obtained FIB-SEM images. The proposed methodology first involved removing “curtain effects” and “charging artefact”, which are specific types of noise commonly associated with FIB-SEM images. Two methods were employed to address this issue and were compared to evaluate their effectiveness: the first method was based on Fourier Transformation and Total Variational Reconstruction, while the second used a stochastic approach formulated as a convex optimization problem. Subsequently, a machine learning technique was integrated to enhance the segmentation process of the images. The final stage of the methodology involved creating a 3D model by reconstructing the clay particles in their spatial configuration. This paper aims to demonstrate how the proposed 3D observation method enables the quantification of the structural organization of clay particles in space in relation to mechanical loading.