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Gaussian graphical models, estimated via precision matrices, are popular for uncovering underlying conditional dependencies among variables, yet the case of multimodal data with blockwise missing remains unaddressed. We propose a novel method, Direct Sparse Graphical Learning for Multimodal Data (DSGL), to handle this issue using only observed data. The DSGL consists of two stages: constructing a pilot estimator for the covariance matrix without imputation as an admissible input for GLASSO; and estimating the DSGL precision matrix via GLASSO with repeated cross-validation tuning. We further propose a thresholded variant to address false-positive edges, a common issue in high-dimensional data. We establish theoretical properties for DSGL with respect to element-wise deviation and its ability to recover the true graphical structure. In simulations and an Alzheimer's Disease Neuroimaging Initiative (ADNI) application, DSGL outperforms imputation-based and other competing approaches, yielding more accurate graph estimation and lower Frobenius-norm error. 

Hydrogen is increasingly recognized as a carbon-free energy carrier, motivating renewed interest in geological natural hydrogen as a subsurface energy resource. Quantitative assessment of hydrogen migration and retention requires robust constraints on gas transport properties and capillary sealing behavior. This study experimentally evaluates hydraulic permeability, gas permeability, snap-off–controlled breakthrough pressure, and wettability using depth-resolved rock core samples collected from Well #6903 in the Midcontinent Rift System (MRS), USA. Radial-flow laboratory measurements show strong lithology-dependent contrasts: sandstone exhibits high hydraulic and gas permeability, whereas shale and altered diabase display very low hydraulic transmissivity. In the low-permeability rocks, gas permeability exceeds hydraulic permeability by orders of magnitude, consistent with pronounced gas slippage effects. Log–log analysis indicates an inverse scaling between the Klinkenberg factor and intrinsic permeability (b ~ k0^-0.322), reflecting pore-network complexity. Snap-off breakthrough tests demonstrate that shale provides substantial resistance to gas invasion, while altered diabase shows no gas breakthrough at injection pressures up to 6.8 MPa. Incorporating measured contact angles into the Young–Laplace framework suggests that shale can sustain hydrogen columns of at least ~10–12 m under capillary-dominated conditions. Collectively, these results provide experimentally constrained pore-scale parameters that are essential for reservoir-scale evaluation of natural hydrogen accumulation and subsurface hydrogen storage potential.