Fluid antenna systems (FAS) have recently emerged as an effective solution for enhancing spatial diversity within compact and flexible wireless architectures. In practical communication systems, however, channel estimation errors are inevitable, which necessitates performance analysis under imperfect channel state information (CSI). This letter investigates the performance of an N-port FAS over Nakagami-m fading channels under imperfect CSI. Closed-form analytical expressions are derived for the cumulative distribution function, probability density function, outage probability, and ergodic capacity, along with high-SNR asymptotic approximations. The average symbol error rate of hexagonal QAM (HQAM) and rectangular QAM (RQAM) is also analyzed under the considered conditions. An asymptotic diversity study reveals that the achievable diversity order is jointly determined by the effective spatial rank of the FAS and the Nakagami fading parameter, while imperfect CSI degrades performance without affecting diversity scaling. Simulation results validate the analysis and demonstrate that HQAM outperforms conventional QAM, achieving approximately 2 dB and 0.8 dB SNR gains over 128-RQAM and 256-SQAM, respectively.
Artificial intelligence (AI) increasingly supports subsurface characterization by integrating heterogeneous geophysical, geochemical, and spatial data. However, existing studies remain fragmented across disciplines and often leave unresolved questions about cross-modal physical coupling, fusion design, uncertainty propagation, validation integrity, and decision relevance. This review synthesizes AI-based multimodal fusion methods for mineral exploration, geothermal systems, geological CO₂ storage, and radioactive-waste disposal. It organizes the field along orthogonal dimensions: the representation stage of fusion; the mechanism and strength of physical coupling; the training signal and model objective; the integration of prior knowledge; the output type; and the intended scope of generalization. The review critically examines scale and support mismatch, spatial autocorrelation, modality dominance, uncertain rock-physics and geochemical relationships, topological and conservation constraints in generative models, and validation leakage. It also distinguishes aleatoric, epistemic, methodological, and ontological uncertainty and considers how these uncertainties propagate through multimodal workflows. Emerging priorities include cross-modally coupled physics-informed learning, constrained generative simulation, foundation-model representations, and risk-aware monitoring. By clarifying conceptual distinctions and evidence limitations, the review provides a rigorous roadmap for developing interpretable, uncertainty-aware, and decision-relevant multimodal subsurface AI.
H. Namazi, M. F. Sulaima, Ondrej Krejcar et al.· Environmental Earth Sciences· 0 citations
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