Methodological Evaluation of Multi-Resolution Hybrid Kolmogorov-Arnold Networks (KAN) - LCS systems - Deep Learning Ensembles for Geophysical Pattern Analysis

Abstract

This paper investigates the computational capabilities of a non-conventional neural architecture designed to analyze highly non-linear, high-energy geophysical patterns within the Japanese region. To bypass the severe infrastructure costs associated with high-performance computing (HPC) clusters and promote decentralized edge-level execution, we introduce a framework integrating Kolmogorov-Arnold Networks (KAN) and optimized Deep Neural Network (DeepNet) topologies. We also added LCS systems a chance to proof their effectivness.In the first verison we used a multi-resolution ensemble gradient (30-day, 7-day, and 3-day windows), the architecture demonstrates the capacity to isolate mathematical convergence peaks within narrow temporal constraints. Numerical tests executed on a simulated benchmark horizon (August 2026) show superior parameter optimization and high algorithmic expressivity compared to traditional un-hybridized multilayer networks.Using this system it was possible estimate with a 3 days time resolution seismic risk:In the second version we had remastered the pipeline from scratch and added also LCS systems with a native time resolution of 7 days.The location is still north of Japan but in week of 24-31 August 2026