Advancements and Challenges in Deep Learning-Driven Marine Data Assimilation: A Comprehensive Review

Abstract

The acquisition and assimilation of high-quality data are fundamental for predictive model development across various domains. In the maritime realm, superior marine data fuels advancements in ship industry innovation, offshore clean energy initiatives, and marine engineering. Recent strides in employing deep learning methodologies have significantly improved data assimilation processes, raising the quality of derived datasets. This review meticulously examines deep learning-driven marine data assimilation, dissecting its challenges, identifying research gaps, and outlining future trajectories. This study employs Citespace's scientometric survey to comprehensively visualize and analyze the constituent elements within the literature, as well as to scrutinize the present state of research across pertinent fields, thereby providing an in-depth exploration and critical assessment of the scholarly landscape. Using bibliometric analysis, keyword exploration, and discipline classification, prevailing research patterns and emerging focal points are dissected. An insightful exploration into marine data nuances illuminates inherent challenges. Moreover, a comparative assessment of diverse algorithmic applications offers insights into their efficacy within this specialized domain. Culminating in a meticulous synthesis, this paper reveals pivotal developmental constraints in marine data assimilation, providing guidance for advancements across multifaceted dimensions in this field.