提出新型海洋锋面检测追踪框架，解决传统方法过检、不连续等痛点。
An ocean front detection and tracking algorithm
- 基于贝叶斯机制融合梯度先验与场算子，自动决策避免人工阈值依赖。
- 通过形态学优化合并碎片化锋面，删除虚假环状结构，实现像素级精度。
- 构建时序度量空间，支持锋面演化系统分析，适合气候与海洋研究者使用。
现有海洋锋面检测方法（如直方图方差分析、李雅普诺夫指数、梯度阈值法和机器学习）存在输出不连续、过检测、依赖单阈值决策及缺乏开源实现等关键缺陷。为此，本文提出贝叶斯锋面检测与追踪框架结合度量空间分析（BFDT-MSA）。该框架引入三项创新：(1) 贝叶斯决策机制，融合梯度先验与场算子，消除对人工阈值的依赖；(2) 形态学精修算法，用于合并碎片锋面、删除虚假环状结构、薄化锋区至像素级精度；(3) 新型度量空间定义，支持锋面时序追踪，实现演化过程系统分析。在2022–2024年全球海表温度数据上验证，相较直方图方法，过检测率降低73%，同时达到0.16℃/km的更高强度、更好连续性与时空一致性。开源发布填补了可复现海洋学研究的关键空白。
原文摘要 · Abstract (English)
Existing ocean front detection methods--including histogram-based variance analysis, Lyapunov exponent, gradient thresholding, and machine learning--suffer from critical limitations: discontinuous outputs, over-detection, reliance on single-threshold decisions, and lack of open-source implementations. To address these challenges, this paper proposes the Bayesian Front Detection and Tracking framework with Metric Space Analysis (BFDT-MSA). The framework introduces three innovations: (1) a Bayesian decision mechanism that integrates gradient priors and field operators to eliminate manual threshold sensitivity; (2) morphological refinement algorithms for merging fragmented fronts, deleting spurious rings, and thinning frontal zones to pixel-level accuracy; and (3) a novel metric space definition for temporal front tracking, enabling systematic analysis of front evolution. Validated on global SST data (2022--2024), BFDT-MSA reduces over-detection by $73\%$ compared to histogram-based methods while achieving superior intensity ($0.16^\circ$C/km), continuity, and spatiotemporal coherence. The open-source release bridges a critical gap in reproducible oceanographic research.
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