Maritime accident adjudication reports contain critical tribunal findings for root cause analysis (RCA), yet retrieving relevant precedents and drafting consistent reports from decades of records remains labor-intensive. This paper proposes a multi-field hybrid retrieval-augmented generation (RAG) framework for automated maritime RCA, utilizing a comprehensive dataset of 13,329 Korea Maritime Safety Tribunal (KMST) reports (1971-2025). We transform raw adjudications into a structured knowledge base of "incident cards", indexing three distinct fields-Summary, Causes, and Disposition-alongside a hierarchical L1/L2 cause taxonomy. Our retrieval strategy employs a field-aware hybrid approach, fusing sparse and dense rankings via Reciprocal Rank Fusion (RRF). Given the lack of large-scale expert relevance labels, we evaluate retrieval performance using ceiling-normalized recall and nDCG based on a metadata-derived proxy relevance score. Experimental results demonstrate that our proposed retrieval significantly outperforms baseline methods, improving NormRecall@100 from 0.18 to 0.55. Furthermore, grounding the generator on the retrieved precedents enhances RCA generation quality over an LLM-only baseline, increasing the LLM-as-a-judge score from 3.34 to 3.72. These findings suggest that field-aware RAG can substantially streamline maritime safety investigation workflows by enabling faster precedent search and more consistent, evidence-based RCA drafting.

翻译：海事事故裁决报告包含了根本原因分析（RCA）所需的关键法庭判定结论，然而从数十年积累的记录中检索相关先例并草拟一致性报告仍是一项劳动密集型工作。本文提出了一种面向自动海事RCA的多字段混合检索增强生成（RAG）框架，利用包含13,329份韩国海事安全法庭（KMST）报告（1971-2025年）的综合数据集。我们将原始裁决转化为结构化知识库中的“事故卡片”，索引三个不同字段——摘要、原因和处理结果——以及层次化的L1/L2原因分类体系。我们的检索策略采用字段感知混合方法，通过倒数排序融合（RRF）将稀疏排序与稠密排序相结合。针对缺乏大规模专家相关性标注的问题，我们基于元数据派生的代理相关性得分，使用天花板归一化召回率和nDCG来评估检索性能。实验结果表明，我们提出的检索方法显著优于基线方法，将NormRecall@100从0.18提升至0.55。此外，以检索到的先例为生成器的基础，相较于仅依赖大语言模型（LLM）的基线，RCA生成质量得到提升，LLM评判得分从3.34增至3.72。这些发现表明，字段感知型RAG能够通过实现更快速的先例搜索以及更一致、基于证据的RCA草拟，显著简化海事安全调查流程。