Generative AI in Geoscientific Document Processing

This paper is a research article that demonstrates the application of generative artificial intelligence (AI) in enhancing geoscientific document processing. It outlines how generative AI improves text analysis, table extraction, and figure classification, addressing challenges faced by traditional workflows, such as domain-specific terminology and low-quality inputs. The study employs domain fine-tuned bidirectional encoder representations from transformers (BERT) models and multimodal large language models for precise data handling. It introduces a domain-optimized retrieval system, GeoRAG, which enhances information retrieval accuracy. The paper also discusses the limitations of traditional machine learning methods in processing geoscientific documents and emphasizes the transformative potential of generative AI in streamlining workflows and improving decision-making processes. The research highlights the need for sophisticated solutions to overcome challenges like data quality and model reliability, ultimately showcasing the scalability and flexibility that generative AI brings to geoscience applications.