Geotechnical reports are crucial for assessing the stability of rock formations and ensuring safety in modern engineering. Traditionally, these reports are prepared manually based on field observations using compasses, magnifying glasses, and notebooks. This method is slow, prone to errors, and subjective in its interpretations. To overcome these limitations, the use of artificial intelligence techniques is proposed for the automatic generation of reports through the processing of images and field data. The methodology was based on the collection of photographs of rock outcrops and manual samples with their respective descriptions, as well as on the reports prepared during the Geotechnical Studies course. These resources were used to define the report outline, prompt engineering, and validate the responses of a multimodal large language model (MLLM). The iterative refinement of prompts until structured and specific instructions were obtained for each section of the report proved to be an effective alternative to the costly process of fine-tuning the MLLM. The system evaluation establishes values of 0.455 and 0.653 for the BLEU and ROUGE-L metrics, respectively, suggesting that automatic descriptions are comparable to those made by experts. This tool, accessible via the web, with an intuitive interface and the ability to export to standardized formats, represents an innovation and an important contribution for professionals and students of field geology.

翻译：地质报告对于评估岩体稳定性及保障现代工程安全至关重要。传统上，这些报告基于野外使用罗盘、放大镜和记录本进行的观测，通过人工方式编制。该方法效率低下，易产生误差，且解释具有主观性。为克服这些局限，本研究提出采用人工智能技术，通过处理图像与野外数据实现报告的自动生成。该方法基于岩石露头照片和人工样本及其对应描述的收集，并结合地质工程研究课程中编制的报告。这些资源被用于定义报告大纲、提示工程设计，并验证多模态大语言模型（MLLM）的响应。通过迭代优化提示，直至获得针对报告各章节的结构化、具体指令，这被证明是替代昂贵MLLM微调过程的有效方案。系统评估显示BLEU和ROUGE-L指标值分别为0.455和0.653，表明自动生成的描述与专家撰写的报告具有可比性。该工具可通过网络访问，具备直观界面并支持导出为标准格式，为野外地质学从业者与学生提供了创新性且重要的技术支持。