多模态无人机影像的碎屑岩露头岩性智能识别

Intelligent Identification of Clastic Rock Outcrops from Multimodal UAV Images

受自然条件影响,野外露头表面存在植被覆盖、风化严重等问题,传统的岩性图像识别方法较难实施,随着地质大数据的兴起和智能地质的发展需求,利用人工智能进行地质领域岩石影像岩性识别成为必然趋势。提出基于注意力机制的多模态碎屑岩露头影像岩性智能识别方法(SE-DeepLabv3+),通过与传统分类方法和语义分割方法的对比,以人工标注结果为参考,SE-DeepLabv3+的岩性识别精度达90%以上,高于其他方法。利用SE-DeepLabv3+对新疆准噶尔盆地南缘清水河-喀拉扎组部分露头剖面进行岩性识别,得到较好的识别结果。利用无人机三维影像数据,结合人工智能技术实现碎屑岩露头的岩性识别,可以大幅提高岩性识别的工作效率,转变传统作业方式,推动地质研究向定量化、智能化发展。

Field outcrops are affected by natural conditions,and the outcrop surfaces are covered with vegetation and severely weathering,which makes the traditional lithology image recognition methods more challenging to implement.Combining artificial intelligence for rock image recognition lithology in the geological field has become an unavoidable trend with the advent of geological big data and the rising demand for intelligent geology.In this study,we propose SEDeepLabv3+,an intelligent lithology recognition approach for multimodal clastic rock outcrop images based on an attention mechanism.The SE-DeepLabv3+achieves more than 90% accuracy in lithology recognition when compared to classical classification methods and semantic segmentation methods,with hand annotation results as a reference,which is greater than other methods.For lithology identification,the SE-DeepLabv3+was used on certain outcrop sections of the Qingshuihe-Karaza Formation along the southern boundary of the Junggar Basin in Xinjiang,and better identification results were obtained.The study employs UAV 3D image data,combined with artificial intelligence technology to identify the lithology of clastic outcrops,which can significantly enhance the efficiency of lithology identification,transform the conventional operation mode,and advance geological research toward quantification and intelligence.