The traditional study of palaeoseismic trenches, involving logging, stratigraphic and structural interpretation, can be time consuming and affected by biases and inaccuracies. To overcome these limitations, a new workflow is presented that integrates infrared hyperspectral and photogrammetric data to support field‐based palaeoseismic observations. As a case study, this method is applied on two palaeoseismic trenches excavated across a post‐glacial fault scarp in northern Finnish Lapland. The hyperspectral imagery (HSI) is geometrically and radiometrically corrected, processed using established image processing algorithms and machine learning approaches, and co‐registered to a structure‐from‐motion point cloud. HSI‐enhanced virtual outcrop models are a useful complement to palaeoseismic field studies as they not only provide an intuitive visualisation of the outcrop and a versatile data archive, but also enable an unbiased assessment of the mineralogical composition of lithologic units and a semi‐automatic delineation of contacts and deformational structures in a 3D virtual environment.

中文翻译：

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用于古地震研究的高光谱露头模型

包括测井，地层和结构解释在内的古地震沟槽的传统研究可能很耗时，并且会受到偏差和误差的影响。为了克服这些限制，提出了一个新的工作流程，该工作流程集成了红外高光谱和摄影测量数据，以支持基于现场的古地震观测。作为案例研究，此方法应用于芬兰拉普兰北部冰川后断层陡坡上开挖的两条古地震沟槽。对高光谱图像（HSI）进行几何和放射线校正，使用已建立的图像处理算法和机器学习方法进行处理，并共同注册到从运动开始的结构点云中。