Hyperspectral outcrop models for palaeoseismic studies

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Abstract

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.

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Kirsch, M., Lorenz, S., Zimmermann, R., Andreani, L., Tusa, L., Pospiech, S., … Ruskeeniemi, T. (2019). Hyperspectral outcrop models for palaeoseismic studies. Photogrammetric Record, 34(168), 385–407. https://doi.org/10.1111/phor.12300

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