Machine learning-based mapping of micro-topographic earthquake-induced paleo-Pulju moraines and liquefaction spreads from a digital elevation model acquired through laser scanning

The advent of public open source airborne laser scanning-produced digital elevationmodels (ALS DEM) has provided new perspectives on glacial geomorphology in the Nordic countries. Seismically-induced microtopographic paleo-landforms can now be identified and mapped throughout the former Fennoscandian Ice Sheet, allowing spatial safety assessment for nuclear waste disposal. Automated machine learning techniques enable recognition of these fine-scale geomorphological features efficiently and in a consistent way nationwide. The current study focuses on automated recognition of paleo-liquefaction spreads and Pulju moraines in northern Finland. Geomorphometric variables in different cell sizes were first derived from the 2 m ALS DEM by Gabor and principal curvature filtering to emphasize the elevational multi-scale texture of these paleo-seismic landforms. The Gabor textural variables were considered as a baseline method and the principal curvature features, including maximum and minimum curvature, were used because they have previously been proven critical in recognition of concave and convex elongated features. Both sets of raster variables were then turned into histogram-based features and input into a non-linear supervised multilayer perceptron early-stop committee which is a neural network classifier. The leave-one-out cross-validation performance results indicated principal curvature features to be highly successful with 94% accuracy. Principal curvatures provided a clear improvement to Gabor based features which provided significantly lower accuracies between 83 and 85%. The study demonstrates the high success of supervised neural network-based classification of ALS DEM data and derived textural features capturing the multi-scale nature of the micro-topographic liquefaction spreads and Pulju moraines. The approach could be utilized for time-efficient mapping of these paleo-seismic geomorphologies to complete paleo-seismic databases in formerly glaciated regions. (C) 2020 Elsevier B.V. All rights reserved.