Abstract
Abstract. This paper presents a novel technique to improve geological understanding in regions of historic mining activity. This is achieved through inferring the orientations of geological structures from the imprints left on the landscape by past mining activities. Open source high resolution LiDAR datasets are used to fine-tune a deep convolutional neural network designed initially for Lunar LiDAR crater identification. By using a transfer learning approach between these two very similar domains, high accuracy predictions of pit locations can be generated in the form of a raster mask of pit location probabilities. Taking the raster of the predicted pit location centres as an input, a Hough transformation is used to fit lines through the centres of the detected pits. The results demonstrate that these lines follow the patterns of known mineralised veins in the area, alongside highlighting veins which are below the scale of the published geological maps.
Highlights
Detection of geological lineaments is a significant part of regional geological analysis, providing information on local geological structures
This paper presents a novel methodology which uses deep learning to detect historic mining remains from LiDAR data, prior to semi-automatically fitting lineaments in the area to infer potentially mineralised features
The result analysis evaluates the accuracy of the deep learning module for detecting the mining pits
Summary
Detection of geological lineaments is a significant part of regional geological analysis, providing information on local geological structures. Lineaments were digitised manually from airborne and spaceborne optical imagery or airborne geophysics; these methods are time consuming, subjective and potentially unreliable (Masoud and Koike, 2017). In addition to the time and subjectivity issues, in many climates direct fault mapping is challenged by a lack of exposed surface rocks across large geographical extents (Yeomans et al, 2019). To address these issues, much research has been focused on developing semi-automatic methods for lineament detection, from early methods using potential field data (Blakely and Simpson, 1986) to modern MATLAB based toolboxes (e.g. TecLines; Rahnama and Gloaguen, 2014). Using LiDAR data instead of optical data can overcome some of these issues, as shown in Grebby et al (2012)
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