Core sample characterisation using 3D terrestrial laser scanning | TU Delft

Core sample characterisation using 3D terrestrial laser scanning | TU Delft

van Berkom, Ivanka (TU Delft Civil Engineering and Geosciences; TU Delft Geoscience and Remote Sensing)

Lindenbergh, Roderik (mentor)
Meijninger, B. (mentor)

Delft University of Technology

Applied Earth Sciences

2017-07

The main purpose of this thesis is to conclude whether terrestrial laser scanning (TLS) can be used as an automated method to improve the classification of soils. It was in collaboration with Fugro N.V., a large Dutch consultancy and engineering firm that is active in onshore and offshore services in the field of geological and geotechnical investigations for a wide field of clients in the petroleum and gas industry but also for other infrastructure. Current methods for the classification of soils comprise of laboratory and in-situ measurements. Fugro is searching for a more accurate and time efficient method to classify soil and provided five soil samples on which a classification had to be performed. The Leica C10 laser scanner, provided by the department of Geoscience and Remote Sensing (TU Delft), was used to scan the soil samples. TLS is based on LIDAR (light detection and ranging) which is known for emitting pulses in a very narrow beam of monochromatic light (one wavelength) in the ultraviolet, visible or near-infrared range of the electromagnetic spectrum. After scanning the soil samples, the following features extracted from the 3D point cloud data were used to perform classification on each soil sample: intensity (backscattered energy), colour (an RGB-image), surface height variations (roughness). The chosen method was iso cluster unsupervised classification. After classification, interpretations were made based on the descriptions of the soil sample and knowledge of bare soil reflectance. The main factors that influenced the soil reflectance were related to characteristics of the samples: glauconite content (an iron-bearing mineral) and surface roughness. Both factors were responsible for a decrease in soil reflectance. Unsupervised classification was applicable on three out of five samples. However, only one sample provided good results. The other two samples were less promising, although some features could clearly be identified after comparing the classified image with ground truth data.

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Student theses

bachelor thesis

© 2017 Ivanka van Berkom