LiDAR, UAV or compass-clinometer? Accuracy, coverage and the effects on structural models

Abstract

a b s t r a c tLight Detection and Ranging (LiDAR) and Structure from Motion (SfM) provide large amounts of digital data from which virtual outcrops can be created. The accuracy of these surface reconstructions is critical for quantitative structural analysis. Assessment of LiDAR and SfM methodologies suggest that SfM results are comparable to high data-density LiDAR on individual surfaces. The effect of chosen acquisition technique on the full outcrop and the efficacy on its virtual form for quantitative structural analysis and prediction beyond single bedding surfaces, however, is less certain. Here, we compare the accuracy of digital virtual outcrop analysis with traditional field data, for structural measurements and along-strike prediction of fold geometry from Stackpole syncline. In this case, the SfM virtual outcrop, derived from UAV imagery, yields better along-strike predictions and a more reliable geological model, in spite of lower accuracy surface reconstructions than LiDAR. This outcome is attributed to greater coverage by UAV and reliable reconstruction of a greater number of bedding planes than terrestrial LiDAR, which suffers from the effects of occlusion. Irrespective of the chosen acquisition technique, we find that workflows must incorporate careful survey planning, data processing and quality checking of derived data if virtual outcrops are to be used for robust structural analysis and along-strike prediction.

Analysis by UAV Digital Photogrammetry of Folds and Related Fractures in the Monte Antola Flysch Formation (Ponte Organasco, Italy)

“…The influence of georeferenced UAVDP models on surface orientation measurements (e.g., beds and fractures dip and dip azimuth) are comparable to the accepted range of errors of the user/instrument orientation measurements [33]. This range (ca.…”

Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization

“…This, in turn, makes metrological work mandatory to any form of modeling that relies on actual measured data. Note that it is acceptable to use preexisting metrological studies to define the priors (Allmendinger et al, 2017;Cawood et al, 2017;Novakova and Pavlis, 2017) provided that the measurement device and procedure used are similar to those of the studies. To gather multiple observations per site is strongly recommended as this practice sharply increases the quality of the disturbance distributions.…”

“…If there is no option for an appropriate transform, it is advisable to perform an empirical analysis of scedasticity beforehand. This is usually achieved through experimental assessment of uncertainty under various conditions (metrological study) of measurement and over the entire range of measured values (Allmendinger et al, 2017;Cawood et al, 2017;Novakova and Pavlis, 2017). The results of such analysis can then be used to define the prior dispersion (ϑ in 5) more accurately as a function of the measurement instead of a constant.…”

“…The perturbation parameters for orientations were chosen to be compatible with metrological data. That is, values for the dispersion of the spherical disturbance distributions used for the foliations were estimated on the basis of the variability in plane measurements observed by other authors (Nelson et al, 1987;Stigsson, 2016;Allmendiger et al, 2017;Cawood et al, 2017;Novakova and Pavlis, 2017) in a variety of settings and for different types of devices. Perturbation parameters for interfaces were designed to meet observed GPS uncertainty (Jennings et al, 2010) and observed experimental interface variability in previous authors' works (Courrioux et al, 2015;Lark et al, 2014Lark et al, , 2013.…”

Influence of Landscape Coverage on Measuring Spatial and Length Properties of Rock Fracture Networks: Insights from Numerical Simulation

“…tens of meters above rock surfaces. The recent unmanned aerial vehicles (UAV) combined with digital photogrammetry are increasingly deployed to obtain aerial photographs in short time intervals with high resolutions (Bemis et al 2014;Clapuyt et al 2016;Cawood et al 2017). These photographs can have a resolution at centimetre to millimetre scales, which can significantly improve the acquisition of geometric and structural information (Vollgger and Cruden 2016).…”