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.
Abstract. Virtual outcrop models are increasingly used in geoscience education to
supplement field-based learning but their efficacy for teaching key 3D
spatial thinking skills has been little tested. With the rapid increase in
online digital learning resources and blended learning, most recently
because of the global COVID-19 pandemic, understanding the role of virtual
field environments in supporting and developing skills conventionally taught through field-based teaching has never been more critical. Here we show the efficacy of virtual outcrop models in improving 3D spatial thinking and provide evidence for positive perceptions amongst participants using virtual outcrops in teaching and learning. Our results show that, in a simple,
multiple-choice scenario, participants were more likely to choose the 3D
block diagram that best represents the structure when using a virtual
outcrop (59 %) compared to more traditional representations, such as a geological
map (50 %) or field photograph (40 %). We add depth to these results by
capturing the perceptions of a cohort of students, within our full
participant set, on the use of virtual outcrops for teaching and learning,
after accessing a virtual field site and outcrops which they had
previously visited during a day's field teaching. We also asked all participants if and how virtual outcrops could be used effectively for teaching and training, recording 87 % of positive responses. However, only 2 % of participants felt that virtual outcrops could potentially replace in-field teaching. We note that these positive findings signal significant potential
for the effective use of virtual outcrops in a blended learning environment and
for breaking barriers to increase the equality, diversity and inclusivity of geoscience field skills and teaching.
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