Terrestrial laser scanning, or lidar, is a recent innovation in spatial information data acquisition, which allows geological outcrops to be digitally captured with unprecedented resolution and accuracy. With point precisions and spacing of the order of a few centimetres, an enhanced quantitative element can now be added to geological fieldwork and analysis, opening up new lines of investigation at a variety of scales in all areas of field-based geology. Integration with metric imagery allows 3D photorealistic models to be created for interpretation, visualization and education. However, gaining meaningful results from lidar scans requires more than simply acquiring raw point data. Surveys require planning and, typically, a large amount of post-processing time. The contribution of this paper is to provide a more detailed insight into the technology, data collection and utilization techniques than is currently available. The paper focuses on the workflow for using lidar data, from the choice of field area and survey planning, to acquiring and processing data and, finally, extracting geologically useful data. Because manufacturer specifications for point precision are often optimistic when applied to real-world outcrops, the error sources associated with lidar data, and the implications of them propagating through the processing chain, are also discussed.

Subsurface reservoir models are typically limited by a lack of spatially accurate geometric data on bedform architecture and geometry, which are key controls on fluid flow. Outcrop analogues have long been utilized as a source of such data, but the capture of sufficiently precise data has always been a challenge. This thesis demonstrates how advances in data capture and computer technology have made possible the collection of three-dimensional, high-resolution, digital geological data generated by ground based laser scanning (lidar) from steep and otherwise inaccessible outcrops. A complete workflow is documented — from outcrop selection through data collection, processing and building of virtual outcrop models (VOMs) — to geological interpretation, data extraction and the building and testing of geocellular models using an industry-standard, reservoir-modelling software. Examples from analogue systems such as the Roda Sandstone, Pyrenees, Spain, and Cretaceous deltaic systems from central Utah, USA, are used to demonstrate the methodology. A parallel objective of the study has been to utilise the developed techniques to improve understand the geometry, genesis and impact of shallow marine, deltaic clinothems. Clinothems are seaward dipping beds, formed when much of the sand in river-dominated deltas is laid down in distributary mouth bars that coalesce during the downstream accretion of the delta to form a delta front. Systematic collection of data from VOMs has allowed for the compilation of a detailed database on clinothems and associated clinoform geometry. Bed thickness and dip measurements have been collected from two ancient, river-dominated deltaic depositional systems, namely the forced regressive Panther Tongue and a high-stand portion of the Ferron Sandstone that both crop out in central Utah, USA. The extensive bed-thickness database of nearly 3000 measurements from 73 individual clinothems at hundreds of meters to about 1000 m. scale has been used to quantify aspects of delta clinothem-beds. A decay gradient parameter permits the thinning of the clinothems to be described as a single number and thus compared between beds, and measurements are used to calculate the average dip angle. Results demonstrate that clinothems in the Panther Tongue are much longer and more shallowly dipping than clinothems in the Ferron Sandstone. Both systems show similar patterns with regards to groups of gradient-values and groups of dip-values: Common boundaries exist between groups defined by gradient values and groups defined by dip values. A cyclic depositional pattern interpreted to be related to autocyclic processes in deltaic mouth bars is documented, and three and nine stratigraphic cycles or bedsets have been identified in the studied parts of the Ferron Sandstone and the Panther Tongue, respectively. The differences between the two systems are attributed to their sequence stratigraphic positions and hence accommodation. Deterministic geocellular reservoir models have been built from surfaces interpreted in the VOMs. The models have been used to test the influence of dipping siltstones heterogeneities on simulated reservoir performance in a reservoir modelling software. Siltstone drapes on clinothem sandstone beds have been modelled and various populations of holes have been placed in the siltstone beds (0, 30, 60, 90 and 100%). The effects of siltstone permeability (1, 0.1, 0.01 and 0.001 mD) have also been tested. A total of 41 individual models were built and flow simulated. Results quantify how the portion of holes governs the production rate/ recovery factor in the Panther Tongue models. Permeability values are more important in the Ferron models, although they are still influenced by the number of holes. Steeper dipping and closer spaced clinothems of the highstand system tract lower the recovery factor by several tens of percent if the related heterogeneities are all or close to continuous and have low enough permeability

Close-range hyperspectral imaging is a new method for geological research, in which imaging spectrometry is applied from the ground, allowing the mineralogy and lithology in near-vertical cliff sections to be studied in detail. Contemporary outcrop studies often make use of photorealistic three-dimensional (3D) models, derived from terrestrial laser scanning (lidar), that facilitate geological interpretation of geometric features. Hyperspectral imaging provides complementary geochemical information that can be combined with lidar models, enhancing quantitative and qualitative analyses. This article describes a complete workflow for applying close-range hyperspectral imaging, from planning the optimal scan conditions and data acquisition, through pre-processing the hyperspectral imagery and spectral mapping, integration with lidar photorealistic 3D models, and analysis of the geological results. Pre-processing of the hyperspectral images involves the reduction of scanner artefacts and image discontinuities, as well as relative reflectance calibration using empirical line correction, based on two calibrated reflection targets. Signal-to-noise ratios better than 70:1 are achieved for materials with 50% reflectance. The lidar-based models are textured with products such as hyperspectral classification maps. Examples from carbonate and siliciclastic geological environments are presented, with results showing that spectrally similar material, such as different dolomite types or sandstone and siltstone, can be distinguished and spectrally mapped. This workflow offers a novel and flexible technique for applications, in which a close-range instrument setup is required and the spatial distribution of minerals or chemical variations is valuable.

Application of 3D seismic reflection data to igneous systems in sedimentary basins has led to a revolution in the understanding of mafic sill complexes. However, there is considerable uncertainty on how geometries and architecture of sill complexes within the subsurface actually relates to geometries in seismic reflection data. To provide constraints on how sill complexes in seismic data should be interpreted, we present synthetic seismograms generated from a seismic-scale (22 × 0.25 km) outcrop in East Greenland constrained by abundant field data. This study highlights how overlying igneous rocks adversely affect imaging of underlying intrusions and rocks by decreasing seismic amplitude, frequency and making steeply dipping features near-impossible to image. Furthermore, seismic modelling in this study shows that, because of the high impedance contrast between siliciclastic host rock and dolerites, very thin (1–5 m) intrusions should in principle be imaged in reflection seismic data at 3 km depth. However, comparison with actual seismic data with well data shows significant amounts of unimaged sill intrusions, and this is likely due to limited seismic resolution, overburden complexity, inadequate velocity-models, and interference between reflections from closely spaced sills and sill splays. Significant improvements to sill imaging and interpretation could be made by better predicting occurrence and geometry of sill intrusions and including these in velocity models. Supplementary material : A scaled version of the input panel (Fig. 5d) used for seismic modelling (A1), and very high resolution versions of subfigures in Figure 11 (A2–3) are available at https://doi.org/10.6084/m9.figshare.5607160