Our understanding of the importance of scale dependency in Earth process has been greatly enhanced by geophysical survey methods that collectively span micro- to global scales. However, at the outcrop scale, traditional field methods are generally poor at capturing precisely georeferenced 3D spatial datasets necessary for scaling relationships to be analysed quantitatively.
New methods of digital geological survey allow high-precision spatial data to be acquired very rapidly. Terrestrial laser-scanning captures a dense point-cloud of data from the outcrop surface. Typical scans contain 105 to 107 individual points, take 10 minutes to 3 hours of scanning to acquire, and have a point spacing of 10-100 mm, depending on the desired level of detail in the scan and the type of equipment used. Real-Time Kinematic (RTK) GPS, with sub-centimetre spatial precision, ensures that individual geological observations can be georeferenced precisely in global coordinates. These digital survey techniques form the basis for quantitative characterisation of rock deformation. Current applications are illustrated with two case studies, addressing scaling issues related to both fol and fracture systems.
For folds, an RTK GPS is used to collect a dense network of georeferenced points across single folded surfaces. These data are used to construct gridded Digital Elevation Models, which form the basis for numerical analysis of fold curvature. Attributes such as principal curvature directions and Gaussian curvature are used to identify invariant fold properties, and form templates which allow a quantitative comparison of the relationship between curvature and fracturing. Importantly, curvature variations can be analysed over a range of wavelengths, and outcrop-scale fold structure can be compared to seismic-scale models used routinely within the petroleum industry.
For fractures, the raw digital survey data, collected using laser-scanning and RTK GPS are interpreted in an immersive 3D visualisation environment to delineate individual fractures. Each fracture can be analysed to quantify attributes such as curvature. Where good 3D exposure is available, individual fractures can be interpolated between measured points. In most cases, in order to increase the volume represented by the interpretation, individual fractures also need to be extrapolated into (and out of) the measured rock volume. Once the digital dataset is fully interpreted, the resultant model represents a quantitative characterisation of spatial distribution of fractures. This can be used to study the dominant scales of fracture distribution (i.e. the fracture granularity), or as a basis for defining representative scale ranges for stochastic models used in reservoir modelling.