On a new robust workflow for the statistical and spatial analysis of fracture data collected with scanlines (or the importance of stationarity)

We present an innovative workflow for the statistical analysis of fracture data collected along scanlines, composed of two major stages, each one with alternative options. A prerequisite in our analysis is the assessment of stationarity of the dataset, which is motivated by statistical and geological considerations. Calculating statistics on non-stationary data can be statistically meaningless, and moreover the normalization and/or sub-setting approach that we discuss here can greatly improve our understanding of geological deformation processes. Our methodology is based on performing non-parametric statistical tests, which allow detecting important features of the spatial distribution of fractures, and on the analysis of the cumulative spacing function (CSF) and cumulative spacing derivative (CSD), which allows defining the boundaries of stationary domains in an objective way. Once stationarity has been analysed, other statistical methods already known in the literature can be applied. Here we discuss in detail methods aimed at understanding the degree of saturation of fracture systems based on the type of spacing distribution, and we evidence their limits in cases in which they are not supported by a proper spatial statistical analysis.

Automatic and Manual Fracture-Lineament Identification on Digital Surface Models as Methods for Collecting Fracture Data on Outcrops: Case Study on Fractured Granite Outcrops, Bangka

Three-dimensional outcrop models, or Digital Surface Models (DSMs), have proved their capacity in many geoscience studies. Along with the advantage in the rapid acquisition, DSMs are capable of creating virtual models of fractured outcrops to be interpreted for further analysis. This paper reports the DSM robustness by comparing the result of fracture-lineament measurement using DSMs and discusses the possible causes of error that might occur. The first method applied in this study is the scanline method to collect fracture data directly from outcrops, measuring more than 1,400 fracture data. The second method is applying fully automatic and manual fracture identification by optimizing hill-shaded DSMs. Two well-exposed granite outcrops in Bangka, Indonesia, are designed for the pilot area. Structure-from-Motion (SfM) photogrammetry is utilized to generate the DSMs, where a series of aerial images are captured using Unmanned Aerial Vehicle (UAV). The images are then processed into hill-shaded DSMs to be automatically analyzed following the algorithm in PCI Geomatics software and manually assessed. The textures of DSMs are also used in fracture identification through RGB filtering as the third method. The results show that the semiautomatic measurement using RGB-filtering texture has the closest pattern to the scanline data compared to the hill-shaded DSM method. The differences rely on several conditions, such as the geometry and texture of the outcrops. Eventually, methods of fracture identification using DSM are expected to be capable as options in preliminary fracture data collecting on outcrops, especially when the scanline is unable to be performed.

On a new robust workflow for the statistical and spatial analysis of fracture data collected with scanlines (or the importance of stationarity)

We present an innovative workflow for the statistical analysis of fracture data collected along scanlines, composed of two major stages, each one with alternative options. A prerequisite in our analysis is the assessment of stationarity of the dataset, that is motivated by statistical and geological motivations. Calculating statistics on non-stationary data can be statistically meaningless, and moreover the normalization and/or sub-setting approach that we discuss here can greatly improve our understanding of geological deformation processes. Our methodology is based on the analysis of the cumulative spacing function (CSF) and cumulative spacing derivative (CSD), that allows defining the boundaries of stationary domains in an objective way. Once stationarity has been analysed, other statistical methods already known in literature can be applied. Here we discuss in details methods aimed at understanding the degree of saturation of fracture systems based on the type of spacing distribution, and we evidence their limits in cases where they are not supported by a proper spatial statistics analysis.

Do different geologists see the same fractures? Quantifying subjective bias in fracture data collection.

<p>The characterisation of natural fracture networks using outcrop analogues is important in understanding subsurface fluid flow and rock mass characteristics in fractured lithologies. It is well known from decision sciences that subjective bias can significantly impact the way data is gathered and interpreted, introducing scientific uncertainty.</p><p>This study investigates the scale of and nature of subjective bias on fracture data collected by geoscientists using four commonly used approaches (linear scanlines, circular scanlines, topology sampling and window sampling) both in the field and in workshops using field photographs.</p><p>We observe considerable variability between each participant’s interpretation of the same scanline, and this variability is seen regardless of participants’ level of geological experience. Geologists appear to be either focussing on the detail or focussing on gathering larger volumes of data; personal character traits that affect the recorded fracture network attributes. As a result, the fracture statistics that are derived from field data can vary considerably for the same scanline, depending on which geologist collected the data. Additionally, the personal bias of geologists collecting the data affects the scanline size (minimum length of linear scanlines, radius of circular scanlines or area of a window sample) needed to collect a statistically representative amount of data.</p><p>Based on our findings and on understanding of bias reduction in decision sciences, we suggest protocols to recognise, understand and limit the effect of subjective bias on fracture data biases during data collection.</p><p>Our work shows the capacity for cognitive biases to introduce uncertainty into observation-based data. Fracture statistics derived from field data often inputs into geological models that are used for a range of applications, from understanding fluid flow to characterising rock strength, and so these uncertainties have ramifications for propagation into a range of outcomes. Importantly, our findings that personal bias can affect data collection have implications well beyond the geosciences.</p>

A review and evaluation of the methodology for digitising 2D fracture networks and topographic lineaments in GIS

Understanding the impact of fracture networks on rock mass properties is an essential part of a wide range of fields in geosciences, from understanding permeability of groundwater aquifers and hydrocarbon reservoirs to erodibility properties and slope stability of rock masses for geotechnical engineering. However, gathering high quality, oriented-fracture datasets in the field can be difficult and time consuming, for example due to constraints on time or access (e.g. cliffs). Therefore, a method for obtaining accurate, quantitative fracture data from photographs is a significant benefit. In this paper we describe and evaluate the method for generating a series of digital fracture traces in GIS-environment, in which spatial analysis of a fracture network can be carried out. The method is not meant to replace the gathering of data in the field, but to be used in conjunction, and is well suited where fieldwork time is limited, or where the section cannot be accessed directly. The basis of the method is the generation of the vector dataset (shapefile) of a fracture network from a georeferenced photograph of an outcrop in a GIS environment. From that shapefile, key parameters such as fracture density and orientation can be calculated. Furthermore, in the GIS-environment more complex spatial calculations and graphical plots can be carried out such as heat maps of fracture density. There are a number of advantages to using a digital method for gathering fracture data including: time efficiency, generating large fracture network datasets, flexibility during data gathering and consistency of data.