Detailed investigation of multi-scale fracture networks in glacially abraded crystalline bedrock at Åland Islands, Finland

Abstract

Abstract. Using multiple scales of observation in studying the fractures of the bedrock increases the reliability and representativeness of the respective studies. This is because the discontinuities, i.e., the fractures, in the bedrock lack any characteristic length and instead occur within a large range of scales of approximately 10 orders of magnitude. Consequently, fracture models need to be constructed based on representative multi-scale datasets to enable valid interpolation and extrapolation of common scaling laws to all fracture sizes. In this paper, we combine a detailed bedrock fracture study from an extensive bedrock outcrop area with lineament interpretation using Light Detection And Ranging (LiDAR) and geophysical data. Our study offers lineament data in an intermediary length range missing from Discrete Fracture Network -modelling conducted at Olkiluoto, a nuclear spent fuel facility in Finland. In addition, this study also provides a robust multi-scale fracture and lineament dataset which has been thoroughly analysed for the purposes of understanding the uncertainties and differences in the different datasets. Our analysis further covers the topological, scale-independent, fracture network characteristics. Results of our study include the discovery of three distinct azimuth sets, N-S, NE-SW and WNW-ESE, both single scale and multi-scale power-law models for fracture and lineaments and further insight into a trend of decreasing apparent connectivity of fracture networks as the scale of observation increases. Specifically, a multi-scale power-law exponent of -1.13 is fitted to fracture and lineament lengths although we found that individually the fractures and lineaments might follow distinct power-laws rather than a common one.

Aerogeophysics and light detecting and ranging (LiDAR)-based lineament interpretation of Finland at the scale of 1:500 000

“…We used strict geometric, topological and metadata validation methods to produce a cohesive vector dataset that is readily 330 available for further analysis and also minimizes trace geometries and interactions that probably do not represent bedrock structures. The importance of the topological relationship between brittle structures and permeability, and subsequent fluid flow, has been highlighted in several studies on faults and fractures (Skyttä et al, 2021;Ceccato et al, 2022;Ovaskainen et al, 2022aOvaskainen et al, , 2022b. Documentation of the topological relationships between lineaments enables, for example, analysis of the abutment relationships between azimuth sets, which could be analysed in a future study to reveal 335 potential age differences between the lineaments (Procter and Sanderson, 2018;Skyttä et al, 2021).…”

“…The trend maxima of NW-SE-trending lineaments (Figure 10) is evident in the whole country, except for northern Finland, where the lineament orientations are more evenly distributed. This might be attributed to the ice movement direction, which in most parts of Finland was parallel to this lineament set and accordingly enhanced the occurrence of lineaments in that orientation due to scouring of the bedrock during glacial flow (Skyttä et al, 2015;Ovaskainen et al, 2022b). In the rose plots for tectonic provinces, the different areas display similar lineament orientations as seen for the 345 whole country, but the Southern Finland Subprovince display more diverse orientations.…”

LinkingLines: Using the Hough Transform to Cluster Line Segments and Mesoscale Feature Extraction

“…Issues with data coverage, incompleteness, and complexity occur in a wide range of Earth and Planetary science data products. As an example we show in our documentation how this work can also be applied to lineaments on Venus (Grosfils et al, 2011) and fracture networks (Ovaskainen et al, 2023). Clustering and machine learning methods are commonly applied geosciences within the subdisciplines of seismology, geochemistry, and planetary science (Li et al, 2023).…”