Are open-source aerial images useful for fracture network characterisation? Insights from a multi-scale approach in the Zagros Mts.

Mercuri, Marco; Tavani, Stefano; Aldega, Luca; Trippetta, Fabio; Bigi, Sabina; Carminati, Eugenio

doi:10.1016/j.jsg.2023.104866

Cited by 6 publications

(1 citation statement)

References 69 publications

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“…However, with the advent of drones and smartphones, attention has shifted to computerised methods that can save time and handle a large amount of data (Ovaskainen et al, 2022;Salvini et al, 2016;Smeraglia et al, 2021;Tavani et al, 2022). Mercuri et al 2023 showed how manually or semi-automatically interpreted satellite and aerial images can be used in characterising fracture networks but that the appropriate scale of interpretation is crucial because the resolution of the dataset strongly influences the fracture network (Bour et al, 2002;Cawood et al, 2023;Ceccato et al, 2022;Espejel et al, 2020). Fracture network characterisation is not limited to satellite and aerial imagery.…”

Section: Introductionmentioning

confidence: 99%

Fracture analyser: a Python toolbox for the 2D analysis of fracture patterns

Borghini,

Striglio,

Bacchiani

et al. 2024

IJG

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Fractures are the most common result of rock brittle failure in the Earth's crust. Their presence, distribution, orientation, and interconnection are some of the main parameters that control rock's mechanical characteristics and fluid flow. The arrangement of fractures on outcrop analogues is critical in predicting rock mass integrity and subsurface fluid migration and storage. Here, we present the application of Fracture Analyser, a Python tool, in analysing two field case studies. We performed the analysis remotely from manually interpreted images of a fractured vertical wall of siliciclastic turbidites and a horizontal pavement of karstified limestone. To test our tool, we analysed the two outcrops at different scales: the first case, i.e., the siliciclastic turbidites vertical wall, from images at a ground resolution of about 0.0005 m/pixel; the second case, the karstified limestone pavement, with a ground resolution of about 0.04 m/pixel. To demonstrate the efficiency of Fracture Analyser in objectively and repeatedly quantifying fracture attributes and the fractured state of an outcrop from a given dataset, after the digitalisation of fractures we quantify fracture pattern attributes such as number, length, spatial orientation, spatial position, angle between scanline and fractures, apparent spacing, fracture density (P20), and fracture intensity (P21). The examples presented in this study demonstrate the efficacy of Fracture Analyser in quantifying fracture pattern attributes.

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