The use of an unmanned aerial vehicle for fracture mapping within a marble quarry (Carrara, Italy): photogrammetry and discrete fracture network modelling

Salvini, Riccardo; Mastrorocco, Giovanni; Seddaiu, Marcello; Rossi, Damiano; Vanneschi, Claudio

doi:10.1080/19475705.2016.1199053

Cited by 74 publications

(23 citation statements)

References 33 publications

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“…Finally, window sampling is a technique all fractures within a given sample area (window) are traced out either by hand, or on a computer, and the resulting traces used to calculate the fracture statistics (Pahl, 1981;Priest, 1993;Wu and D. 5 Pollard, 1995). This technique is often utilised to analyse remote-sampling data such as aerial photographs (Healy et al, 2017), Unmanned Arial Vehicle (UAV) images (Salvini et al, 2017), bathymetry (Nixon et al, 2012), or satellite imagery (Koike et al, 1998), as well as in outcrop studies (Belayneh et al, 2009). It has been suggested that a minimum of 110 fractures are sampled to be able to statistically describe the fracture network using window sampling (Zeeb et al, 2013).…”

Section: Fracture Data Collection and Analysismentioning

confidence: 99%

How do we see fractures? Quantifying subjective bias in fracture data collection

Andrews¹,

Roberts²,

Shipton³

et al. 2019

Preprint

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Abstract. The characterisation of natural fracture networks using outcrop analogues is important in understanding sub-surface fluid flow and rock mass characteristics in fractured lithologies. It is well known from decision-sciences that subjective bias significantly impacts the way data is gathered and interpreted. This study investigates the impact of subjective bias on fracture data collected using four commonly used approaches (linear scanlines, circular scanlines, topology sampling and window sampling) both in the field and in workshops using field photographs. Considerable variability is observed between each participant's interpretation of the same scanline, and this variability is seen regardless of geological experience. Geologists appear to be either focussing on the detail or focussing on gathering larger volumes of data, and this innate personality trait affects the recorded fracture network attributes. As a result, fracture statistics derived from the field data and which are often used as inputs for geological models, can vary considerably between different geologists collecting data from the same scanline. Additionally, the personal bias of geologists collecting the data affects the size (minimum length of linear scanlines, radius of circular scanlines or area of a window sample) required of the scanline that is needed to collect a statistically representative amount of data. We suggest protocols to recognise, understand and limit the effect of subjective bias on fracture data biases during data collection.

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Section: Fracture Data Collection and Analysismentioning

confidence: 99%

How do we see fractures? Quantifying subjective bias in fracture data collection

Andrews¹,

Roberts²,

Shipton³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…In the stability analysis of cliff faces, the identification of joint systems and fractures are key issues. The remote sensing techniques are capable of identifying these structural geological elements [30,37]. Novel methods and close range surveys using digital techniques include the use of thermo-camera [38], close-range photography [34,39], or TLS [40].…”

Section: Introductionmentioning

confidence: 99%

“…This is where the application of remote sensing techniques demonstrates many advantages. In the assessment of the structural geological conditions, these tools are often applied [29,41] for representing faults [37,42] or folds [43].…”

Section: Introductionmentioning

confidence: 99%

Application of UAV in Topographic Modelling and Structural Geological Mapping of Quarries and Their Surroundings—Delineation of Fault-Bordered Raw Material Reserves

Török

Bögöly

Somogyi

et al. 2020

Sensors

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A 3D surface model of an active limestone quarry and a vegetation-covered plateau was created using unmanned aerial vehicle (UAV) technique in combination with terrestrial laser scanning (TLS). The aim of the research was to identify major fault zones that dissect the inaccessible quarry faces and to prepare a model that shows the location of these fault zones at the entire study area. An additional purpose was to calculate reserves of the four identified lithological units. It was only possible to measure faults at the lowermost two meters of the quarry faces. At the upper parts of the quarry and on the vegetation-covered plateau where no field geological information was available, remote sensing was used. Former logs of core drillings were obtained for the modelling of the spatial distribution of four lithological units representing cover beds and various quality of limestone reserves. With the comparison of core data, field measurements and remote sensing, it was possible to depict major faults. Waste material volumes and limestone reserves were calculated for five blocks that are surrounded by these faults. The paper demonstrates that, with remote sensing and with localised control field measurements, it is possible: (a) to provide all geometric data of faults and (b) to create a 3D model with fault planes even at no exposure or at hardly accessible areas. The surface model with detected faults serves as a basis for calculating geological reserves.

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“…Accordingly, a multitude of UAS applications [9] have emerged over the past five years including mine surveillance [10], slope stability assessment in mining [11], agriculture [12], infrastructure and construction work [13], archaeology [14], forestry [15], disaster monitoring [16], coastal environmental studies [17] and ground water research [18]. Digital elevation models obtained from UAS can deliver resolutions in (sub-)centimeter range, and are superior to conventional mapping techniques in terms of survey area, time, and target accessibility [19,20].…”

Section: Introductionmentioning

confidence: 99%

Drone-Borne Hyperspectral and Magnetic Data Integration: Otanmäki Fe-Ti-V Deposit in Finland

et al. 2019

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The technical evolution of unmanned aerial systems (UAS) for mineral exploration advances rapidly. Recent sensor developments and improved UAS performance open new fields for research and applications in geological and geophysical exploration among others. In this study, we introduce an integrated acquisition and processing strategy for droneborne multisensor surveys combining optical remote sensing and magnetic data. We deploy both fixedwing and multicopter UAS to characterize an outcrop of the Otanmäki FeTiV deposit in central Finland. The lithology consists mainly of gabbro intrusions hosting ore bodies of magnetiteilmenite. Large areas of the outcrop are covered by lichen and low vegetation. We use two droneborne multi and hyperspectral cameras operating in the visible to nearinfrared parts of the electromagnetic spectrum to identify dominant geological features and the extents of ore bodies via ironindicating proxy minerals. We apply band ratios and unsupervised and supervised image classifications on the spectral data, from which we can map surficial ironbearing zones. We use two setups with threeaxis fluxgate magnetometers deployed both by a fixedwing and a multicopter UAS to measure the magnetic field at various flight altitudes (15 m, 40 m, 65 m). The total magnetic intensity (TMI) computed from the individual components is used for further interpretation of ore distribution. We compare to traditional magnetic groundbased survey data to evaluate the UASbased results. The measured anomalies and spectral data are validated and assigned to the outcropping geology and ore mineralization by performing surface spectroscopy, portable Xray fluorescence (pXRF), magnetic susceptibility, and traditional geologic mapping. Locations of mineral zones and magnetic anomalies correlate with the established geologic map. The integrated survey strategy allowed a straightforward mapping of ore occurrences. We highlight the efficiency, spatial resolution, and reliability of UAS surveys. Acquisition time of magnetic UAS surveying surpassed ground surveying by a factor of 20 with a comparable resolution. The proposed workflow possibly facilitates surveying, particularly in areas with complicated terrain and of limited accessibility, but highlights the remaining challenges in UAS mapping.

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The use of an unmanned aerial vehicle for fracture mapping within a marble quarry (Carrara, Italy): photogrammetry and discrete fracture network modelling

Cited by 74 publications

References 33 publications

How do we see fractures? Quantifying subjective bias in fracture data collection

How do we see fractures? Quantifying subjective bias in fracture data collection

Application of UAV in Topographic Modelling and Structural Geological Mapping of Quarries and Their Surroundings—Delineation of Fault-Bordered Raw Material Reserves

Drone-Borne Hyperspectral and Magnetic Data Integration: Otanmäki Fe-Ti-V Deposit in Finland

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