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3. The Frinco Castle: From an Integrated Survey to 3D Modelling and a Stratigraphic Analysis for Helping Knowledge and Reconstruction.

Author

Diara, Filippo and Roggero, Marco

Subjects
*CASTLES, *HISTORICAL literacy, *LOCAL government, *MASONRY
Abstract

The Frinco Castle (AT-Italy) was the focus of a critical requalification and restoration project and historical knowledge. The initial medieval nucleus was modified and enriched by other architectural parts giving the current shape over the centuries. These additions gave the castle its actual internal and external complexity and an extreme structural fragility: in 2014, a significant portion collapsed. The main objective of this work was to obtain 3D metric documentation and a historical interpretation of the castle for reconstruction and fruition purposes. The local administration has planned knowledge processes from 2021: an integrated 3D geodetic survey of the entire castle and stratigraphic investigations of masonries. Both surveys were essential for understanding the architectural composition as well as the historical evolution of the court. NURBS modelling and a stratigraphic analysis of masonries allowed for the implementation of 3D immersion related to the historical interpretation. Furthermore, this modelling choice was essential for virtually reconstructing the collapsed area and helping the restoration phase. [ABSTRACT FROM AUTHOR]

Published
2023
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10. Detecting offsets in GPS time series: first results from the Detection of Offsets in GPS Experiment

Author

Gazeaux, Julien, Williams, Simon, King, Matt, Bos, Machiel, Dach, Rolf, Deo, Manoj, Moore, Angelyn W, Ostini, Luca, Petrie, Elizabeth, Roggero, Marco, Teferle, Felix Norman, Olivares, German, and Webb, Frank H.

Subjects
520 Astronomy
Abstract

The accuracy of Global Positioning System (GPS) time series is degraded by the presence of offsets. To assess the effectiveness of methods that detect and remove these offsets, we designed and managed the Detection of Offsets in GPS Experiment. We simulated time series that mimicked realistic GPS data consisting of a velocity component, offsets, white and flicker noises (1/f spectrum noises) composed in an additive model. The data set was made available to the GPS analysis community without revealing the offsets, and several groups conducted blind tests with a range of detection approaches. The results show that, at present, manual methods (where offsets are hand picked) almost always give better results than automated or semi‒automated methods (two automated methods give quite similar velocity bias as the best manual solutions). For instance, the fifth percentile range (5% to 95%) in velocity bias for automated approaches is equal to 4.2 mm/year (most commonly ±0.4 mm/yr from the truth), whereas it is equal to 1.8 mm/yr for the manual solutions (most commonly 0.2 mm/yr from the truth). The magnitude of offsets detectable by manual solutions is smaller than for automated solutions, with the smallest detectable offset for the best manual and automatic solutions equal to 5 mm and 8 mm, respectively. Assuming the simulated time series noise levels are representative of real GPS time series, robust geophysical interpretation of individual site velocities lower than 0.2–0.4 mm/yr is therefore certainly not robust, although a limit of nearer 1 mm/yr would be a more conservative choice. Further work to improve offset detection in GPS coordinates time series is required before we can routinely interpret sub‒mm/yr velocities for single GPS stations.

Published
2013

37. Long Range RTK

Author

Cina, Alberto, Manzino, Ambrogio, and Roggero, Marco

Published
2003

42. Detecting offsets in GPS time series: First results from the detection of offsets in GPS experiment

Author

Leverhulme Trust, United Kingdom Natural Environment Research Council, COST Action ES0701 [sponsor], Gazeaux, Julien, Williams, Simon, King, Matt, Bos, Machiel, Dach, Rolf, Deo, Manoj, Moore, Angelyn, Ostini, Luca, Petrie, Elizabeth, Roggero, Marco, Teferle, Felix Norman, Olivares Pulido, German, Webb, Frank H., Leverhulme Trust, United Kingdom Natural Environment Research Council, COST Action ES0701 [sponsor], Gazeaux, Julien, Williams, Simon, King, Matt, Bos, Machiel, Dach, Rolf, Deo, Manoj, Moore, Angelyn, Ostini, Luca, Petrie, Elizabeth, Roggero, Marco, Teferle, Felix Norman, Olivares Pulido, German, and Webb, Frank H.

Abstract

The accuracy of Global Positioning System (GPS) time series is degraded by the presence of offsets. If these are not detected and adjusted correctly they bias velocities, and hence geophysical estimates, and degrade the terrestrial reference frame. They also alter apparent time series noise characteristics as undetected offsets resemble a random walk process. As such, offsets are a substantial problem. A number of offset detection methods have been developed across a range of fields, and some of these are now being tested in geodetic time series. The DOGEx (Detection of Offsets in GPS Experiment) project aims to test the effectiveness of automated and manual offset detection approaches and the subsequent effect on GPS-derived velocities. To do this, simulated time series were first generated that mimicked realistic GPS data consisting of a velocity component, offsets, white and flicker noises (1/f spectrum noises) composed in an additive model. We focus on offset detection and together with velocity biases induced by incorrect offset detection. We show that, at present, manual methods (where offsets are hand -picked by GPS time series experts) almost always give better results than automated or semi-automated methods (two automated methods give quite similar velocity bias as the best manual solutions). For instance, the 5th percentile ranges (5% to 95%) in velocity bias for automated approaches is equal to 4.2mm/year,whereas it is equal to 1.8mm/yr for the manual solutions. However the True Positive detection rate of automated solutions is significantly higher than those for the manual solutions, being around 37% for the best automated, and 42% for the best manual solution. The amplitude of offsets detectable by automated solutions is greater than for hand picked solutions, with the smallest detectable offset for the two best manual solutions equal to 5mm and 7mm and to 8mm and 10mm for the two best automated solutions. The best manual solutions yielded velocity bia

Published
2013

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