Tuning the CARIS implementation of CUBE for Patagonian Waters.pdf

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1. INTRODUCTION The Chilean Hydrographic Office (SHOA) is currently cleaning its bathymetric data using interactive editing. This is a time consuming task since multibeam data is used for the creation of nautical products. This chapter introduces the Chilean scenario and the objectives of this research for implementing the latest algorithm known as CUBE (Combined Uncertainty and Bathymetry Estimator) in the Chilean bathymetric data analysis procedure. 1.1 Problem Statement. The CUBE [Calder and Mayer, 2001; Calder, 2003; Calder and Wells, 2007] algorithm generates point-wise estimates of depth from dense soundings. Applications of CUBE have become an excellent tool for bathymetric data analysis and cleaning. However, CUBE will not always make the right decision, especially if the data is corrupted by noise and if the area is affected by extreme terrain conditions. That is the case with the Chilean data. According to the nature of the seafloor and failures in bottom detection, the CUBE algorithm is not suitable with its default parameter values for this kind of scenario. 1.2 Background. SHOA has been using the echosounders FANSWEEP 20 [ATLAS, 2002; 2003] (for areas up to 250 metres depth) and HYDROSWEEP MD 2 (for deeper sectors) to collect bathymetric data. These echosounders use electrical beamforming and interferometric techniques to produce a high-resolution seafloor representation. 1
In the case of the bathymetric data from the Patagonian area, SHOA faces a difficult situation, since the topography of the seafloor changes abruptly from shallow to very deep waters in just a few minutes of surveying. This seafloor is complex in its geomorphology of high mountains and extreme roughness. Also, its oceanographic aspects are very variable, making the survey a difficult task. These features produce failures in the data acquisition system (e.g., sea bottom mistracking). Since hydrographic offices have implemented multibeam echosounder technology, the enormous amount of data available has turned the operator’s work into something extremely laborious. The advantage of having the seafloor mapped in high resolution has the disadvantage of being time consuming in the cleaning procedure. Also, the operators must deal with many subjective decisions that sometimes (depending on their expertise and dedication) could be wrong. The Chilean Hydrographic Office is currently processing its multibeam data using interactive editing with CARIS HIPS software. This means performing data analysis through the entire area surveyed, swath-by-swath, selecting and rejecting data noticed as noise by the operators. This implies a time consuming task by SHOA to complete the cleaning process. Reducing post-processing time is critical for any organization since it makes the hydrographic surveys less expensive. On the other hand, the data cleaning needs to guarantee safety of navigation for the relevant nautical charts. For this reason the implementation of new statistical cleaning tools in the Chilean bathymetric data cleaning processes is necessary. 2
Page 1 and 2: Tuning the CARIS implementation of
Page 3 and 4: Abstract This thesis is focused on
Page 5 and 6: Resumen Esta tesis está orientada
Page 7 and 8: Acknowledgements My sincere gratitu
Page 9 and 10: 4.3 Bathymetry Associated with Stat
Page 11 and 12: List of Figures Figure 2.1: Illustr
Page 13: List of Symbols, Nomenclature or Ab
Page 17 and 18: ATLAS FANSWEEP 20 is not one of the
Page 19 and 20: Capture Distance Minimum were modif
Page 21 and 22: • HIPS CUBE deep configuration, t
Page 23 and 24: is performed. Several CARIS surface
Page 25 and 26: are dependent on the nature of the
Page 27 and 28: 2.2 Beamwidth, Resolution and Beam
Page 29 and 30: In the FANSWEEP 20 electronic beamf
Page 31 and 32: For vessel motion compensation ATLA
Page 33 and 34: . Transducer referenced depth: trav
Page 35 and 36: wave lost traveling through the wat
Page 37 and 38: Sea bottom mistracking Figure 2.3:
Page 39 and 40: deal with echoes that arrive at the
Page 41 and 42: Recalling the previous chapter, the
Page 43 and 44: The fjords’ basins are basically
Page 45 and 46: 44m 124m Noise-data 168m Figure 3.4
Page 47 and 48: contained in CARIS HIPS, where main
Page 49 and 50: The advantage for bathymetric data
Page 51 and 52: uncertainty. The BASE surface meets
Page 53 and 54: The algorithm also has built-in che
Page 55 and 56: 4.4.2 Assimilation (Gathering proce
Page 57 and 58: hypothesis. Whether incoming soundi
Page 59 and 60: The Density-Locale rule in HIPS is
Page 61 and 62: the respective BASE surface. Since
Page 63 and 64: dimension and Reference Point (RP)
Page 65 and 66:
5.2.3 Construction of a new Device
Page 67 and 68:
Figure 5.3: Estimates of the perfor
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A new Field Sheet called CUBE was c
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In order to get comparison metrics
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6. RESULTS AND DISCUSSION In this c
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Table 6.1: Different CUBE configura
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noise-data affecting the surface re
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20% of depth or 2 metres from the n
Page 81 and 82:
Figure 6.3: Capture Distance Scale
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Figure 6.4: The effect of using mul
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Figure 6.5: The CARIS surface gener
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Figure 6.6: (a). The results of CAR
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The grid resolution selected was 5
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6.3.2 CUBE new configurations. Seve
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(a) (b) Figure 6.8: (a). Subset of
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51,23m 88,93m Figure 6.10: Top left
Page 97 and 98:
6.4 Summary of results. 1. SHOA sur
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from the node. Thus, the node estim
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Interactive editing needs to be hel
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CARIS intends future implementation
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References and Bibliography Aber, J
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CARIS, Geomatics Software. (2006a).
Page 109 and 110:
Llewellyn, K. (2006). Correction fo
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APPENDIX A Ancillary sensors of the
Page 113 and 114:
100 APPENDIX B Device model for ATL
Page 115 and 116:
102 APPENDIX C DUMP ATLAS SURF Util
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104 `B' Event Nr. 5 time 152.03 sec
Page 119 and 120:
106 APPENDIX D HVF population. 106
Page 121:
108 Vita Name: Miguel Ernesto Vásq
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