臺灣博碩士論文加值系統

多音束測深系統的問市對於某些海洋工程應用上來說是令人振奮的﹐其量測而得之高密度資料使得吾人對海床之地形及其變化有更深刻的了解﹐然而﹐由於其資料之獲取乃由多個感測器的資料整合解算而來﹐相對的﹐不但其誤差傳播計算不易﹐且一些水深值不規則的跳動﹐著實影響了出圖成果﹐進而牽連隨後決策的品質。若待測海床極為平坦﹐剔除部份跳動水深值或許並不困難﹐但對佈有人工魚礁甚至地形崎嶇度高的岩岸海域﹐則需更具邏輯力之異常值判斷法則。
綜觀現行關於多音束測深資料之自動篩選演算法﹐多以資料面的相互比對來達成錯誤偵測之目的﹐因而使得資料篩選本身之效能竟全然仰賴著資料品質之優劣。然本篩選演算法除了藉由各剖面間之各音束投射位置及其投射角度找出相互間之對應關係外﹐同時發現因海床特徵物所造成的遮蔽效應﹐此外﹐由於相鄰測線之資料比對而得到之具時間性之可移式平均﹐由於業經鄰居之預檢俾使對欲篩選之資料點水深估計的偏差減至最低。再者﹐上述之相鄰測線間之資料比對亦提供了兩測線於同一區域之量測結果一致度資訊及該區域之地形變動趨勢。
經由本研究成果顯示﹐在一般地形連續之沙岸海域﹐經由本研究兩階段式篩選之錯誤標記﹐除了可將嚴重錯誤篩選出來外﹐小範圍之跳動水深於兩階段之標記結果也相當一致﹐而在佈有人工魚礁的海床﹐由於實際量得之測帶與測帶間之重疊比例不夠使得很多擊中魚礁的音束在第二階篩選並沒有足夠的比對點﹐然而﹐經由相鄰測線連續剖面的比較仍可分離出何者為音束擊中人工魚礁之水深點而何者是錯誤。

Abstract:
The invention of multi-beam echo sounder system is a wonderful news for the people who have dealing with marine research and engineering. Its character of high data density gives us better understanding about the seabed topography and its change. However, the error propagation of multibeam data is hard to derive because all the sounding data is computed from related sensors. Besides, some irregular soundings or outliers indeed influence the charting result, and may spoil the quality of decision making. It may not be difficult to remove the outliers from the data measured in the absolutely flat and smooth seabed. But for the seabed with artificial reef or rocky coast, we need more logical rule to identify the anomalous data.
Most current published automatic error detection algorithms for multi-beam data rely on comparison with its neighbor soundings. Thus the efficiency of error detection is very much related to the data quality. However, our automatic error detection algorithm for multi-beam data has following characteristics. At first, we find the relationship between the beam angles and the positions where the beams hit the seabed in a profile. Secondly, the "shadow effect" caused by the features on the seabed is been investigated. Thirdly, time-based moving average algorithm was used for previous stages flagged data points by comparing with its neighbors from its adjacent line. The outliers are removed due to above mentioned prefiltering processes. And finally, the consistency of the inter-swath data and the seabed roughness were also checked by comparing inter-swath data .
The result shows that the two phases error flagging process originated from our error detection algorithm not only filter out the big mistake but detect small unreasonable soundings. However, it is found that a lot of soundings around artificial reefs don''t have enough neighbor soundings for depth accuracy estimation owing to lack of inter-swath overlay area at the second flagging phase. We can easily distinguish the depth points located on the surface of artificial reefs from outliers by overlapping two adjacent survey lines.

目 錄
中文摘要 Ⅰ
英文摘要 Ⅱ
目錄 Ⅳ
圖目錄 Ⅵ
表目錄 Ⅷ
第一章 前言 1
1.1 研究背景 1
1.2 相關文獻回顧 3
1.3 研究方法與內容 4
第二章 多音束測深系統簡介 11
2.1 多音束測深聲納之原理 11
2.2 多音束測深系統之架構 11
2.3 多音束測深系統之資料演算數學模式 15
2.3.1 關於資料點之水深值及平面座標之計算 15
2.3.2 計算實際深度誤差之可能範圍 20
2.4 多波束測深系統之資料特性 21
2.5 影響多音束測深系統資料品質之主要因素 25
2.5.1 系統因素 25
2.5.1.1 正確之各感測器相對位置分量之量測 25
2.5.1.2 有效之校正程序(Patch Test Calibration) 25
2.5.1.3 正確之聲速剖面輸入及潮汐水位變化 26
2.5.2 非系統因素 26
2.5.2.1 正確地航駛勘測路線 26
2.5.2.2 異常值檢測 27
第三章 多音束測深系統之資料篩選演算法 28
3.1 目前發表之相關資料篩選演算法 28
3.1.1 The NOAA COP method(1992) 28
3.1.2 Ware. Knight and Wells method(1991) 28
3.1.3 Egg method(1995) 30
3.1.4 Du Wells and Mayer method(1995) 31
3.1.5 HDCS系統(Hydrographic Data Cleaning System)(1992) 33
3.2 本研究之資料篩選演算法原理 35
3.2.1 對於實測區域範圍之估計(data coverage estimation) 35
3.2.2 波束投射角與波束投射位置的關係 35
3.2.3 遮蔽效應(shadow effect) 41
3.2.4 以時間為基礎之具權重之可移式平均(time based -weighted moving average) 49
3.2.5 可移式平均之衍生功能 52
3.2.5.1 相鄰測線之資料一致性(consistency of interswath data) 52
3.2.5.2 地形之變動性與異常值之關係(variability V.S. spike) 53
3.2.6 關於如何驗證本研究之各部原理 53
3.2.7 本演算法之限制 54
3.3 綜合與比較 55
第四章 本研究之資料篩選演算法工作流程及成果展示 58
4.1 各程序式之功能及輸入輸出格式 58
4.1.1 資料點含蓋範圍估計(data coverage estimation) 58
4.1.2 第一階段之標記(First Phase Flagging) 58
4.1.3 第二階段之標記(Second Phase Flagging) 59
4.1.4 刪除錯誤資料點 60
4.1.5 關於驗證本研究篩選結果之程式 61
4.1.6 其他輔助程序式 61
4.2 本研究之成果展示 63
第五章 結論與建議 95
參考文獻 99

參考文獻：
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