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1.
中国科学院海洋研究所在开展西太平洋马里亚纳海山区多学科综合科学考察的过程中,利用“科学”轮船载的全水深多波束测深系统Seabeam3012对多个海山进行了地形测量工作。针对作业过程中遇到的恶劣海况导致采集数据质量差、多波束系统易检测错误海底信息、测线布设难度大等问题,提出了基于船体姿态对数据质量影响分析的多波束测线方向优化、基于地形变化并参考浅地层剖面资料的作业参数优化和基于实时采集情况的多波束采集测线布设优化等一系列措施,有效地提高了海山区多波束数据采集质量,并提高了作业效率。获得的高品质地形数据,为多学科协同研究奠定了基础,为ROV等设备的现场作业提供了安全保障。 相似文献
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多波束测深技术是目前水下地形测量的主要技术手段,测量平台的瞬时姿态及方位是影响多波束测深系统最终成果准确度的重要因素。GNSS方位辅助惯性导航系统,作为目前应用较为广泛的方位、姿态、及位置综合测量系统,不仅能够提供高精度位置信息,同时也能提供测量平台的瞬时姿态及方位数据,而且因为具有GNSS方位辅助测量,使得最终方位测量结果比传统方位测量精度大大提高,这对于多波束最终测量成果精度提高具有重要意义。文中从GNSS方位辅助惯性导航系统原理及技术优势出发,结合Trimble RTX后处理技术,从姿态测量、方位测量及辅助高程测量方面分析了在多波束水下地形测量中的应用,并以实际测量成果来展现其在水下地形精密测量技术方面的优势,结果显示,定位精度可以达到优于2 cm级别,方位精度可以优于0.01°(依赖于双GNSS天线之间的基线长度),该技术对水下地形测量准确度提升作用显著。 相似文献
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斜向扩张是超慢速扩张洋中脊独特的构造特征,其地形分段特征明显区别于经典的快速-慢速端元洋中脊模型,是理解超慢速扩张洋中脊地质过程的重要切入点。基于西南印度洋中脊Indomed-Gallieni和Shaka-DuToit段多波束地形数据,分析了不同斜向扩张角度(α)洋中脊的地形分段样式。其中,46.5°~47.5°E(α=5°)、16°~25°E(α=10°)和48.5°~52°E(α=15°)为近正向扩张段,发育雁列式叠置的中央火山脊;47.5°~48.5°E(α=50°)和16°~25°E(α=60°)为斜向扩张段,仅在洋脊段中部形成中央火山脊。利用有限差分+颗粒法(FD+MIC)数值模拟技术研究了洋中脊应变分布特征对不同α值的响应,结合地形分析,认为斜向扩张角度和温度异常分布共同控制了洋中脊地形分段样式。近正向扩张洋中脊(α<20°)在温度异常处形成地壳伸展应变的集中区,有利于岩浆汇聚,发育雁列式叠置的中央火山脊,其位置随温度异常分布的变化而改变;斜向扩张洋中脊(α>20°)地壳伸展应变集中区的位置受斜向扩张几何样式控制,在洋脊段中部发育中央火山脊,对温度异常不敏感,形成位置长期固定的岩浆活动中心。 相似文献
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本文在总结和比较中国现有黄海、东海海域漫衰减系数反演算法的基础上,利用水色测量数据分析得到漫衰减系数Kd(532)与Kd(490)之间的关系,为激光测深系统(以CZMIL为例)的性能评估提供了基础。利用MODIS二级数据产品评估了中国黄海、东海海域在CZMIL海道测量模式下最大可测水深的空间分布。研究结果表明CZMIL在黄海、东海海域可测水深基本在0—50 m,该区域的面积占研究区域面积的比例为76.2%,为浅海海域开展激光测深作业提供了测深能力评估的依据和方法。 相似文献
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Kohsaku Arai Hideaki Machiyama Shun Chiyonobu Hiroki Matsuda Keiichi Sasaki Marc Humblet Yasufumi Iryu 《Island Arc》2014,23(1):1-15
Bathymetric mapping and observations of the seafloor using a remotely operated vehicle (ROV, Hyper‐Dolphin 3K) were carried out on the slopes of the Miyako‐Sone submarine platform, east of Miyako‐jima in the Ryukyu Islands, northwestern Pacific Ocean. The bathymetric map indicates that terraces are present at water depths of approximately 140 m, 330 m, 400 m, and 680 m on the northwestern slope of the platform. A number of NW–SE trending lineaments, probably faults, extend perpendicular to the axis of the Ryukyu Island Arc. Two ROV surveys were conducted at water depths ranging from 519 m (on the slope) to 121 m (shallowest part of the platform). The surveys revealed that well‐indurated carbonate rocks are exposed at terrace margins and on upper slopes, and that the lower slopes are covered with modern sediments consisting of unconsolidated, coarse‐sand‐sized bioclastic carbonates. Calcareous nannofossils from the well‐indurated carbonate rocks indicate a Middle–Late Pleistocene age, which suggests that the rocks correlate with the Quaternary reef and fore‐reef deposits of the Ryukyu Group (Ryukyu Limestone) on the Ryukyu Islands. No siliciclastic deposits corresponding to the upper Miocene–lower Pleistocene Shimajiri Group (as exposed on Okinawa‐jima and Miyako‐jima islands) were recovered during the surveys. Coeval well‐indurated carbonate rocks, all of which formed in a similar sedimentary environment, have been downthrown towards the west due to displacements on the western sides of normal faults. Subsidence of the Miyako‐Sone submarine platform was the result of large vertical displacements on such normal faults. The timing of initial subsidence cannot be tightly constrained, but the presence of the youngest limestone at progressively lower levels towards the west suggests the subsidence continued until after 0.265 Ma. 相似文献
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Airborne light detection and ranging (LiDAR) bathymetry appears to be a useful technology for bed topography mapping of non‐navigable areas, offering high data density and a high acquisition rate. However, few studies have focused on continental waters, in particular, on very shallow waters (<2 m) where it is difficult to extract the surface and bottom positions that are typically mixed in the green LiDAR signal. This paper proposes two new processing methods for depth extraction based on the use of different LiDAR signals [green, near‐infrared (NIR), Raman] of the SHOALS‐1000T sensor. They have been tested on a very shallow coastal area (Golfe du Morbihan, France) as an analogy to very shallow rivers. The first method is based on a combination of mathematical and heuristic methods using the green and the NIR LiDAR signals to cross validate the information delivered by each signal. The second method extracts water depths from the Raman signal using statistical methods such as principal components analysis (PCA) and classification and regression tree (CART) analysis. The obtained results are then compared to the reference depths, and the performances of the different methods, as well as their advantages/disadvantages are evaluated. The green/NIR method supplies 42% more points compared to the operator process, with an equivalent mean error (?4·2 cm verusu ?4·5 cm) and a smaller standard deviation (25·3 cm verusu 33·5 cm). The Raman processing method provides very scattered results (standard deviation of 40·3 cm) with the lowest mean error (?3·1 cm) and 40% more points. The minimum detectable depth is also improved by the two presented methods, being around 1 m for the green/NIR approach and 0·5 m for the statistical approach, compared to 1·5 m for the data processed by the operator. Despite its ability to measure other parameters like water temperature, the Raman method needed a large amount of reference data to provide reliable depth measurements, as opposed to the green/NIR method. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
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