Seamount detection and isolation with a modified wavelet transform
Seamount detection and isolation with a modified wavelet transform
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使用改进的小波变换进行海山检测和隔离
DOI：
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发表时间：
2008
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通讯作者：
John K. Hillier
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作者：
John K. Hillier
海山的大小、形状和数量一旦被发现并与海洋高原或海沟等其他地貌隔离开来，就有可能对重要的固体地球过程，例如海洋火山作用，提供宝贵的制约。然而，海山大小和形态的多变性给海山隔离的计算方法带来了问题。本文开发了一种新颖有效的基于小波的海山检测程序"空间小波变换“;首次使用多尺度分析从测深数据中直接分离出建筑物。只使用与形状有关的弱标准，不需要关于海山规模和位置的先验知识。对于在巡航v3312 SWT上收集的测深剖面，与最佳统计（例如平均值、中位数或众数）滑动窗口滤波器相比，人工检查确定的特征数量的五倍尺寸在25%以内匹配。大小-频率分布是海山种群的一个关键描述符，用SWT方法估计也好得多。因此，SWT是朝着实现海山客观和可靠的量化和分类目标迈出的一步。
The size, shape and number of seamounts, once detected and isolated from other features such as oceanic plateaus or trenches, have the potential to provide valuable constraints on important solid Earth processes, e.g. oceanic volcanism. The variability of seamount size and morphology, however, presents problems for computational approaches to seamount isolation. This paper develops a novel and efficient wavelet‐based seamount detection routine ‘Spatial Wavelet Transform (SWT)’; the first use of multiple scales of analysis to directly isolate edifices from bathymetric data. Only weak shape‐related criteria are used and no a priori knowledge of the scale and location of the seamounts is required. For a bathymetric profile collected on cruise v3312 SWT matches, to within 25%, the dimensions of five times the number of the features determined by manual inspection than does the best statistically based (e.g. mean, median or mode) sliding window filter. The size–frequency distribution, a key descriptor of seamount populations, is also much better estimated by the SWT method. As such, the SWT represents a step towards the goal of objective and robust quantification and classification of seamounts.