Seamount detection and isolation with a modified wavelet transform

Abstract

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 'SWT'; the first use of multiple scales of analysis to directly isolate edifices from bathymetric data. Only weak shaperelated 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.

Testing 3D landform quantification methods with synthetic drumlins in a real digital elevation model

“…Wessel, 1998;Hillier 167 and , Hillier, 2008. Distinctively for an RRS technique the cookie cutter alone 168 requires a manually digitized outline as an input.…”

Manual mapping of drumlins in synthetic landscapes to assess operator effectiveness

“…This has also made automated identification a non-trivial task and it is only in the last decade that significant progress has been made (e.g. Anders, Seijmonsbergen, & Bouten, 2011;Drȃguţ & Blaschke, 2006;Hillier, 2008). Even then, anecdotal observation of researchers' preferences and its usage in publications suggests that manual interpretation is generally still considered to be more reliable.…”

3.11 Geovisualization

“…Sithole, 2004). For geomorphic features, several algorithms also exist that identify the smaller features directly (Hillier & Watts, 2004;Hillier, 2008). These seek to avoid the problems experienced by the older approaches above, and have the advantage that features are implicitly mapped during the separation but, like the de-cluttering algorithms, are somewhat task specific.…”

“…identifying and determining spatial limits for) all individual features of a certain type within landscapes (e.g. Hillier & Watts, 2004;Hillier, 2008) leaving the regional trend. But, the same naming convention is still used.…”