Global Distribution and Morphology of Seamounts

Seamounts are valuable characteristics of the ocean floor geographically, ecologically, and oceanographically since they provide insight on many of the Earths cycles. However, the global distribution of small seamounts is still incomplete because about 80% of the seafloor remains unmapped by multibeam sonar. The vertical gravity gradient (VGG) can be used to better understand small-scale features on the ocean floor. The accuracy of the VGG has increased by about a factor of two since the last seamount census [Kim and Wessel, 2011] because of the new altimeter data provided by Cryosat-2, SARAL/Altika, Jason-1/2, and Sentinel-3. We used the latest grid of VGG to update and refine the global seamount catalog. Known features such as fracture zones, transform faults, ridge axes, and see-saw propagators were masked since these can give signals that may look like seamounts in the VGG. We visually identified approximately 10,000 new seamounts, expanding the seamount catalog by 1/3. These new seamounts were then recentered using the nearby local maximum in the VGG grid. Most of these newly-discovered seamounts are relatively small (< 2.5 km) and have conical morphology. To further refine the characteristic shapes of small seamounts, we searched the global 15-arc second bathymetry for seamounts having the most data in a 12 km radius. We found 206 well-surveyed seamounts having heights ranging from 421 m to 2514 m. Two approaches were used to estimate the typical radially-symmetric seamount morphology. First, normalized seamount height versus normalized radius was used in an Empirical Orthogonal Function (EOF) analysis to find the characteristic seamount shape. Mode-1 explained nearly all the variance (91.01%) and so the higher modes were not needed. The second approach was to combine the radial normalized height data from all 206 seamounts to form median and median-absolute deviation. These data were fit to a 1-parameter Gaussian model that explained 99.82% of the variance. The slope of the best-fit model is 0.26 which is in good agreement with a previous study based on the analysis of 88 seamounts [Smith, 1988]. By understanding the distribution and shape of these seamounts, scientists can build a better visualization about parts of the ocean floor that are yet to be surveyed and explain their implications on other Earth processes.