The Mid-Cayman spreading centre is an ultraslow-spreading ridge in the Caribbean Sea. Its extreme depth and geographic isolation from other mid-ocean ridges offer insights into the effects of pressure on hydrothermal venting, and the biogeography of vent fauna. Here we report the discovery of two hydrothermal vent fields on the Mid-Cayman spreading centre. The Von Damm Vent Field is located on the upper slopes of an oceanic core complex at a depth of 2,300 m. High-temperature venting in this off-axis setting suggests that the global incidence of vent fields may be underestimated. At a depth of 4,960 m on the Mid-Cayman spreading centre axis, the Beebe Vent Field emits copper-enriched fluids and a buoyant plume that rises 1,100 m, consistent with >400 °C venting from the world's deepest known hydrothermal system. At both sites, a new morphospecies of alvinocaridid shrimp dominates faunal assemblages, which exhibit similarities to those of Mid-Atlantic vents.
We surveyed Antarctic krill (Euphausia superba) under sea ice using the autonomous underwater vehicle Autosub-2. Krill were concentrated within a band under ice between 1 and 13 kilometers south of the ice edge. Within this band, krill densities were fivefold greater than that of open water. The under-ice environment has long been considered an important habitat for krill, but sampling difficulties have previously prevented direct observations under ice over the scale necessary for robust krill density estimation. Autosub-2 enabled us to make continuous high-resolution measurements of krill density under ice reaching 27 kilometers beyond the ice edge.
Terrain-aided navigation (TAN) is a localisation method which uses bathymetric measurements for bounding the growth in inertial navigation error. The minimisation of navigation errors is of particular importance for long-endurance autonomous underwater vehicles (AUVs). This type of AUV requires simple and effective on-board navigation solutions to undertake long-range missions, operating for months rather than hours or days, without reliance on external support systems. Consequently, a suitable navigation solution has to fulfil two main requirements: (a) bounding the navigation error, and (b) conforming to energy constraints and conserving on-board power. This study proposes a low-complexity particle filter-based TAN algorithm for Autosub Long Range, a long-endurance deep-rated AUV. This is a light and tractable filter that can be implemented on-board in real time. The potential of the algorithm is investigated by evaluating its performance using field data from three deep (up to 3,700 m) and long-range (up to 195 km in 77 hr) missions performed in the Southern Ocean during April 2017. The results obtained using TAN are compared to on-board estimates, computed via dead reckoning, and ultrashort baseline (USBL) measurements, treated as baseline locations, sporadically recorded by a support ship. Results obtained through postprocessing demonstrate that TAN has the potential to prolong underwater missions to a range of hundreds of kilometres without the need for intermittent surfacing to obtain global positioning system fixes. During each of the missions, the system performed 20 Monte Carlo runs. Throughout each run, the algorithm maintained convergence and bounded error, with high estimation repeatability achieved between all runs, despite the limited suite of localisation sensors. K E Y W O R D S long-range AUV navigation, marine robotics, nonlinear filtering, terrain-aided navigation
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