Michael V. Jakuba

Diverse styles of submarine venting on the ultraslow spreading Mid-Cayman Rise

Thirty years after the first discovery of high-temperature submarine venting, the vast majority of the global mid-ocean ridge remains unexplored for hydrothermal activity. Of particular interest are the world’s ultraslow spreading ridges that were the last to be demonstrated to host high-temperature venting but may host systems particularly relevant to prebiotic chemistry and the origins of life. Here we report evidence for previously unknown, diverse, and very deep hydrothermal vents along the ∼110 km long, ultraslow spreading Mid-Cayman Rise (MCR). Our data indicate that the MCR hosts at least three discrete hydrothermal sites, each representing a different type of water-rock interaction, including both mafic and ultramafic systems and, at ∼5,000 m, the deepest known hydrothermal vent. Although submarine hydrothermal circulation, in which seawater percolates through and reacts with host lithologies, occurs on all mid-ocean ridges, the diversity of vent types identified here and their relative geographic isolation make the MCR unique in the oceans. These new sites offer prospects for an expanded range of vent-fluid compositions, varieties of abiotic organic chemical synthesis and extremophile microorganisms, and unparalleled faunal biodiversity—all in close proximity.

Monitoring of Benthic Reference Sites: Using an Autonomous Underwater Vehicle

We have established an Australia-wide observation program that exhibits recent developments in autonomous underwater vehicle (AUV) systems to deliver precisely navigated time series benthic imagery at selected reference stations on Australias continental shelf. These observations are designed to help characterize changes in benthic assemblage composition and cover derived from precisely registered maps collected at regular intervals. This information will provide researchers with the baseline ecological data necessary to make quantitative inferences about the long-term effects of climate change and human activities on the benthos. Incorporating a suite of observations that capitalize on the unique capabilities of AUVs into Australias integrated marine observation system (IMOS) [1] is providing a critical link between oceanographic and benthic processes. IMOS is a nationally coordinated program designed to establish and maintain the research infrastructure required to support Australias marine science research. It has, and will maintain, a strategic focus on the impact of major boundary currents on continental shelf environments, ecosystems, and biodiversity. The IMOS AUV facility observation program is designed to generate physical and biological observations of benthic variables that cannot be cost effectively obtained by other means.

Explosive volcanism on the ultraslow-spreading Gakkel ridge, Arctic Ocean

Roughly 60% of the Earth’s outer surface is composed of oceanic crust formed by volcanic processes at mid-ocean ridges. Although only a small fraction of this vast volcanic terrain has been visually surveyed or sampled, the available evidence suggests that explosive eruptions are rare on mid-ocean ridges, particularly at depths below the critical point for seawater (3,000 m). A pyroclastic deposit has never been observed on the sea floor below 3,000 m, presumably because the volatile content of mid-ocean-ridge basalts is generally too low to produce the gas fractions required for fragmenting a magma at such high hydrostatic pressure. We employed new deep submergence technologies during an International Polar Year expedition to the Gakkel ridge in the Arctic Basin at 85° E, to acquire photographic and video images of ‘zero-age’ volcanic terrain on this remote, ice-covered ridge. Here we present images revealing that the axial valley at 4,000 m water depth is blanketed with unconsolidated pyroclastic deposits, including bubble wall fragments (limu o Pele), covering a large (>10 km2) area. At least 13.5 wt% CO2 is necessary to fragment magma at these depths, which is about tenfold the highest values previously measured in a mid-ocean-ridge basalt. These observations raise important questions about the accumulation and discharge of magmatic volatiles at ultraslow spreading rates on the Gakkel ridge and demonstrate that large-scale pyroclastic activity is possible along even the deepest portions of the global mid-ocean ridge volcanic system.

An efficient approach to bathymetric SLAM

In this paper we propose an approach to SLAM suitable for bathymetric mapping by an Autonomous Underwater Vehicle (AUV). AUVs typically do not have access to GPS while underway and the survey areas of interest are unlikely to contain features that can easily be identified and tracked using bathymetric sonar. We demonstrate how the uncertainty in the vehicle state can be modeled using a particle filter and an Extended Kalman Filter (EKF), where each particle maintains a 2D depth map to model the seafloor. Efficient methods for maintaining and resampling the joint maps and particles using Distributed Particle Mapping are then described. Our algorithm was tested using field data collected by an AUV equipped with multibeam sonar. The results achieved by Bathymetric distributed Particle SLAM (BPSLAM) demonstrate how observations of the seafloor structure improve the estimated trajectory and resulting map when compared to dead reckoning fused with USBL observations, the best navigation solution during the trials. Furthermore, the computational run time to deliver these results falls well below the total mission time, providing the potential for the algorithm to be implemented in real time.

Mapping multiple gas/odor sources in an uncontrolled indoor environment using a Bayesian occupancy grid mapping based method

In this paper we address the problem of autonomously localizing multiple gas/odor sources in an indoor environment without a strong airflow. To do this, a robot iteratively creates an occupancy grid map. The produced map shows the probability each discrete cell contains a source. Our approach is based on a recent adaptation (Jakuba, 2007) [16] to traditional Bayesian occupancy grid mapping for chemical source localization problems. The approach is less sensitive, in the considered scenario, to the choice of the algorithm parameters. We present experimental results with a robot in an indoor uncontrolled corridor in the presence of different ejecting sources proving the method is able to build reliable maps quickly (5.5 minutes in a 6 mx2.1 m area) and in real time.

A featureless approach to efficient bathymetric SLAM using distributed particle mapping

This paper presents an approach to simultaneous localization and mapping (SLAM) suitable for efficient bathymetric mapping that does not require explicit identification, tracking, or association of seafloor features. This is accomplished using a Rao–Blackwellized particle filter, in which each particle maintains a hypothesis of the current vehicle state and a grid-based, two-dimensional depth map, efficiently stored by exploiting redundancies between different maps. Distributed particle mapping is employed to remove the computational expense of map copying during the resampling process. The proposed approach to bathymetric SLAM is validated using multibeam sonar data collected by an autonomous underwater vehicle over a small-timescale mission (2 h) and a remotely operated vehicle over a large-timescale mission (11 h). The results demonstrate how observations of the seafloor structure improve the estimated trajectory and resulting map when compared to dead reckoning fused with ultrashort-baseline or long-baseline observations. The consistency and robustness of this approach to common errors in navigation is also explored. Furthermore, results are compared with a preexisting state-of-the art bathymetric SLAM technique, confirming that similar results can be achieved at a fraction of the computation cost.

Towards autonomous habitat classification using Gaussian Mixture Models

Robotic agents that can explore and sample in a completely unsupervised fashion could greatly increase the amount of scientific data gathered in dangerous and inaccessible environments. Our application is imaging the benthos using an autonomous underwater vehicle with limited communication to surface craft. Robotic exploration of this nature demands in situ data analysis. To this end, this paper presents results of using a Gaussian Mixture Model (GMM), a Hidden Markov Model (HMM) filter, an Infinite Gaussian Mixture Model (IGMM) and a Variation Dirichlet Process model (VDP) for the classification of benthic habitats. All of the models are trained using unsupervised methods. Furthermore, the IGMM and VDP are trained without knowing the the number of classes in the dataset. It was found that the sequential information the HMM filter provides to the classification process adds lag to the habitat boundary estimates, reducing the classification accuracy. The VDP proved to be the most accurate classifier of the four tested, and also one of the fastest to train. We conclude that the VDP is a powerful model for entirely autonomous labelling of benthic datasets.

Bathymetric Particle Filter SLAM Using Trajectory Maps

We present an efficient and featureless approach to bathymetric simultaneous localization and mapping (SLAM) that utilizes a Rao–Blackwellized particle filter (RBPF) and Gaussian process (GP) regression to provide loop closures in areas with little to no overlap with previously explored terrain. To significantly reduce the memory requirements (thereby allowing for the processing of large datasets) a novel map representation is also introduced that, instead of directly storing estimates of seabed depth, records the trajectory of each particle and synchronizes them to a common log of bathymetric observations. Upon detecting a loop closure each particle is weighted by matching new observations to the current predictions generated from a local reconstruction of their map using GP regression. Here the spatial correlation in the environment is fully exploited, allowing predictions of seabed depth in areas that may not have been directly observed previously. The results demonstrate how observations of seafloor structure with partial overlap can be used by bathymetric SLAM to improve map self consistency when compared with dead reckoning fused with long-baseline (LBL) observations. In addition we show how mapping corrections can still be achieved even when no map overlap is present.

A novel trigger-based method for hydrothermal vents prospecting using an autonomous underwater robot

In this paper we address the problem of localizing active hydrothermal vents on the seafloor using an Autonomous Underwater Vehicle (AUV). The plumes emitted by hydrothermal vents are the result of thermal and chemical inputs from submarine hot spring systems into the overlying ocean. The Woods Hole Oceanographic Institution’s Autonomous Benthic Explorer (ABE) AUV has successfully localized previously undiscovered hydrothermal vent fields in several recent vent prospecting expeditions. These expeditions utilized the AUV for a three-stage, nested survey strategy approach (German et al. in Deep Sea Res. I 55:203–219, 2008). Each stage consists of a survey flown at successively deeper depths through easier to detect but spatially more constrained vent fluids. Ideally this sequence of surveys culminates in photographic evidence of the vent fields themselves. In this work we introduce a new adaptive strategy for an AUV’s movement during the first, highest-altitude survey: the AUV initially moves along pre-designed tracklines but certain conditions can trigger an adaptive movement that is likely to acquire additional high value data for vent localization. The trigger threshold is changed during the mission, adapting the method to the different survey profiles the robot may find. The proposed algorithm is vetted on data from previous ABE missions and measures of efficiency presented.

Toward automatic classification of chemical sensor data from autonomous underwater vehicles

Autonomous underwater vehicles (AUVs) are commonly used to support oceanographic science by providing water-column mapping, seafloor bathymetric and photographic survey, and deep-sea exploration capabilities. In practice, the mapping activities carried out by AUVs consist of flying either pre-programmed tracklines (most propeller-driven AUVs), or else reporting data to human operators at regular intervals that permit retasking (typical for month-long underwater glider deployments). AUVs equipped with the ability to reason about scientific objectives in real time could significantly increase the value of individual deployments by enabling sampling efforts to be focused on targets or areas identified autonomously or semi-autonomously as scientifically interesting [1]. In this paper, we focus on AUV autonomy as it pertains to water-column sensing and argue that the classification of water-column sensor data represents an important enabling capability. We demonstrate practical, semi-supervised classification of water-column sensor data using a particular Bayesian, non-parametric clustering method, the Variational Dirichlet Process, combined with operator-supplied semantic labeling. The method is applied to the detection of a deep subsea hydrocarbon plume using data collected by the Woods Hole Oceanographics Sentry AUV during an expedition to the Gulf of Mexico following the Deepwater Horizon blowout disaster.