James H. Lever

Accretion rate of cosmic spherules measured at the South Pole.

Micrometeorites are terrestrially collected, extraterrestrial particles smaller than about 1 mm, which account for most of the mass being accreted to the Earth,. Compared with meteorites, micrometeorites more completely represent the Earth-crossing meteoroid complex, and should include fragments of asteroids, comets, Mars and our Moon, as well as pre-solar and interstellar grains,. Previous measurements of the flux of micrometeoroids that survive to the Earths surface have large uncertainties owing to the destruction of particles by weathering, inefficiencies in magnetic collection or separation techniques, low particle counts,, poor age constraint,, or highly variable concentrating processes,. Here we describe an attempt to circumvent these problems through the collection of thousands of well preserved and dated micrometeorites from the bottom of the South Pole water well, which supplies drinking water for the Scott–Amundsen station. Using this collection, we have determined precise estimates of the flux and mass distribution for 50–700-µm cosmic spherules (melted micrometeorites). Allowing for the expected abundance of unmelted micrometeorites in the samples, our results indicate that about 90% of the incoming mass of submillimetre particles evaporates during atmospheric entry. Our data indicate the loss of glass-rich and small stony spherules from deep-sea deposits,, and they provide constraints for models describing the survival probability of micrometeoroids,.

Simulated Rainfall-Driven Dissolution of TNT, Tritonal, Comp B and Octol Particles

Live-fire military training can deposit millimeter-sized particles of high explosives (HE) on surface soils when rounds do not explode as intended. Rainfall-driven dissolution of the particles then begins a process whereby aqueous HE solutions can enter the soil and groundwater as contaminants. We dripped water onto individual particles of TNT, Tritonal, Comp B and Octol to simulate how surface-deposited HE particles might dissolve under the action of rainfall and to use the data to verify a model that predicts HE dissolution as a function of particle size, particle composition and rainfall rate. Particle masses ranged from 1.1 to 17 mg and drip rates corresponded to nominal rainfall rates of 6 and 12 mmh(-1). For the TNT and Tritonal particles, TNT solubility governed dissolution time scales, whereas the lower-solubility of RDX controlled the dissolution time of both RDX and TNT in Comp B. The large, low-solubility crystals of HMX slowed but did not control the dissolution of TNT in Octol. Predictions from a drop-impingement dissolution model agree well with dissolved-mass timeseries for TNT, Tritonal and Comp B, providing some confidence that the model will also work well when applied to the rainfall-driven, outdoor dissolution of these HE particles.

Outdoor Weathering and Dissolution of TNT and Tritonal

Low-order detonations of military munitions scatter cm-sized chunks of high-explosives onto military range soils, where rainfall can dissolve and then transport the explosives to groundwater. We present 1 year of mass-loss data obtained from cm-sized chunks of the frequently used explosives TNT (2,4,6-trinitrotoluene) and Tritonal (an 80:20 mixture of TNT and aluminum flakes) exposed outdoors to weather and dissolve under natural conditions. The explosive chunks rested on glass frits in individual funnels and all precipitation interacting with them was collected and analyzed. Mass balance data reveal that TNT in the water samples accounts for only about one-third of the TNT lost from the chunks. The creation of photo-transformation products on the solid chunks, and their subsequent dissolution or sublimation, probably accounts for the other two-thirds. Although these products cannot, as yet, be quantified they are intrinsic to the outdoor weathering and fate of TNT-based explosives. TNT in our water samples was not photo-transformed. Thus, we used the yearlong, dissolved-mass time-series to validate a drop-impingement dissolution model for TNT. The model used measured rainfall and air temperature data as input, and the results agreed remarkably well with TNT dissolved-mass time-series measured for the year. This model can estimate annual TNT influx into range soils using annual rainfall and particle-size distributions. Nevertheless, large uncertainties remain in the numbers and sizes of TNT particles scattered on military ranges and the identities and fates of the photo-transformation products.

Design and power management of a solar-powered “Cool Robot” for polar instrument networks

The Cool Robot is a four-wheel-drive, solar-powered, autonomous robot designed to support summertime science campaigns in Antarctica and Greenland over distances exceeding 500 km. This paper provides an overview of key features of the robot, including design for good mobility, high efficiency, and long-term deployment under solar power in harsh polar environments. The Cool Robots solar panel box, comprising panels on four sides and a top panel, encounters insolation variations with a bandwidth of up to 1 Hz due to sastrugi. The paper details a unique photovoltaic control algorithm to accommodate these variations. We deployed the robot at Summit Camp, Greenland to validate its mobility and power budget and to assess the photovoltaic control system. The 61 kg robot drove continuously at 0.78 m/s on soft snow, its 160 W average power demand met by solar power alone under clear skies above 16° sun elevation. The power-control system reliably matched input with demand as insolation varied during testing. A simple GPS waypoint-following algorithm provides low-bandwidth path planning and course correction and demonstrated reliable autonomous navigation during testing over periods of 5–8 h. Field data validate the Cool Robot design models and indicate that it will exceed its design goal of carrying a 15 kg payload 500 km across Antarctica in 2 weeks. A brief description of instrument payloads and scientific studies aided by networks of such autonomous solar robots is provided.

Seeking Unbiased Collections of Modern and Ancient Micrometeorites

Micrometeorites are sub-millimeter-sized extraterrestrial particles that survive atmospheric entry. An unbiased collection of micrometeorites should contain samples of all of the dust-producing objects in the solar system. However, because of low concentrations and rapid weathering in terrestrial environments, unbiased collections are difficult to find. Additionally, most particles have been severely heated during atmospheric entry, and the resulting changes must be understood to derive compositional information about the parent micrometeoroids. Large modern collections that can be characterized by the flux, size distribution, and micrometeorite compositional types can help constrain heating models that predict how micrometeorites are heated while entering the Earth’s atmosphere. These collections can also be used as a reference to deduce the effects of weathering on collections of ancient micrometeorites.

Performance of a solar-powered robot for polar instrument networks

The Cool robot is a four-wheel-drive, solar-powered autonomous vehicle designed to support summertime science campaigns in Antarctica and Greenland. We deployed the robot at Summit Camp, Greenland, during 2005 to validate its power budget and to assess its unique control system that matches solar power input with power demand as the robot drives over rough terrain. The 61-kg robot drove continuously at 0.78 m/s on soft snow, its 160-W average power demand met by solar power alone under clear skies when sun elevation exceeded 16deg. The power-control system reliably matched input with demand as insolation changed during the tests. A simple GPS waypoint-following algorithm provided reliable autonomous navigation over periods of 5-8 hours. The data validate our design models and indicate that the Cool Robot exceeds its design goal of carrying a 15-kg payload 500 km in two weeks on the Antarctic plateau

Autonomous GPR Surveys using the Polar Rover Yeti

The National Science Foundation operates stations on the ice sheets of Antarctica and Greenland to investigate Earths climate history, life in extreme environments, and the evolution of the cosmos. Understandably, logistics costs predominate budgets due to the remote locations and harsh environments involved. Currently, manual ground-penetrating radar (GPR) surveys must preceed vehicle travel across polar ice sheets to detect subsurface crevasses or other voids. This exposes the crew to the risks of undetected hazards. We have developed an autonomous rover, Yeti, specifically to conduct GPR surveys across polar ice sheets. It is a simple four-wheel-drive, battery-powered vehicle that executes autonomous surveys via GPS waypoint following. We describe here three recent Yeti deployments, two in Antarctica and one in Greenland. Our key objective was to demonstrate the operational value of a rover to locate subsurface hazards. Yeti operated reliably at −30 °C, and it has has good oversnow mobility and adequate GPS accuracy for waypoint-following and hazard georeferencing. It has acquired data on hundreds of crevasse encounters to improve our understanding of heavily crevassed traverse routes and to develop automated crevasse-detection algorithms. Importantly, it helped to locate a previously undetected buried building at the South Pole. Yeti can improve safety by decoupling survey personnel from the consequences of undetected hazards. It also enables higher-quality systematic surveys to improve hazard-detection probabilities, increase assessment confidence, and build datasets to understand the evolution of these regions. Yeti has demonstrated that autonomous vehicles have great potential to improve the safety and efficiency of polar logistics.

An autonomous robotic platform for ground penetrating radar surveys

Detection of hidden surface crevasses on glaciers is a vital process involved in over-snow traverses for science and resupply missions in Polar regions. There are several areas warranting improvement in the current protocol for crevasse detection, which employs a human-operated ground penetrating radar (GPR) on a mid-weight tracked vehicle. In this fashion, a GPR scout team must plan an appropriate crevasse-free route by investigating paths across the glacier. This paper presents methods supporting a completely autonomous robotic system employing GPR probing of the glacier surface. We tested and evaluated three machine learning algorithms on post-processed Antarctic GPR data, collected by our robot and a Pisten Bully in 2009 and 2010 at McMurdo Station. We achieved 82% classification rate for a linear SVM, compared to 82% using logistic regression and 80% using a Bayes network for contrast. We also discuss independent versus sequential classification of GPR scans, and suggest improvements to or combinations of the most successful training models. Our experiment demonstrates the promise and reliability of real-time object detection with GPR.

Crevasse Detection in Ice Sheets Using Ground Penetrating Radar and Machine Learning

This paper presents methods to automatically classify ground penetrating radar (GPR) images of crevasses on ice sheets. We use a combination of support vector machines (SVMs) and hidden Markov models (HMMs) with down sampling, a preprocessing step that is unbiased and suitable for real-time analysis and detection. We perform modified cross-validation experiments with 129 examples of Greenland GPR imagery from 2012, collected by a lightweight robot towing a GPR. In order to minimize false positives, an HMM classifier is trained to prescreen the data and mark locations in the GPR files to evaluate with an SVM, and we evaluate the classification results with a similar modified cross-validation technique. The combined HMM-SVM method retains all of the correct classifications by the SVM, and reduces the false positive rate to 0.0007. This method also reduces the computational burden in classifying GPR traces because the SVM is evaluated only on select prescreened traces. Our experiments demonstrate the promise, robustness, and reliability of real-time crevasse detection and classification with robotic GPR surveys.

Development of an autonomous robot for ground penetrating radar surveys of polar ice

This paper describes the design and fabrication of a low cost, battery-powered mobile robot for ground penetrating radar surveys in support of Polar science and logistics. Key features of the design include lightweight construction for low resistance and high energy efficiency in deformable terrain; a passive, articulated chassis for high mobility; and design simplicity for low cost. Deployment in Greenland in spring 2008 over crevasse fields demonstrated the ability of the robot to traverse rough terrain characterized by both firm and soft snow, while gathering data from a ground penetrating radar to detect crevasses. A simple navigation and control algorithm provides low-bandwidth path planning and course correction. Mobility assessment during deployment highlights the need for non-visual means of assessing mobility autonomously. A proprioceptive sensor suite and sample data for autonomous detection of terrain traversability are described.