We make a first-order calculation of accumulation rates in the catchment of Thwaites Glacier (TG), West Antarctica using the Nye and Daansgard-Johnson methodologies. Both formulations compute accumulations as a function of the age-depth relationship, including a thinning correction due to ice flow. For this purpose, I track and firn-correct two continuous, shallow ice layers obtained from radio echo soundings surveyed during the 2004-05 AGASEA expedition. The layers range from 60 to 700 meters depth between the ice divide and the coast. Dating of layers come from the ice core WDC06A, located on the West Antarctic Ice Sheet (WAIS) ice divide, which have ages 548 and 725 years, respectively. We compare our accumulation results with four independent datasets: 1)IceBridge snow radar (2009-2010), optimized for tracking near-surface layers; 2) a contemporary model of snowfall precipitation, 3) an interpolation of ice core data using satellite passive microwave; 4) ice cores data. We test the hypothesis that accumulation rates have increased since the beginning of the industrial era, a change that has not been observed. Indeed, I find that observations indicate that accumulation rates in the TG catchment have not changed during the past ~700 years. From here I assess the mass balance of the system and analyze what it tells about the history of the glacier. / text

The West Antarctic Ice Sheet (WAIS) is mostly grounded in broad, deep basins (down to 2.5 km below sea level) that are stretched between five crustal blocks. The geometry of the bedrock, being mostly below sea level, induces a fundamental instability in the WAIS through the possibility of runaway grounding line retreat. The crustal environment of the WAIS further influences the ice sheet’s fast flow through conditions at the ice-bedrock boundary. This study focuses on understanding the WAIS by examining the subglacial geology (such as volcanoes and sedimentary basins) at the icebedrock boundary and the continent’s deeper crustal structure- primarily using airborne gravity anomalies. The keystone of this study is a 2004-2005 aerogeophysical survey over one of the most negative mass balance glaciers on the continent: Thwaites Glacier (TG). The gravity anomalies derived from this dataset- as well as gravity-based modeling and spectral crustal boundary depth estimates- reveal a heterogeneous crustal environment beneath the glacier. The widespread Mesozoic rifting observed in the Ross Sea Embayment (RSE) of West Antarctica extends beneath TG, where the crust is ~27 km thick and cool. Adjacent to TG, spectrally-derived shallow Moho depths for the Marie Byrd Land (MBL) crustal block can be explained by thermal support from warm mantle. I assemble here new compilations of free-air and Bouguer gravity anomalies across West Antarctica (from both airborne and satellite datasets) and re-interpret the extents of West Antarctic crustal block and their boundaries with the rift system. Airy isostatic gravity anomalies reveal that TG is relatively sediment starved, in contrast to the sediment-rich RSE. TG’s fast flow velocities could be sustained in this sediment poor environment if higher heat flux in MBL was providing an ample source of subglacial melt water to the glacier. The isostatic anomalies also indicate that TG’s outlet rests on a bedrock sill that will impede future grounding line retreat (up to ~100 km) and temporarily stabilize the glacier. / text

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

This thesis presents a deep learning tool able to identify ice in radar images fromthe sea-ice environment of the Twhaites glacier outlet. The project is motivatedby the threatening situation of the Thwaites glacier that has been increasingits mass loss rate during the last decade. This is of concern considering thelarge mass of ice held by the glacier, that in case of melting, could increasethe mean sea level by more than +65 cm [1]. The algorithm generated alongthis work is intended to help in the generation of navigation charts and identificationof icebergs in future stages of the project, outside of the scope of this thesis.The data used for this task are ICEYE’s X-band radar images from the Thwaitessea-ice environment, the target area to be studied. The corresponding groundtruth for each of the samples has been manually generated identifying the iceand icebergs present in each image. Additional data processing includes tiling,to increment the number of samples, and augmentation, done by horizontal andvertical flips of a random number of tiles.The proposed tool performs semantic segmentation on radar images classifyingthe class "Ice". It is developed by a deep learning Convolutional Neural Network(CNN) model, trained with prepared ICEYE’s radar images. The model reachesvalues of F1 metric higher than 89% in the images of the target area (Thwaitessea-ice environment) and is able to generalize to different regions of Antarctica,reaching values of F1 = 80 %. A potential alternative version of the algorithm isproposed and discussed. This alternative score F1 values higher than F1 > 95 %for images of the target environment and F1 = 87 % for the image of the differentregion. However, it must not be confirmed as the final algorithm due to the needfor further verification.

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