Yucca Mountain Climate Technical Support Representative

The principal investigator (PI), Saxon Sharpe, for Task ORD-FY04-012, DOE Cooperative Agreement DE-FC28-04RW12232, will serve as Yucca Mountain Climate Technical Support Representative for the Department of Energy (DOE) in a series of activities related to past, present, and future climate for the Yucca Mountain Project (YMP) climate program. As stated in the Viability Assessment of a Repository at Yucca Mountain: “Climate and its changes over time directly affect system performance at Yucca Mountain.” Currently, information from climate studies is used in models that support the Total System Performance Assessment and Licensing Application. It is a model component of all key attributes in the repository safety strategy (limited water contacting waste package, long waste package lifetime, low rate of release of radionuclides from breached waste packages, and radionuclide concentration reduction during transport from the waste packages). Elements of the climate program are also directly related to the Nuclear Regulatory Commission’s (NRC) Key Technical Issue of Unsaturated and Saturated Zone Flow Under Isothermal Conditions and, in addition, address other NRC Key Technical Issues

Realistic Adversarial Data Augmentation for MR Image Segmentation

Neural network-based approaches can achieve high accuracy in various medical image segmentation tasks. However, they generally require large labelled datasets for supervised learning. Acquiring and manually labelling a large medical dataset is expensive and sometimes impractical due to data sharing and privacy issues. In this work, we propose an adversarial data augmentation method for training neural networks for medical image segmentation. Instead of generating pixel-wise adversarial attacks, our model generates plausible and realistic signal corruptions, which models the intensity inhomogeneities caused by a common type of artefacts in MR imaging: bias field. The proposed method does not rely on generative networks, and can be used as a plug-in module for general segmentation networks in both supervised and semi-supervised learning. Using cardiac MR imaging we show that such an approach can improve the generalization ability and robustness of models as well as provide significant improvements in low-data scenarios

Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer

The recent progress of computing, machine learning, and especially deep learning, for image recognition brings a meaningful effect for automatic detection of various diseases from chest X-ray images (CXRs). Here efficiency of lung segmentation and bone shadow exclusion techniques is demonstrated for analysis of 2D CXRs by deep learning approach to help radiologists identify suspicious lesions and nodules in lung cancer patients. Training and validation was performed on the original JSRT dataset (dataset #01), BSE-JSRT dataset, i.e. the same JSRT dataset, but without clavicle and rib shadows (dataset #02), original JSRT dataset after segmentation (dataset #03), and BSE-JSRT dataset after segmentation (dataset #04). The results demonstrate the high efficiency and usefulness of the considered pre-processing techniques in the simplified configuration even. The pre-processed dataset without bones (dataset #02) demonstrates the much better accuracy and loss results in comparison to the other pre-processed datasets after lung segmentation (datasets #02 and #03).Comment: 10 pages, 7 figures; The First International Conference on Computer Science, Engineering and Education Applications (ICCSEEA2018) (www.uacnconf.org/iccseea2018) (accepted

Potential Future Igneous Activity at Yucca Mountain, Nevada

Location, timing, and volumes of post-Miocene volcanic activity, along with expert judgment, provide the basis for assessing the probability of future volcanism intersecting a proposed repository for nuclear waste at Yucca Mountain, Nevada. Analog studies of eruptive centers in the region that may represent the style and extent of possible future igneous activity at Yucca Mountain have aided in defining the consequence scenarios for intrusion into and eruption through a proposed repository. Modeling of magmatic processes related to magma/proposed repository interactions has been used to assess the potential consequences of a future igneous event through a proposed repository at Yucca Mountain. Results of work to date indicate future igneous activity in the Yucca Mountain region has a very low probability of intersecting the proposed repository. Probability of a future event intersecting a proposed repository at Yucca Mountain is approximately 1.7 x 10{sup -8} per year. Since completion of the Probabilistic Volcanic Hazard Assessment (PVHA) in 1996, anomalies representing potential buried volcanic centers have been identified from aeromagnetic surveys. A re-assessment of the hazard is currently underway to evaluate the probability of intersection in light of new information and to estimate the probability of one or more volcanic conduits located in the proposed repository along a dike that intersects the proposed repository. US Nuclear Regulatory Commission regulations for siting and licensing a proposed repository require that the consequences of a disruptive event (igneous event) with annual probability greater than 1 x 10{sup -8} be evaluated. Two consequence scenarios are considered: (1) igneous intrusion-poundwater transport case and (2) volcanic eruptive case. These scenarios equate to a dike or dike swarm intersecting repository drifts containing waste packages, formation of a conduit leading to a volcanic eruption through the repository that carries the contents of the waste packages into the atmosphere, deposition of a tephra sheet, and redistribution of the contaminated ash. In both cases radioactive material is released to the accessible environment either through groundwater transport or through the atmospheric dispersal and deposition. Six Quaternary volcanic centers exist within 20 h of Yucca Mountain. Lathrop Wells cone (LWC), the youngest (approximately 75,000 yrs), is a well-preserved cinder cone with associated flows and tephra sheet that provides an excellent analogue for consequence studies related to future volcanism. Cone, lavas, hydrovolcanic ash, and ash-fall tephra have been examined to estimate eruptive volume and eruption type. LWC ejecta volumes suggest basaltic volcanism may be waning in the Yucca Mountain region. The eruptive products indicate a sequence of initial fissure fountaining, early Strombolian ash and lapilli deposition forming the scoria cone, a brief hydrovolcanic pulse (possibly limited to the NW sector), and a violent Strombolian phase. Mathematical models have been developed to represent magmatic processes and their consequences on proposed repository performance. These models address dike propagation, magma interaction and flow into drifts, eruption through the proposed repository, and post intrusion/eruption effects. These models continue to be refined to reduce the uncertainty associated with the consequences from a possible future igneous event