Martin Ross

Subglacial landforms in northern Manitoba, Canada, based on remote sensing data

Abstract Please click here to download the map associated with this article. This paper presents a new subglacial landform map for northern Manitoba (58°-60° N). The region was formerly covered by the Laurentide Ice Sheet and is located at the southern margin of the Late Wisconsinan (∼25-10 Ka BP) deglacial Keewatin Ice Divide just west of Hudson Bay. Mapping was focused on determining the location and orientation of streamlined landforms (drumlins and megaflutes), Rogen moraines, and eskers for the 109,366 km2 region. Based on the theory that landforms such as drumlins and Rogen moraines all result from subglacial ice flow processes, this map forms the basis for reconstruction of ice flow sets that indicate past glacial phases in northern Manitoba. It shows that the geomorphologic record, and hence ice flow history, is more complex than previously reported. There are several successive generations of ice flow indicators superimposed on top of each other, sometimes at abrupt (90°) angles. Special attention is paid to the location and ridge crest orientation of Rogen moraines in northern Manitoba. Hence the map also provides insights into the characteristics of Rogen moraines in northern Manitoba, which are critical for investigating formative processes. 11,007 individual landforms were mapped using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) satellite imagery in combination with a Shuttle Radar Topography Mission (SRTM) digital elevation model and several SPOT 4 satellite images. The results are presented as a printable map at 1:1,125,000 scale.

An Open, Object-Based Framework for Generating Anisotropy in Sedimentary Subsurface Models: An Open, Object-Based Framework for Generating Anisotropy in Sedimentary Subsurface Models

The spatial distribution of hydraulic properties in the subsurface controls groundwater flow and solute transport. However, many approaches to modeling these distributions do not produce geologically realistic results and/or do not model the anisotropy of hydraulic conductivity caused by bedding structures in sedimentary deposits. We have developed a flexible object-based package for simulating hydraulic properties in the subsurface-the Hydrogeological Virtual Realities (HyVR) simulation package. This implements a hierarchical modeling framework that takes into account geological rules about stratigraphic bounding surfaces and the geometry of specific sedimentary structures to generate realistic aquifer models, including full hydraulic-conductivity tensors. The HyVR simulation package can create outputs suitable for standard groundwater modeling tools (e.g., MODFLOW), is written in Python, an open-source programming language, and is openly available at an online repository. This paper presents an overview of the underlying modeling principles and computational methods, as well as an example simulation based on the Macrodispersion Experiment site in Columbus, Mississippi. Our simulation package can currently simulate porous media that mimic geological conceptual models in fluvial depositional environments, and that include fine-scale heterogeneity in distributed hydraulic parameter fields. The simulation results allow qualitative geological conceptual models to be converted into digital subsurface models that can be used in quantitative numerical flow-and-transport simulations, with the aim of improving our understanding of the influence of geological realism on groundwater flow and solute transport.

Regional VS30 model for the St. Lawrence Lowlands, Eastern Canada

ABSTRACT Shear-wave velocity of the top 30 m, VS30, is commonly used for prediction of the seismic site response. This paper presents development, validation and uncertainty assessment of a regional VS30 model based on a combination of simplified 3D geology and statistically representative velocity values. Results identify soft marine sediments in deep sedimentary basins as zones most susceptible to seismic shaking. Compared to the available urban-scale seismic zonation studies, the regional model showed a success rate of roughly 64% in predicting local site category. The standard deviation was in average 30% of the expected VS30 value.