3 Oct 2022

Landform Identification in the Chihuahuan Desert for Dust Source Characterization Applications: Developing a Landform Reference Data Set

Abstract: ERDC-Geo is a surface erodibility parameterization developed to improve dust predictions in weather forecasting models. Geomorphic landform maps used in ERDC-Geo link surface dust emission potential to landform type. Using a previously generated southwest United States landform map as training data, a classification model based on machine learning (ML) was established to generate ERDC-Geo input data. To evaluate the ability of the ML model to accurately classify landforms, an independent reference landform data set was created for areas in the Chihuahuan Desert. The reference landform data set was generated using two separate map-ping methodologies: one based on in situ observations, and another based on the interpretation of satellite imagery. Existing geospatial data layers and recommendations from local rangeland experts guided site selections for both in situ and remote landform identification. A total of 18 landform types were mapped across 128 sites in New Mexico, Texas, and Mexico using the in situ (31 sites) and remote (97 sites) techniques. The final data set is critical for evaluating the ML-classification model and, ultimately, for improving dust forecasting models.

30 Sep 2022

Automated Ground-Penetrating-Radar Post-Processing Software in R Programming

Abstract: Ground-penetrating radar (GPR) is a nondestructive geophysical technique used to create images of the subsurface. A major limitation of GPR is that a subject matter expert (SME) needs to post-process and interpret the data, limiting the technique’s use. Post-processing is time-intensive and, for detailed processing, requires proprietary software. The goal of this study is to develop automated GPR post-processing software, compatible with Geophysical Survey Systems, Inc. (GSSI) data, in open-source R programming. This would eliminate the need for an SME to process GPR data, remove proprietary software dependencies, and render GPR more accessible. This study collected GPR profiles by using a GSSI SIR4000 control unit, a 100 MHz antenna, and a Trimble GPS. A standardized method for post-processing data was then established, which includes static data removal, time-zero correction, distance normalization, data filtering, and stacking. These steps were scripted and automated in R programming, excluding data filtering, which was used from an existing package, RGPR. The study compared profiles processed using GSSI soft-ware to profiles processed using the R script developed here to ensure comparable functionality and output. While an SME is currently still necessary for interpretations, this script eliminates the need for one to post-process GSSI GPR data.

30 Sep 2022

Investigations into the Ice Crystallization and Freezing Properties of the Antifreeze Protein ApAFP752

Abstract: Antifreeze proteins (AFPs) allow biological organisms, including insects, fish, and plants, to survive in freezing temperatures. While in solution, AFPs impart cryoprotection by creating a thermal hysteresis (TH), imparting ice recrystallization inhibition (IRI), and providing dynamic ice shaping (DIS). To leverage these ice-modulating effects of AFPs in other scenarios, a range of icing assays were performed with AFPs to investigate how AFPs interact with ice formation when tethered to a surface. In this work, we studied ApAFP752, an AFP from the beetle Anatolica polita, and first investigated whether removing the fusion protein attached during protein expression would result in a difference in freezing behavior. We performed optical microscopy to examine ice-crystal shape, micro-structure, and the recrystallization behavior of frozen droplets of AFP solutions. We developed a surface chemistry approach to tether these proteins to glass surfaces and conducted droplet-freezing experiments to probe the interactions of these proteins with ice formed on those surfaces. In solution, ApAFP752 did not show any DIS or TH, but it did show IRI capabilities. In surface studies, the freezing of AFP droplets on clean glass surfaces showed no dependence on concentration, and the results from freezing water droplets on AFP-decorated surfaces were inconclusive.

30 Sep 2022

Buried-Object-Detection Improvements Incorporating Environmental Phenomenology into Signature Physics

Abstract: The ability to detect buried objects is critical for the Army. Therefore, this report summarizes the fourth year of an ongoing study to assess environmental phenomenological conditions affecting probability of detection and false alarm rates for buried-object detection using thermal infrared sensors. This study used several different approaches to identify the predominant environmental variables affecting object detection: (1) multilevel statistical modeling, (2) direct image analysis, (3) physics-based thermal modeling, and (4) application of machine learning (ML) techniques. In addition, this study developed an approach using a Canny edge methodology to identify regions of interest potentially harboring a target object. Finally, an ML method was developed to improve automatic target detection and recognition performance by accounting for environmental phenomenological conditions, improving performance by 50% over standard automatic target detection and recognition software.

8 Sep 2022

Ground-penetrating Radar Studies of Permafrost, Periglacial, and Near-surface

Abstract: Installations built on ice, permafrost, or seasonal frozen ground require careful design to avoid melting issues. Therefore, efforts to rebuild McMurdo Station, Antarctica, to improve operational efficiency and consolidate energy resources require knowledge of near-surface geology. Both 200 and 400 MHz ground-penetrating radar (GPR) data were collected in McMurdo during January, October, and November of 2015 to detect the active layer, permafrost, excess ice, fill thickness, solid bedrock depth, and buried utilities or construction and waste debris. Our goal was to ultimately improve surficial geology knowledge from a geotechnical perspective. Radar penetration ranged between approximately 3 and 10 m depth for the 400 and 200 MHz antennas, respectively. Both antennas successfully detect buried utilities and near-surface stratified material to ~0.5–3.0 m whereas 200 MHz profiles were more useful for mapping deeper stratified and un-stratified fill over bedrock. Artificially generated excess ice which appears to have been created from runoff, water pooling and refreezing, aspect shading from buildings, and snowpack buried under fill, are prevalent. Results show that McMurdo Station has a complex myriad of ice-rich fill, scoria, fractured volcanic bedrock, permafrost, excess ice, and buried anthropogenically generated debris, each of which must be considered during future construction.

7 Sep 2022

Extracting Sintered Snow Properties from MicroCT Imagery to Initialize a Discrete Element Method Model

Abstract: Modeling snow’s mechanical behavior is important for many cold regions engineering problems. Because snow’s microstructure plays a significant role in its mechanical response, it is imperative to initialize models with accurate bond characteristics and realistic snow-grain geometries to precisely capture the microstructure interactions. Previous studies have processed microcomputed tomography scans of snow samples with a watershed method to extract grain geometries. This approach relies on identification of seed points to segment each grain. Our new methodology, called the “moving window method,” does not require prior knowledge of the snow-grain-size distribution to identify seed points. We use the interconnectivity of the segmented grains to identify bond characteristics. We compare the resultant grain-size and bond-size distributions to the known grain sizes of the laboratory-made snow samples. The grain-size distributions from the moving window method closely match the known grain sizes, while both results from the traditional method produce grains that are too large. We propose that the bond net-work identified using the traditional method underestimates the number of bonds and overestimates bond radii. Our method allows us to segment realistic snow grains and their associated bonds, without prior knowledge of the samples, from which we can initialize numerical models of the snow.

6 Sep 2022

Short-range Near-surface Seismic Ensemble Predictions and Uncertainty Quantification for Layered Medium

Abstract: To make a prediction for seismic signal propagation, one needs to specify physical properties and subsurface ground structure of the site. This information is frequently unknown or estimated with significant uncertainty. This paper describes a methodology for probabilistic seismic ensemble prediction for vertically stratified soils and short ranges with no in situ site characterization. Instead of specifying viscoelastic site properties, the methodology operates with probability distribution functions of these properties taking into account analytical and empirical relationships among viscoelastic variables. This yields ensemble realizations of signal arrivals at specified locations where statistical properties of the signals can be estimated. Such ensemble predictions can be useful for preliminary site characterization, for military applications, and risk analysis for remote or inaccessible locations for which no data can be acquired. Comparison with experiments revealed that measured signals are not always within the predicted ranges of variability. Variance-based global sensitivity analysis has shown that the most significant parameters for signal amplitude predictions in the developed stochastic model are the uncertainty in the shear quality factor and the Poisson ratio above the water table depth.

6 Sep 2022

Brine, Englacial Structure, and Basal Properties near the Terminus of McMurdo Ice Shelf, Antarctica

Abstract: We collected ∼1300 km of ground-penetrating radar profiles over McMurdo Ice Shelf, Antarctica, using frequencies between 40 and 400 MHz to determine extent, continuity and depth to the brine. We also used profiles to determine meteoric ice thickness and locate englacial features, which may suggest ice shelf instability. The brine extends 9–13 km inland from the ice shelf terminus and covers the entire region between Ross, White and Black Islands. Jump unconformities and basal fractures exist in the brine and ice shelf, respectively, suggesting prior fracturing and re-suturing. One 100 MHz profile, the most distal from the ice shelf edge while still being situated over the brine, simultaneously imaged the brine and bottom of meteoric ice. This suggests a negative brine salinity gradient moving away from the terminus. The meteoric ice bottom was also imaged in a few select locations through blue ice in the ablation zone near Black Island. We suggest that brine, sediment-rich ice and poor antenna coupling on rough ice attenuates the signal in this area. When combined with other recent mass-balance and structural glaciology studies of MIS, our results could contribute to one of the most high-resolution physical models of an ice shelf in Antarctica.

31 Aug 2022

Photographic Aerial Transects of Fort Wainwright, Alaska

Abstract: This report presents the results of low-altitude photographic transects conducted over the training areas of US Army Garrison Fort Wainwright, in the boreal biome of central Alaska, to document baseline land-cover conditions. Flights were conducted via a Cessna™ 180 on two flight paths over portions of the Tanana Flats, Yukon, and Donnelly Training Areas and covered 486 mi (782 km) while documenting GPS waypoints. Nadir photographs were made with two GoPro™ cameras operating at 5 sec time-lapse intervals and with a handheld digital camera for oblique imagery. This yielded 6,063 GoPro photos and 706 oblique photos. Each image was intersected with a land-cover-classification map, collectively representing 38 of the 44 cover categories.

30 Aug 2022

Automated Detection of Austere Entry Landing Zones: A “GRAIL Tools” Validation Assessment

Abstract: The Geospatial Remote Assessment for Ingress Locations (GRAIL) Tools software is a geospatial product developed to locate austere entry landing zones (LZs) for military aircraft. Using spatial datasets like land classification and slope, along with predefined LZ geometry specifications, GRAIL Tools generates binary suitability filters that distinguish between suitable and unsuitable terrain. GRAIL Tools combines input suitability filters, searches for LZs at user‐defined orientations, and plots results. To refine GRAIL Tools, we: (a) verified software output; (b) conducted validation assessments using five unpaved LZ sites; and (c) assessed input dataset resolution on outcomes using 30 and 1‐m datasets. The software was verified and validated in California and the Baltics, and all five LZs were correctly identified in either the 30 or the 1‐m data. The 30‐m data provided numerous LZs for consideration, while the 1‐m data highlighted hazardous conditions undetected in the 30‐m data. Digital elevation model grid size affected results, as 1‐m data produced overestimated slope values. Resampling the data to 5 m resulted in more realistic slopes. Results indicate GRAIL Tools is an asset the military can use to rapidly assess terrain conditions.