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Mt. Zion image

The image above is cropped from "Guggenheim Hall, carillon tower" with the "M" on Mount Zion in the background. 

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  • Satellite data reveals the start of Canada's wildfire season

    Zhizhin, Mikhail; Ziv, Kristin; Elvidge, Christopher; Bazilian, Morgan; Colorado School of Mines. Payne Institute for Public Policy (Colorado School of Mines. Arthur Lakes LibraryColorado School of Mines. Payne Institute for Public Policy, 2024-05-16)
    Payne Institute Earth Observation Group Research Associate Mikhail Zhizhin, Communications Associate Kristin Ziv, Senior Research Associate Christopher Elvidge, and Director Morgan Bazilian write about how as of May 14, 2024, there are 143 active wildfires in Canada, and 39 are out of control, according to Canadian experts and officials. The Earth Observation Group has calculated the temperatures and spatial extent of active burning across Canada with their Nightfire algorithm applied to data collected by NOAA's Visible Infrared Imaging Spectrometer Suite (VIIRS).
  • Navigating commercial advisory in the VCM

    Andreatta, Jared; Handler, Bradley P.; Colorado School of Mines. Payne Institute for Public Policy (Colorado School of Mines. Arthur Lakes LibraryColorado School of Mines. Payne Institute for Public Policy, 2024-05-16)
    School of Mines Mineral and Energy Economics Masters candidate Jared Andreatta and Sustainable Finance Lab Program Manager Brad Handler write an explainer of the various types of commercial advisory services firms that participate in the Voluntary Carbon Market (VCM). These advisory firms primarily help buyers find, evaluate and transact carbon offset credits, but offer distinct approaches.
  • Satellite data captures power outages in Sudan's civil war

    Zhizhin, Mikhail; Ziv, Kristin; Elvidge, Christopher; Bazilian, Morgan; Colorado School of Mines. Payne Institute for Public Policy (Colorado School of Mines. Arthur Lakes LibraryColorado School of Mines. Payne Institute for Public Policy, 2024-05-19)
    Payne Institute Earth Observation Group Research Associate Mikhail Zhizhin, Communications Associate Kristin Ziv, Senior Research Associate Christopher Elvidge, and Director Morgan Bazilian write about how a horrible full-scale civil war in Sudan is creating chaos, anarchy, mass starvation, and the world's largest population of internal refugees – approximately nine million.  The researchers have created a temporal profile of nighttime lights for Khartoum where seasonal variations in lights within a year can be seen, but the interannual radiance was stable until the conflict started in April 2023.
  • Classifying pulse shapes from superconducting tunnel junctions for the BeEST experiment

    BeEST Collaboration; Taylor, John; Leach, Kyle G.
    The Beryllium Electron capture in Superconducting Tunnel junctions (BeEST) experiment aims to detect physics beyond the Standard Model by measuring atomic recoils from the electron capture decay of Beryllium-7 (Be-7). The experiment utilizes Superconducting Tunnel Junction (STJ) sensors to measure the daughter recoil kinetic energy spectrum to search for neutrino-coupled Beyond-Standard-Model physics. This work presents systematic studies that aim to distinguish between events occurring in the top and bottom electrodes of the STJs to search for the presence of a line-splitting artifact that could mimic a heavy neutrino signal. This is accomplished by analyzing the rise and fall times of the electrical pulses generated by the nuclear decays. Two primary techniques, 10-90% Rise Time Analysis and Charge Integration, are employed to investigate the pulse characteristics. While the former exhibits challenges in noise and pile-up events, the latter reveals a clear separation in the data, indicating a splitting effect caused by the STJ detector or the data acquisition system. The study proposes further investigation into the segregation observed and explores alternative methods for event separation.
  • Reconstructing high-resolution 2D data from low-resolution inputs using a super-resolution conditional generative adversarial network

    Alnabbat, Mohammed; Bernstein, Brett; Zhang, Mengli
    Super-resolution is an image processing technique that takes a low-resolution image and makes it high-resolution (Nasrollahi and Moeslund, 2014). Recent studies in image processing and medical imaging use Super-Resolution Conditional Generative Adversarial Networks (SRCGANs) to achieve super-resolution, successfully generating high-resolution images that are perceptually indistinguishable from real images (Nasser et al., 2022). We apply the concept of super-resolution to gridded gravity anomalies and train an SRCGAN to learn their complex signal structures and generate high-resolution data from coarsely-sampled grids. Typical generative adversarial networks (GANs) consist of two neural networks, a generator G and discriminator D, which learn and improve by competing with each other. G generates an image and D attempts to determine if it is a real or generated image. G then updates to make the generated output more realistic, and D updates to better distinguish between real and generated images. We adapt the SRCGAN architecture from Ledig et al. (2017) for use with gridded gravity data. Our generator G takes a low-resolution grid X, coarsely-sampled from a full data set Y, as input, and outputs an up-sampled, high resolution grid ˆY . Our network is trained and tested using gridded, regional gravity data from Australia (GeoscienceAustralia, 2023). These fully-sampled data Y are resized to a common shape of 128x128 and down-sampled by a factor of 4 to shape 32x32 to obtain the network inputs X. A total of 3546 pairs of fully-sampled and coarsely-sampled grids are used for training, 799 pairs are used for validation during training, and 88 pairs are reserved to test the network after training. The trained generator is tested with one of the reserved data pairs. Figure 1 shows the fully-sampled grid Y, coarse grid X, and the high-resolution grid ˆY generated from X. We are interested in how well the generator can reconstruct high-resolution signal from just the low-resolution input, so we quantify the increase in information by comparing the differences Y −X and Y −ˆY , histograms of which are in figure 1, their mean absolute error (MAE), and their root mean square error (RMSE). These metrics are summarized in table 1. The difference between the fully-sampled data and the coarse data Y −X produces a high MAE and RMSE and is characterized by a noticeably-broad spectrum of differences. The comparison between Y and ˆY yields lower metrics and a sharpening of the histogram. The improved metrics from the generated high-resolution data show that our trained SRCGAN generator can reconstruct missing signal structure from coarsely-sampled, gridded gravity data. The generated grid is upscaled in size by a factor of 4 in both directions while maintaining the integrity of the input signal. From the histogram of differences, we understand that the generated high-resolution data closely resembles the full data, discrepancies in which are due to the SRCGAN not completely reconstructing the high-frequency content. This may be improved through further adjusting and tuning of the network and the network training process. These results are a promising look into what may be a robust and versatile data reconstruction method.

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