Bulletin of the American Physical Society
2020 Annual Meeting of the APS Four Corners Section (Virtual)
Volume 65, Number 16
Friday–Saturday, October 23–24, 2020; Albuquerque, NM (Virtual)
Session L02: Atmospheric Physics / GeophysicsLive
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Chair: Walter Toki, Colorado State University |
Saturday, October 24, 2020 11:00AM - 11:12AM Live |
L02.00001: A Deep Neural Network Approach to the Inversion of Gravity and Cosmic Ray Muon Data for Shallow Subsurface Density Prediction Katherine Cosburn, Brady Spears, Mousumi Roy Estimating subsurface density is important for imaging and monitoring many geologic structures, such as volcanic edifices, reservoirs and aquifers. Muon tomography has been used in recent years to complement traditional gravity measurements in Bayesian joint inversion techniques as an effective method for probing shallow subsurface density variations. Also in recent years, the use of deep learning to address complex inverse problems in the geosciences has gained increasing attention, particularly in the field of seismology. Using deep neural networks on the inversion of gravity and muon data, however, has yet to be explored and we present a first study here. In this study, we examine the results of a deep learning model on sets of noisy synthetic gravity and muon data, which are generated based on theoretical knowledge of the forward kernels that relate these datasets to density. The deep learning model is trained with a suite of possible density values and variation patterns and its accuracy is determined by comparing against the known forward calculation. After testing our neural network on a toy model structure, we use an optimized placement of muon detectors and gravimeters on real topography to test the feasibility of our model for use by geologists in the field. [Preview Abstract] |
Saturday, October 24, 2020 11:12AM - 11:24AM Live |
L02.00002: 2D Machine learning modeling of space weather Nicolette Clark, Matthew McHarg, Casey Pellizzari, Gabriel Wilson A real-time map of space weather provides an operational advantage to satellite missions. In this paper, we present a technique for modelling ionospheric plasma density in two dimensions based on sparsely-sampled data from a single satellite. Three global magnetic indices, along with location and solar elevation angle, are used as the drivers for a feed-forward neural network model of ionospheric space weather. In situ measurements taken by an integrated miniaturized electrostatic analyzer are used as target values for training the network. The model results are used to construct a two-dimensional map of ionospheric conditions, which we then compare to International Reference Ionosphere data as an external validation measure. PA Number: USAFA-DF-2020-330. [Preview Abstract] |
Saturday, October 24, 2020 11:24AM - 11:36AM Live |
L02.00003: Analysis of Downward Terrestrial Gamma-Ray Flashes at Telescope Array Jackson Remington The Telescope Array cosmic ray observatory in central Utah reports the first close, high-resolution observations of downward-directed TGFs. The array consists of 512 3~m$^2$ thin scintillator detector units on a grid covering 700~km$^2$, and is overlooked by a broadband VHF interferometer, Lightning Mapping Array, and fast electric field change sensors. This unique collection of detectors provides high-resolution measurements of lightning activity alongside particle data at ground level. Four TGF-producing lightning flashes from 2018 were analyzed in this study with currents between --21 and --37~kA. The particle data can resolve individual particles and place lower limits on their energies --- this study finds unambiguous evidence of gamma photons with energy of at least 6.4~MeV. These TGFs occurred in the first couple milliseconds of lightning activity during strong, complex initial breakdown pulses. The precision timing analyses are able to further correlate the TGFs to individual sub-pulses in the streamer-based fast negative breakdown, implying the existence of impulsive, spark-like events. These events may shed light on many poorly-understood processes involved in negative lightning development and TGF production in general. [Preview Abstract] |
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