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3 results for “particle backscatter”
Paired field measurements of suspended-sediment concentration, turbidity, acoustic backscatter, and particle size compiled from various estuaries in the United States and Australia
<p>Field measurements of suspended-sediment concentration, turbidity, acoustic backscatter, and particle size are compiled from various estuaries in the United States and Australia to investigate the utility of combining optical and acoustic backscatter measurements for the estimation of suspended-sediment concentration under changes in floc particle size and density. </p> <p>Theory, analysis, and interpretation of the data is available in Livsey et al (2023). Data collected from the Chesapeake Bay, US were compiled from Fall et al (2022). Data collected on the Brisbane River were collected by Livsey et al (2022). Data collected for all other locations were compiled from Livsey et al (2022). </p> <p>Data collected by Fall et al (2022) utilized a LISST 100x. Data collected by Livsey et al (2022, 2023) utilized a LISST 200x. Data files for each instrument are provided. </p> <p>Funding for this research was provided by an Advance Queensland Industry Research Fellowship, Queensland University of Technology, and Queensland Department of Environment and Science.</p> <p>References</p> <p>Fall, Kelsey A., Massey, Grace M., and Friedrichs, Carl T., (2020). The importance of organic content to fractal floc properties in estuarine surface waters, insights from video, LISST, and pump sampling: Supporting data. Data. William & Mary. https://doi.org/10.25773/7gbc-794 6739</p> <p>Livsey, D., Turner, R., Grace, P., and Crosswell, & Andy Steven. (2022). Field and laboratory measurements of suspended-sediment particle size and concentration from nine rivers draining to the Great Barrier Reef (1.0). Data. Zenodo. https://doi.org/10.5281/zenodo.6788303</p> <p>Livsey, D., Turner, R., and Grace, P. (2023). Combining optical and acoustic backscatter measurements for monitoring of fine suspended-sediment concentration under changes in particle size and density. Water Resources Research. <a href="https://doi.org/10.1029/2022WR033982">https://doi.org/10.1029/2022WR033982</a></p> <p> </p>
Backscattering from randomly placed Dirichlet particles
<p><strong>Summary:</strong></p> <p>A simulated data set of backscattered waves, governed by the 2D wave equation, from randomly placed Dirichlet particles. The particles are circular with 0.2 < radius < 2.0 and 1% < volume fraction < 20%. The incident wave is of the form e<sup>i k (x - x<sub>R</sub> - t)</sup> where the particles are placed in the halfspace x>0 and the backscattering is emitted and received at (x<sub>R</sub>, 0) with x<sub>R</sub> = -10.0. The data named bunny and bunnytest is for incident waves with 0 < k < 1 (where the term e<sup>- i k t</sup> factors out). The data named train and test is for incident waves of the form e<sup>0.1</sup><sup>(x - xR - t)^2</sup>, with 9.5 < t < 98. </p> <p><strong>Format:</strong></p> <p>An example of how to use the data is given in [4]. To load the data requires the packages [1] and [2] in the language Julia v0.5> [3]. When using load(filename) the data will automatically be loaded as an array of type FrequencySimulation or of type TimeSimulation. </p> <p>[1] https://github.com/jondea/MultipleScattering.jl</p> <p>[2] https://github.com/JuliaIO/JLD.jl </p> <p>[3] https://julialang.org/</p> <p>[4] https://github.com/gowerrobert/MultipleScatteringLearnMoments/tree/master/examples</p>
Detecting Lunar and Martian Water via Backscattered Cosmic Particles using Muon Tomography
<p><strong>Introduction</strong></p> <p>The search for water on the Lunar and Martian surfaces is a cornerstone of space exploration, playing a key role in expanding our understanding of the history and evolution of these celestial bodies. Despite its importance, current knowledge about the distribution, concentration, origin, and migration of water on the Moon and Mars is still limited. This study aims to address these gaps by employing a novel approach that leverages cosmic-ray muon detectors and backscattered radiation. Through the use of advanced muon tracking systems and preliminary simulations conducted with GEANT4, the research suggests that muon tomography holds significant promise for improving our understanding of water-ice content on the Lunar and Martian surfaces.</p> <p><strong>Data Description</strong></p> <p>Data and detector models were generated using GEANT4. The simulations include:</p> <ul> <li>Lunar and Martian dry regolith</li> <li>Lunar and Martian regolith with water-ice beneath the surface</li> </ul> <p><strong>Contents</strong></p> <p>This record includes:</p> <ul> <li><code>*.csv</code>: Output raw files from GEANT4, including 5D information, scattering angle, detector plate position, and particle type.</li> <li><code>backscatter_eventselection.py</code>: Python code to filter events and generate a CSV file of selected backscattered events.</li> <li><code>*.tiff</code>: Visualization files depicting Lunar and Martian scenarios, including detector geometry and particle events.</li> <li><code>ml_classifier.py</code>: Python code for machine learning tasks to classify backscattered events.</li> <li><code>OP_Muographers_2023.pdf</code>: Detailed description of chemical composition and simulated scenarios.</li> <li>Tracking_EKF: Performs track reconstruction and computes track lengths using extended Kalman Filter.</li> </ul> <p><strong>Disclaimer</strong></p> <p>The provided datasets are simulated samples suitable for conceptual R&D and performance studies. They have not been calibrated against real data and should not be used for physics projections about the detectors.</p>
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