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Articles 1 - 8 of 8
Full-Text Articles in Physical Sciences and Mathematics
Quantification Of Mineral Reactivity Using Machine Learning Interpretation Of Micro-Xrf Data, Julie J. Kim, Florence Ling, Dan A. Plattenberger, Andres F. Clarens, Catherine A. Peters
Quantification Of Mineral Reactivity Using Machine Learning Interpretation Of Micro-Xrf Data, Julie J. Kim, Florence Ling, Dan A. Plattenberger, Andres F. Clarens, Catherine A. Peters
Environmental Science Faculty Work
Accurate characterizations of mineral reactivity require mapping of spatial heterogeneity, and quantifications of mineral abundances, elemental content, and mineral accessibility. Reactive transport models require such information at the grain-scale to accurately simulate coupled processes of mineral reactions, aqueous solution speciation, and mass transport. In this work, millimeter-scale mineral maps are generated using a neural network approach for 2D mineral mapping based on synchrotron micro x-ray fluorescence (μXRF) data. The approach is called Synchrotron-based Machine learning Approach for RasTer (SMART) mapping, which reads μXRF scans and provides mineral maps of the same size and resolution. The SMART mineral classifier is trained …
Diffusional Fractionation Of Helium Isotopes In Silicate Melts, Haiyang Luo, Bijaya Karki, Dipta B. Ghosh, Huiming Bao
Diffusional Fractionation Of Helium Isotopes In Silicate Melts, Haiyang Luo, Bijaya Karki, Dipta B. Ghosh, Huiming Bao
Faculty Publications
Estimating Helium (He) concentration and isotope composition of the mantle requires quantifying He loss during magma degassing. The knowledge of diffusional He isotope fractionation in silicate melts may be essential to constrain the He loss. Isotopic mass dependence of He diffusion can be empirically expressed as D3He/D4He = (4/3)^β, where D is the diffusivity of a He isotope. However, no studies have reported any β values for He in silicate melts due to technical challenges in both experiments and computations. Here, molecular dynamics simulations based on deep neural network potentials trained by ab initio data …
Computer Simulations Of Diffusional Isotope Effects And Dynamical Properties Of Silicate Melts, Haiyang Luo
Computer Simulations Of Diffusional Isotope Effects And Dynamical Properties Of Silicate Melts, Haiyang Luo
LSU Doctoral Dissertations
Silicate melts have served as transport agents in the chemical and thermal evolution of Earth. Diffusional isotope effect in silicate melts is the key to interpret isotope variations in lots of geological samples. Isotopic mass dependence of diffusion is commonly expressed as (Di/Dj)=(mj/mi)^β, where Di and Dj are diffusion coefficients of two isotopes whose masses are mi and mj. However, how the dimensionless empirical parameter β depends on temperature, pressure, and composition remains poorly constrained. Viscosity and electrical conductivity are two fundamental dynamical properties of silicate melts needed to constrain melt distribution in Earth's interior but remain unclear for most …
A Machine Learning Approach To The Detection Of Ghosting And Scattered Light Artifacts In Dark Energy Survey Images, C. Chang, A. Drlica-Wagner, S. M. Kent, B. Nord, D. M. Wang, M.H.L.S. Wang
A Machine Learning Approach To The Detection Of Ghosting And Scattered Light Artifacts In Dark Energy Survey Images, C. Chang, A. Drlica-Wagner, S. M. Kent, B. Nord, D. M. Wang, M.H.L.S. Wang
Student Publications & Research
Astronomical images are often plagued by unwanted artifacts that arise from a number of sources including imperfect optics, faulty image sensors, cosmic ray hits, and even airplanes and artificial satellites. Spurious reflections (known as “ghosts”) and the scattering of light off the surfaces of a camera and/or telescope are particularly difficult to avoid. Detecting ghosts and scattered light efficiently in large cosmological surveys that will acquire petabytes of data can be a daunting task. In this paper, we use data from the Dark Energy Survey to develop, train, and validate a machine learning model to detect ghosts and scattered light …
Towards Advancing The Earthquake Forecasting By Machine Learning Of Satellite Data, Pan Xiong, Lei Tong, Kun Zhang, Xuhui Shen, Roberto Battiston, Dimitar Ouzounov, Roberto Iuppa, Danny Crookes, Cheng Long, Huyui Zhou
Towards Advancing The Earthquake Forecasting By Machine Learning Of Satellite Data, Pan Xiong, Lei Tong, Kun Zhang, Xuhui Shen, Roberto Battiston, Dimitar Ouzounov, Roberto Iuppa, Danny Crookes, Cheng Long, Huyui Zhou
Mathematics, Physics, and Computer Science Faculty Articles and Research
Earthquakes have become one of the leading causes of death from natural hazards in the last fifty years. Continuous efforts have been made to understand the physical characteristics of earthquakes and the interaction between the physical hazards and the environments so that appropriate warnings may be generated before earthquakes strike. However, earthquake forecasting is not trivial at all. Reliable forecastings should include the analysis and the signals indicating the coming of a significant quake. Unfortunately, these signals are rarely evident before earthquakes occur, and therefore it is challenging to detect such precursors in seismic analysis. Among the available technologies for …
A Coastal N₂ Fixation Hotspot At The Cape Hatteras Front: Elucidating Spatial Heterogeneity In Diazotroph Activity Via Supervised Machine Learning, Corday R. Selden, P. Dreux Chappell, Sophie Clayton, Alfonso Macías-Tapia, Peter W. Bernhardt, Margaret R. Mulholland
A Coastal N₂ Fixation Hotspot At The Cape Hatteras Front: Elucidating Spatial Heterogeneity In Diazotroph Activity Via Supervised Machine Learning, Corday R. Selden, P. Dreux Chappell, Sophie Clayton, Alfonso Macías-Tapia, Peter W. Bernhardt, Margaret R. Mulholland
OES Faculty Publications
In the North Atlantic Ocean, dinitrogen (N2) fixation on the western continental shelf represents a significant fraction of basin‐wide nitrogen (N) inputs. However, the factors regulating coastal N2 fixation remain poorly understood, in part due to sharp physico‐chemical gradients and dynamic water mass interactions that are difficult to constrain via traditional oceanographic approaches. This study sought to characterize the spatial heterogeneity of N2 fixation on the western North Atlantic shelf, at the confluence of Mid‐ and South Atlantic Bight shelf waters and the Gulf Stream, in August 2016. Rates were quantified using the 15N2 …
Volcan De Fuego: A Machine Learning Approach In Understanding The Eruptive Cycles Using Precursory Tilt Signals, Kay Sivaraj
Volcan De Fuego: A Machine Learning Approach In Understanding The Eruptive Cycles Using Precursory Tilt Signals, Kay Sivaraj
Dissertations, Master's Theses and Master's Reports
Volcan de Fuego is an active stratovolcano located in the Central Guatemalan segment of the 1100 m long Central America Volcanic Arc System (CAVAS). Fuego-Acatenango massif consists of at least four major vents of which the Fuego summit vent is the most active and the youngest member. The volcano exhibits primarily Strombolian and Vulcanian behavior along with occasional paroxysms and pyroclastic flows. Historically, Fuego has produced basaltic-andesitic rocks with more recent eruptions progressively trending towards maficity. Several studies have used short-term deployments of broadband seismometers, infrasound, and long-term remote sensing techniques to characterize the mechanism of Fuego. In our study, …
Inference Of Surface Velocities From Oblique Time Lapse Photos And Terrestrial Based Lidar At The Helheim Glacier, Franklyn T. Dunbar Ii
Inference Of Surface Velocities From Oblique Time Lapse Photos And Terrestrial Based Lidar At The Helheim Glacier, Franklyn T. Dunbar Ii
Graduate Student Theses, Dissertations, & Professional Papers
Using time dependent observations derived from terrestrial LiDAR and oblique
time-lapse imagery, we demonstrate that a Bayesian approach to glacial motion es-
timation provides a concise way to incorporate multiple data products into a single
motion estimation procedure effectively producing surface velocity estimates with
an associated uncertainty. This approach brings both improved computational effi-
ciency, and greater scalability across observational time-frames when compared to
existing methods. To gauge efficacy, we apply these methods to a set of observa-
tions from the Helheim Glacier, a critical actor in contemporary mass loss trends
observed in the Greenland Ice Sheet. We find that …