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Now showing items 11-17 of 17
Examining Seismic Amplitude Responses of Gaseous Media Using Unsupervised Machine Learning
(2020-12-18)
The presence of gas in the rock’s or sediment’s pore space significantly affects its seismic amplitude response and modifies its subsurface signature. Gas hydrates in the subsurface are often difficult to image with ...
Using Machine Learning Applications and HREFv2 to Enhance Hail Prediction for Operations
(2019-08)
In this thesis, I demonstrate how hail prediction can be improved through post-processing numerical weather prediction (NWP) data from the new High-Resolution Ensemble Forecast system version 2 (HREFv2) with machine learning ...
Carbon-Based Pollutant Analysis and Remote Sensor Validation Using Column-Observing Fourier Transform Infrared (FTIR) Spectrometers During the TRACER Campaign
(2023-08-04)
Greenhouse gases methane (CH4), and carbon dioxide (CO2), along with carbon monoxide (CO), while produced by both anthropogenic and natural sources, all contribute to atmospheric warming. Additionally, CO poses health risks ...
Nurturing as Safe Exploration Promotes the Evolution of Generalized Supervised Learning
(2017-08-01)
The ability to learn is often a desirable property of intelligent systems which can make them more adaptive. However, it is difficult to develop sophisticated learning algorithms that are effective. One approach to the ...
Energy Efficient Machine Learning-Based Classification of ECG Heartbeat Types
(2018-12-05)
To meet the accuracy, latency and energy efficiency requirements during real-time collection and analysis of health data, a distributed edge computing environment is the answer, combined with 5G speeds and modern computing ...
APPLICATIONS OF MACHINE LEARNING METHODS IN THE GENERATION OF SUBSURFACE MEASUREMENTS
(2018-05-11)
Machine learning methods have been used in the Oil and Gas industry for about thirty years. Applications range from interpretations of geophysical, well and seismic responses, identification of minerals, analysis of rock ...
Spatiotemporal gap-filling of NASA Deep Blue aerosol optical depth over CONUS using the UNet 3+ architecture
(2024-05-10)
Due to sensor and algorithmic constraints, satellite aerosol optical depth (AOD) retrievals are spatially incomplete over clouds, deserts, and other bright surfaces. These gaps in satellite AOD datasets represent a significant ...