Publications (1-3 of 3)

1. Evaluating Machine Learning and Statistical Models for Greenland Bed Topography

   15 May 2024 | Contributor(s):: Katherine Yi, Angelina Dewar, Tartela Tabassum, Jason Lu, Ray Chen, Homayra Alam, Omar Faruque, Sikan Li, Mathieu Morlighem, Jianwu Wang

   Abstract:The purpose of this research is to study how different machine learning and statistical models can be used to predict bed topography in Greenland using ice-penetrating radar and satellite imagery data. Accurate bed topography representations are crucial for understanding ice sheet...

2. Evaluating Machine Learning and Statistical Models for Greenland Subglacial Bed Topography

   15 May 2024 | Contributor(s):: Katherine Yi, Angelina Dewar, Tartela Tabassum, Jason Lu, Ray Chen, Homayra Alam, Omar Faruque, Sikan Li, Mathieu Morlighem, Jianwu Wang

   Abstract:The purpose of this research is to study how different machine learning and statistical models can be used to predict bedrock topography under the Greenland ice sheet using ice-penetrating radar and satellite imagery data. Accurate bed topography representations are crucial for...

3. Evaluating Machine Learning and Statistical Models for Greenland Subglacial Bed Topography

   15 May 2024 | Contributor(s):: Katherine Yi, Angelina Dewar, Tartela Tabassum, Jason Lu, Ray Chen, Homayra Alam, Omar Faruque, Sikan LI, Mathieu Morlighem, Jianwu Wang

   Abstract:The purpose of this research is to study how different machine learning and statistical models can be used to predict bedrock topography under the Greenland ice sheet using ice-penetrating radar and satellite imagery data. Accurate bed topography representations are crucial for...