Print Email Facebook Twitter Glacial Isostatic Adjustment Forward Model with a Convolutional Neural Network Title Glacial Isostatic Adjustment Forward Model with a Convolutional Neural Network Author Bouma, Quinten (TU Delft Aerospace Engineering) Contributor van der Wal, W. (mentor) Degree granting institution Delft University of Technology Programme Aerospace Engineering Date 2023-06-30 Abstract This thesis introduces a new approach to Glacial Isostatic Adjustment (GIA) modeling using Machine Learning (ML) techniques. The work addresses two main challenges – uncertainty in historical ice load history and the complexity of inverse problems – by developing two ML-based surrogate models (emulators) to rapidly estimate Relative Sea-Level (RSL) history and uplift rates from varying ice load histories, given a constant Earth model. The emulators are constructed using a Convolutional Neural Network (CNN) with U-Net architecture and spherical data representation, enabling an efficient, cost-effective approach to the GIA forward model. The performance of these emulators was evaluated in two separate experiments, displaying encouraging results in efficiency, accuracy, and versatility. The prediction performance exceeded existing models in computational speed and offers accuracy comparable to other GIA studies. The successful implementation of these emulators could advance GIA modeling by integrating ML, but enhanced resolution is needed for direct scientific application. Subject Glacial Isostatic AdjustmentConvolutional Neural NetworkMachine Learning (ML) To reference this document use: http://resolver.tudelft.nl/uuid:8d919051-4cdd-45e3-b59d-d76d6ae3929b Part of collection Student theses Document type master thesis Rights © 2023 Quinten Bouma Files PDF Glacial_Isostatic_Adjustm ... etwork.pdf 16.83 MB Close viewer /islandora/object/uuid:8d919051-4cdd-45e3-b59d-d76d6ae3929b/datastream/OBJ/view