34 Outer Space Rocks! Enhancing the Understanding of our Planetary Neighbors
Sunday, 30 March 2025
8:00 AM - 12:00 PM
120
DEEP LEARNING-BASED TRACKING OF SUBDUCTION ZONES IN MANTLE CONVECTION MODELS
Paper number: 34-11
Program: Theme Sessions
Time: 11:00 AM – 11:20 AM
Authors
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Bradford J. FoleyPennsylvania State University, State College, PA
Abstract
Our method involves converting temperature, fineness (defined as the inverse of grain size, a key parameter in grain-damage rheology), and vertical velocity fields into RGB images to improve classification accuracy. A semantic segmentation-based FCN model is then constructed to determine subduction zones on a per-pixel basis by exploring the relationships among these RGB images. This approach enables a more accurate and detailed analysis of subduction zone dynamics without the need for arbitrary thresholds to define what constitutes a subduction zone. By using efficient image segmentation based on deep learning, we enhance our detection and analysis of subduction zones, potentially providing valuable insights into the mechanisms that trigger subduction and influence global tectonic processes. The ability to accurately track subduction zones over time opens new avenues for investigating the initiation, evolution, and termination of subduction processes, as well as their broader implications for mantle dynamics and plate tectonics. This new technique represents a significant advancement in automated subduction zone analysis, offering geoscientists a powerful tool for investigating Earth's dynamic processes.
Geological Society of America Abstracts with Programs. Vol. 57, No. 3
doi: 10.1130/abs/2025NE-408957
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