T215 Structure and Tectonic Studies, from Outcrop to Supercontinent II: In Honor of Ian Dalziel

Tuesday, 24 September 2019
1:30 PM - 5:30 PM
Room 229A, North Building

AUTOMATICALLY IDENTIFYING AND EXAMINING GEODYNAMIC FEATURES IN PLATE TECTONIC MODELS

Paper number: T215-5
Program: Technical Sessions
Time: 2:30 PM – 2:45 PM

Authors

Abstract

Plate tectonic reconstructions facilitate visual links between geokinematics and geodynamics of mappable features and the symptoms of geological processes reflecting the evolution of the Earth. For example, geodynamic process episodically fertilise crust or mantle and strongly influence the location of economic mineralisation. Geological processes, plate boundaries and their locations through time from the geosphere impact the behaviour and the interaction among the atmosphere, biosphere and hydrosphere. Accurate plate tectonic boundaries for any reconstruction model provide researchers a mechanism to validate and improve plate models but also to constrain the location of important geological processes. In most detailed reconstruction models, crustal blocks are represented by complex shaped geodynamic unit (GDU) polygons, and the motion of preserved crustal blocks older than the Mesozoic is complicated. There is also very little preserved evidence of oceanic blocks prior to the Mesozoic. Thus, there are no detailed reconstruction models with continually closing plate boundary topologies prior to the Mesozoic and late Paleozoic. The current manual methodology of geodynamic boundary identification is very time consuming and easily introduces errors when applied to detailed models. We are developing methods to automate the procedure of identifying and delineating divergent zones (rifts and mid-ocean ridges), convergent zones (subduction zones) and transform faults using a variety of open source software code. Different approaches have been evaluated for optimization in run-time and memory for general laptop or desktop computer systems. Our approach also promotes the integration of multiple geo-information layers for machine learning to derive additional insights and to further constrain the interaction among tectonic plates in the development of reconstruction models.

Geological Society of America Abstracts with Programs. Vol. 51, No. 5

doi: 10.1130/abs/2019AM-334431

© Copyright 2019 The Geological Society of America (GSA), all rights reserved. Permission is hereby granted to the author(s) of this abstract to reproduce and distribute it freely, for noncommercial purposes. Permission is hereby granted to any individual scientist to download a single copy of this electronic file and reproduce up to 20 paper copies for noncommercial purposes advancing science and education, including classroom use, providing all reproductions include the complete content shown here, including the author information. All other forms of reproduction and/or transmittal are prohibited without written permission from GSA Copyright Permissions.