Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands

Abstract: Bayesian inference provides a rigorous methodology for estimation and uncertainty quantification of unknown parameters in geophysical forward models. Badlands is a landscape evolution model that simulates topography development at various space and time scales. Badlands consists of a number of geophysical parameters that needs estimation with appropriate uncertainty quantification; given the observed present-day ground truth … Read more…

EarthByte Honours and Masters Projects 2018

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EarthByte has now released a list of Honours/Masters projects to be offered in 2018. These projects are outlined below. Project Title Supervisor(s) Dynamic Earth models, landscape dynamics and basin evolution in Australasia Dietmar Müller, Sabin Zahirovic, Tristan Salles, Rohit Chandra, Sally Cripps (Centre for Translational Data Science) Incorporating modern plate tectonic reconstructions into box models of the deep-time deep-Earth … Read more…

Understanding the Deep Carbon Cycle from Icehouse to Greenhouse Climates

Sydney Research Excellence Initiative grant (2017-2018) Research area, key questions, significance, and innovation. The planet is experiencing a major transition from an icehouse climate, one dominated by permanent continental ice sheets at high latitudes, to a greenhouse climate that favours ice-free conditions. Although part of the deglaciation trend is influenced by a natural orbital cycle, … Read more…