Dr Rohitash Chandra

Integration of Selective Dimensionality Reduction Techniques for Mineral Exploration Using ASTER Satellite Data

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Abstract: There are a significant number of image processing methods that have been developed during the past decades for detecting anomalous areas, such as hydrothermal alteration zones, using satellite images. Among these methods, dimensionality reduction or transformation techniques are known to be a robust type of methods, which are helpful, as they reduce the extent … Read more…

Surrogate-assisted Bayesian inversion for landscape and basin evolution models

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Abstract: The complex and computationally expensive nature of landscape evolution models poses significant challenges to the inference and optimization of unknown model parameters. Bayesian inference provides a methodology for estimation and uncertainty quantification of unknown model parameters. In our previous work, we developed parallel tempering Bayeslands as a framework for parameter estimation and uncertainty quantification … Read more…

Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models

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Abstract: Traditional approaches to develop 3D geological models employ a mix of quantitative and qualitative scientific techniques, which do not fully provide quantification of uncertainty in the constructed models and fail to optimally weight geological field observations against constraints from geophysical data. Here, using the Bayesian Obsidian software package, we develop a methodology to fuse … Read more…

Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data

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Abstract: The extraction of tectonic lineaments from digital satellite data is a fundamental application in remote sensing. The location of tectonic lineaments such as faults and dykes are of interest for a range of applications, particularly because of their association with hydrothermal mineralization. Although a wide range of applications have utilized computer vision techniques, a … Read more…

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

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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…

Modeling geochemical anomalies of stream sediment data through a weighted drainage catchment basin method for detecting porphyry Cu-Au mineralization

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Abstract: Stream sediment surveying is a geochemical sampling method which is typically applied in the preliminary stages of mineral prospecting. Both continuous and discrete mapping approaches have been proposed to delineate geochemical anomalies at large scales using stream sediment samples. We aim to enhance the efficiency of a recent discrete mapping method called Weighted Drainage … Read more…

EarthByte Honours and Masters Projects 2020

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EarthByte has now released a list of Honours/Masters projects to be offered in 2020. These projects are outlined below. We can also tailor projects to your interests. Feel free to contact us by clicking the supervisor links below. Project Title Supervisor(s) How climate and subsidence control the sedimentation along the Norwegian Margin? Claire Mallard , … Read more…

EarthByte Honours and Masters Projects 2019

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EarthByte has now released a list of Honours/Masters projects to be offered in 2019. These projects are outlined below. Project Title Supervisor(s) How is landscape complexity driving biodiversity over geological time scales? Tristan Salles & Patrice Rey How well are tectonic and climatic signals preserved in the stratigraphic record? Tristan Salles & Claire Mallard Vertical motions … Read more…

Workshop on “Bayeslands: Bayesian inference for Badlands”

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Overview: In recent years, the Bayesian inference has become a popular methodology for the estimation and uncertainty quantification of parameters in geological and geophysical forward models via the posterior distribution. Badlands is a basin and landscape evolution model for simulating topography development at various space and time scales. This workshop will present  BayesLands which provides … 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…