61 citations · 116 across the 5 of their papers we have counts for
5 papers
A review of machine learning in processing remote sensing data for mineral exploration
Hojat Shirmard, Ehsan Farahbakhsh, R. Dietmar Muller +1
The decline of the number of newly discovered mineral deposits and increase in demand for different minerals in recent years has led exploration geologists to look for more efficie…
Surrogate-assisted Bayesian inversion for landscape and basin evolution models
Rohitash Chandra, Danial Azam, Arpit Kapoor +1
The complex and computationally expensive nature of landscape evolution models pose significant challenges in the inference and optimisation of unknown parameters. Bayesian inferen…
Computer vision-based framework for extracting geological lineaments from optical remote sensing data
Ehsan Farahbakhsh, Rohitash Chandra, Hugo K. H. Olierook +4
The extraction of geological lineaments from digital satellite data is a fundamental application in remote sensing. The location of geological lineaments such as faults and dykes a…
Multi-core parallel tempering Bayeslands for basin and landscape evolution
Rohitash Chandra, R. Dietmar Müller, Danial Azam +4
The Bayesian paradigm is becoming an increasingly popular framework for estimation and uncertainty quantification of unknown parameters in geo-physical inversion problems. Badlands…
Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands
Rohitash Chandra, Danial Azam, R. Dietmar Müller +2
Bayesian inference provides a rigorous methodology for estimation and uncertainty quantification of parameters in geophysical forward models. Badlands (basin and landscape dynamics…