Dr Ehsan Farahbakhsh

Dr. Ehsan Farahbakhsh is a Research Associate in the EarthByte Group, School of Geosciences, University of Sydney. He holds a PhD degree in Mining Engineering – Mineral Exploration from Tehran Polytechnic. Along with his research activities, he has been involved in several projects as an exploration geologist or spatial data analyst for the exploration industry, primarily for providing prospectivity maps of various ore deposit types from regional to deposit scale. His research interests are multidimensional mineral prospectivity modeling, geological remote sensing, geostatistics, and the application of data science and UAVs in mineral exploration.

Leveraging Machine Learning and Geophysical Data for Automated Detection of Interior Structures of Cratons

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The internal structures and discontinuities of cratons hold considerable economic value due to their tendency for reactivation and different horizontal stress, serving as conduits for fluid flow and mineral deposition over time. Detecting these structures at various depths is critical for accurately mapping prospective zones of metallic mineralisation. This study demonstrates the effectiveness of integrating … Read more…

Net Zero Institute White Paper on Critical Minerals and Materials released

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The University of Sydney’s Net Zero Institute has just released their White Paper on Critical Minerals & Materials. It represents a massive collaborative effort of over 50 colleagues from the University and international partners. The EarthByte Group has made a contribution to it with an outline of AI-powered mineral prospectivity mapping, particularly applied to copper … Read more…

PLATO – PLAte Tectonics and Ore deposits

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Project PLATO is an ARC Linkage project as a collaboration between the EarthByte Group and Lithodat. CIs, PIs and AIs include Dietmar Müller (Usyd) Maria Seton (Usyd) Sabin Zahirovic (Usyd) Sara Polanco (Usyd) Brent McInnes (Curtin Univ.) Fabian Kohlmann (Lithodat) In addition, Dr Ehsan Farahbakhsh is a research fellow and Elnaz Heidari is a PhD student … Read more…

Applied Geochemistry: Multivariate statistical analysis and bespoke deviation network modeling for geochemical anomaly detection of rare earth elements

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Rare earth elements (REEs), a significant subset of critical minerals, play an indispensable role in modern society and are regarded as “industrial vitamins,” making them crucial for global sustainability. Geochemical survey data proves highly effective in delineating metallic mineral prospects. Separating geochemical anomalies associated with specific types of mineralization from the background reflecting geological processes … Read more…

Keynote Talk at Exploration in the House: Critical minerals – prospectivity mapping using generative AI

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In the recent Exploration in the House event at Parliament House in Sydney Dietmar provided an overview of the use of generative AI for assessing copper, nickel and cobalt prospectivity in the Lachlan fold belt, based on the Honours thesis of Nathan Wake, and work by Ehsan Farahbakhsh and Vera Nolte-Wilson. The event also featured … Read more…

Part-time research assistant position in mineral exploration

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The EarthByte Group is looking for a research assistant as part of our STELLAR industry project in collaboration with BHP. The casual position will be for up to 10 hours per week on average. The successful applicant can complete these hours through a regular weekly schedule or clump their hours into intensive weekly blocks (i.e., … Read more…

The use of machine learning in processing remote sensing data for mineral exploration

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ASEG will be hosting their next technical meeting on Wednesday 20th April, featuring EarthByter Ehsan Farahbakhsh Title: The use of machine learning in processing remote sensing data for mineral exploration     Time:                    5:30 pm for 6:00 pm start Address:              Level 2, 99 on York (99 York St, Sydney. Room ‘York 2’) For virtual attendance, … Read more…

Remote Sensing: A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data

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Lithological mapping is a critical aspect of geological mapping that can be useful in studying the mineralization potential of a region and has implications for mineral prospectivity mapping. This is a challenging task if performed manually, particularly in highly remote areas that require a large number of participants and resources. The combination of machine learning … Read more…

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…

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…