mineral exploration

Why Does the Broken Hill Deposit Sit in Resistive Crust? Magnetotelluric Evidence for Metamorphic Decoupling of a World‐Class Mineral System

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Regional gravity and magnetic anomaly maps showing tectonic boundaries and the location of the Broken Hill deposit, NSW, Australia.

Researchers used magnetotelluric surveys to image the electrical structure of the crust around the world-class Broken Hill Pb-Zn-Ag deposit in NSW, Australia. They found that the upper crust here is mostly resistive, with only small, isolated conductive zones linked to the ore body and its host rocks, which do not connect down to a deeper … Read more…

DEEP-SEAM: an explainable semi-supervised deep learning framework for mineral prospectivity mapping

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Abstract. The global transition to clean energy is sharply increasing demand for rare earth elements (REEs), yet discovery rates are declining, especially in areas concealed by younger cover. Deep learning (DL) offers new opportunities for mineral prospectivity mapping (MPM), but its application is challenged by sparse labelled mineral occurrences, strong class imbalance, and limited model … Read more…

Subducting seafloor anomalies promote porphyry copper formation

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Oceanic seafloor is scarred by age discontinuities, seamounts, and large igneous provinces (LIPs) over normal bathymetry. The recycling of these seafloor anomalies has been speculated to alter subduction regimes which may locally prime some regions for porphyry copper deposit formation. Using a tectonic plate reconstruction encompassing the last 100 Ma paired with a machine learning classifier … Read more…

Convolutional Neural Networks for Mineral Prospecting Through Alteration Mapping with Remote Sensing Data

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Traditional geological mapping methods, which rely on field observations and rock sample analysis, are inefficient for continuous spatial mapping of geological features such as alteration zones. Deep learning models such as convolutional neural networks (CNNs) have ushered in a transformative era in remote sensing data analysis. CNNs excel in automatically extracting features from image data … Read more…

Remote sensing framework for geological mapping via stacked autoencoders and clustering

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Supervised machine learning methods for geological mapping via remote sensing face limitations due to the scarcity of accurately labelled training data that can be addressed by unsupervised learning, such as dimensionality reduction and clustering. Dimensionality reduction methods have the potential to play a crucial role in improving the accuracy of geological maps. Although conventional dimensionality … Read more…

Machine Learning-Based Spatio-Temporal Prospectivity Modeling of Porphyry Systems in the New Guinea and Solomon Islands Region

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Abstract. The discovery of new economic copper deposits is critical for the development of renewable energy infrastructure and zero-emissions transport. The majority of existing copper mines are located within current or extinct continental arc systems, but our understanding of the tectonic and geodynamic conditions favoring the formation of porphyry systems is still incomplete. Traditionally, exploration … Read more…

Lateritic Ni-Co prospectivity modelling in eastern Australia using an enhanced generative adversarial network and positive-unlabelled bagging

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The surging demand for nickel (Ni) and cobalt (Co), driven by the acceleration of clean energy transitions, has sparked interest in the Lachlan Orogen of New South Wales for its potential lateritic Ni-Co resources. Despite recent discoveries, a substantial knowledge gap exists in understanding the full scope of these critical metals in this geological province. … Read more…

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…

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…

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…

Fulbright Postdoctoral Fellowship awarded to Behnam Sadeghi

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Geologic Map of the Moon from (USGS) Astrogeology Science Center A Fulbright Postdoctoral Fellowship has been awarded to EarthByter Behnam Sadeghi. He will complete his Fulbright project at the Carnegie Institution for Science, Earth and Planets Laboratory (EPL) in Washington which has close collaborations with NASA, as well as at Stanford University. His research project, focused … Read more…

Remote Sensing of the Enivronment: A review of machine learning in processing remote sensing data for mineral exploration

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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 efficient and innovative methods for processing different data types at each stage of mineral exploration. As a primary step, various features, such as lithological units, alteration types, … Read more…

The Conversation: Travelling through deep time to find copper for a clean energy future

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More than 100 countries, including the United States and members of the European Union, have committed to net-zero carbon emissions by 2050. The world is going to need a lot of metal, particularly copper. Recently, the International Energy Agency sounded the warning bell on the global supply of copper as the most widely used metal in renewable … Read more…