Clustering-Based Integration of Magnetotelluric (MT) and Ambient Noise Tomography (ANT) for High-Resolution Imaging of Crustal Anomalies in the Curnamona-Mundi Mundi Region, Australia

In geophysical exploration, combining magnetotelluric (MT) and ambient noise tomography (ANT) has proven to be an effective approach for investigating subsurface structures and detecting crustal anomalies. MT gives invaluable insights into variations in electrical conductivity, which can be a sign of fluid pathways, differences in rock types, and thermal anomalies. On the other hand, ANT provides versatile shear-wave velocity models that help map out structural differences and changes in crustal composition. The Curnamona Province, known for having one of the most electrically conductive crusts globally holds significant potential as a target for the MT and ANT methods. This area is particularly intriguing because of its series of conductivity anomalies that reach mid-crustal depths, with a notable eastern boundary located beneath the Mundi Mundi region. In this research, we combine 3D seismic and MT models through statistical clustering. Our goal is to connect these models in a way that allows us to identify regions with similar physical property groupings. Statistical clustering aids in organizing geophysical data, improving the clarity of crustal differences, and uncovering hidden structures like mineralized zones, fault systems, and geothermal reservoirs. The combination of MT and ANT via statistical clustering has provided valuable insights into the subsurface layout of the Curnamona-Mundi Mundi area. The findings highlight important subsurface characteristics, allowing us to distinguish rock types beneath sediment layers and pinpoint potential mineralized areas. This method effectively addresses the limitations of using individual geophysical methods, tackling resolution issues and minimizing interpretational uncertainties. By modeling structural features such as faults and lithological boundaries, this approach enhances the identification of key targets for mineral exploration.

Additionally, a 2D k-means clustering analysis is utilized on the post-inversion resistivity, gravity, and magnetic datasets to map out geological units and rock types. This method combines geophysical signatures to overcome the drawbacks of interpreting individual datasets by using a data-driven approach. The clusters match up well with existing geological maps offering more detailed insights and spotting underground geological formations and their links to possible mineralization areas.