In hyperspectral core analysis, distinct mineral phases manifest diagnostic absorption features across the visible to near-infrared (400–1000 nm) and short-wave infrared (1000–2500 nm) regions, driven by electronic transitions of transition metal ions and vibrational overtones of hydroxyl (OH), water (H2O), and carbonate (CO3) functional groups in their crystal lattices. For instance, iron-bearing minerals (hematite, goethite, jarosite) exhibit characteristic Fe3+ and Fe2+ absorption features in the 400–600 nm and 800–1000 nm intervals. Clay minerals (kaolinite, illite, smectite) display distinct Al-OH, Mg-OH, and Si-OH absorption profiles near 1400 nm, 1900 nm, and 2200 nm, while carbonate minerals (calcite, dolomite) exhibit diagnostic combination bands of the CO3 functional group in the 2300–2350 nm window. These spectral signatures constitute explicit "spectral fingerprints" for mineral discrimination, establishing the physical foundation for hyperspectral core scanning. The workflow for hyperspectral data-driven mineral identification typically encompasses spectral preprocessing, endmember extraction, spectral matching, and abundance inversion. Preprocessing includes radiometric calibration, reflectance conversion, denoising, and continuum removal to isolate and enhance target absorption geometries. Endmember extraction algorithms, such as the Pixel Purity Index (PPI), Vertex Component Analysis (VCA), and Convex Cone Analysis (CMASS), automatically extract diagnostic endmember spectra from the hyperspectral data cube for automated matching against standardized reference libraries (e.g., USGS, JPL, and ASTER spectral libraries). Spectral matching methods, including Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF), and binary encoding, compute spectral similarity vectors to rapidly classify mineral species. The HG-HyperCore-Scan hyperspectral drill core logging system, engineered by Hagorun Technology Limited, integrates these advanced processing and analytical pipelines to execute fully automated hyperspectral imaging and mineral mapping. Compared with traditional polarized light microscopy and X-ray diffraction (XRD) analyses, hyperspectral imaging offers non-destructive, rapid (scanning speeds typically span tens of seconds to a few minutes per meter), and spatially continuous coverage. It yields mineral composition metrics for every pixel along the core axis to generate continuous mineral distribution profiles, compensating for the limited spatial representation of discrete point sampling. This makes it uniquely suited for hydrothermal alteration zonation research and mineralization vector modeling.

Hydrothermal alteration zonation serves as a critical vectoring tool for locating concealed orebodies. The systematic spatial arrangement of distinct alteration assemblages (sericitic, chloritic, silicic, carbonate, and potassic) typically maps the paleofluid migration pathways and mineralization centers. Utilizing hyperspectral imaging systems for continuous core scanning resolves characteristic mineral absorption profiles to automatically delineate alteration mineral distribution logs and zonation boundaries. For example, in a classic porphyry copper deposit system, the alteration sequence vectors outward from the core zone via potassic → phyllic (quartz-sericite) → argillic → propylitic zones. The spectral assemblages of key diagnostic minerals across these zones exhibit sharp contrasts, enabling the core scanning system to rapidly log the exact depths, intervals, and true thicknesses of individual alteration facies. Regarding the quantification of alteration intensity, relative mineral abundance inversion derived from hyperspectral data arrays provides quantitative metrics for resource prospective evaluation. Applying spectral unmixing algorithms—such as the Multiple Endmember Linear Spectral Mixture Model (MESMA) or sparse unmixing—estimates the relative fractional coverage of diverse alteration phases down the core axis to generate continuous mineral abundance curves. Correlating these curves with trace-element geochemistry grids constructs statistical relationships between alteration indices (such as illite crystallinity or chlorite composition chemistry) and mineral grade variations, guiding strategic drill target optimization. Empirical applications demonstrate that hyperspectral core logging yields excellent results in the exploration of porphyry copper, epithermal gold, IOCG-type, and uranium deposits. The HG-HyperCore-Scan hyperspectral drill core logging system from Hagorun Technology Limited supports an expansive suite of spectral matching and abundance inversion algorithms, exporting precise mineral classification maps and relative abundance logs optimized for automated core digitization and alteration vectoring across exploration grids.

Drill cores represent invaluable physical assets in geological exploration. However, traditional core storage and management facilities struggle with massive spatial footprints, physical core weathering/fragmentation, and low information extraction efficiency. Hyperspectral imaging provides a stable solution for core digitization and long-term asset preservation. By capturing co-registered hyperspectral reflectance imagery and calibrated data streams, physical core trays are transformed into digital core assets embedded with spatial coordinates and spectral attributes, archived within relational databases paired with interactive visual platforms. Geologists can remotely query, browse, and re-evaluate historical core metrics across exploration regions, minimizing physical core handling and extending the operational lifespan of the archive. Building upon these digital core repositories, the integration of machine learning and deep learning architectures enables automated, intelligent mineral classification. Training Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN) on accurately labeled hyperspectral core datasets permits end-to-end mineral identification and automated alteration mapping for newly scanned cores. This framework yields diagnostic classification accuracies that match or exceed conventional spectral matching techniques while dramatically accelerating data processing throughput. Developing regional knowledge graphs of spectral signatures for typical deposit styles delivers a robust data foundation for rapid core comparative analysis and proactive prospecting target forecasting in greenfield terrains.

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