Hyperspectral Imaging

Surface Mapping

Scientific Basis:

• Each mineral reflects specific wavelengths due to its molecular vibrations and electronic transitions, particularly in the SWIR region.

• AI and machine learning models process this spectral data to correlate reflectance curves with mineralogical and geochemical datasets, enabling real-time grade estimation.

• This fusion of spectral science and data analytics helps bridge the gap between visual surface data and subsurface grade models, bringing lab-grade accuracy to field-scale decision-making.

In essence:
Spectropy turns light into data — and data into actionable geological intelligence.

Hyperspectral imaging is a remote sensing technology that captures information across hundreds of narrow and contiguous spectral bands, extending far beyond what the human eye or standard cameras can detect. Each mineral and material has a unique spectral signature — a distinct way it reflects and absorbs light across different wavelengths. By analyzing these spectral fingerprints, Spectropy identifies and quantifies minerals, grades, and compositional variations with laboratory-level precision from aerial or ground-based platforms.

Spectral Range Used:
Spectropy operates across the Visible to Short-Wave Infrared (VNIR–SWIR) range: 400 nm to 2500 nm, covering the key spectral absorption features of most rock-forming and alteration minerals.

• VNIR (400–1000 nm): Sensitive to iron oxides, vegetation, and surface reflectance.

• SWIR (1000–2500 nm): Critical for identifying clay minerals, carbonates, sulfates, and hydroxyl-bearing minerals that define alteration zones and ore boundaries.

Mineral Exploration