Jupyter Notebook is a versatile tool designed for interactively developing and showcasing data science projects. It allows you to seamlessly combine code, visualizations, descriptive text, and other rich media into one document, offering an integrated and expressive workflow. This guide will walk you through installing Jupyter Notebook locally and creating your first project.
This post explores how X-ray plunge projection can be used to analyze ore grades effectively, offering new insights into mineralisation patterns and geological structures. Maximum Intensity Projection (MIP) highlights the highest-value point from a point cloud along a specific line of sight, enabling a fast, intuitive view of structural controls, especially where high-grade zones are concealed by surrounding lower-grade material.
Drillhole desurveying computes accurate XYZ coordinates along a drillhole's length using collar and downhole survey data. By reconstructing the true 3D path from azimuth and dip measurements, it ensures geological data are positioned correctly, improving grade estimation, modelling, and resource evaluation while minimising errors in exploration and mining workflows.
A practical Jupyter Notebook that teaches an end-to-end Sentinel-2 workflow in Python: define an AOI, fetch cloud-filtered scenes from Earth Engine, build monthly composites (median for spectral bands, mode for SCL), compute key indices (NDVI, NDWI, MNDWI, NDTI, NDCI, ferric/ferrous iron, AlOH, FeOH, MgOH, silica, carbonate, clay), export multi-band GeoTIFFs, and render MP4 time-series with geographic axes. Includes QA checks and CSV block tables. This demo focuses on clarity and teaching rather than national/continental optimisation.