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Consulting & analytics

geochemistry exploration

Data generation

The Field Data Acquisition Services team focuses on data generation: regional soil/rock/stream sampling, geophysical surveys, drilling supervision and logging, geological mapping, and site visits for historic data validation. In addition, we offer software to manually capture and prepare data as well as utilising Computer Vision image analysis for logging. Our data generation efforts are oriented toward the efficient creation of fit-for-purpose datasets.


Orebody knowledge, modelling & estimation

Ore Body Knowledge refers to the understanding and information gained about a mineral deposit, including its size, shape, grade, structure, and location. This information is used to make informed decisions about mining and processing operations, including mine design, resource evaluation, and decision-making related to investment and production. Having accurate and comprehensive Ore Body Knowledge is crucial for the success and profitability of a mining operation.


Artificial intelligence analysis & automation

ALS GoldSpot’s Artificial Intelligence technologies and workflows, guided by expert input, help rapidly and accurately interpret large volumes of data, allowing your expert geoscientists to spend their time thinking about geoscience. Geological signals are embedded in many data types and layers. Our geoscience and data science teams work together to process data to create prospectivity analyses, exploration drill targeting, geological maps, automated core logging, and more. The geoscientists and data scientists at ALS GoldSpot are leading experts at creating predictive, data-driven models underpinned by geological expertise to ensure the resulting maps have accuracy and reliability, not geo-fantasy.


Geoscience expertise

Historic geological and mining data are challenging, as they are commonly incomplete, sparse, and noisy. However, insight signals can be extracted if suitable approaches and workflows are applied. ALS GoldSpot Data Scientists and Artificial Intelligence experts work with the geoscience team to select the appropriate processing of datasets. Internally developed data science tools process data more quickly, accurately, and repeatably than through subjective methods traditionally applied by eye. Projects benefit, especially when using data collected by our Data Generation team.


Downhole data domaining

The domains of a mineral deposit are made up of unique geological features (lithological, structural, petrophysical, or geotechnical), which are combined with a spatial component to create coherent blocks, which are then used throughout the mining value chain. Typical machine learning clustering algorithms, without reference to spatial information, often result in numerous small-scale units. The advantage of downhole data domaining is that this technique incorporates the spatial component (neighbourhood analysis) as a variable together with the other numeric variables, resulting in more accurate “real world” domains.