XRF systems—whether portable units or CoreScan‑based platforms—have reshaped how geologists acquire geochemical information. They deliver rapid, high‑resolution, multi‑element datasets directly from drill core or outcrop, producing far more geochemical detail than traditional assays alone. Interpreting these dense datasets remains challenging because elements co‑vary, mineralogical associations overlap, and nonlinear relationships are... [Read More]

Which wavelet is best for my signal? The answer depends on the nature of your data and what features you’re trying to extract. Wavelets are mathematical functions that decompose signals into time-frequency space. Each wavelet has its own shape, symmetry, and localization properties. Choosing the best wavelet means finding one... [Read More]

Signals present irregularities such as sudden drops, peaks or subtle shifts that break the pattern. These features mark important transitions wether the data represent financial markets, seismic data, geological scans and others. Detecting of these patterns requires precision and a correct analytical approach. Available algorithms are tailored to specific applications.... [Read More]

Distributed training is the process of partitioning the workload across multiple processing units to speed up the training especially in ML. [Read More]

Image analysis extracts feature information to quantify shapes, enumerate object’s structures and characterize shape of structures. Rock particles result from erosion, blasting, comminution, and others. The geometric characteristics of these particles encode information about their generating process. The mineralogy influences mechanical properties and determines the shape and size of individual... [Read More]