Learning characteristic natural gamma shale marker signatures in iron ore deposits

Uncertainty in the location of stratigraphic boundaries in stratiform deposits has a direct impact on the uncertainty of resource estimates. The interpretation of stratigraphic boundaries in banded iron formation (BIF)-hosted deposits in the Hamersley province of Western Australia is made by recognizing shale markers which have characteristic signatures from natural gamma wireline logs. This paper presents a novel application of a probabilistic sequential model, named a continuous profile model, which is capable of jointly modelling the uncertainty in the amplitude and alignment of characteristic signatures. We demonstrate the accuracy of this approach by comparing three models that incorporate varying intensities of distortion and alignment in their ability to correctly identify a shale band of the West Angelas member of the Wittenoom Formation which overlies the Marra Mamba Iron Formation in the Hamersley Basin. Our experiments show that the proposed approach recovers 98.72% of interpreted shale band intervals and importantly quantifies the uncertainty in scale and alignment that contribute to probabilistic interpretations of stratigraphic boundaries.