Application of seismic to mineral deposit exploration and evaluation

Gibson, Mark A. S.

doi:10.1190/1.3599147

The Leading Edge

2011

DOI: 10.1190/1.3599147

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Application of seismic to mineral deposit exploration and evaluation

Mark A. S. Gibson

Abstract: Mineral deposits occur in different geological envi-ronments than oil and gas deposits, and are usually at very shallow depths (from surface outcrop, to hundreds of meters). Because of internal heterogeneities within most deposit types, extensive use of borehole sampling is required to satisfy resource and reserve reporting requirements. While borehole sampling can be designed to satisfy the geostatistical variability of a deposit, it is usually insufficient to adequately sample the structural continuity of de… Show more

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Diffractivity — Another attribute for the interpretation of seismic data in hard rock environment, a case study

et al. 2016

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The primary objective of seismic exploration in a hard rock environment is the detection of heterogeneities such as fracture zones, small-scale geobodies, intrusions, and steeply dipping structures that are often associated with mineral deposits. Prospecting in such environments using seismic-reflection methods is more challenging than in sedimentary settings due to lack of continuous reflector beds and predominance of steeply dipping hard rock formations. The heterogeneities and “fractal” aspect of hard rock geologic environment produce considerable scattering of the seismic energy in the form of diffracted waves. These scatterers can be traced back to irregular and often “sharp-shaped” mineral bodies, magmatic intrusions, faults, and complex and heterogeneous shear zones. Due to the natural lack of reflectors and abundant number of diffractors, there are only a few case studies of diffraction imaging in hard rock environments. There are almost no theoretical models or field examples of diffraction imaging in prestack domain. We have filled this gap by applying a 3D prestack diffraction imaging method to image point diffractors. We calculated the diffractivity by computing the semblance of seismic data along diffraction traveltime curves in the prestack domain. The performance of the method is evaluated on a synthetic case and a field seismic data set collected over the Kevitsa mineral deposit in northern Finland. The high-resolution results obtained by the application of prestack diffraction imaging suggest that diffractivity is a robust attribute that can be used in addition to other seismic attributes for the interpretation of seismic data in hard rock environment.

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Diffractivity — Another attribute for the interpretation of seismic data in hard rock environment, a case study

et al. 2016

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Use of novel 3D seismic technology and machine learning for pothole detection, characterization, and classification — Case study in the Bushveld Complex (South Africa)

Zhang¹,

Sehoole

Manzi

et al. 2021

The Leading Edge

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We demonstrate that integrating 3D reflection seismics with machine learning (ML) can bring many benefits for the future development of the mining industry. We use a serial integration of reflection seismics, which identifies economic horizon-depression structures known as potholes within the western Bushveld Complex. Thereafter, agglomerative clustering is applied to the resulting data, using features engineered from the physical characteristics of the potholes. Our results indicate that potholes can be divided into several classes based on characteristic features; e.g., large potholes are substantially less steep than small potholes. Furthermore, we model this empirical relationship and show that it can be used to predict average sizes of potholes given their typical in-mine exposures. We also demonstrate that pothole formation is likely to have been initiated depth-wise, followed by lateral increases in size. Lastly, we demonstrate that our serial application of seismically based data generation and ML-based data analytics is a viable alternative to conventional geostastistical analysis, especially for the classification, prediction, and modeling of geologic structures such as potholes.

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Application of seismic to mineral deposit exploration and evaluation

Cited by 2 publications

References 2 publications

Diffractivity — Another attribute for the interpretation of seismic data in hard rock environment, a case study

Diffractivity — Another attribute for the interpretation of seismic data in hard rock environment, a case study

Use of novel 3D seismic technology and machine learning for pothole detection, characterization, and classification — Case study in the Bushveld Complex (South Africa)

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