Automated pattern recognition to support geological mapping and exploration target generation – A case study from southern Namibia

Eberle, D.; Hutchins, D.; Das, Sonali; Majumdar, Anandamayee; Paasche, Hendrik

doi:10.1016/j.jafrearsci.2015.03.011

Journal of African Earth Sciences

2015

DOI: 10.1016/j.jafrearsci.2015.03.011

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Automated pattern recognition to support geological mapping and exploration target generation – A case study from southern Namibia

D. Eberle

D. Hutchins

Sonali Das

et al.

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Cited by 7 publications

(1 citation statement)

References 26 publications

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“…In seabed as well as terrestrial land cover mapping unsupervised techniques have been previously used to create attribute classes and maps from geophysical and remote sensing data which could then be linked to geology by means of ground-truth samples (e.g. Paasche et al 2006;De & Chakraborty 2009;Eberle et al 2015). More recently supervised machine learning techniques became popular for predictive modelling of marine sediment (e.g.…”

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confidence: 99%

Predictive modelling of grain-size distributions from marine electromagnetic profiling data using end-member analysis and a radial basis function network

Baasch

Müller

Dobeneck

2018

Geophysical Journal International

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In this work, we present a new methodology to predict grain-size distributions from geophysical data. Specifically, electric conductivity and magnetic susceptibility of seafloor sediments recovered from electromagnetic profiling data are used to predict grain-size distributions along shelf-wide survey lines. Field data from the NW Iberian shelf are investigated and reveal a strong relation between the electromagnetic properties and grain-size distribution. The here presented workflow combines unsupervised and supervised machine-learning techniques. Non-negative matrix factorization is used to determine grain-size end-members from sediment surface samples. Four end-members were found, which well represent the variety of sediments in the study area. A radial basis function network modified for prediction of compositional data is then used to estimate the abundances of these end-members from the electromagnetic properties. The end-members together with their predicted abundances are finally back transformed to grain-size distributions. A minimum spatial variation constraint is implemented in the training of the network to avoid overfitting and to respect the spatial distribution of sediment patterns. The predicted models are tested via leave-one-out crossvalidation revealing high prediction accuracy with coefficients of determination (R 2) between 0.76 and 0.89. The predicted grain-size distributions represent the well-known sediment facies and patterns on the NW Iberian shelf and provide new insights into their distribution, transition and dynamics. This study suggests that electromagnetic benthic profiling in combination with machine learning techniques is a powerful tool to estimate grain-size distribution of marine sediments.

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confidence: 99%

Predictive modelling of grain-size distributions from marine electromagnetic profiling data using end-member analysis and a radial basis function network

Baasch

Müller

Dobeneck

2018

Geophysical Journal International

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The application of artificial intelligence for the identification of the maceral groups and mineral components of coal

Młynarczuk

Skiba

2017

Computers & Geosciences

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The use of decision tree induction and artificial neural networks for recognizing the geochemical distribution patterns of LREE in the Choghart deposit, Central Iran

Zaremotlagh

Hezarkhani

2017

Journal of African Earth Sciences

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Automated pattern recognition to support geological mapping and exploration target generation – A case study from southern Namibia

Cited by 7 publications

References 26 publications

Predictive modelling of grain-size distributions from marine electromagnetic profiling data using end-member analysis and a radial basis function network

Predictive modelling of grain-size distributions from marine electromagnetic profiling data using end-member analysis and a radial basis function network

The application of artificial intelligence for the identification of the maceral groups and mineral components of coal

The use of decision tree induction and artificial neural networks for recognizing the geochemical distribution patterns of LREE in the Choghart deposit, Central Iran

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