Automated recognition of stratigraphic marker shales from geophysical logs in iron ore deposits

The mining of stratiform ore deposits requires a means of determining the location of stratigraphic boundaries. A variety of geophysical logs may provide the required data but, in the case of banded iron formation hosted iron ore deposits in the Hamersley Ranges of Western Australia, only one geophysical log type (natural gamma) is collected for this purpose. The information from these logs is currently processed by slow manual interpretation. In this paper we present an alternative method of automatically identifying recurring stratigraphic markers in natural gamma logs from multiple drill holes.Our approach is demonstrated using natural gamma geophysical logs that contain features corresponding to the presence of stratigraphically important marker shales. The host stratigraphic sequence is highly consistent throughout the Hamersley and the marker shales can therefore be used to identify the stratigraphic location of the banded iron formation (BIF) or BIF hosted ore.The marker shales are identified using Gaussian Processes (GP) trained by either manual or active learning methods and the results are compared to the existing geological interpretation. The manual method involves the user selecting the signatures for improving the library, whereas the active learning method uses the measure of uncertainty provided by the GP to select specific examples for the user to consider for addition.The results demonstrate that both GP methods can identify a feature, but the active learning approach has several benefits over the manual method. These benefits include greater accuracy in the identified signatures, faster library building, and an objective approach for selecting signatures that includes the full range of signatures across a deposit in the library. When using the active learning method, it was found that the current manual interpretation could be replaced in 78.4% of the holes with an accuracy of 95.7%. We apply Gaussian Processes to identify marker shales in iron ore mines.We examine manual and active learning methods of training Gaussian Processes.The active learning's use of uncertainty increases the accuracy of results.The active learning method can replace more than 3/4 of the manual interpretations.

[1]  Eric Nettleton,et al.  Detection of geological structure using gamma logs for autonomous mining , 2011, 2011 IEEE International Conference on Robotics and Automation.

[2]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[3]  L. Li,et al.  New Method of Horizon Recognition in Seismic Data , 2012, IEEE Geoscience and Remote Sensing Letters.

[4]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[5]  Changhyup Park,et al.  A Fuzzy Model Integrated with Electrofacies Characterization for Permeability Prediction , 2013 .

[6]  D. Lascelles.  The Genesis of the Hope Downs Iron Ore Deposit, Hamersley Province, Western Australia , 2006 .

[7]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[8]  J. Hoyes,et al.  A review of “global” interpretation methods for automated 3D horizon picking , 2011 .

[9]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[10]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[11]  Probabilistic Modeling of Ore Lens Geometry: An Alternative to Deterministic Wireframes , 2005 .

[12]  E. Ziegel.  Statistics and Data Analysis in Geology (3rd ed.) , 2005 .

[13]  David A. Cohn,et al.  Active Learning with Statistical Models , 1996, NIPS.

[14]  S. Hagemann,et al.  Banded Iron Formation-Related Iron Ore Deposits of the Hamersley Province, Western Australia , 2008 .

[15]  T Aizawa,et al.  Automated lithology prediction from PGNAA and other geophysical logs. , 2006, Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine.

[16]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[17]  H. C. Chen,et al.  Identification of lithofacies using Kohonen self-organizing maps , 2002 .

[18]  Maria Petrou,et al.  Horizon picking in 3D seismic data volumes , 2004, Machine Vision and Applications.

[19]  Marra Mamba Iron Formation stratigraphy in the eastern Chichester Range, Western Australia , 2000 .

[20]  Timothy R. Carr,et al.  Methodology of organic-rich shale lithofacies identification and prediction: A case study from Marcellus Shale in the Appalachian basin , 2012, Comput. Geosci..

[21]  Geoffrey E. Hinton,et al.  Learning representations by back-propagation errors, nature , 1986 .

[22]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[23]  R. Reyment,et al.  Statistics and Data Analysis in Geology. , 1988 .