Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery

Mastoureh Yousefi,Amin Beiranvand Pour,Reyhaneh Rikhtehgaran,Seyed Hassan Tabatabaei,Biswajeet Pradhan

doi:10.3390/min11111235

Abstract

The application of machine learning (ML) algorithms for processing remote sensing data is momentous, particularly for mapping hydrothermal alteration zones associated with porphyry copper deposits. The unsupervised Dirichlet Process (DP) and the supervised Support Vector Machine (SVM) techniques can be executed for mapping hydrothermal alteration zones associated with porphyry copper deposits. The main objective of this investigation is to practice an algorithm that can accurately model the best training data as input for supervised methods such as SVM. For this purpose, the Zefreh porphyry copper deposit located in the Urumieh-Dokhtar Magmatic Arc (UDMA) of central Iran was selected and used as training data. Initially, using ASTER data, different alteration zones of the Zefreh porphyry copper deposit were detected by Band Ratio, Relative Band Depth (RBD), Linear Spectral Unmixing (LSU), Spectral Feature Fitting (SFF), and Orthogonal Subspace Projection (OSP) techniques. Then, using the DP method, the exact extent of each alteration was determined. Finally, the detected alterations were used as training data to identify similar alteration zones in full scene of ASTER using SVM and Spectral Angle Mapper (SAM) methods. Several high potential zones were identified in the study area. Field surveys and laboratory analysis were used to validate the image processing results. This investigation demonstrates that the application of the SVM algorithm for mapping hydrothermal alteration zones associated with porphyry copper deposits is broadly applicable to ASTER data and can be used for prospectivity mapping in many metallogenic provinces around the world.

Highlights

Because of the importance of minerals in industry and other aspects of human life, appropriate methods to explore minerals are essential
The specific objectives of this research are: (i) to detect alteration zones in the Zefreh porphyry copper deposit using Relative Band Depth (RBD), Linear Spectral Unmixing (LSU), Orthogonal Subspace Projection (OSP), Spectral Feature Fitting (SFF) algorithms; (ii) to determine the exact expansion of alteration zones in the Zefreh porphyry copper deposit using the Dirichlet Process (DP) method and use its results as training data for supervised methods; (iii) to perform Support Vector Machine (SVM) and Spectral Angle Mapper (SAM) methods using training data obtained from the DP method and specify analogous alteration areas in the ASTER scene; and (iv) to verify the classification results using field checking of alteration zones
In order to accurately determine the training data to use in the SVM and SAM algorithms, firstly, the alteration zones were identified by several mapping methods such as RBD, LSU, OSP, and SFF [57,58,59,60]

Summary

Introduction

Because of the importance of minerals in industry and other aspects of human life, appropriate methods to explore minerals are essential. Remote sensing satellite imagery is extensively used in different sectors of Earth science such as mineral mapping [1,2,3,4]. The results of remote sensing studies, by means of saving time and cost in identifying alteration zones, have greatly contributed to the exploration of minerals, especially in the reconnaissance stages [5,6,7,8]. Remote sensing has been used successfully in the identification of lithological units, structure features, and alterations zones with the development of new algorithms and ML techniques [9,10,11]. Owing to the high volume of remote sensing satellite data, data mining methods to extract the desired information are necessary [12,13]

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Discussion

Conclusion