Application of Concentration-Area fractal modeling and artificial neural network to identify Cu, Zn±Pb geochemical anomalies in Hashtjin area, NW of Iran

Document Type : Research Article

Authors

• Ali Imamalipour ¹
• Hamed Ebrahimi ²
• Amir reza Abdollahpur ³

¹ Associate Professor, Department of Mining Engineering, Faculty of Engineering, Urmia University, Urmia, Iran

² Ph.D. Student, Department of Mining Engineering, Faculty of Engineering, Urmia University, Urmia, Iran

³ M.Sc. Student, Department of Geology, Faculty of Science, Tabriz University, Tabriz, Iran

10.22067/econg.2024.1117

Abstract

Identification of geochemical anomalies plays an essential role in mineral exploration. Recent research investigations have shown that Machine Learning (ML) algorithms can identify geochemical anomalies associated with mineralization that represent targets for mineral exploration. Machine Learning algorithms are widely used in various fields due to their strong capability to extract and display high-level features of training samples. Autoencoder networks show a high ability to identify geochemical anomalies. In this study, the combined method of autoencoder network with the fractal concentration-area method was used to identify geochemical anomalies. First, using multivariate factor analysis, the elements barium, lead, zinc, copper, gold, iron, gold and arsenic were selected as indicators. Subsequently, the uni-element geochemical maps of these elements were prepared, and to standardize the maps in terms of minimum and maximum values, all maps were fuzzified and scaled. Using the fuzzy gamma operator, uni-element geochemical maps were combined.  Then the resulting map applied the deep autoencoder method with eight layers of encoder and decoder were reconstructed. Finally, a mineral prospectively map was prepared for the potential area using the concentration-area fractal method. The mixed model proposed in this study introduces the region with high mineralization potential northeast of the studied area.
 
Introduction
Over the past few decades, the identification of geochemical anomalies has played an important role in mineral exploration (Coates et al., 2011; Lecun et al., 2015; Bergen et al., 2019) Various methods have been used to identify geochemical anomalies in the last few decades, including statistical analysis, geostatistical approaches (Nabavi, 1976), fractal modeling (Ziaii et al., 2009; Ziaii et al., 2012) and many other methods. Fractal/multifractal models are powerful tools that have been widely used to detect geochemical anomalies.
Recent developments in machine learning methods have led to significant advances in geoscience. ML-based approaches to mineral prospectivity mapping using geochemical data can more effectively identify statistical correlations between geochemical patterns than other non-ML methods. Recent research shows that machine-learning approaches enable the integration of geochemical data and the successful identification and separation of geochemical anomalies associated with mineralization that may be overlooked using non-machine learning methods (Tukey, 1977; Cheng, 2006; Cheng et al., 2010).
In this study, the combined method of autoencoder network with the fractal concentration-area method was applied to identify geochemical anomalies in the Hashtjin area (Ardabil province), NW Iran.
 
Materials and methods
The combined method of the autoencoder network with the fractal concentration-area method was used to identify the geochemical anomalies. The flowchart of this study is as follows:
 1- First single-element geochemical maps of Pb, Zn, Cu, Au, As, Fe, and Ba elements were prepared, and to incorporate the minimum and maximum values, all of them became fuzzy to be in the range of (0 and 1).
2- Geochemical maps were combined using the fuzzy gamma operator.
3- The deep autoencoder method was implemented on the resulting map, and the reconstructed output was obtained.
4- Using the fractal concentration-area method, the final map of mineral prospectivity maps was prepared.
 
Discussion
An autoencoder is a type of artificial neural network used for learning efficient encoding of unlabeled data (unsupervised learning). Autoencoder consists of two functions: an encoder function that encodes the input data into a lower-dimensional hidden layer and a decoder function that reconstructs the encoded input data.
The encoder part of an autoencoder network attempts to reduce the dimensionality of the input data while preserving the majority of the information, and it encodes the input data in a blinded space. The decoder part attempts to capture the encoded data and reconstruct the original data with minimal error. The deep autoencoder network is an autoencoder network in which the neural network is designed profoundly, and the number of layers in it is greater. Each layer looks at the data as a new perspective. This neural network automatically and through unsupervised learning identifies patterns, complex structures, and high-level features of the input data.
Considering the evidence of the fractal nature of element distribution, the use of these methods in geochemical exploration for separating anomalous populations from the background with high confidence levels is one of the most potent known methods. The concentration-area fractal method is based on the amount of area occupied by a particular concentration in the study area.
 
Results
The study area exhibits complex geochemical features. In such a complex setting, it is essential to implement multiple methods in combination to separate anomalies from the background accurately. The combined method introduced in this study is a powerful tool for identifying geochemical anomalies in areas with complex geological history and diverse geochemical backgrounds. According to the results, all three methods used in this study represent a high favorability of ore mineralization in the northeast of the study area; Therefore, further investigations and investigation are recommended in the introduced area during later stages.
The results obtained from applying the fractal method to the output of the deep autoencoder method indicated a potentially favorable area for the mineralization with approximately 9,047,500 m²
In the predictive map obtained in the northeast of the studied area, an area with high mineralization potential is introduced, which is geologically represented by the Upper Jurassic (Lar Formation) and Cretaceous formation controlled by, fault boundaries and in the northeastern part of the Hashtjin.

Keywords

• Geochemical anomaly
• Machin learning
• Fractal modeling
• Mineral prospectivity mapping
• Hashtjin

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References

Afzal, P., Aramesh Asl, R., Adib, A. and Yasrebi, A.B., 2015. Application of fractal modelling for Cu mineralisation reconnaissance by ASTER multispectral and stream sediment data in Khoshname area, NW Iran. Journal of the Indian Society of Remote Sensing, 43: 0255–660. https://doi.org/10.1007/s12524-014-0384-6

Alavi, M., 1996. Tectonostratigraphic synthesis and structural style of the Alborz Mountains system northern Iran. Journal of Geodynamic, 21(1): 1–33. https://doi.org/10.1016/0264-3707(95)00009-7

Aramesh, Asl,R., Afzal, P., Adib, A. and Yasrebi, A.B., 2015. Application of multifractal modeling for the identification of alteration zones and major faults based on ETM+ multispectral data. Arabian Journal of Geosciences, 8: 2997–3006. https://doi.org/10.1007/s12517-014-1366-2

Bergen, K.J., Johnson, P.A., De Hoop, M.V. and Beroza, G.C., 2019. Machine learning for data-driven discovery in solid earth geoscience, Science, 363(6433). https://doi.org/10.1126/science.aau0323

Cheng, Q., 2006. Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China. Ore Geology Reviews. 32(1–2): 314–324. https://doi.org/10.1016/j.oregeorev.2006.10.002

Cheng, Q., Agterberg, F.P. and Ballantyne, S.B., 1994. The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical Exploration. 51(2): 109–130. https://doi.org/10.1016/0375-6742(94)90013-2

Cheng, Q., Agterberg, F.P. and Bonham Carter, G.F., 1996. A spatial analysis method for geochemical anomaly separation. Journal of Geochemical Exploration.56(3): 183–195. https://doi.org/10.1016/S0375-6742(96)00035-0

Cheng, Q., Xia, Q., Li, W., Zhang, S., Chen, Z., Zuo, R. and Wang, W., 2010. Density/area power-law models for separating multi-scale anomalies of ore and toxic elements in stream sediments in Gejiu mineral district, Yunnan Province, China. Biogeosciences.7(10): 3019–3025. https://doi.org/10.5194/bg-7-3019-2010

Coates, A., Lee, H., and Ng, A. Y., 2011. An analysis of singlelayer networks in unsupervised feature learning. 14th International Conference on Artificial Intelligence and Statistics (AISTATS) 2011, Fort Lauderdale, FL, USA

Daneshvar Saein, L., Afzal, P., Shahbazi, S. and Sadeghi, B., 2020. Application of an improved zonality index model integrated with multivariate fractal analysis: epithermal gold deposits. Geopersia,12(2): 379–394.  https://doi.org/10.22059/GEOPE.2022.339864.648652

Farhadi, S., Afzal, P., Boveiri Konari, M., Daneshvar Saeini, L. and Sadeghi, B., 2022. Combination of machine learning algorithms with concentration-area fractal method for soil geochemical anomaly detection in sediment-hosted Irankuh Pb-Zn deposit, Central Iran. Minerals, 12(6): 689. https://doi.org/10.3390/min12060689

Farhadi, S., Tatullo, S., Konari, MB. and Afzal, P., 2024. Evaluating Stacking C and ensemble models for enhanced lithological classification in geological mapping. Journal of Geochemical Exploration 260: 107–441. https://doi.org/10.1016/j.gexplo.2024.107441

Faridi, M., Anvari, A. and Ghassemi, M.R., 2000. Geological map of Hashtchin, scale 1:100000, Geological Organization of the country. Sheet Index 5664.

Grunsky, E.C. and Agterberg, F.P., 1988. Spatial and multivariate analysis of geochemical data from metavolcanic rocks in the Ben Nevis area, Ontario. Mathematical Geology, 20(7): 825–861. https://doi.org/10.1007/BF00890195

Hinton, G.E., Osindero, S. and Teh, Y.W., 2006. A fast-learning algorithm for deep belief nets. Neural Computation 18(7): 1527–54. https://doi.org/10.1162/neco.2006.18.7.1527

LeCun, Y., Bengio, Y. and Hinton, G., 2015. Deep learning. Nature, 521: 436–444. https://doi.org/10.1038/nature14539

Lecun, Y., Bottou, L., Bengio, Y. and Haffner, P., 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11). https://doi.org/10.1109/5.726791

Nabavi, M., 1976. An introduction to the geology of Iran. Geological Survey of Iran, 109: 104–109. (in Persian)

Pourgholam, M.M., Afzal, P., Adib, A., Rahbar, K. and Gholinejad, M., 2024. Recognition of REEs anomalies using an image Fusion fractal-wavelet model in Tarom metallogenic zone, NW Iran. Geochemistry, 84(2): 126093. https://doi.org/10.1016/j.chemer.2024.126093

Qaderi, S., 2014. Survey of Remote sensing and geochemical exploration of a region in Kermanshah 1:100000 sheet. Master thesis, Urmia University, Urmia, Iran. Retrieved September 26, 2024 from https://elmnet.ir/doc/10927102-41181

Redlich, A.N., 1993. Redundancy reduction as a strategy for unsupervised learning. Neural Computation, 5(2): 289–304. https://doi.org/10.1162/neco.1993.5.2.289

Reichstein, M., Camps Valls, G., Stevens, B., Jung, M., Denzler, J. and Carvalhais, N., 2019. Deep learning and process understanding for data-driven earth system science. Nature, 77(43): 195–204. https://doi.org/10.1038/s41586-019-0912-1

Scott, A.J. and Knott, M., 1974. A cluster analysis method for grouping means in the analysis of variance. Biometrics, 30(30): 507–512. https://doi.org/10.2307/2529204

Silverman, B.W., 1986. Density Estimation for Statistics and Data Analysis. Chapman & Hall, London – New York, 175 pp. https://doi.org/10.1002/bimj.4710300745

Sim, B.L., Agterberg, F.P. and Beaudry, C., 1999. Determining the cutoff between background and relative base metal smelter contamination levels using multifractal methods. Computers & Geosciences, 25(9): 1023–1041. https://doi.org/10.1016/S0098-3004(99)00064-3

Sun, T., Chen, F., Zhong, L., Liu, W. and Wang, Y., 2019. GIS-based mineral prospectivity mapping using machine learning methods: a case study from Tongling ore district, eastern China. Ore Geology Reviews, 109: 26–49. https://doi.org/10.1016/j.oregeorev.2019.04.003

Tukey, J.W., 1977. Some thoughts on clinical trials, especially problems of multiplicity. Science. 198(4318): 679–684. https://doi.org/10.1126/science.333584

Vapnik, V., 1995. The Nature of Statistical Learning Theory. Springer-Verlag, New York. 188 pp. http://doi.org/10.1007/978-1-4757-2440-0

Wackernagel, H., 2003. Multivariate geostatistics: An introduction with applications. Springer Science & Business Media, Mathematical Geology, 388 pp. http://doi.org/10.1007/978-3-662-05294-5

Ziaii, M., Ardejani, F.D., Ziaei, M. and Soleymani, A.A., 2012. Neuro-fuzzy modeling based genetic algorithms for identification of geochemical anomalies in mining geochemistry. Applied Geochemistry, 27(3): 663–676. https://doi.org/10.1016/j.apgeochem.2011.12.020

Ziaii, M., Pouyan, A.A. and Ziaei M., 2009.  Neuro-fuzzy modelling in mining geochemistry: Identification of geochemical anomalies. Journal of Geochemical Exploration.100(1): 25–36. https://doi.org/10.1016/j.gexplo.2008.03.004

Zuo, R., 2017. Machine learning of mineralization-related geochemical anomalies: a review of potential methods. Natural Resources Research, 26: 457–464. https://doi.org/10.1007/s11053-017-9345-4

Zuo, R., Cheng, Q., Agterberg, F.P. and Xia, Q., 2009. Application of singularity mapping technique to identify local anomalies using stream sediment geochemical data, a case study from Gangdese, Tibet, western China. Journal of Geochemical Exploration,101(3): 225–235. https://doi.org/10.1016/j.gexplo.2008.08.003

Zuo, R. and Wang, J., 2016. Fractal/multifractal modeling of geochemical data. Journal of Geochemical Exploration. 164: 33–41. https://doi.org/10.1016/j.gexplo.2015.04.010

Zuo, R., Xiong, Y., Wang, J. and Carranza, E., 2019. Deep learning and its application in geochemical mapping. Earth- Science Reviews, 192: 1–14. https://doi.org/10.1016/j.earscirev.2019.02.023