Adankon, M.M., Dansereau, J. and Cheriet, F., 2012. Analysis of scoliosis trunk deformities using ica. 11th International Conference on Information Science, Signal Processing and Their Applications (ISSPA).
https://doi.org/10.1109/isspa.2012.6310543
Andriyani, F. and Puspitarani, Y., 2022. Performance comparison of k-means and dbscan algorithms for text clustering product reviews. SinkrOn, 6(3): 944–949.
https://doi.org/10.33395/sinkron.v7i3.11569
Berberian, M. and King, G.C.P., 1981. Towards a paleogeography and tectonic evolution of Iran. Canadian Journal of Earth Sciences, 18(2): 210–265.
https://doi.org/10.1139/e81-019
Bulut, O., Gorgun, G. and He, S., 2024. Unsupervised anomaly detection in sequential process data. Zeitschrift Für Psychologie, 232(2): 74–94.
https://doi.org/10.1027/2151-2604/a000558
Chen, L., Guan, Q., Feng, B., Yue, H., Wang, J. and Zhang, F., 2019a. A multi-convolutional autoencoder approach to multivariate geochemical anomaly recognition. Minerals, 9(5): 270.
https://doi.org/10.3390/min9050270
Chen, L., Guan, Q., Xiong, Y., Liang, J., Wang, Y. and Xu, Y., 2019b. A spatially constrained multi-autoencoder approach for multivariate geochemical anomaly recognition. Computers & Geosciences, 125: 43–54.
https://doi.org/10.1016/j.cageo.2019.01.016
Chen, J. and Okle, R.A.N., 2024. A study on the application of response surface methodology and bayesian optimization in parameter tuning of genetic algorithms. Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024), Kaifeng, China.
https://doi.org/10.1117/12.3036921
Ester, M., Kriegel, H., Sander, J. and Xu, X., 1996. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. 2nd International Conference on Knowledge Discovery and Data Mining. Retrieved from July 19, 2025 from
https://www.dbs.ifi.lmu.de/Publikationen/Papers/KDD-96.final.frame.pdf
Farhadi, S., Tatullo, S., Boveiri Konari, M. and Afzal, P., 2024. Evaluating StackingC and ensemble models for enhanced lithological classification in geological mapping. Journal of Geochemical Exploration, 260: 107441
https://doi.org/10.1016/j.gexplo.2024.107441
Feng, B., Chen, L. and Xu, Y., 2022. Comparative study on three autoencoder‐based deep learning algorithms for geochemical anomaly identification. Earth and Space Science, 9(11).
https://doi.org/10.1029/2022ea002626
Ghawi, R. and Pfeffer, J., 2019. Efficient hyperparameter tuning with grid search for text categorization using knn approach with bm25 similarity. Open Computer Science, 9(1): 160-180.
https://doi.org/10.1515/comp-2019-0011
Hariri, S., Kind, M.C. and Brunner, R., 2021. Extended isolation forest. IEEE Transactions on Knowledge and Data Engineering, 33(4): 1479–1489.
https://doi.org/10.1109/tkde.2019.2947676
Hezarkhani, A., 2006. Petrology of the intrusive rocks within the Sungun Porphyry Copper Deposit, Azerbaijan, Iran. Journal of Asian Earth Sciences, 27(3): 326–340.
https://doi.org/10.1016/j.jseaes.2005.04.005
Horrocks, T., 2019. Integrated analysis of geological, geophysical, and geochemical data of the kevitsa ni-cu-pge deposit: machine learning approaches. Ph.D. Thesis, The University of western Australia, Crawley, Western Australia, Australia,180 pp.
https://doi.org/10.26182/5c5ba16237b57
Iwamori, H., Yoshida, K., Nakamura, H., Kuwatani, T., Hamada, M., Haraguchi, S., and Ueki, K., 2017. Classification of geochemical data based on multivariate statistical analyses: complementary roles of cluster, principal component, and independent component analyses. Geochemistry, Geophysics, Geosystems, 18(3): 994–1012.
https://doi.org/10.1002/2016gc006663
Jin, Z., Risk, B.B. and Matteson, D.S., 2019. Optimization and testing in linear non‐gaussian component analysis. Statistical Analysis and Data Mining: The ASA Data Science Journal, 12(3): 141–156.
https://doi.org/10.1002/sam.11403
Kingma, D.P. and Welling, M., 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.
https://doi.org/10.48550/arXiv.1312.6114
Li, H., Correa, N.M., Rodríguez, P. and Calhoun, V.D., 2011. Application of independent component analysis with adaptive density model to complex-valued fmri data. IEEE Transactions on Biomedical Engineering, 58(10): 2794–2803.
https://doi.org/10.1109/tbme.2011.2159841
Li, L., Jamieson, K.G., DeSalvo, G., Rostamizadeh, A. and Talwalkar, A., 2016. Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization. Journal of Machine Learning Research 18(2018): 1–52.
https://doi.org/10.48550/arxiv.1603.06560
Li, H., Li, X., Yuan, F., Jowitt, S.M., Zhang, M., Zhou, J., Zhou, T., Li, X., Ge, C. and Wu, B., 2020. Convolutional neural network and transfer learning based mineral prospectivity modeling for geochemical exploration of au mineralization within the guandian–zhangbaling area, anhui province, china. Applied Geochemistry, 122: 104747.
https://doi.org/10.1016/j.apgeochem.2020.104747
Luo, Z., Xiong, Y. and Zuo, R., 2020. Recognition of geochemical anomalies using a deep variational autoencoder network. Applied Geochemistry, 122: 104710.
https://doi.org/10.1016/j.apgeochem.2020.104710
Luo, Z., Zuo, R., Xiong, Y. and Wang, X., 2021. Detection of geochemical anomalies related to mineralization using the ganomaly network. Applied Geochemistry, 131: 105043.
https://doi.org/10.1016/j.apgeochem.2021.105043
Pourgholam, M.M., Adib, A., Afzal, P., Rahbar, K. and Gholinejad, M., 2025. Deep learning and fractal-wavelet techniques for magnetite-apatite exploration in Tarom Iran. Scientific Reports 15(1): 31907.
https://doi.org/10.1038/s41598-025-16040-2
Praveena, S.M., Kwan, O.W. and Aris, A.Z., 2011. Effect of data pre-treatment procedures on principal component analysis: a case study for mangrove surface sediment datasets. Environmental Monitoring and Assessment, 184(11): 6855–6868.
https://doi.org/10.1007/s10661-011-2463-2
Rezende, D., Mohamed, S. and Wierstra, D., 2014. Stochastic Back-propagation and Variational Inference in Deep Latent Gaussian Models. ArXiv,abs/1401.4082.
https://doi.org/10.48550/arxiv.1401.4082
Richards, J.P., 2003. Tectono-magmatic precursors for porphyry Cu-(Mo-Au) deposit formation. Economic Geology, 98(8): 1515–1533.
https://doi.org/10.2113/gsecongeo.98.8.1515
Rijn, J.N.v. and Hutter, F., 2018. Hyperparameter importance across datasets. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2367-2376.
https://doi.org/10.1145/3219819.3220058
Sarikhani, P., Ferleger, B.I., Mitchell, K.T., Ostrem, J.L., Herron, J.A., Mahmoudi, B. and Miocinovic, S., 2022. Automated deep brain stimulation programming with safety constraints for tremor suppression in patients with parkinson’s disease and essential tremor. Journal of Neural Engineering, 19(4): 046042.
https://doi.org/10.1088/1741-2552/ac86a2
Snoek, J., Larochelle, H. and Adams, R.P., 2012. Practical Bayesian Optimization of Machine Learning Algorithms. Neural Information Processing Systems.
https://doi.org/10.48550/arxiv.1206.2944
Taghipour, N., Aftabi, A. and Mathur, R., 2008. Geology and Re-Os geochronology of mineralization of the miduk porphyry copper deposit, Iran. Resource Geology, 58(2): 143–160.
https://doi.org/10.1111/j.1751-3928.2008.00054.x
Thavasimani, K. and Srinath, N.K., 2022. Hyperparameter optimization using custom genetic algorithm for classification of benign and malicious traffic on internet of things–23 dataset. International Journal of Electrical and Computer Engineering (IJECE), 12(4): 4031.
https://doi.org/10.11591/ijece.v12i4.pp4031-4041
Thu, T.N.T. and Armitage, G., 2008. A survey of techniques for internet traffic classification using machine learning. IEEE Communications Surveys & Tutorials, 10(4): 56–76.
https://doi.org/10.1109/surv.2008.080406
Ueki, K., Hino, H. and Kuwatani, T., 2018. Geochemical discrimination and characteristics of magmatic tectonic settings: a machine‐learning‐based approach. Geochemistry, Geophysics, Geosystems, 19(4): 1327–1347.
https://doi.org/10.1029/2017gc007401
Verma, S.P., 1997. Sixteen statistical tests for outlier detection and rejection in evaluation of international geochemical reference materials: example of microgabbro pm‐s. Geostandards Newsletter, 21(1): 59–75.
https://doi.org/10.1111/j.1751-908x.1997.tb00532.x
Wein, S., Tomé, A.M., Goldhacker, M., Greenlee, M.W. and Lang, E., 2020. A constrained ica-emd model for group level fmri analysis. Frontiers in Neuroscience, 14.
https://doi.org/10.3389/fnins.2020.00221
Xiong, Y. and Zuo, R., 2016. Recognition of geochemical anomalies using a deep autoencoder network. Computers & Geosciences, 86: 75–82.
https://doi.org/10.1016/j.cageo.2015.10.006
Xiong, Y. and Zuo, R., 2020. Recognizing multivariate geochemical anomalies for mineral exploration by combining deep learning and one-class support vector machine. Computers & Geosciences, 140: 104484.
https://doi.org/10.1016/j.cageo.2020.104484
Yang, N., Zhang, Z., Yang, J., Hong, Z. and Shi, J., 2021. A convolutional neural network of googlenet applied in mineral prospectivity prediction based on multi-source geoinformation. Natural Resources Research, 30(6): 3905–3923.
https://doi.org/10.1007/s11053-021-09934-1
Yasukawa, K., Nakamura, K., Fujinaga, K., Iwamori, H. and Kato, Y., 2016. Tracking the spatiotemporal variations of statistically independent components involving enrichment of rare-earth elements in deep-sea sediments. Scientific Reports, 6: 29603.
https://doi.org/10.1038/srep29603
Zhang, S., Carranza, E.J.M., Fu, C., Wen-zhi, Z. and Xiang, Q., 2024. Interpretable machine learning for geochemical anomaly delineation in the yuanbo nang district, gansu province, china. Minerals, 14(5): 500.
https://doi.org/10.3390/min14050500
Zhang, S., Xiao, K., Carranza, E.J.M., Yang, F. and Zhao, Z., 2019. Integration of auto-encoder network with density-based spatial clustering for geochemical anomaly detection for mineral exploration. Computers & Geosciences, 130: 43–56.
https://doi.org/10.1016/j.cageo.2019.05.011
Zhang, C. and Zuo, R., 2021. Recognition of multivariate geochemical anomalies associated with mineralization using an improved generative adversarial network. Ore Geology Reviews, 136: 104264.
https://doi.org/10.1016/j.oregeorev.2021.104264
Zhang, C. and Zuo, R., 2024. Incorporating geological knowledge into deep learning to enhance geochemical anomaly identification related to mineralization and interpretability. Mathematical Geosciences, 56(6): 1233–1254.
https://doi.org/10.1007/s11004-023-10133-2
Zuo, R., 2011. Identifying geochemical anomalies associated with cu and pb–zn skarn mineralization using principal component analysis and spectrum–area fractal modeling in the gangdese belt, tibet (china). Journal of Geochemical Exploration, 111(1–2): 13–22.
https://doi.org/10.1016/j.gexplo.2011.06.012
