Computers in Earth Sciences, Economic Geology, Geology, Geochemistry and Petrology
16
Scopus Publications
208
Scholar Citations
10
Scholar h-index
10
Scholar i10-index
Scopus Publications
Predictive modeling of mineral prospectivity by deep self-organizing map: Implications for copper exploration targeting in saveh district, central Iran Zohre Hoseinzade, Mohammad Hassan Bazoobandi, Saeid Esmaeiloghli, Mobin Saremi Ore Geology Reviews, 2026 • Combining unsupervised deep learning (DAN) for dimensionality reduction with SOM’s clustering to delineate ore-related spatial patterns. • Validating results through multi-source geospatial data from the Saveh district (Central Iran), P-A plot analysis , and field evidence ( petrography, microprobe data ). • Demonstrating superior targeting accuracy compared to conventional methods, with direct implications for cost-effective copper exploration . The increasing challenges in mineral prospectivity mapping (MPM), particularly for deep-seated and complex metal deposits, demand the application of cutting-edge computational workflows to model mineralization-related spatial patterns effectively. This study discusses a novel hybrid algorithm combining a deep autoencoder network (DAN) with a self-organizing map (SOM) to recognize mineralization-related spatial patterns and enhance the MPM procedure. By integrating the dimensionality reduction power of the DAN with the robust clustering capabilities of the SOM, the proposed workflow aims to achieve more precise prospectivity predictions. The proposed hybrid algorithm was applied to multi-source exploration data pertaining to the Saveh district, Central Iran. A comparative analysis with traditional approaches was constituted, whereby prediction-area (P-A) plots were used to assess the relevance of models in predictive modeling of mineral prospectivity within the study area. The results derived from the P-A plots demonstrated the superiority of the proposed hybrid model in delimiting target areas for further metal exploration. Moreover, field surveys and mineralogical studies, including petrographic and microprobe analyses, confirmed the presence of ore-bearing evidence within the identified target areas. The findings suggest that the proposed methodology has the potential to enhance MPM accuracy and mitigate costs and risks in regional-scale exploration programs. The research demonstrates the potential of integrated deep learning and clustering techniques in boosting MPM procedures and discovering new metal deposits within complex metallogenic systems.
A deep embedded clustering algorithm in conjunction with an ensemble technique for mineral prospectivity mapping Mobin Saremi, Zohre Hoseinzade, Mahyar Yousefi Scientific Reports, 2025 Traditional clustering algorithms are popular unsupervised methods and have been widely applied in mineral prospectivity mapping (MPM). Despite the advantages of these algorithms in terms of simplicity and popularity, they are not strong enough to struggle with high-dimensional, complex, and non-linear geospatial data. Consequently, they may lead to suboptimal clustering performance, a reason for not being able to precisely recognize and discriminate complex mineralization-related anomaly patterns in mineral exploration datasets. To improve the clustering performance, we propose a deep embedded clustering (DEC) approach for MPM. DEC is an unsupervised method that uses deep neural networks to learn from the feature representations and optimize cluster assignments simultaneously. In this study, evidence layers, representing porphyry copper mineralization, were first generated. Then, four clustering techniques were applied to generate prospectivity models. The prediction rate of the models was evaluated using the prediction-area (P-A) plot. The results showed that the prediction rates of K-means, Gaussian mixture model (GMM), DEC-K-means, and DEC-GMM prospectivity models were 66, 68, 69, and 72%, respectively. This demonstrates that DEC-based clustering outperforms conventional clustering algorithms and that DEC-GMM effectively recognizes mineralization-related patterns. Finally, to benefit from the advantages of all the applied clustering methods, we calculated a confidence index, as an ensemble technique, to recognize exploration targets, those that support further mineral exploration programs in terms of low uncertainty.
Enhancing porphyry copper prospectivity mapping: A deep autoencoder-based approach to identify non-deposit points in varzaghan region, NW Iran Mobin Saremi, Abbas Maghsoudi, Ardeshir Hezarkhani, Amin Beiranvand Pour, Zohre Hoseinzade, Seyyed Ataollah Agha Seyyed Mirzabozorg, Mahyar Yousefi Ore Geology Reviews, 2025 • Comparison of Unsupervised and Expert-Based Methods for Selecting Non-Deposit Points in Mineral Prospectivity Modeling. • Improving the Prediction Rate of the Random Forest Model for Copper Deposit Prediction Using Deep Autoencoder Output. Supervised machine learning algorithms have shown enormous potential to predict mineral prospectivities and to identify mineral exploration targets within study areas. However, accurately selecting non-deposit points remains a critical challenge, as improper selection can mitigate the prediction rate and introduce systematic bias. This study describes the idea of leveraging and comparing deep autoencoder (DAE) network (as a first experiment) with expert knowledge (as a second experiment) to tackle the problem of non-deposit selection in predictive modeling of mineral prospectivity. For this, according to the conceptual model of porphyry copper deposits evidence layers of fault density, multi-element geochemical signatures, proximity to phyllic and argillic alterations, and proximity to intrusive rocks, were first generated to represent ore-forming subsystems. Within the first experiment, a DAE technique was used to integrate multiple exploration criteria whereby non-deposit locations within the recognized non-prospective regions were determined. Within the second experiment, expert opinions were set as criteria to define non-deposit locations. Both sets of non-deposit points were fed into a random forest (RF) algorithm, generating two prospectivity models. The effectiveness of these models was evaluated using the prediction-area (P-A) plot and the normalized density index (Nd). The Nd values for all models exceed one, indicating their effectiveness in integrating exploration evidence to delineate potential targets. However, the DAE-based experiment improved the prediction rate of RF and reduced systematic uncertainties. The proposed methodology was shown to be a robust approach to enhance the relevance of mineral prospectivity mapping, and it may possess the potential to predict new porphyry copper exploration targets in analogous mineral systems.
Evaluation of Deep Isolation Forest (DIF) Algorithm for Mineral Prospectivity Mapping of Polymetallic Deposits Mobin Saremi, Milad Bagheri, Seyyed Ataollah Agha Seyyed Mirzabozorg, Najmaldin Ezaldin Hassan, Zohre Hoseinzade, Abbas Maghsoudi, Shahabaldin Rezania, Hojjatollah Ranjbar, Basem Zoheir, Amin Beiranvand Pour Minerals, 2024 Mineral prospectivity mapping (MPM) is crucial for efficient mineral exploration, where prospective zones are identified in a cost-effective manner. This study focuses on generating prospectivity maps for hydrothermal polymetallic mineralization in the Feizabad area, in northeastern Iran, using unsupervised anomaly detection methods, i.e., isolation forest (IForest) and deep isolation forest (DIF) algorithms. As mineralization events are rare and complex, traditional approaches continue to encounter difficulties, despite advances in MPM. In this respect, unsupervised anomaly detection algorithms, which do not rely on ground truth samples, offer a suitable solution. Here, we compile geospatial datasets on the Feizabad area, which is known for its polymetallic mineralization showings. Fourteen evidence layers were created, based on the geology and mineralization characteristics of the area. Both the IForest and DIF algorithms were employed to identify areas with high mineralization potential. The DIF, which uses neural networks to handle non-linear relationships in high-dimensional data, outperformed the traditional decision tree-based IForest algorithm. The results, evaluated through a success rate curve, demonstrated that the DIF provided more accurate prospectivity maps, effectively capturing complex, non-linear relationships. This highlights the DIF algorithm’s suitability for MPM, offering significant advantages over the IForest algorithm. The present study concludes that the DIF algorithm, and similar unsupervised anomaly detection algorithms, are highly effective for MPM, making them valuable tools for both brownfield and greenfield exploration.
Clay minerals characterization of the Miduk ball mill output through spectral analysis Zohre Hoseinzade, Ahmad Reza Mokhtari, Hamid Zekri Ore Geology Reviews, 2023 Porphyry copper deposits usually include typical alteration types, which are often composed of clay minerals. The mixing of clay minerals has a significant impact on their spectral absorption characteristics, and their identification and separation becomes complicated. One of the most accurate and widely used methods for identifying minerals is spectroscopy, which is a suitable alternative to traditional methods. Spectroscopy assists in identifying the mineral composition and classification. The aim of the following research is to quantitatively characterize clay minerals in mineral assemblage of ball mill output. Pure and composite samples were prepared through sampling from two alteration types and creating synthetic samples containing different percentages of clay minerals. Then they were subjected to spectral analysis in the range of 2500-350 nm and their output was subjected to necessary pre-processing. The results were used for classification by employing a hybrid algorithm. The analysis identified the following minerals in the Miduk deposit: hematite, carbonates, illite, biotite, kaolinite, chlorite and muscovite. Additionally, microscopic studies were conducted on the samples for further analysis. Finally, total and clay diffraction XRD analysis were used to validate the results.
Prediction of concentrated phosphorus grade of iron ore using mathematical analysis Journal of Analytical and Numerical Methods in Mining Engineering, 2019
Predictive modeling of mineral prospectivity by deep self-organizing map: Implications for copper exploration targeting in saveh district, central Iran Z Hoseinzade, MH Bazoobandi, S Esmaeiloghli, M Saremi Ore Geology Reviews, 107194 , 2026 2026
Uncertainty Analysis of Deep Learning-Based Geochemical Models Using a New Approach (Southeast Jiroft, Kerman) F Khademi, D Esmaeily, A Kananian, Z Hoseinzade 2026
A deep embedded clustering algorithm in conjunction with an ensemble technique for mineral prospectivity mapping M Saremi, Z Hoseinzade, M Yousefi Scientific Reports 15 (1), 38086 , 2025 2025 Citations: 3
Fusion of remote sensing and geochemical data using hybrid Variational Autoencoder-BIRCH deep learning algorithm for copper prospectivity mapping Z Hoseinzade, M Saremi, M Shojaei, AR Mokhtari, AB Pour, ... Remote Sensing Applications: Society and Environment, 101738 , 2025 2025 Citations: 5
Enhancing porphyry copper prospectivity mapping: A deep autoencoder-based approach to identify non-deposit points in Varzaghan region, NW Iran M Saremi, A Maghsoudi, A Hezarkhani, AB Pour, Z Hoseinzade, ... Ore Geology Reviews 183, 106705 , 2025 2025 Citations: 9
Integration of deep learning models for mineral prospectivity mapping: a novel Bayesian index approach to reducing uncertainty in exploration Z Hoseinzade, M Shojaei, F Khademi, AR Mokhtari, M Saremi Modeling Earth Systems and Environment 11 (3), 161 , 2025 2025 Citations: 11
Data-driven AHP: a novel method for porphyry copper prospectivity mapping in the Varzaghan District, NW Iran M Saremi, A Maghsoudi, Z Hoseinzade, AR Mokhtari Earth Science Informatics 17 (6), 5063-5078 , 2024 2024 Citations: 20
Applying Deep Embedded Self Organizing Map (DE-SOM) method to separate geochemical anomalous areas of copper-gold mineralization in Moalleman region, Iran Z Hoseinzade, MH Bazoobandi Journal of Mining and Environment , 2024 2024 Citations: 10
Integrated remote sensing and geochemical studies for enhanced prospectivity mapping of porphyry copper deposits: A case study from the Pariz district, Urmia-Dokhtar … M Saremi, Z Hoseinzade, SAAS Mirzabozorg, AB Pour, B Zoheir, A Almasi Remote Sensing Applications: Society and Environment 36, 101343 , 2024 2024 Citations: 30
Deep embedded clustering: Delineating multivariate geochemical anomalies in the Feizabad region Z Hoseinzade, MH Bazoobandi Geochemistry 84 (4), 126208 , 2024 2024 Citations: 18
Evaluation of deep isolation forest (DIF) algorithm for mineral prospectivity mapping of polymetallic deposits M Saremi, M Bagheri, SA Agha Seyyed Mirzabozorg, NE Hassan, ... Minerals 14 (10), 1015 , 2024 2024 Citations: 16
Clay minerals characterization of the Miduk ball mill output through spectral analysis Z Hoseinzade, AR Mokhtari, H Zekri Ore Geology Reviews 161, 105629 , 2023 2023 Citations: 6
Identification of areas at the risk of landslide via the short-time Fourier transform Z Hoseinzade, M Mokhtari, K Shirani, NS Miresmaeili Earth Science Informatics 15 (4), 2405-2413 , 2022 2022 Citations: 3
Application of prediction–area plot in the assessment of MCDM methods through VIKOR, PROMETHEE II, and permutation Z Hoseinzade, A Zavarei, K Shirani Natural Hazards 109 (3), 2489-2507 , 2021 2021 Citations: 23
A comparison study on landslide prediction through FAHP and Dempster–Shafer methods and their evaluation by P – A plots M Mokhtari, Z Hoseinzade, K Shirani Environmental Earth Sciences 79 (3), 76 , 2020 2020 Citations: 18
Prediction of concentrated phosphorus grade of iron ore using mathematical analysis Z Hoseinzade, S Sharestani, SH Mojtahedzadeh, M Babaie, H Daneshfar, ... Journal of Analytical and Numerical Methods in Mining Engineering 9 (18 … , 2019 2019
Application of radial basis function in the analysis of irregular geochemical patterns through spectrum-area method Z Hoseinzade, AR Mokhtari, H Zekri Journal of Geochemical Exploration 194, 257-265 , 2018 2018 Citations: 10
A comparison study on detection of key geochemical variables and factors through three different types of factor analysis Z Hoseinzade, AR Mokhtari Journal of African Earth Sciences 134, 557-563 , 2017 2017 Citations: 26
MOST CITED SCHOLAR PUBLICATIONS
Integrated remote sensing and geochemical studies for enhanced prospectivity mapping of porphyry copper deposits: A case study from the Pariz district, Urmia-Dokhtar … M Saremi, Z Hoseinzade, SAAS Mirzabozorg, AB Pour, B Zoheir, A Almasi Remote Sensing Applications: Society and Environment 36, 101343 , 2024 2024 Citations: 30
A comparison study on detection of key geochemical variables and factors through three different types of factor analysis Z Hoseinzade, AR Mokhtari Journal of African Earth Sciences 134, 557-563 , 2017 2017 Citations: 26
Application of prediction–area plot in the assessment of MCDM methods through VIKOR, PROMETHEE II, and permutation Z Hoseinzade, A Zavarei, K Shirani Natural Hazards 109 (3), 2489-2507 , 2021 2021 Citations: 23
Data-driven AHP: a novel method for porphyry copper prospectivity mapping in the Varzaghan District, NW Iran M Saremi, A Maghsoudi, Z Hoseinzade, AR Mokhtari Earth Science Informatics 17 (6), 5063-5078 , 2024 2024 Citations: 20
Deep embedded clustering: Delineating multivariate geochemical anomalies in the Feizabad region Z Hoseinzade, MH Bazoobandi Geochemistry 84 (4), 126208 , 2024 2024 Citations: 18
A comparison study on landslide prediction through FAHP and Dempster–Shafer methods and their evaluation by P – A plots M Mokhtari, Z Hoseinzade, K Shirani Environmental Earth Sciences 79 (3), 76 , 2020 2020 Citations: 18
Evaluation of deep isolation forest (DIF) algorithm for mineral prospectivity mapping of polymetallic deposits M Saremi, M Bagheri, SA Agha Seyyed Mirzabozorg, NE Hassan, ... Minerals 14 (10), 1015 , 2024 2024 Citations: 16
Integration of deep learning models for mineral prospectivity mapping: a novel Bayesian index approach to reducing uncertainty in exploration Z Hoseinzade, M Shojaei, F Khademi, AR Mokhtari, M Saremi Modeling Earth Systems and Environment 11 (3), 161 , 2025 2025 Citations: 11
Applying Deep Embedded Self Organizing Map (DE-SOM) method to separate geochemical anomalous areas of copper-gold mineralization in Moalleman region, Iran Z Hoseinzade, MH Bazoobandi Journal of Mining and Environment , 2024 2024 Citations: 10
Application of radial basis function in the analysis of irregular geochemical patterns through spectrum-area method Z Hoseinzade, AR Mokhtari, H Zekri Journal of Geochemical Exploration 194, 257-265 , 2018 2018 Citations: 10
Enhancing porphyry copper prospectivity mapping: A deep autoencoder-based approach to identify non-deposit points in Varzaghan region, NW Iran M Saremi, A Maghsoudi, A Hezarkhani, AB Pour, Z Hoseinzade, ... Ore Geology Reviews 183, 106705 , 2025 2025 Citations: 9
Clay minerals characterization of the Miduk ball mill output through spectral analysis Z Hoseinzade, AR Mokhtari, H Zekri Ore Geology Reviews 161, 105629 , 2023 2023 Citations: 6
Fusion of remote sensing and geochemical data using hybrid Variational Autoencoder-BIRCH deep learning algorithm for copper prospectivity mapping Z Hoseinzade, M Saremi, M Shojaei, AR Mokhtari, AB Pour, ... Remote Sensing Applications: Society and Environment, 101738 , 2025 2025 Citations: 5
A deep embedded clustering algorithm in conjunction with an ensemble technique for mineral prospectivity mapping M Saremi, Z Hoseinzade, M Yousefi Scientific Reports 15 (1), 38086 , 2025 2025 Citations: 3
Identification of areas at the risk of landslide via the short-time Fourier transform Z Hoseinzade, M Mokhtari, K Shirani, NS Miresmaeili Earth Science Informatics 15 (4), 2405-2413 , 2022 2022 Citations: 3
Predictive modeling of mineral prospectivity by deep self-organizing map: Implications for copper exploration targeting in saveh district, central Iran Z Hoseinzade, MH Bazoobandi, S Esmaeiloghli, M Saremi Ore Geology Reviews, 107194 , 2026 2026
Uncertainty Analysis of Deep Learning-Based Geochemical Models Using a New Approach (Southeast Jiroft, Kerman) F Khademi, D Esmaeily, A Kananian, Z Hoseinzade 2026
Prediction of concentrated phosphorus grade of iron ore using mathematical analysis Z Hoseinzade, S Sharestani, SH Mojtahedzadeh, M Babaie, H Daneshfar, ... Journal of Analytical and Numerical Methods in Mining Engineering 9 (18 … , 2019 2019
