Adel Shirazy has written more than 100 titles in science, sports and arts. He is the manager in STS Publishers and one of the activists in the field of expanding book culture. He has published over 150 scientific papers. Shirazy worked on the foundation of artificial intelligence in mining exploration. He is the chairman of the STS Knowledge based company. In 2024, Shirazy was a candidate of the Islamic Consultative Assembly in the 12th Islamic Consultative Assembly and was on the list of the Javanan-e-enghelabi Tehran and also the coalition of innovators of Islamic Iran. He published material to explain the policies of elitism and youthism. He is the vice president of AIKIKempo self-defense style in the Iran Judo Federation (IJF).he received the title of the country's best doctorate from the Economic Geology Association of Iran.
EDUCATION
Adel Shirazy is an Iranian scientist, author and politician who is one of the pioneers of artificial intelligence training in mineral exploration. He is also a scientific and practical activist of conservation affairs. Adel Shirazy was born in Pasteur Hospital in Tehran on 28 March 1991. After completing his high school and pre-university education from Shohadaye-Enghelab High School in Tehran (2009), he continued to complete his bachelor's degree in mining engineering and completed his master's degree in Birjand University of Technology in 2017. After completing a specialized doctorate course at Shahroud University of Technology in 2017-2019, he completed post-doctorate studies at Amir Kabir University of Technology in 2022 with the publication of numerous articles. He is among the top 2% scientists in his field according to Researchgate. Along with his studies, he practiced Aikido.
RESEARCH, TEACHING, or OTHER INTERESTS
Earth and Planetary Sciences, Computers in Earth Sciences, Geophysics, Multidisciplinary
20
Scopus Publications
1088
Scholar Citations
22
Scholar h-index
36
Scholar i10-index
Scopus Publications
Petrogenesis of Serpentinites and Chromitites in the Neoproterozoic Bou Azzer Ophiolite, Morocco: From Mantle Depletion to High-Pressure Exhumation Amina Wafik, Mohamed Ben Massoude, Youssef Atif, Atman Ait Lamqadem, Reza Rooki, Aref Shirazi, Adel Shirazy, Amin Beiranvand Pour Minerals, 2026 Serpentinites and associated chromitites of the Neoproterozoic Bou Azzer ophiolite (Central Anti-Atlas, Morocco) provide key constraints on mantle depletion, melt–rock interaction, and the tectono-metamorphic evolution of a supra-subduction zone (SSZ) system. This study integrates field observations, petrography, Raman spectroscopy, and whole-rock/mineral chemistry to decipher the history of this highly dismembered ultramafic suite. The mantle sequence is dominated by antigorite-bearing serpentinites derived primarily from refractory harzburgitic and dunitic protoliths. Whole-rock geochemistry and highly depleted chromite compositions (Cr# = 0.50–0.68; Mg# = 0.43–0.77; TiO2 ≤ 0.18 wt.%) demonstrate that these peridotites represent refractory residues formed after high degrees of partial melting (~15–25%). The data delineate a clear evolutionary trend from abyssal to fore-arc and back-arc environments, where infiltrating boninitic melts drove localized podiform chromitite formation through intense melt–rock interaction. Crucially, thermodynamic and mineral–chemical constraints challenge previous models of simple greenschist-facies obduction. Equilibration temperatures exceeding 600 °C and chromite stability within the lower amphibolite to near-granulite facies indicate that the oceanic lithosphere underwent deep subduction prior to its exhumation. This high-temperature, high-pressure metamorphism was followed by multistage retrogressive serpentinization and intense CO2-rich metasomatism (talc-magnesite alteration) during Pan-African transpressional tectonics. Ultimately, the Bou Azzer ophiolite represents a mature SSZ mantle wedge, recording a complete geodynamic cycle from deep subduction-zone metamorphism to final tectonic emplacement along the northern margin of the West African Craton.
Machine learning-driven mineral prospectivity mapping: A predictive performance analysis in Janja, Iran , Mohammad Ebdali, Ardeshir Hezarkhani, , Adel Shirazy, , Amin Beiranvnd Pour, and Nafta Gaz, 2025 Geochemical analysis is an effective technique for detecting mineral deposits by examining element concentrations. Various statistical techniques have been developed to differentiate abnormal values from background values. A more accurate analysis can be obtained by employing multivariate statistical methods. The use of these methods enables the simultaneous analysis of changes in multiple variables. This research utilized correlation coefficients, cluster analysis, and factor analysis to demonstrate the genetic connections among various elements. The factor analysis method was additionally applied to generate multivariable maps and comprehensive multivariable results. Moreover, the stepwise factor analysis (SFA) method, an enhanced version of traditional factor analysis, was utilized to produce geochemical distribution maps. This technique entails initially recognizing and removing non-representative elements, followed by identifying the most important and impactful representative factors. This study demonstrates the efficacy of the SFA method when applied to geochemical data. This approach removes superfluous elements and increases the variance attributed to the predictive mineralization factor, thereby improving the geochemical halos. Additionally, this research evaluated multivariate analysis approaches alongside machine learning techniques. To achieve this, a multilayer perceptron neural network (MLP) was used to evaluate the levels of gold, silver, copper, lead, and zinc in the study area. The output variable represented the grade of a particular element individually, whereas the input variables encompassed the grades of the remaining four elements. To optimize the model, different quantities of hidden layers and a range of activation functions were applied. Ultimately, an ideal model was developed for each element. The model achieved accuracies of 95%, 88%, 73%, 80%, and 72% for the gold, silver, copper, lead, and zinc, respectively. The results show the significant computational efficiency of this method in assessing element grades. Finally, the element distribution maps generated by both methods indicate that the MLP approach identified the anomalous areas with higher accuracy.
Unsupervised Anomaly Detection for Mineral Prospectivity Mapping Using Isolation Forest and Extended Isolation Forest Algorithms Mobin Saremi, Ardeshir Hezarkhani, Seyyed Ataollah Agha Seyyed Mirzabozorg, Ramin DehghanNiri, Adel Shirazy, Aref Shirazi Minerals, 2025 Unsupervised anomaly detection algorithms have gained significant attention in the field of mineral prospectivity mapping (MPM) due to their ability to reveal hidden mineralization zones by effectively modeling complex, nonlinear relationships between exploration data and mineral deposits. This study utilizes two tree-based anomaly detection algorithms, namely, isolation forest (IF) and extended isolation forest (EIF), to enhance MPM and exploration targeting. According to the conceptual model of porphyry copper deposits, several evidence layers were generated, including fault density, multi-element geochemical signatures, proximity to various alteration types (phyllic, argillic, propylitic, and iron oxide), and proximity to intrusive rocks. These layers were integrated using IF and EIF algorithms, and their results were subsequently compared with a geological map of the study area. The comparison revealed a high degree of overlap between the identified anomalous zones and geological features, such as andesitic rocks, tuffs, rhyolites, pyroclastics, and intrusions. Additionally, quantitative assessments through prediction-area plots validated the efficacy of both models in generating prospective targets. The results highlight the significant influence of hyperparameter tuning on the accuracy of prospectivity models. Furthermore, the study demonstrates that hyperparameter tuning is more intuitive and straightforward in IF, as it provides a clear and distinct tuning pattern, whereas EIF lacks such clarity, complicating the optimization process.
Modeling the Barite Deposition Process in the Jbel Irhoud Deposit, Western Jebilet, Morocco: Implications for Barite Exploration and Insights Into Mineralization Mechanisms Fouad Benchekroun, Abdel Mohsine Aghzer, Amina Wafik, Mohamed Ben massoude, Youssef Atif, Amin Beiranvand Pour, Adel Shirazy, Aref Shirazi Geofluids, 2025 The barite of the Jbel Irhoud deposit in Western Jebilet, Morocco, is a Paleozoic massif known for its mineralization, which occurs mainly in shale, sandstone, and Middle Cambrian limestone. Three main types of barite deposits are known in the area: karst, vein fillings, and limestone replacement. The karst formations make up the majority of the reserves. Barite‐quartz‐galena and Fe‐Cu‐Zn‐ and Ag‐sulfides, as well as hematite‐carbonates, form the mineral paragenesis. Oxidation and mixing models are proposed for the deposition of barite. To check the effects of oxidation, numerical modeling of aqueous fluid composition for Irhoud barite deposition (at 150°C–250°C, Psat, and 1–6 m NaCl) was performed using a program developed by Professor Moine at the Paul Sabatier University in Toulouse, France. It shows that a large amount of barium can be transported as Ba2+ (barium chloride becomes more significant at relatively high temperatures) and that the decrease in solubility of barium under the given conditions can be caused by an increase in fO2, with or without a decrease in temperature, pressure, and/or salinity. Moreover, it is shown that the mixing of two fluids with different compositions leads to an oxidation (and a partial decrease in temperature) that causes a significant decrease in the solubility of barium (more than 130 ppm) and thus an efficient precipitation of barite in the Jbel Irhoud deposit. This modeling could be used to explain the manifestation of fluids with different compositions associated with the deposition of barite worldwide. The hydrothermal and structurally controlled Irhoud barite is the result of rapid decompression and Ba2+/BaCl+ transport under moderate to high P–T conditions, suggesting an epigenetic, postsedimentary system.
Employing Statistical Algorithms and Clustering Techniques to Assess Lithological Facies for Identifying Optimal Reservoir Rocks: A Case Study of the Mansouri Oilfields, SW Iran Seyedeh Hajar Eftekhari, Mahmoud Memariani, Zahra Maleki, Mohsen Aleali, Pooria Kianoush, Adel Shirazy, Aref Shirazi, Amin Beiranvand Pour Minerals, 2024 The crucial parameters influencing drilling operations, reservoir production behavior, and well completion are lithology and reservoir rock. This study identified optimal reservoir rocks and facies in 280 core samples from a drilled well in the Asmari reservoir of the Mansouri field in SW Iran to determine the number of hydraulic flow units. Reservoir samples were prepared, and their porosity and permeability were determined by measuring devices. The flow zone index (FZI) was calculated for each sample using MATLAB software; then, a histogram analysis was performed on the logarithmic data of the FZI, and the number of hydraulic flow units was determined based on the obtained normal distributions. Electrical facies were determined based on artificial neural network (ANN) and multi-resolution graph-based clustering (MRGC) approaches. Five electrical facies with dissimilar reservoir conditions and lithological compositions were ultimately specified. Based on described lithofacies, shale and sandstone in zones three and five demonstrated elevated reservoir quality. This study aimed to determine the Asmari reservoir’s porous medium’s flowing fluid according to the C-mean fuzzy logic method. Furthermore, the third and fourth flow units in the Asmari Formation have the best flow units with high reservoir quality and permeability due to determining the siliceous–clastic facies of the rock units and log data. Outcomes could be corresponded to the flow unit determination in further nearby wellbores without cores.
Geochemical Modeling of Copper Mineralization Using Geostatistical and Machine Learning Algorithms in the Sahlabad Area, Iran Aref Shirazi, Ardeshir Hezarkhani, Adel Shirazy, Amin Pour Minerals, 2023 Analyzing geochemical data from stream sediment samples is one of the most proactive tools in the geochemical modeling of ore mineralization and mineral exploration. The main purpose of this study is to develop a geochemical model for prospecting copper mineralization anomalies in the Sahlabad area, South Khorasan province, East Iran. In this investigation, 709 stream sediment samples were analyzed using inductively coupled plasma mass spectrometry (ICP-MS), and geostatistical and machine learning techniques. Subsequently, hierarchical analysis (HA), Spearman’s rank correlation coefficient, concentration–area (C–A) fractal analysis, Kriging interpolation, and descriptive statistics studies were performed on the geochemical dataset. Machine learning algorithms, namely K-means clustering, factor analysis (FA), and linear discriminant analysis (LDA) were employed to deliver a comprehensive geochemical model of copper mineralization in the study area. The identification of trace elements and the predictor composition of copper mineralization, the separation of copper geochemical communities, and the investigation of the geochemical behavior of copper vs. its trace elements were targeted and accomplished. As a result, the elements Ag, Mo, Pb, Zn, and Sn were distinguished as trace elements and predictors of copper geochemical modeling in the study area. Additionally, geochemical anomalies of copper mineralization were identified based on trace elements. Conclusively, the nonlinear behavior of the copper element versus its trace elements was modeled. This study demonstrates that the integration and synchronous use of geostatistical and machine learning methods can specifically deliver a comprehensive geochemical modeling of ore mineralization for prospecting mineral anomalies in metallogenic provinces around the globe.
Remote Sensing, Petrological and Geochemical Data for Lithological Mapping in Wadi Kid, Southeast Sinai, Egypt Wael Fahmy, Hatem M. El-Desoky, Mahmoud H. Elyaseer, Patrick Ayonta Kenne, Aref Shirazi, Ardeshir Hezarkhani, Adel Shirazy, Hamada El-Awny, Ahmed M. Abdel-Rahman, Ahmed E. Khalil, Ahmed Eraky, Amin Beiranvand Pour Minerals, 2023 The Wadi Samra–Wadi Kid district in southeastern Sinai, Egypt, has undergone extensive investigation involving remote sensing analysis, field geology studies, petrography, and geochemistry. The main aim of this study is the integration between remote sensing applications, fieldwork, and laboratory studies for accurate lithological mapping for future mineral exploration in the study region. The field relationships between these coincident rocks were studied in the study area. Landsat-8 (OLI) data that cover the investigated area were used in this paper. The different rock units in the study area were studied petrographically using a polarizing microscope, in addition to major and trace analysis using ICP-OES tools. The Operational Land Imager (OLI) images were used with several processing methods, such as false color composite (FCC), band ratio (BR), principal component analysis (PCA), and minimum noise fraction (MNF) techniques for detecting the different types of rock units in the Wadi Kid district. This district mainly consists of a volcano-sedimentary sequence as well as diorite, gabbro, granite, and albitite. Geochemically, the metasediments are classified as pelitic graywackes derived from sedimentary origin (i.e., shales). The Al2O3 and CaO contents are medium–high, while the Fe2O3 and TiO2 contents are very low. Alkaline minerals are relatively low–medium in content. All of the metasediment samples are characterized by high MgO contents and low SiO2, Fe2O3, and CaO contents. The granitic rocks appear to have alkaline and subalkaline affinity, while the subalkaline granites are high-K calc-alkaline to shoshonite series. The alkaline rocks are classified as albitite, while the calc-alkaline series samples vary from monzodiorites to granites. The outcomes of this study can be used for prospecting metallic and industrial mineral exploration in the Wadi Kid district.
Ore Genesis of the Abu Ghalaga Ferro-Ilmenite Ore Associated with Neoproterozoic Massive-Type Gabbros, South-Eastern Desert of Egypt: Evidence from Texture and Mineral Chemistry Hatem M. El-Desoky, Ahmed M. Abdel-Rahman, Wael Fahmy, Ibrahim Khalifa, Salah A. Mohamed, Aref Shirazi, Ardeshir Hezarkhani, Adel Shirazy, Amin Beiranvand Pour Minerals, 2023 Massif-type mafic intrusions (gabbro and anorthosite) are known for their considerable resources of vanadium-bearing iron–titanium oxide ores. Massive-type gabbroic and anorthosite rocks are frequently associated with magmatic rocks that have significant quantities of iron, titanium, and vanadium. The most promising intrusions that host Fe-Ti oxide ores are the gabbroic rocks in the south-eastern desert. The ilmenite ore deposits are hosted in arc gabbroic and anorthosite rocks. They are classified into three types, namely black ore, red ore, and disseminated ore. The black ilmenite ore is located at the deeper level, while the oxidized red ore is mainly located at or near the surface. Petrographically, the gabbro and ilmenite ores indicate a crystallization sequence of plagioclase, titaniferous pyroxene, and ilmenite. This reveals that the ilmenite is a magmatic deposit formed by the liquid gravity concentration of ilmenite following the crystallization of feldspar and pyroxene. Meanwhile, quartz, tremolite, zoisite, and opaque minerals are accessory minerals. The Fe-Ti ores are composed of ilmenite hosting exsolved hematite lamellae of variable sizes and shapes, gangue silicate minerals, and some sulfides. The X-ray diffraction (XRD) data reveal the presence of two mineral phases: ilmenite and hematite formed by the unmixing of the ferroilmenite homogeneous phase upon cooling. As a result, the ore is mostly made up of hemo-ilmenite. Using an electron microscope (SEM), as well as by observing the textures seen by the ore microscope, ilmenite is the dominant Fe-Ti oxide and contains voluminous hematite exsolved crystals. Under the scanning electron microscope, ilmenite contained intergrowths of hematite as a thin sandwich and lens shape. The formation of hematite lamellae indicates an oxidation process. Mineral chemistry-based investigations reveal late/post-magmatic activity at high temperatures. The examined ilmenite plots on the ferro-ilmenite line were created by continuous solid solution over 800 °C, whereas the analyzed magnetite and Ti-magnetite plot near the magnetite line and were formed by continuous solid solution exceeding 600 °C.
Petrogenesis of Serpentinites and Chromitites in the Neoproterozoic Bou Azzer Ophiolite, Morocco: From Mantle Depletion to High-Pressure Exhumation A Wafik, M Ben Massoude, Y Atif, A Ait Lamqadem, R Rooki, A Shirazi, ... Minerals 16 (5), 460 , 2026 2026
Thermo-poroelastic analysis of drilling fluid pressure and temperature on wellbore stresses in the Mansouri oilfield, SW Iran A Pirhadi, P Kianoush, S Varkouhi, R Shirinabadi, A Shirazy, A Shirazi, ... Results in Earth Sciences 3, 100061 , 2025 2025 Citations: 42
AI-Driven Mineral Exploration: Enhancing Geochemical Anomaly Detection with Generative adversarial Networks and Transfer Learning, A Case Study from Janja polymetallic deposit … M Ebdali, A Hezarkhani, A Shirazy, A Beiranvand Pour Journal of Mining and Environment 16 (5), 1693-1710 , 2025 2025 Citations: 1
Comparison of clustering methods in determining gold mineralization anomalies in the Janja area, Iran (vol 24, geochem2024-029, 2024) M Ebdali, A Hezarkhani, A Shirazy, AB Pour GEOCHEMISTRY-EXPLORATION ENVIRONMENT ANALYSIS 25 (2) , 2025 2025
Correction to:‘Comparison of clustering methods in determining gold mineralization anomalies in the Janja area, Iran’(2024), by M. Ebdali and A. Hezarkhani M Ebdali, A Hezarkhani, A Shirazy, AB Pour Geochemistry: Exploration, Environment, Analysis 25 (2), geochem2025-026 , 2025 2025
Machine learning-driven mineral prospectivity mapping: A predictive performance analysis in Janja, Iran M Ebdali, A Hezarkhani, A Shirazy, AB Pour Nafta-Gaz, 295-317 , 2025 2025
Unsupervised anomaly detection for mineral prospectivity mapping using isolation forest and extended isolation forest algorithms M Saremi, A Hezarkhani, SAAS Mirzabozorg, R DehghanNiri, A Shirazy, ... Minerals 15 (4), 411 , 2025 2025 Citations: 17
بررسی و تهیه نقشه پتانسیل مطلوب کانی سازی پنهان مس-طلادار پورفیری ورقههای یکصد هزارم بصیران و کودگان ضیائی, منصور, شبانی, سلیمانی منفرد, شیرازی, عارف, حامدی, سید امیر علی روش های تحلیلی و عددی در مهندسی معدن 15 (42), 37-50 , 2025 2025
Mineral Prospectivity Mapping of the Hidden Cu-Au Porphyry Mineralization in the Basiran and Kodegan 1: 100,000 Sheets M Ziaii, A Shabani, M Soleimani Monfared, A Shirazi, SA Hamedi Journal of Analytical and Numerical Methods in Mining Engineering 15 (42), 37-50 , 2025 2025
Geochemical Evolution and Tectonic Setting of Neoproterozoic Serpentinites from the Bou Azzer Ophiolite, Morocco: Evidence for Subduction-Related Mantle Wedge Processes A Shirazy, AB Pour 2025
Modeling the Barite Deposition Process in the Jbel Irhoud Deposit, Western Jebilet, Morocco: Implications for Barite Exploration and Insights Into Mineralization Mechanisms F Benchekroun, AM Aghzer, A Wafik, MB Massoude, Y Atif, AB Pour, ... Geofluids 2025 (1), 6364240 , 2025 2025
An Artificial Intelligence (AI)-based Model for Optimal Exploratory Surveys A Shirazi, A Shirazy, A Hezarkhani GRIN Verlag , 2024 2024 Citations: 2
Employing statistical algorithms and clustering techniques to assess lithological facies for identifying optimal reservoir rocks: A case study of the Mansouri oilfields, SW Iran SH Eftekhari, M Memariani, Z Maleki, M Aleali, P Kianoush, A Shirazy, ... Minerals 14 (3), 233 , 2024 2024 Citations: 43
Employing statistical algorithms and clustering techniques to assess lithological facies for identifying optimal reservoir rocks: A case study of the Mansouri oilfields, SW … SH Eftekhari, M Memariani, Z Maleki, M Aleali, P Kianoush, A Shirazy, ... d o i. o r g/1 0 3 (3), 9 , 2024 2024 Citations: 16
An Artificial Intelligence based Model for Optimal Exploratory Surveys: Geophysics and Geochemistry A Shirazi, A Shirazy, A Hezarkhani LAMBERT Academic Publishing , 2024 2024 Citations: 1
Remote Sensing, petrological and geochemical data for lithological mapping in Wadi Kid, Southeast Sinai, Egypt W Fahmy, HM El-Desoky, MH Elyaseer, P Ayonta Kenne, A Shirazi, ... Minerals 13 (9), 1160 , 2023 2023 Citations: 13
Geochemical modeling of copper mineralization using geostatistical and machine learning algorithms in the Sahlabad area, Iran A Shirazi, A Hezarkhani, A Shirazy, AB Pour Minerals 13 (9), 1133 , 2023 2023 Citations: 12
Geospatial analysis applied to mineral exploration: remote sensing, GIS, geochemical, and geophysical applications to mineral resources AB Pour, M Parsa, AM Eldosouky Elsevier , 2023 2023 Citations: 14
Ore genesis of the Abu Ghalaga ferro-ilmenite ore associated with neoproterozoic massive-type gabbros, south-eastern desert of Egypt: evidence from texture and mineral chemistry HM El-Desoky, AM Abdel-Rahman, W Fahmy, I Khalifa, SA Mohamed, ... Minerals 13 (3), 307 , 2023 2023 Citations: 8
Remote Sensing and Petrological and Geochemical Data for Lithological Mapping in Wadi Kid, Southeast Sinai, Egypt. Minerals 2023, 13, 1160 W Fahmy, HM El-Desoky, MH Elyaseer, P Ayonta Kenne, A Shirazi, ... 2023
MOST CITED SCHOLAR PUBLICATIONS
Neuro-Fuzzy-AHP (NFAHP) technique for copper exploration using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and geological datasets in the Sahlabad … A Shirazi, A Hezarkhani, A Beiranvand Pour, A Shirazy, M Hashim Remote Sensing 14 (21), 5562 , 2022 2022 Citations: 57
Remote Sensing Studies for Mapping of Iron Oxide Regions, South of Kerman, IRAN A Shirazi, A Hezarkhani, A Shirazy International Journal of Science and Engineering Applications (IJSEA) 7 (4 … , 2018 2018 Citations: 52
Fusion of remote sensing, magnetometric, and geological data to identify polymetallic mineral potential zones in Chakchak Region, Yazd, Iran AA Aali, A Shirazy, A Shirazi, AB Pour, A Hezarkhani, A Maghsoudi, ... Remote Sensing 14 (23), 6018 , 2022 2022 Citations: 46
Employing statistical algorithms and clustering techniques to assess lithological facies for identifying optimal reservoir rocks: A case study of the Mansouri oilfields, SW Iran SH Eftekhari, M Memariani, Z Maleki, M Aleali, P Kianoush, A Shirazy, ... Minerals 14 (3), 233 , 2024 2024 Citations: 43
Remote sensing to identify copper alterations and promising regions, Sarbishe, South Khorasan, Iran A Shirazi, A Shirazy, J Karami International Journal of Geology and Earth Sciences 4 (2), 36-52 , 2018 2018 Citations: 43
Thermo-poroelastic analysis of drilling fluid pressure and temperature on wellbore stresses in the Mansouri oilfield, SW Iran A Pirhadi, P Kianoush, S Varkouhi, R Shirinabadi, A Shirazy, A Shirazi, ... Results in Earth Sciences 3, 100061 , 2025 2025 Citations: 42
Geostatistical and remote sensing studies to identify high metallogenic potential regions in the Kivi area of Iran A Shirazy, M Ziaii, A Hezarkhani, T Timkin Minerals 10 (10), 869 , 2020 2020 Citations: 42
Hybrid fuzzy-analytic hierarchy process (AHP) model for porphyry copper prospecting in simorgh area, eastern lut block of Iran V Khosravi, A Shirazi, A Shirazy, A Hezarkhani, AB Pour Mining 2 (1), 1-12 , 2021 2021 Citations: 39
Fusion of lineament factor (Lf) map analysis and multifractal technique for massive sulfide copper exploration: The Sahlabad area, East Iran A Shirazi, A Hezarkhani, AB Pour Minerals 12 (5), 549 , 2022 2022 Citations: 37
Geostatistical studies and anomalous elements detection, Bardaskan Area, Iran S Alahgholi, A Shirazy, A Shirazi Open Journal of Geology 8 (7), 697-710 , 2018 2018 Citations: 34
Geostatistics studies and geochemical modeling based on core data, sheytoor iron deposit, Iran A Shirazi, A Shirazy, S Saki, A Hezarkhani Journal of Geological Resource and Engineering 6, 124-133 , 2018 2018 Citations: 34
Multi-Dimensional Data Fusion for Mineral Prospectivity Mapping (MPM) Using Fuzzy-AHP Decision-Making Method, Kodegan-Basiran Region, East Iran A Shabani, M Ziaii, MS Monfared, A Shirazy, A Shirazi Minerals 12 (12), 1629 , 2022 2022 Citations: 33
Application of remote sensing in earth sciences–A review A Shirazy, A Shirazi, H Nazerian International Journal of Science and Engineering Applications 10 (5), 45-51 , 2021 2021 Citations: 30
Exploration Geochemistry Data-Application for Cu Anomaly Separation Based On Classical and Modern Statistical Methods in South Khorasan, Iran A Shirazi, A Hezarkhani, A Shirazy International Journal of Science and Engineering Applications (IJSEA) 7 (4 … , 2018 2018 Citations: 30
Investigation of magneto-/radio-metric behavior in order to identify an estimator model using K-means clustering and Artificial Neural Network (ANN)(Iron Ore Deposit, Yazd, IRAN) A Shirazy, A Hezarkhani, T Timkin, A Shirazi Minerals 11 (12), 1304 , 2021 2021 Citations: 26
Geochemical and geostatistical studies for estimating gold grade in tarq prospect area by k-means clustering method A Shirazy, A Shirazi, MH Ferdossi, M Ziaii, S Adel, S Aref, HF Mohammad, ... Open Journal of Geology 9 (6), 306-326 , 2019 2019 Citations: 26
Copper oxide ore leaching ability and cementation behavior, mesgaran deposit in Iran S Khakmardan, A Shirazi, A Shirazy, H Hosseingholi Open Journal of Geology 8 (09), 841 , 2018 2018 Citations: 26
Geochemical behavior investigation based on k-means and artificial neural network prediction for titanium and zinc, Kivi region, Iran S Adel, Z Mansour, H Ardeshir Известия Томского политехнического университета. Инжиниринг георесурсов 332 … , 2021 2021 Citations: 25
Evaluation of chromite recovery from shaking table tailings by magnetic separation method S Khakmardan, RJ Doodran, A Shirazy, A Shirazi, E Mozaffari Open Journal of Geology 10 (12), 1153-1163 , 2020 2020 Citations: 25
Introducing a software for innovative neuro-fuzzy clustering method named NFCMR A Shirazi, A Shirazy, S Saki, A Hezarkhani Global Journal of Computer Sciences: theory and research 8 (2), 62-69 , 2018 2018 Citations: 25
