Abedi F., Gholami A., Norouzi Gh. 2013. A stable downward continuation of airborne magnetic data: A case study for mineral prospectivity mapping in Central Iran. Comput. Geosci. 52, 269-280.
ANANABA S.E., AJAKAIYE D.E. 1987. Evidence of tectonic control of mineralization in Nigeria from lineament density analysis A Landsat-study. Int J Remote Sens. 8, 1445–1453.
Anderson E.D., Hitzman M.W., Monecke T., Bedrosian P.A., Shah A.K., Kelley K.D. 2013. Geological Analysis of Aeromagnetic Data from Southwestern Alaska: Implications for Exploration in the Area of the Pebble Porphyry Cu-Au-Mo Deposit. Economic Geology. 108, 421–436.
Arogundade A.B., Awoyemi M.O., Ajama O.D., Falade S.C., Hammed O.S., Dasho O.A., Adenika C.A. 2022. Integrated Aeromagnetic and Airborne Radiometric Data for Mapping Potential Areas of Mineralisation Deposits in Parts of Zamfara, North West Nigeria. Pure Appl Geophys. 179, 351–369.
Baranov V., Naudy H. 1964. NUMERICAL CALCULATION OF THE FORMULA OF REDUCTION TO THE MAGNETIC POLE. GEOPHYSICS. 29, 67–79.
Barbarin B. 1999. A review of the relationships between granitoid types, their origins and their geodynamic environments. Lithos. 46, 605–626.
Eldosouky A., Alkhateeb S. 2016. Detection of Porphyry Intrusions Using Analytic Signal (AS), Euler Deconvolution, and Center for Exploration Targeting (CET) Technique at Wadi Allaqi Area, South Eastern Desert, Egypt. Int. j. sci. eng. Res. 7, 471-477.
Holden E., Dentith M., Kovesi, P. 2008. Towards the automatic analysis of regional aeromagnetic data to identify regions prospective for gold deposits. Comput. Geosci.34, 1505–1513.
Holden E.-J., Fu S.C., Kovesi P., Dentith M., Bourne B., Hope M. 2011. Automatic identification of responses from porphyry intrusive systems within magnetic data using image analysis. J Appl Geophy. 74, 255–262.
Loy G., and Zelinsky A. 2003. Fast Radial Symmetry for Detecting Points of Interest. IEEE PAMI. 25, 8, 959–973.
Mohamed A., Abdelrady M., Alshehri F., Mohammed M.A., Abdelrady A. 2022. Detection of Mineralization Zones Using Aeromagnetic Data. Applied Sciences. 12, 9078.
Montsion R.M., Saumur B.M., Acosta-Gongora P., Gadd M.G., Tschirhart P., Tschirhart V. 2019. Knowledge-driven mineral prospectivity modelling in areas with glacial overburden: porphyry Cu exploration in Quesnellia, British Columbia, Canada. Applied Earth Science. 128, 181–196.
Osinowo O.O., Alumona K., Olayinka A.I. 2020. Analyses of high resolution aeromagnetic data for structural and porphyry mineral deposit mapping of the nigerian younger granite ring complexes, North - Central Nigeria. Journal of African Earth Sciences. 162, 103705.
Riahi S., Bahroudi A., Abedi M., Aslani S., Lentz D.R. 2022. Evidential data integration to produce porphyry Cu prospectivity map, using a combination of knowledge and data‐driven methods. Geophys Prospect. 70, 421–
437
Riahi S., Bahroudi A., Abedi M., Aslani S., Lentz D.R. 2022. Evidential data integration to produce porphyry Cu prospectivity map, using a combination of knowledge and data‐driven methods. Geophys Prospect. 70, 421–437.
Sanusi S.O., Amigun J.O. 2020. Logistic-Based Translation of Orogenic Gold Forming Processes into Mappable Exploration Criteria for Fuzzy Logic Mineral Exploration Targeting in the Kushaka Schist Belt, North-Central Nigeria. Natural Resources Research. 29, 3505–3526.
Sun T., Li H., Wu K., Chen F., Zhu Z., Hu Z. 2020. Data-Driven Predictive Modelling of Mineral Prospectivity Using Machine Learning and Deep Learning Methods: A Case Study from Southern Jiangxi Province, China. Minerals. 10, 102.
Xiao F., Wang Z. 2017. Geological interpretation of Bouguer gravity and aeromagnetic data from the Gobi-desert covered area, Eastern Tianshan, China: Implications for porphyry Cu-Mo polymetallic deposits exploration. Ore Geol Rev. 80, 1042–1055.
Vallée M., Byrne K., King J., Lee R, Lesage G., Farquharson C., Chouteau M., Enkin R., 2020. Imaging porphyry copper alteration using aeromagnetic data at Highland Valley Copper, British Columbia, Canada, Explor. Geophys. 51:3, 388-400.
Williams D., and Shah M. 1990. A Fast Algorithm for Active Contours. Third International Conference on Computer Vision. 592–595.