Integration of airborne geophysics and satellite imagery data for exploration targeting in porphyry Cu systems: Chahargonbad district, Iran

Abstract

This study illustrates the application of a geometric average integration of aeromagnetic, radiometric and satellite imagery data over a region prone to Cu‐bearing mineralization at Chahargonbad area in Kerman province of Iran. Processing aeromagnetic, radiometric and Advanced Spaceborne Thermal Emission and Reflection Radiometer satellite data can provide exploratory insights about favourable zones in association with porphyry‐type ore occurrences, which can be synthesized through a combination of knowledge‐ and data‐driven approaches as a geometric average and be represented in a mineral prospectivity map. The existence of known deposits in a prospect region can facilitate the investigation of significant exploratory footprints extracted from airborne data by calculating each indicator layer's weight by plotting a prediction–area curve accompanied by a concentration–area fractal curve. Among various indicators, the most important ones are determined based on derived weights from the prediction–area plots to be synthesized in a single Cu favorability map. To fulfil this aim, indicator layers from airborne geophysics (magmatic bodies, magnetic lineaments and potassium radiometry) and remote‐sensing data (alterations such as argillic, phyllic, propylitic and iron oxide along with geological lineaments) were prepared and evaluated using the known porphyry Cu mineralization by the simultaneous plot of the concentration–area fractal model and the prediction–area curve to attain the ore prediction rate and the relevant occupied area of each map for weight assignment of indicators. The geometric average prospectivity model was applied to synthesize the leading indicators, and the result was compared with a multi‐class index overlay map. This study's significance lies in improvement of the performance of the mineral prospectivity/potential mapping after running a geometric average by a higher ore prediction rate of 79%, which has occupied 21% of the area as potential zones for further mining investigations.

Fusion of Remote Sensing, Magnetometric, and Geological Data to Identify Polymetallic Mineral Potential Zones in Chakchak Region, Yazd, Iran

“…These characteristics are matched with band 12 (2.100-2.280 µm) and band 11 (1.565-1.655 µm) of Sentinel-2, as well as band 7 (2.11-2.29 µm) and band 5 (1.57-1.65 µm) of Landsat-7, respectively. Therefore, the 11/12 band ratio of Sentinel-2 and 5/7 band ratio of Landsat-7 were used to map hydroxyl bearing altera- The multi-class overlap index method is an extended version of the binary index overlap modeling method [35][36][37][38][39]. Each of the j patterns (classes) of the i control map has a S ij weight to indicate the degree of dependence on mineral deposits.…”

“…The pattern of the inference engine would imitate the geospatial process being modeled [32]. The multi-class overlap index method is an extended version of the binary index overlap modeling method [35][36][37][38][39]. Each of the j patterns (classes) of the i control map has a Sij weight to indicate the degree of dependence on mineral deposits.…”

“…This score is usually a positive integer. Weighted control maps are then combined using Equation (4) [36,40], which calculates the average weights for each location in Equation ( 4):…”

Mapping and litho‐geophysical classification of potential mining areas by spatial remote sensing and helicopter‐borne gamma‐ray spectrometry (Tikirt, Anti‐Atlas, Morocco)

“…clay alterations and iron oxides) on the radioactive elements K, eU and eTh distribution (Aisabokhae & Osazuwa, 2021;Badr, 2021;Dentith & Mudge, 2014, pp. 222;Ghoneim et al, 2021;Mamouch et al, 2022;Riahi et al, 2022Riahi et al, , 2021.…”

Simulation Analysis Based on COMSOL Helicopter Time-domain Aeromagnetic Method

“…To combine the high transmitting power of fixed-wing systems for deep penetration with the slower speeds and altitudes of HFEM systems for higher spatial resolution or more rugged terrain surveys, research began on helicopter airborne systems [2], where time-domain AEM had a greater depth of detection and a wider range of applications compared to frequency-domain AEM. Therefore, the research and development of time-domain aeromagnetic simulation methods are of great importance for the optimization of aeromagnetic system parameters and the processing and interpretation of measurement data [3]. For time-domain airborne EM problems in complex media, including rugged terrain and geological layers, differently shaped target objects, etc., where the target objects often represent local bodies with different structures with anomalous conductivity [4], it is necessary to construct effective 3D models for computation.…”