Automated Scanning Electron Microscopy (QEMSCAN®)-based mineral identification and quantification of the Jabali Zn-Pb-Ag nonsulfide deposit (Yemen).

Supergene nonsulfide Zn-Pb deposits consist mainly of Zn-Pb carbonates, Zn silicates, Fe hydroxides, and minor Zn-Pb phosphates, commonly associated with remnants of primary sulfides (sphalerite and galena). The relative abundances of these mineral phases are strongly dependent on the type of host rock. Their variable mineralogy is complex to characterize, and can lead to problems during the processing and recovery of the minerals from the ores. In this case study, mineralogical characterization and quantitative evaluation of the Jabali (Yemen) nonsulfide Zn-Pb deposit was carried out using automated scanning electron microscopy (QEMSCAN®). Both primary and secondary ores at Jabali are hosted in Jurassic dolostones. Smithsonite is the most abundant economic ore mineral in the supergene deposit, and it is locally intergrown with Fe hydroxides and remnants of primary sulfides (sphalerite and galena). The host dolomite is locally replaced by broad bands of Zn-rich dolomite, where Zn has substituted for Mg. The ZnO content in this dolomite can reach 22 wt %. Other Zn minerals such as hemimorphite and hydrozincite occur in limited amounts, as well as cerussite and anglesite. Small particles of Ag sulfide are associated with the secondary minerals. Gypsum, Fe (hydr)oxides (goethite > hematite), Zn-Mn (hydr)oxides, and Pb-Mn (hydr)oxides have been detected locally. The QEMSCAN technique, combined with data previously obtained from other analytical techniques (X-ray diffraction [XRD], scanning electron microscope-energy dispersive spectrometry [SEM-EDS], optical petrography), has provided detailed mineralogical and textural information on the Jabali mineralization. A key outcome from this QEMSCAN study is the textural data and quantification of the Zn dolomite, which is fairly abundant in this deposit. The combination of techniques used to examine the Jabali supergene ore provides high-quality information that not only characterizes the deposit in detail, but also offers a better understanding for the design of ore processing options and a more realistic predicted recovery of economic minerals.