3D Modeling and Determination of Factors Responsible for Zinc‐Lead Mineralization in the Mehdiabad Deposit, Central Iran, based on Statistical Analysis of Geochemical Data

Abstract

Although the Mehdiabad zinc‐lead deposit is one of the most well‐known deposits in the central Iran structural zone, the genesis of the deposit remains controversial. The host rock of the ore is a dolomitic limestone of the Lower Cretaceous Taft Formation. In the two main orebodies of the deposit, which includes the Black Hill and East Ridge ore zones, the oxide and sulfide ores are observed at the surface and at depth, respectively. The elements Zn, Fe, Mn and Mg are more abundant in the East Ridge ore zone (in both sulfide and oxide ores), with Ba, Pb, Ag and Cu being more abundant in the Black Hill oxide ore. Based on the distribution of elements and their correlation with each other in these ore zones, the elements are divided into three general groups, that of terrigenous elements, chemically‐deposited elements and ore‐forming (hydrothermally deposited) elements, a division that is supported by the results of factor analyses. The spatial distribution of elements is jointly affected by contact with host rocks, the boundary of oxide‐sulfide ores and fault zones. The main factors governing the distribution of elements are the mechanical transfer of detrital sediments, chemical sedimentation, transfer by hydrothermal fluids, oxidation and surface dissolution, all of which affected the spatial distribution of elements. The ore‐forming elements are mostly affected by hydrothermal fluids and oxidation. This study not only provides additional information about the genesis of the Mehdiabad deposit, but also could assist in the exploitation of ore and further exploration purposes. The results of this study can aid in the exploration and exploitation of the Mehdiabad deposit and similar deposits in the region.

Three‐dimensional Modeling of Ore‐forming Elements and Mineralization Prognosis for the Yechangping Mo Deposit, Henan Province, China

“…Geochemical 3D modeling has emerged as a leading method for deep mineral resource prediction, focusing on establishing 3D models based on primary halos (oreforming elements) to precisely depict anomalous space geometry and achieve 3D visualization of quantitative mineralization prediction for deep minerals, guiding exploration of deep concealed mines. This method represents a significant shift in geochemical prediction techniques and is increasingly recognized as essential for locating deep blind ore bodies (Abdul-Rahman and Pilouk, 2008;Calcagno et al, 2008;Mao et al, 2012Mao et al, , 2016Wang et al, 2013Wang et al, , 2019Zhang, 2014;Chen, 2016;Liu et al, 2016;Xiang, 2018;Zhang et al, 2018;Nielsen et al, 2019;Chen et al, 2020;Gao et al, 2020;Huang et al, 2020;Yang et al, 2020;Bonyadi, 2022;Wang, 2022).…”