Distinguishing Algorith for Gold Deposit Types

The determination of gold deposit type holds a great economic significance since each gold deposit type displays its own grade and tonnage and consequently requires different exploration and exploitation strategies. The considerable diversity of gold deposits, combined with the distinctive features inherent to each type and the notable overlap among many deposits, renders the accurate classification of these deposits a complex endeavor. To differentiate between these deposit types, we collected geological, mineralogical, and geochemical characteristics, as well as ore-forming parameters, for 12 gold deposit types. A detailed classification scheme is utilized, covering four specific categories of gold deposits, namely orogenic, including greenstone-hosted, banded iron formation-hosted, and turbidite-hosted; reduced intrusion-related deposits; and oxidized intrusion-related gold deposits, which encompass Au-Cu-porphyry, Au-skarn, and high-sulfidation epithermal deposits, with a fourth class incorporating other deposit types, such as low-sulfidation epithermal, Carlin-type, and Au-volcanic massive sulfide deposits. The tabulated distinctive characteristics were used to construct a series of decision trees for gold deposit type identification. The distinguishing algorithm is formulated in the form of a Java computer application. Three decision trees are implemented for the purpose of ascertaining the type of gold deposit. If two decision trees yield a consensus on a particular type, the ore type identification is made accordingly. To validate the outcome, the user is prompted to respond to a series of questions pertaining to the identified type, with the accuracy rate of the responses must exceed 90%. Failure to meet this criterion will result in the decision tree being revisited, and the accurate data will need to be re-entered.