Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery

The application of machine learning (ML) algorithms for processing remote sensing data is momentous, particularly for mapping hydrothermal alteration zones associated with porphyry copper deposits. The unsupervised Dirichlet Process (DP) and the supervised Support Vector Machine (SVM) techniques can be executed for mapping hydrothermal alteration zones associated with porphyry copper deposits. The main objective of this investigation is to practice an algorithm that can accurately model the best training data as input for supervised methods such as SVM. For this purpose, the Zefreh porphyry copper deposit located in the Urumieh-Dokhtar Magmatic Arc (UDMA) of central Iran was selected and used as training data. Initially, using ASTER data, different alteration zones of the Zefreh porphyry copper deposit were detected by Band Ratio, Relative Band Depth (RBD), Linear Spectral Unmixing (LSU), Spectral Feature Fitting (SFF), and Orthogonal Subspace Projection (OSP) techniques. Then, using the DP method, the exact extent of each alteration was determined. Finally, the detected alterations were used as training data to identify similar alteration zones in full scene of ASTER using SVM and Spectral Angle Mapper (SAM) methods. Several high potential zones were identified in the study area. Field surveys and laboratory analysis were used to validate the image processing results. This investigation demonstrates that the application of the SVM algorithm for mapping hydrothermal alteration zones associated with porphyry copper deposits is broadly applicable to ASTER data and can be used for prospectivity mapping in many metallogenic provinces around the world.

The Origins of Sulfate in Cenozoic Non-Marine Evaporites in the Basin and-Range Province, Southwestern North America

Cenozoic evaporites (gypsum and anhydrite) in southwestern North America have wide ranges of δ34S (−30 to +22‰; most +4 to +10‰) and δ18OSO4 (+3 to +19‰). New data are presented for five basins in southern Arizona. The evaporites were deposited in playas or perennial saline lakes in closed basins of Oligocene or younger age. Very large accumulations in Picacho, Safford and Tucson Basins have isotope compositions plotting close to a linear δ34S-δ18OSO4 relationship corresponding to mixing of two sources of sulfur: (1) sulfate recycled from Permian marine gypsum and (2) sulfate from weathering of Laramide-age igneous rocks that include porphyry copper deposits. In the large evaporites, sulfate with δ34S > +10‰ is dominantly of Permian or Early Cretaceous marine origin, but has locally evolved to higher values as a result of bacterial sulfate reduction (BSR). Sulfate with δ34S < −10‰ formed following exposure of sulfides, possibly formed during supergene enrichment of a porphyry copper deposit by BSR, and have values of δ18OSO4 higher than those of local acid rock drainage because of participation of evaporated water in BSR. Accumulations of 30 to 100 km3 of gypsum in Picacho and Safford Basins are too large to explain as products of contemporaneous erosion of Permian and Laramide source materials, but may represent recycling of Late Cretaceous to Miocene lacustrine sulfate.

Construction and applications of knowledge graph of porphyry copper deposits

A knowledge graph is becoming popular due to its ability to describe the real world by using a graph language that can be understood by both humans and machines using computer technologies. A case study to construct the knowledge graph of porphyry copper deposits is presented in this paper. First of all, the raw text data is collected and integrated from selected porphyry copper deposits and porphyry-skarn copper deposits in the Qinzhou Bay – Hangzhou Bay metallogenic belt, South China. Second, the text's entities, relations, and attributes are labeled and extracted with reference to the conceptual model of porphyry copper deposits in the study area. The third, a knowledge graph of porphyry copper deposits, was constructed using Neo4j 4.3. The resulted knowledge graph of porphyry copper deposit has the basic functions of an application. Furthermore, as part of a planned integrated knowledge graph from a single deposit, through an upper-geared metallogenic series, to a high-top metallogenic province, the understanding from the present study may be extended to mineral resource prospectivity and assessment beyond today. The interrelationship between the earth system, the metallogenic system, the exploration system, and the prospectivity and assessment (ES-MS-ES-PS) should be completely understood, and a knowledge graph system for ES-MS-ES-PS is needed. The key scientific and technological problems for achieving the ES-MS-ES-PS knowledge graph system are included in the progressively relative system of the domain ontology and knowledge graph of ES-MS-ES-PS, the automatic construction technology of complicated ESMS-ES-PS domain ontology and knowledge graph, the self-evolution and complementary techniques for multi-modal correlation data embedding in the ES-MS-ES-PS knowledge graph, and the knowledge graph, big data mining and artificial intelligence based on ES-resource prospectivity, and assessment theory, and methods.

The Mineralization and Structural Geology of the Porphyry Copper Deposits of Pakistan

The purpose of this review is to shed light on copper deposits found in different regions of Pakistan. The geological attributes of copper deposits have been considered with their tectonic context. The porphyry copper deposits can be traced in Pakistan from the north through Kohistan Island Arc (KIA) up to the south to Chaghi Magmatic Arc (CMA). These deposits are mainly found in and around the Late Tertiary–Early Tertiary Himalayan Belt, Kohistan magmatic arc, Karakorum Block Foreland fold and thrust belt, Ophiolite Thrust belt, Suture zone and Chaghi Magmatic Arc. These deposits in Pakistan are chiefly established in different episodes of tectonic regimes, including subduction processes, oceanic island arc, continental arc, along with Chaman- OrnachNal Fault system and post-collisional settings.