Journal of Geology and Exploration

Correlation Study Of Sulphur and Ash Content In Patappa Coal Area Pujananting Sub-District Barru District

Muhammad Idris Juradi — 2024-12-31

Coal is an organic sedimentary rock derived from the decomposition of various plant remains which is a heterogeneous mixture of organic compounds and inorganic substances that fuse under the weight of the strata that crush it. This research is devoted to determining the correlation between ash content and sulfur content in Patappa coal area, Pujananting sub-district, Barru Regency. The purpose of this study is to determine whether there is a correlation between ash content and sulfur content in Patappa coal. The research method carried out in this research is the first preliminary stage including administration, literature study and discussion, then the data collection stage includes primary data and secondary data, the stage of data analysis and processing is to determine the correlation between ash content and sulfur content based on the analysis results. The materials used in this research include geological hammer, compass, GPS, sample bag, camera and roll meter. the sampling process carried out is by using channel sampling. The results of the analysis of this study are in sample 1, it can be seen that the value of the ash content is 18.32%, while the sulfur content has a value of 7.22%. In sample 2, the ash content value is 15.58% while the sulfur content has a value of 8.63%. So it can be concluded that the relationship between the two analyses is inversely proportional where the higher the ash content value, the lower the sulfur content value, and vice versa, the lower the ash content value, the higher the sulfur content value.

Identification of Asphalt Distribution and Thickness Using Drill Data

Nurfadli Nurfadli — 2024-12-31

Cross section and block model of sediment is a method to determine the distribution and thickness of the deposition material is layered and constantly. The aim of this study to determine the direction of distribution of asphalt on the research area. Some of the activities carried out in advance starting from the analysis of core drilling, where this activity is to delineate the results of drilling activity to find out some impurities on the asphalt. Furthermore, the data obtained from the drilling include a collar of data, assay data, survey data and lithologic data is processed in tools that are customized. From the data processing result: string average A-A '17.571meters, string B-B' 14.142meters, string C-C '14.857meters, string D-D', 15meters string, E-E '11.142meters, string F-F' 12.75meters, and string G-G '15.833meters. From the results that have been obtained, it can be concluded that the average 14.47109 meters thick string and direction of spread slightly thickened to the west.

Characteristics of Laterite Nickel Based on Geochemical Data and Electrical Resistivity Tomography (ERT) of Ultramafic Rocks in the Sorowako Area, East Luwu Regency

Erika Patadungan — 2024-12-31

In laterite nickel exploration, PT.Vale Indonesia Tbk, initialy relied solely on drilling methods to define profile boundaries based on mineral content and rock characteristics, but discrepancies of around 2% between reserve estimates and actual mining outcomes led to the adoption of geophysical methods as a complementary approach in 2014. This study aims to determine the characteristics of laterite nickel profiles by correlating resistivity values with geochemical data. Using Datamine and Leapfrog software, a 3D model of laterite nickel profiles was generated, identifying limonite (0–10 m depth, 201–250 Ohm.m resistivity), saprolite (0–10 m depth, 101–200 Ohm.m resistivity), and bedrock (>10 m depth, 101 to >801 Ohm.m resistivity). Variations in resistivity are influenced by factors such as mineral content and morphology. The volume estimated from resistivity correlation and drillhole data is 1,625,300 m³ for limonite and 1,902,600 m³ for saprolite, compared to 1,523,100 m³ and 1,390,100 m³ based on drillhole-only data, showing discrepancies of 6% and 27%, respectively. This study provides a clearer understanding of geological modeling using drillhole and ERT data to support laterite nickel ore mining and correlation modeling.

Analysis of Coal Seam Identification Based on Drilling Data in Nunukan Regency, North Kalimantan

A. Aenul Tul Mukarramah — 2024-12-31

Coal deposition results in the formation of continuous layers with specific thicknesses and slopes. This study aimed to determine coal seams based on drilling data. The method involved correlating the rocks flanking the coal seams using AutoCAD 2014 software. The findings revealed that points BC-054, BC-045, and BC-038 belong to the same coal seam based on the associated flanking rocks. Similarly, points BC-029, BC-022, BC-004, and BC-087 were also found to be interconnected within a coal seam, sharing similar thicknesses and clamping rock layers. However, the third layer at BC-087 does not correspond with other boreholes and is categorized as a separate seam. Correlation analysis identified three coal seams with an average thickness ranging from 0.6 m to 1.7 m.

Distinguishing Algorith for Gold Deposit Types

Abdelhalim Mahmoud — 2024-12-31

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.