首页|Reports from University for Development Studies Describe Recent Advances in Machine Learning (Provenance Studies of Aubearing Stream Sediments and Performance Assessment of Machine Learning-based Models: Insight From Whole-rock Geochemistry ...)
Reports from University for Development Studies Describe Recent Advances in Machine Learning (Provenance Studies of Aubearing Stream Sediments and Performance Assessment of Machine Learning-based Models: Insight From Whole-rock Geochemistry ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learning have been published. According to news originating from Tamale, Ghana, by NewsRx correspondents, research stated, "The source of clastic sediments generally, can be traced to their source through provenance studies using the whole rock geochemistry of clastic sediments. However, the provenance of the Au-bearing stream sediments within the central parts of Tanzania is yet to be deciphered." Our news journalists obtained a quote from the research from University for Development Studies, "Hence, in this study, to enhance exploration targeting, the source of the Au-bearing stream sediments was characterized using whole-rock geochemistry. The performance of linear regression (LR), decision tree (DT), and polynomial regression (PR) models as prediction models for the Au mineralization in the area, were also compared as additional Au exploration techniques worth exploring in the area. The weathering condition proxies, CIA, ICV, CIW, and PIA as well as discriminant diagrams suggest weakly to intensely weathered sediments. The values of SiO2/Al2O3 and K2O/Al2O3 are indicative of felsic source rocks rather than compositional maturity due to sediments reworking. From Th/Cr, Cr/Th, Th/U, La/Sc, and Th/Sc proxies, the Au-bearing stream sediments are sourced from felsic igneous rocks. These indications are corroborated by the correlation matrix assessment. However, Au is not sourced from the same source rocks as the host sediments due probably, to a prior depositional mixing of the sediments before subsequent transportation to their current depositional environment. With R2 (0.62), MAE (0.6035), MSE (0.6546), and RMSE (0.8091) for LR, R2 (1.0), MAE (0.7500), MSE (1.6273), and RMSE (1.2752) for DT, and R2 (1.0), MAE (2.6608), MSE (12.7840), and RMSE (3.5755), for PR."
TamaleGhanaAfricaChemistryCyborgsEmerging TechnologiesGeochemistryMachine LearningUniversity for Development Studies
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