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Machine learning based prospect targeting: A case of gold occurrence in central parts of Tanzania, East Africa
(Elsevier, 2024) Gawusu, Sidique; Mvile, Benatus Norbert; Abu, Mahamuda; Kalimenze, John Desderius
Soil geochemical analyses from central Tanzania reveal significant gold (Au) values, highlighting the potential for further exploration in the region. This study employs ensemble machine learning models—XGBoost-RF, XGBoost-SVM, and XGBoost-ANN—to enhance predictions of Au distribution. Among these, the XGBoost-ANN model showed the highest accuracy during the training phase, achieving a Mean Absolute Percentage Error (MAPE) of 1.275, a Root Mean Square Error (RMSE) of 0.031, an R² of 0.999, and a Pearson Correlation Coefficient (PCC) of 0.999. However, its performance declined in the testing phase with a MAPE of 0.0668 and an RMSE of 0.2491, indicating reduced predictiveness on new data. Spatial analyses using Global and Local Moran's I tests revealed no significant global spatial autocorrelation but identified localized clusters of high and low Au concentrations. Specific areas showed significant spatial dependence, enhancing our understanding of the complex geospatial distribution of Au. These findings support the combined use of predictive modeling and spatial statistical methods to refine mineral exploration strategies, highlighting the value of advanced analytics in identifying promising exploration targets.
Provenance studies of Au-bearing stream sediments and performance assessment of machine learning-based models: insight from whole-rock geochemistry central Tanzania, East Africa
(Springer Science and Business Media LLC, 2024) Abu, Mahamuda; Mvile, Benatus Norbert; Kalimenze, John Desderius
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. 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. The LR model performs better in predicting the Au occurrence in the area.
Provenance studies of Au-bearing stream sediments and performance assessment of machine learning-based models: insight from whole-rock geochemistry central Tanzania, East Africa
(Springer Science and Business Media LLC, 2024-01-29) Abu, Mahamuda; Mvile, Benatus Norbert; Kalimenze, John Desderius
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. 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. The LR model performs better in predicting the Au occurrence in the area.
Quantification of modelled 4D response and viability of repeated seismic reservoir monitoring in J-Area Field, Central North Sea
(Springer Nature, 2020) Mvile, Benatus Norbert; Abu, Mahamuda; Bishoge, Obadia Kyetuza; Yousif, Ibrahim Mohamed; Kazapoe, Raymond
4D reservoir monitoring has recently become a major tool used to manage the hydrocarbon production of reservoirs. When combined to production well data, high quality 4D seismic is very useful to address production changes in a reservoir over time. This becomes very challenging though, for most of the clastic reservoirs from the J-Area field, in Central North Sea. These reservoirs are frequently compartmentalized with complex faulting which can result in different initial fluid contacts and pressures across the same field. Full understanding of which faults are acting as hydrocarbon baffles or flow barriers would be very useful in optimizing drilling. This work aimed to determine whether 4D seismic techniques could realistically aid this understanding by quantifying the reservoir production effects relating to pore pressure and water saturation changes in J-Area Triassic using real and predicted well data. It further aimed at testing the viability of repeated seismic reservoir monitoring in this field using the normalized root mean square (NRMS) technique. The modelled 4D seismic response derived from synthetic seismic traces based on seismic volumes generated from the well data was used to design a new dedicated 4D survey. The results show that both pore pressure depletion and water saturation changes produce a significant 4D effects in the reservoir. Yet, pore pressure depletion become the major production effect in this field as the majority of 4D effects are due to pore pressure reduction through depletion of the reservoir. The study suggest that, for optimum reservoir monitoring, a baseline survey must be reprocessed in parallel with the monitor survey to reduce the NRMS noise or alternatively a dedicated repeat survey is acquired matching the design of the latest vintage of seismic.
Sources and pollution assessment of trace elements in soils of the central, Dodoma region, East Africa: implication for public health monitoring
(Elsevier, 2021) Abu, Mahamuda; Kalimenze, John; Mvile, Benatus Norbert; Kazapoe, Raymond Webrah
The study assesses the pollution levels and sources in soils of As, Pb, Cu, Zn, Cd, Mn, Cr, Co, Ni and Se in the central Dodoma region of Tanzania from estimations of CF, EF, Igeo, PLI and RI together with PCA, FA and HCA multivariate techniques. Generally, the capital city’s regional area is polluted with these heavy metals with PLI > 1 and EFs > 40. The Igeo also shows concerning concentration levels of As with 60.77 % of the study area being moderately to extremely polluted by As and As, Cd and Pb are the heavy metals that requires immediate monitoring within the Dodoma region. The multivariate analysis supports a dominant geogenic source of these heavy metals with the mafic ore bearing lithology controlling these elemental concentrations in soils via chemical weathering of pyrites, arsenopyrite and chalcopyrite as the most probable geological process releasing these heavy metals into the soil. The fast growing industrialization of the region with its associated commercial agriculture activities, also contributes although it may be small for now, to the heavy metals contents in soils within the area. With the pace of industrialization coupled with the desire of the country to create jobs through small scale activities, it is appropriate and timely to assess the levels of As, Cd and Pb in surface and groundwater as well as in some cereals like maize or millet which are the main stay of the people, and should be centered at the northern and central parts of the region. This is necessary for effective public health monitoring and to enhance environmental management practices in the region.
The search for plausible economic mineral deposits in the central parts of Tanzania; insight from stream sediment geochemistry, multivariate statistics and geostatistics
(Elsevier BV, 2023) Nunoo, Samuel; Mvile, Benatus Norbert; Abu, Mahamuda; Kelimenze, John Desderius
Exploration success relies heavily on the data obtained, but, significantly on the type of analytical methods deployed and the interpretation reached. A poorly analyzed data may obscure the true reflectivity of the data, and thus, compromised the decision made. A combined data processing approach of descriptive statistics, enrichment-depletion data normalization, geospatial elemental distribution, and stacked overlayed comparison of elements have been used in this study. The prime purpose was to demonstrate potential elemental anomalies, and predict areas of higher degree of confidence for subsequent exploration and mineral resource evaluation. One-hundred and sixty-six stream sediment samples from the Dodoma Region of the Tanzania Craton have been examined; to reveal potential elements or mineral commodity that warrant further exploration. Forty-three elements of target were examined, as this craton is globally known for its rich earth mineral commodity. Our result indicates an enrichment of transition metals (TMs) (Cu, Ni, Co, Cr, Mn and Zn), High Field Strength Elements (Y, Th, U, Zr, Nb, Hf, Ta and Pb), Large Ion Lithophile Elements (Ba and Rb) and Rare-Earth Elements (La and Ce), Platinum Group Element (Pd and Pt) and other metals (Au, As, Bi, W, Mo and Li). Obtained results point to a likely poly-metallic sources and processes; as the underlain geology is marked largely by pegmatite and migmatites, and moderate proportion of fine clastic sedimentary rocks, and minor volcanic rocks mostly to the northern domain. Theoretically, the Large Ion Lithophile Elements (LILEs), Rare-Earth Elements (REEs) and Platinum Group Elements (PGEs) are associated with felsic rocks or variable stages of plutonic granitization. Although, the TMs are often associated with mafic-ultramafic rocks, the linkage of such metals with organic-rich shales been reported elsewhere. These rocks may equally contribute to the occurrence of other metals as stated in this paper. Its intriguing to note a strong positive correlation of Li with TMs, possibility of Li control by mafic minerals in pegmatite bodies. This work proposes a polymetallic enrichment controlled by the area geology. To suggest an alluvial mining potential of the above elements in the area, resource evaluation is a requirement. The geospatial maps reveal areas worth focusing for subsequent exploration. The adopted geostatistical methods and other approach utilized in this research are effective, indicative of handling bulk exploration data for decision and subsequent exploration.

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