Mafic rocks comprising tholeiitic pillow basalt, dolerite and minor gabbro form the basal stratigraphic unit in the ca. 2.8 to 2.6 Ga Geita Greenstone Belt situated in the NW Tanzania Craton. They outcrop mainly along...Mafic rocks comprising tholeiitic pillow basalt, dolerite and minor gabbro form the basal stratigraphic unit in the ca. 2.8 to 2.6 Ga Geita Greenstone Belt situated in the NW Tanzania Craton. They outcrop mainly along the southern margin of the belt, and are at least 50 million years older than the supracrustal assemblages against which they have been juxtaposed. Geochemical analyses indicate that parts of the assemblage approach high Mg-tholeiite （more than 8 wt.% MgO）. This suite of samples has a restricted compositional range suggesting derivation from a chemically homogenous reservoir. Trace element modeling suggests that the mafic rocks were derived by partial melting within the spinel peridotite field from a source rock with a primitive mantle composition. That is, trace elements maintain primitive mantle ratios （Zr/Hf = 32-35, Ti/Zr - 107-147）, producing flat REE and HFSE profles [（La/Yb）pm = 0.9 -1.3], with abundances of 3-10 times primitive mantle and with minor negative anomalies of Nb [（Nb/ La）pm - 0.6-0.8] and Th [（Th/La）pm = 0.6-0.9]. Initial isotope compositions （εNd） range from 1.6 to 2.9 at 2.8 Ga and plot below the depleted mantle line suggesting derivation from a more enriched source compared to present day MORB mantle. The trace element composition and Nd isotopic ratios are similar to the mafic rocks outcropping -50 km south. The mafic rocks outcropping in the Geita area were erupted through oceanic crust over a short time period, between -2830 and-2820 Ma; are compositionally homogenous, contain little to no associated terrigenous sediments, and their trace element composition and short emplacement time resemble oceanic plateau basalts. They have been interpreted to be derived from a plume head with a primitive mantle composition.展开更多
Shale gas resources have been regarded as a viable energy source, and it is of great significance to characterize the shale composition of different cements, such as quartz and dolomite. In this research, chemical ana...Shale gas resources have been regarded as a viable energy source, and it is of great significance to characterize the shale composition of different cements, such as quartz and dolomite. In this research, chemical analysis and the multifractal method have been used to study the mineral compositions and petrophysical structures of cements in shale samples from the Longmaxi Formation, China. X-ray diffraction, electron microprobe, field emission scanning electron microscopy, cathodoluminescence microscopy and C-O isotope analyses confirmed that cements in the Longmaxi Formation shales are mainly composed of Fe-bearing dolomite and quartz. Fe-bearing dolomite cements concentrate around dolomite as annuli, filling micron-sized inorganic primary pores. Quartz cements in the form of nanoparicles fill primary inter-crystalline pores among clay minerals. Theoretical calculation shows that the Fe-bearing dolomite cements formed slightly earlier than the quartz cements, but both were related to diagenetic illitization of smectite. Moreover, multifractal analysis reveals that the quartz cements are more irregularly distributed in pores than the Fe-bearing dolomite cements. These results suggest that the plugging effect of the quartz cements on the primary inoraganic pore structures is the dominant factor resulting in low interconnected porosity of shales, which are unfavorable for the enrichment of shale gas.展开更多
Mineral prospectivity mapping(MPM)is designed to reduce the exploration search space by combining and analyzing geological prospecting big data.Such geological big data are too large and complex for humans to effectiv...Mineral prospectivity mapping(MPM)is designed to reduce the exploration search space by combining and analyzing geological prospecting big data.Such geological big data are too large and complex for humans to effectively handle and interpret.Artificial intelligence(AI)algorithms,which are powerful tools for mining nonlinear mineralization patterns in big data obtained from mineral exploration,have demonstrated excellent performance in MPM.However,AI-driven MPM faces several challenges,including difficult interpretability,poor generalizability,and physical inconsistencies.In this study,based on previous studies,we devised a novel workflow that aims to constructing more transparent and explainable artificial intelligence(XAI)models for MPM by embedding domain knowledge throughout the AI-driven MPM,from input data to model design and model output.This newly proposed approach provides strong geological and conceptual leads that guide the entire AI-driven MPM model training process,thereby improving model interpretability and performance.Overall,the development of XAI models for MPM is capable of embedding prior and expert knowledge throughout the modeling process,presenting a valuable and promising area for future research designed to improve MPM.展开更多
文摘Mafic rocks comprising tholeiitic pillow basalt, dolerite and minor gabbro form the basal stratigraphic unit in the ca. 2.8 to 2.6 Ga Geita Greenstone Belt situated in the NW Tanzania Craton. They outcrop mainly along the southern margin of the belt, and are at least 50 million years older than the supracrustal assemblages against which they have been juxtaposed. Geochemical analyses indicate that parts of the assemblage approach high Mg-tholeiite （more than 8 wt.% MgO）. This suite of samples has a restricted compositional range suggesting derivation from a chemically homogenous reservoir. Trace element modeling suggests that the mafic rocks were derived by partial melting within the spinel peridotite field from a source rock with a primitive mantle composition. That is, trace elements maintain primitive mantle ratios （Zr/Hf = 32-35, Ti/Zr - 107-147）, producing flat REE and HFSE profles [（La/Yb）pm = 0.9 -1.3], with abundances of 3-10 times primitive mantle and with minor negative anomalies of Nb [（Nb/ La）pm - 0.6-0.8] and Th [（Th/La）pm = 0.6-0.9]. Initial isotope compositions （εNd） range from 1.6 to 2.9 at 2.8 Ga and plot below the depleted mantle line suggesting derivation from a more enriched source compared to present day MORB mantle. The trace element composition and Nd isotopic ratios are similar to the mafic rocks outcropping -50 km south. The mafic rocks outcropping in the Geita area were erupted through oceanic crust over a short time period, between -2830 and-2820 Ma; are compositionally homogenous, contain little to no associated terrigenous sediments, and their trace element composition and short emplacement time resemble oceanic plateau basalts. They have been interpreted to be derived from a plume head with a primitive mantle composition.
基金financially funded by the National Key R&D Program of China(No.2016YFC0600501)the Natural Science Foundation of China(Nos.41572315,41872250)the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)(No.CUG170104)
文摘Shale gas resources have been regarded as a viable energy source, and it is of great significance to characterize the shale composition of different cements, such as quartz and dolomite. In this research, chemical analysis and the multifractal method have been used to study the mineral compositions and petrophysical structures of cements in shale samples from the Longmaxi Formation, China. X-ray diffraction, electron microprobe, field emission scanning electron microscopy, cathodoluminescence microscopy and C-O isotope analyses confirmed that cements in the Longmaxi Formation shales are mainly composed of Fe-bearing dolomite and quartz. Fe-bearing dolomite cements concentrate around dolomite as annuli, filling micron-sized inorganic primary pores. Quartz cements in the form of nanoparicles fill primary inter-crystalline pores among clay minerals. Theoretical calculation shows that the Fe-bearing dolomite cements formed slightly earlier than the quartz cements, but both were related to diagenetic illitization of smectite. Moreover, multifractal analysis reveals that the quartz cements are more irregularly distributed in pores than the Fe-bearing dolomite cements. These results suggest that the plugging effect of the quartz cements on the primary inoraganic pore structures is the dominant factor resulting in low interconnected porosity of shales, which are unfavorable for the enrichment of shale gas.
基金supported by the National Natural Science Foundation of China(Grant Nos.42321001,42172326)the Natural Science Foundation of Hubei Province(China)(Grant No.2023AFA001)。
文摘Mineral prospectivity mapping(MPM)is designed to reduce the exploration search space by combining and analyzing geological prospecting big data.Such geological big data are too large and complex for humans to effectively handle and interpret.Artificial intelligence(AI)algorithms,which are powerful tools for mining nonlinear mineralization patterns in big data obtained from mineral exploration,have demonstrated excellent performance in MPM.However,AI-driven MPM faces several challenges,including difficult interpretability,poor generalizability,and physical inconsistencies.In this study,based on previous studies,we devised a novel workflow that aims to constructing more transparent and explainable artificial intelligence(XAI)models for MPM by embedding domain knowledge throughout the AI-driven MPM,from input data to model design and model output.This newly proposed approach provides strong geological and conceptual leads that guide the entire AI-driven MPM model training process,thereby improving model interpretability and performance.Overall,the development of XAI models for MPM is capable of embedding prior and expert knowledge throughout the modeling process,presenting a valuable and promising area for future research designed to improve MPM.
