The world’s greatest deposit of gold is hosted by the Archaean Witwatersrand sedimentary basin,situated in the central portion of the Kaapvaal Craton of South Africa.The geological setting of this remarkable clastic ...The world’s greatest deposit of gold is hosted by the Archaean Witwatersrand sedimentary basin,situated in the central portion of the Kaapvaal Craton of South Africa.The geological setting of this remarkable clastic sedimentary deposit,which has yielded more than one third of all the gold ever produced on the planet,is discussed.The stratigraphy and structure of the Witwatersrand Supergroup is reviewed together with the sedimentology,mineralogy and geochronology of the more important auriferous conglomerate(reef)horizons.展开更多
The Kaapvaal Craton in South Africa hosts one of the largest gold placer deposits in the world. Mining in the Witwatersrand Basin here has been the source of about one third to one half of the gold ever produced in th...The Kaapvaal Craton in South Africa hosts one of the largest gold placer deposits in the world. Mining in the Witwatersrand Basin here has been the source of about one third to one half of the gold ever produced in the world. Gold was discovered in the Johannesburg area in 1886 and after 120 years of continuous operation, mining is currently approaching depths of 4 000 m. In spite of the challenges and risks that the industry has had to deal with including rock temperature, ventilation and water, one of the most feared hazards in the basin has been the threat from the ongoing occurrence of seismicity and rockbursts. The problem first manifested itself by way of the occurrence of tremors roughly twenty years after the commencement of mining operations. This paper traces the history of the approach to rockbursts and seismicity during the 120 year history of mining in the basin. It portrays a picture of the mining seismicity in terms of monitoring phases, mechanisms and mitigation strategies. The work of research organizations over the years is highlighted with a brief mention of current regulation strategies on the part of the mining inspectorate.展开更多
Remote sensing data is a cheap form of surficial geoscientific data,and in terms of veracity,velocity and volume,can sometimes be considered big data.Its spatial and spectral resolution continues to improve over time,...Remote sensing data is a cheap form of surficial geoscientific data,and in terms of veracity,velocity and volume,can sometimes be considered big data.Its spatial and spectral resolution continues to improve over time,and some modern satellites,such as the Copernicus Programme’s Sentinel-2 remote sensing satellites,offer a spatial resolution of 10 m across many of their spectral bands.The abundance and quality of remote sensing data combined with accumulated primary geochemical data has provided an unprecedented opportunity to inferentially invert remote sensing data into geochemical data.The ability to derive geochemical data from remote sensing data would provide a form of secondary big geochemical data,which can be used for numerous downstream activities,particularly where data timeliness,volume and velocity are important.Major benefactors of secondary geochemical data would be environmental monitoring and applications of artificial intelligence and machine learning in geochemistry,which currently entirely relies on manually derived data that is primarily guided by scientific reduction.Furthermore,it permits the usage of well-established data analysis techniques from geochemistry to remote sensing that allows useable insights to be extracted beyond those typically associated with strictly remote sensing data analysis.Currently,no generally applicable and systematic method to derive chemical elemental concentrations from large-scale remote sensing data have been documented in geosciences.In this paper,we demonstrate that fusing geostatistically-augmented geochemical and remote sensing data produces an abundance of data that enables a more generalized machine learning-based geochemical data generation.We use gold grade data from a South African tailing storage facility(TSF)and data from both the Landsat-8 and Sentinel remote sensing satellites.We show that various machine learning algorithms can be used given the abundance of training data.Consequently,we are able to produce a high resolution(10 m grid size)gold concentration map of the TSF,which demonstrates the potential of our method to be used to guide extraction planning,online resource exploration,environmental monitoring and resource estimation.展开更多
文摘The world’s greatest deposit of gold is hosted by the Archaean Witwatersrand sedimentary basin,situated in the central portion of the Kaapvaal Craton of South Africa.The geological setting of this remarkable clastic sedimentary deposit,which has yielded more than one third of all the gold ever produced on the planet,is discussed.The stratigraphy and structure of the Witwatersrand Supergroup is reviewed together with the sedimentology,mineralogy and geochronology of the more important auriferous conglomerate(reef)horizons.
文摘The Kaapvaal Craton in South Africa hosts one of the largest gold placer deposits in the world. Mining in the Witwatersrand Basin here has been the source of about one third to one half of the gold ever produced in the world. Gold was discovered in the Johannesburg area in 1886 and after 120 years of continuous operation, mining is currently approaching depths of 4 000 m. In spite of the challenges and risks that the industry has had to deal with including rock temperature, ventilation and water, one of the most feared hazards in the basin has been the threat from the ongoing occurrence of seismicity and rockbursts. The problem first manifested itself by way of the occurrence of tremors roughly twenty years after the commencement of mining operations. This paper traces the history of the approach to rockbursts and seismicity during the 120 year history of mining in the basin. It portrays a picture of the mining seismicity in terms of monitoring phases, mechanisms and mitigation strategies. The work of research organizations over the years is highlighted with a brief mention of current regulation strategies on the part of the mining inspectorate.
基金provided by the Department of Science and Innovation(DSI)-National Research Foundation(NRF)Thuthuka Grant(Grant UID:121,973)DSI-NRF CIMERA.Yousef Ghorbani acknowledges financial support from the Centre for Advanced Mining and Metallurgy(CAMM),a strategic research environment established at the LuleåUniversity of Technology funded by the Swedish governmentWe also thank Sibanye-Stillwater Ltd.For their funding through the Wits Mining Institute(WMI).
文摘Remote sensing data is a cheap form of surficial geoscientific data,and in terms of veracity,velocity and volume,can sometimes be considered big data.Its spatial and spectral resolution continues to improve over time,and some modern satellites,such as the Copernicus Programme’s Sentinel-2 remote sensing satellites,offer a spatial resolution of 10 m across many of their spectral bands.The abundance and quality of remote sensing data combined with accumulated primary geochemical data has provided an unprecedented opportunity to inferentially invert remote sensing data into geochemical data.The ability to derive geochemical data from remote sensing data would provide a form of secondary big geochemical data,which can be used for numerous downstream activities,particularly where data timeliness,volume and velocity are important.Major benefactors of secondary geochemical data would be environmental monitoring and applications of artificial intelligence and machine learning in geochemistry,which currently entirely relies on manually derived data that is primarily guided by scientific reduction.Furthermore,it permits the usage of well-established data analysis techniques from geochemistry to remote sensing that allows useable insights to be extracted beyond those typically associated with strictly remote sensing data analysis.Currently,no generally applicable and systematic method to derive chemical elemental concentrations from large-scale remote sensing data have been documented in geosciences.In this paper,we demonstrate that fusing geostatistically-augmented geochemical and remote sensing data produces an abundance of data that enables a more generalized machine learning-based geochemical data generation.We use gold grade data from a South African tailing storage facility(TSF)and data from both the Landsat-8 and Sentinel remote sensing satellites.We show that various machine learning algorithms can be used given the abundance of training data.Consequently,we are able to produce a high resolution(10 m grid size)gold concentration map of the TSF,which demonstrates the potential of our method to be used to guide extraction planning,online resource exploration,environmental monitoring and resource estimation.
