北大西洋中脊海底多金属硫化物资源预测
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摘要
海底热液多金属硫化物是继大洋铁锰结核和富钴结壳之后发现的叉一巨大具有开发远景的海底矿产资源,具有巨大的潜在经济价值和良好的开发前景。通过综合分析国内外海底矿产资源特征,总结海底多金属硫化物资源的找矿标志,初步建立了海底多金属硫化物资源的描述性找矿模型,并开展了北大西洋中脊海底多金属硫化物资源预测工作。证据权预测结果表明海底多金属硫化物的分布与水深地形、地球物理、海底扩张速率、沉积物厚度、海底火山地震等标志有很大的关系。预测结果与已有热液点具有较好的叠合度,预测方法、预测模型具有可信度及可操作性,对海底多金属硫化物资源调查和评价具有重要的指导意义。
Seafloor polymetallic sulphides are a huge development prospect of seabed mineral resources after the discovery of the Fe-Mn nodules and Cobalt-rich crusts.Seafloor polymetallic sulphides are produced by seafloor hydrothermal activities,which are high rich in copper,lead,zinc,gold and silver and other metals.Seafloor polymetallic sulphides mainly distribute in the mid-ocean ridge areas which have huge potential economic value and good development prospect.Currently,the main research focuses on the distribution of seafloor hvdrothermal,mineralization mechanism,formation mechanism,environmental effect and so on,and the study and prediction of regional sulfide resources at this stage have almost not been carried out.Base on the information collection of the domestic and foreign researches of seabed mineral resources and comprehensive analysis of the characteristics of seabed mineral resources,the seafloor polymetallic sulfides resources ore-controlling factors and prospecting criteria were summarized,and a seafloor polymetallic sulphides descriptive resource prospecting model was initially established.The North Atlantic ridge area was chosen as the first stage to make a study to carry out the seafloor polymetallic sulfides resource forecasting.The predicted results of the weights-of-evidence method showed that there was a great relationship between the distribution of seafloor polymetallic sulfides resources and the indicators such as the depth of seabed topography,geophysical features,seafloor spreading rate,sediment thickness,submarine volcano seismics.The predicted results had a good superimposed effect with the existing hydrothermal point.Forecasting methods and model are credible and operational.The work of sulfide resource prediction and evaluation has a good guidance.
引文
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