Weighted Weights of Evidence and Stepwise Weights of Evidence and Their Applications in Sn-Cu Mineral Potential Mapping in Gejiu, Yunnan Province, China
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摘要:
为了消除和减弱当证据层不满足条件独立性假设时对预测结果产生的影响, 提出了逐步证据权模型和加权证据权模型.加权证据权模型通过对logit模型进行修改, 对各个证据层给予一定的权重, 以调整由于证据层与其他证据层的条件相关性对模型的影响; 逐步证据权模型是将证据层按照一定的顺序逐步加入到模型中, 在加入到模型的过程中依次用已经获得的后验概率作为模糊训练层的方法.以个旧锡铜多金属矿产资源预测为例, 应用4种证据权模型的后验概率进行异常圈定, 结果表明两种新的模型对减弱证据层不满足条件独立性假设所产生的影响是有效的.
Abstract:This paper proposes two kinds of new models of weight of evidence: weighted weights of evidence and stepwise weights of evidence to reduce the influence of correlation among evidence layers when hypothesis of conditional independence is not held.In the weighted weights of evidence model, an adjustment of weight of evidence is made to reduce the influence of correlation among evidence layers.In the stepwise model, each evidence layer is added into the logistic model as if a single layer whose weight can be calculated by using the previously calculated posterior probability as a new prior probability.These two models are compared with other models through a case study of calculating posterior probability maps for Sn-Cu mineral deposits in Gejiu, Yunnan, China.The result shows that both models are effective to reduce the influence of correlations among evidence layers on delineation of Sn-Cu anomalies.
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图 1 采用证据权方法确定的控矿要素二态图层
a.构造交汇点距离6 km缓冲区, 白色点表示构造交汇点; b.采用S-A方法分解得到的地球化学综合异常图; c.采用局部奇异性方法得到的局部地球化学异常图; d.个旧组地层, 粉红色多边形区域为出露个旧岩体, 白色圆圈表示Sn矿床, 黑色线条表示断裂构造
Fig. 1. Binary maps of ore control factors obtained by using fuzzy weights of evidence method
图 2 采用4种证据权模型锡铜矿床预测后验概率图
a.模糊证据权模型(Ⅰ); b.逐步证据权模型(Ⅱ); c.逐步证据权模型(Ⅲ); d.加权证据权模型(Ⅳ); 图中三角形符号表示11个Sn矿床单元
Fig. 2. Posterior probability map using four kinds of weights of evidence model
表 1 模糊证据权模型参数统计
Table 1. Statistical parameters calculated by using fuzzy weights of evidence method
表 2 基于各个子区域4种模型后验概率从大到小排序
Table 2. The rank of 4 posterior probability maps in unique condition
表 3 4种模型后验概率相关系数
Table 3. Correlation among four posterior probability maps
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