基于深度学习的山东大尹格庄金矿床深部三维预测模型     

邓浩  郑扬  陈进  魏运凤  毛先成 《地球学报》2020,41(2):157-165

在隐伏矿体三维预测中,预测模型的准确性在很大程度上取决于找矿指标对矿化富集部位的指示性。然而,找矿指标容易受到找矿概念模型可靠性和成矿信息提取有效性限制,从而影响预测的准确性。论文以山东大尹格庄金矿隐伏矿体三维预测为例,基于深度学习方法,构建矿床深部隐伏矿体三维预测模型,旨在利用深度网络模型,学习获得对矿化具有显著指示性的找矿指标,提升三维预测的准确性。该方法将三维地质模型及其形态特征转换为适合卷积网络二维图像,采用卷积神经网络实现找矿指标的自动提取,并构建三维地质模型到矿化富集地段的定量关联。利用该方法建立了大尹格庄金矿的三维预测模型,经与几种人工建立找矿指标预测模型的对比分析,表明基于深度学习的预测模型较大地提升了预测准确性。  相似文献   

A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena     

Guiming Zhang  A-Xing Zhu 《International journal of geographical information science》2019,33(9):1873-1893

Volunteered geographic information (VGI) contains valuable field observations that represent the spatial distribution of geographic phenomena. As such, it has the potential to provide regularly updated low-cost field samples for predictively mapping the spatial variations of geographic phenomena. The predictive mapping of geographic phenomena often requires representative samples for high mapping accuracy, but samples consisting of VGI observations are often not representative as they concentrate on specific geographic areas (i.e. spatial bias) due to the opportunistic nature of voluntary observation efforts. In this article, we propose a representativeness-directed approach to mitigate spatial bias in VGI for predictive mapping. The proposed approach defines and quantifies sample representativeness by comparing the probability distributions of sample locations and the mapping area in the environmental covariate space. Spatial bias is mitigated by weighting the sample locations to maximize their representativeness. The approach is evaluated using species habit suitability mapping as a case study. The results show that the accuracy of predictive mapping using weighted sample locations is higher than using unweighted sample locations. A positive relationship between sample representativeness and mapping accuracy is also observed, suggesting that sample representativeness is a valid indicator of predictive mapping accuracy. This approach mitigates spatial bias in VGI to improve predictive mapping accuracy.  相似文献   

基于带状混合矩阵ICA实现地震盲反褶积   总被引：3，自引：2，他引：1       下载免费PDF全文

刘喜武  高伟  张宁  刘婉莹 《地球物理学进展》2007,22(4):1153-1163

基于对地震反褶积本质上是一个盲过程的认识,引入高阶统计学盲源分离技术——独立分量分析（ICA）实现地震盲反褶积.在无噪声假设条件下,利用地震记录时间延迟矩阵和地震子波带状褶积矩阵,将地震褶积模型转化为一般线性混合ICA模型,采用FastICA算法,将带状性质作为先验信息,实现所谓带状ICA算法（B\|ICA）,得到个数与子波算子长度相等的多个估计反射系数序列和估计子波序列,最后利用褶积模型提供的附加信息从中优选出最佳的反射系数序列及相应的地震子波.模型数据和实际二维地震道数值算例表明：对于统计性反褶积,在不对反射系数作高斯白噪假设,不对子波作最小相位假设的所谓“全盲”条件下,基于ICA方法（反射系数非高斯分布,地震子波非最小相位）可以较好解决地震盲反褶积问题,是基于二阶统计特性的地震信号统计性反褶积方法的提升,具有可行性和应用前景.  相似文献   

结合长时序遥感监测和机器学习算法预测藻类增殖风险时空变化          下载免费PDF全文

时晨燚  刘凤  祝铠  张媛  刘海 《湖泊科学》2024,36(3):670-684

饮用水源地藻类增殖监测和预测对于改善水生态系统环境和保护人类健康具有重要意义。利用多源遥感数据能够获取高时空分辨率的藻类动态信息,结合长时序遥感监测与机器学习算法能够适应藻类生长复杂的影响机制和非线性特征,实现藻类增殖风险时空变化的预测。本文利用Landsat与MODIS长时间序列卫星遥感数据,采用FAI与NDVI两种方法提取2000—2020年丹江口水库藻类浓度的时空变化信息,在此基础上分析藻类增殖对气象因子(气温、气压、相对湿度、风速和累计日照时间)的时间滞后效应。利用支持向量机、朴素贝叶斯与随机森林3种机器学习算法预测藻类增殖风险,并对3种算法的预测性能进行评价和对比。结果表明:丹江口水库藻类浓度年际变化呈现出先增后降的趋势,呈现出明显的季节性周期变化,春末夏初是藻类快速增殖时期。空间上入库支流和库湾等局部地区藻类浓度相对较高,为藻类增殖高风险区,丹江口水库藻类增殖风险预测模型能够较为准确地确定藻类增殖高风险区位置并反映短期内的空间变化情况,3种算法的预测结果呈现出整体上的一致性,其中支持向量机与朴素贝叶斯算法表现出更高的精度,提前4～5天是最佳预测时间。  相似文献