Identification of geochemical anomalies from geological background is of great significance in the exploration of complex mineralization systems. For a 2D problem, the window-based local singularity mapping has been used widely to identify the distribution patterns of geochemical data. However, the optimal window parameters for calculating the singularity index are hard to determine. Previous studies commonly use the default parameters when applying singularity mapping. In this study, window-based local singularity mapping was performed and improved by comprehensive analysis of multiple parameters to explore geochemical anomalies associated with gold mineralization in the Xishan deposit, North China, with the aim of revealing undiscovered mineralization. By using Au anomalies as an example, the parameters that may influence the result of window-based local singularity processing have been analyzed and discussed to improve the mapping result. The parameters include the average concentration calculation algorithm, the shape of the sliding windows, the window size increment and the number of windows. Success-rate curves and area under the success-rate curve have been used to assess the spatial correlation of the singularity map with the known mineral occurrences. While square sliding window is the most regularly used window shape, circular and elliptical windows can be alternative choices. We found that the directions of major axis of the ellipses parallel or quasi-parallel to geological strike fit the locations of ore deposits better if the mineralization system is controlled by regional faults. After taking the influence of different parameters into account, geochemical anomalies were successfully separated from background and have been enhanced compared to anomalies identified solely from concentration values. Singularity–quantile analysis has been applied to recognize and separate multiple geochemical anomaly populations based on the singularity map in frequency and spatial domain. While the Au concentration map shows quite scattered strong and weak geochemical anomalies, the linear regions of positive singularity resolved by singularity–quantile analysis coincide well with the location of regional faults and alteration zones, which might indicate footprints of ore-forming fluids. Based on the singularity maps of multi-elements (Au, Ag, Cu and Pb), we resolved two prospect areas of mineralization bounded by regional faults, hydrothermally altered rocks and lamprophyres with positive singularity, which warrant further investigation for undiscovered mineralization.

从地质背景识别地球化学异常对勘探复杂的成矿系统具有重要意义。对于二维问题，基于窗口的局部奇异映射已被广泛用于识别地球化学数据的分布模式。但是，难以确定用于计算奇异指数的最佳窗口参数。以前的研究在应用奇点映射时通常使用默认参数。在这项研究中，通过对多个参数进行综合分析，对基于窗口的局部奇异点进行了绘制和改进，以探索与华北西山矿床金矿化有关的地球化学异常，目的是揭示未发现的矿化。以金异常为例，分析和讨论了可能影响基于窗口的局部奇异处理结果的参数，以改善映射结果。这些参数包括平均浓度计算算法，滑动窗口的形状，窗口大小增量和窗口数。成功率曲线和成功率曲线下的面积已用于评估奇异图与已知矿物发生的空间相关性。尽管方形滑动窗口是最常用的窗口形状，但是圆形和椭圆形窗口可以作为替代选择。我们发现，如果矿化系统受区域性断裂控制，则椭圆长轴平行于或准平行于地质走向的方向更好地适应了矿床的位置。在考虑了不同参数的影响后，地球化学异常成功地与背景分离，并且与仅从浓度值中识别出的异常相比已得到增强。基于频率和空间域上的奇异图，奇异量分析已用于识别和分离多个地球化学异常种群。尽管金浓度图显示出强而弱的地球化学异常分布，但通过奇异-分位数分析所解析的正奇异的线性区域与区域断层和蚀变带的位置非常吻合，这可能表明了成矿流体的足迹。根据多元素（Au，Ag，Cu和Pb）的奇异图，我们解析了两个受区域断层限制的成矿前景区，