论文标题
使用点过程对水力地球化学数据的贝叶斯统计分析：多组分流体混合物中源检测的新工具
Bayesian statistical analysis of hydrogeochemical data using point processes: a new tool for source detection in multicomponent fluid mixtures
论文作者
论文摘要
水力地球化学数据可以看作是多维空间中的点云。该空间的每个维度都代表水力地球化学参数（即盐度，溶质浓度，浓度比，同位素组成...）。虽然许多地质流体的组成是通过多种来源之间的混合来控制的，但与水力地球化学数据集有关的关键问题是检测来源。通过将水力地球化学数据视为空间数据，本文为基于点过程的源检测问题提供了新的解决方案。结果显示在地热流体的模拟和实际数据上。
Hydrogeochemical data may be seen as a point cloud in a multi-dimensional space. Each dimension of this space represents a hydrogeochemical parameter (i.e. salinity, solute concentration, concentration ratio, isotopic composition...). While the composition of many geological fluids is controlled by mixing between multiple sources, a key question related to hydrogeochemical data set is the detection of the sources. By looking at the hydrogeochemical data as spatial data, this paper presents a new solution to the source detection problem that is based on point processes. Results are shown on simulated and real data from geothermal fluids.
