论地质研究中的因果关系和相关关系——大数据研究的启示

我们对大数据两年多来的研究表明,大数据是一个非常有效的方法。一直以来,人们都是通过因果关系来认识世界的;而大数据不是,大数据是从数据出发,挖掘数据之间的相关关系,从而提升数据的价值。例如在矿床学研究中,人们往往过分关注矿床的成因,关注成矿与岩浆、流体与岩体之间的因果关系。实际上,流体与岩体、矿床与岩浆之间主要不是因果关系而是相关关系。早先的玄武岩构造环境判别图几乎都是按照因果关系的思路设计的,虽然取得了一定的效果但并不完美。我们采用大数据方法对全球全体玄武岩和安山岩的数据进行挖掘,取得了极佳的效果,主要依据的是元素之间的相关关系而非因果关系。多少年来,人们在科学研究的实践活动中习惯于对因果关系的追求。现在,科学的发展要求我们更加重视数据之间的相关关系,从对因果关系的追求转变为对相关关系的追求。实践表明,追求因果关系不可避免人为因素的干扰,而大数据方法挖掘数据之间的相关关系,在很大程度上避免了人为因素的干扰,因此,大数据的结果是真实可靠的。

Discussion on causality and correlation in geological research

Big data is an effective method through our study in the recent two years. For a long time, human is to know the world by causality, but big data is to increase the value of the data by mining data correlation. In the study of ore deposit, for example, people tend to focus too much on the genesis of ore deposit, the causality between mineralization and magma, the fluid and rock mass. In fact, the relationship is not causality, but correlation. Earlier the basalt tectonic environment discrimination diagrams are almost designed according to the causality thinking, and obtained the certain effect, but are not perfect. We use global basalt and andesite data mining, achieved excellent result. It is mainly based on correlation rather than the causality between elements. For many years, people in the scientific research activities are used to pursuit of causality. Now, the scientific development requires us to pay more attention to the correlation between data, change from pursuit of causality to the pursuit of the correlation. The practice shows that the interference of human factors inevitable during the pursuit of causality. But big data avoid interference with human factors through data mining to pursuit the correlation. Therefore, the result of big data is reliable.