Basalts are ubiquitous, mantle-derived mafic rocks found within all tectonic settings. Studying the chemical composition of basalts has proven to be an effective way to understand tectonics-related mantle processes for more recent periods of Earth history when other geological and tectonic records are still well preserved. However, using basalt geochemistry to decipher ancient tectonic environments and mantle processes on Earth remains a significant challenge; interpretations are often non-unique, and weathering, erosion, and orogenic processes can modify the chemical composition of basalts, and eliminate or obscure other complementary geotectonic records. This is most apparent in the oceanic realm, where subduction-related processes have erased much of the geological record for times older than ∼200 Ma, only leaving small and rare dismembered blocks of oceanic lithosphere as ophiolite mélanges along tectonic sutures. As a result, workers must rely heavily on analyzing the chemical composition of preserved basalts to identify the tectonic settings of their original melt extraction from the mantle using various tectonic discrimination diagrams from the literature (e.g., Pearce and Cann, 1973). In this paper, we demonstrate that such widely used classic discrimination diagram approaches often suffer from the intrinsic shortfall of large ambiguity. Instead, we apply here a big-data approach to characterize basalts generated in some typical tectonic settings. We start with a bespoke data correction and machine learning workflow. Our results show that mid-ocean ridge basalts, ocean island basalts, continental flood basalts, arc basalts (from both the oceanic and continental realm), and oceanic flood basalts are statistically chemically different, thus presenting a novel and unique chemical ‘fingerprinting’ approach to more accurately discriminate basalts. The method successfully predicts the tectonic settings of basalt emplacement at a prediction accuracy of >96%.

玄武岩是在所有构造环境中发现的普遍存在的地幔衍生基性岩。在其他地质和构造记录仍然保存完好的情况下，研究玄武岩的化学成分已被证明是了解地球历史更近时期与构造相关的地幔过程的有效方法。然而，利用玄武岩地球化学来破译地球上的古代构造环境和地幔过程仍然是一项重大挑战。解释通常是非唯一的，风化、侵蚀和造山过程可以改变玄武岩的化学成分，并消除或掩盖其他互补的大地构造记录。这在海洋领域最为明显，在那里与俯冲相关的过程已经消除了大部分超过~200 Ma的地质记录，只留下小而稀有的海洋岩石圈碎片作为沿构造缝线的蛇绿岩混杂。因此，工作人员必须严重依赖分析保存下来的玄武岩的化学成分，以使用文献中的各种构造判别图（例如，Pearce 和 Cann，1973）来确定从地幔中提取原始熔体的构造环境。在本文中，我们证明了这种广泛使用的经典判别图方法经常遭受大歧义的内在缺陷。相反，我们在这里应用大数据方法来表征在一些典型构造环境中产生的玄武岩。我们从定制的数据校正和机器学习工作流程开始。我们的研究结果表明，洋中脊玄武岩、洋岛玄武岩、陆洪玄武岩，弧形玄武岩（来自海洋和大陆领域）和海洋洪水玄武岩在化学上是不同的，因此提出了一种新颖而独特的化学“指纹”方法来更准确地区分玄武岩。该方法成功地预测了玄武岩就位的构造环境，预测准确度>96%。