Drastic changes before the 2011 Tohoku earthquake, revealed by exploratory data analysis

Predicting earthquakes is of the utmost importance, especially to those countries of high risk, and although much effort has been made, it has yet to be realised. Nevertheless, there is a paucity of statistical approaches in seismic studies to the extent that an old theory is believed without verification. Seismic records of time and magnitude in Japan were analysed by exploratory data analysis (EDA). EDA is a parametric statistical approach based on the characteristics of data and is suitable for data-driven investigations. The distribution style of each dataset was determined, and the important parameters were found. This enabled us to identify and evaluate the anomalies in the data. Before the huge 2011 Tohoku earthquake, swarm earthquakes occurred before the main earthquake at improbable frequencies. The frequency and magnitude of all earthquakes increased. Both changes made larger earthquakes more likely to occur: even an M9 earthquake was expected every two years. From these simple measurements, the EDA succeeded in extracting useful information. Detecting and evaluating anomalies using this approach for every set of data would lead to a more accurate prediction of earthquakes.

地震预测至关重要，尤其针对地震高风险国家而言。尽管学界已付出诸多努力，但可靠的地震预测至今仍未得以实现。然而，当前地震研究领域的统计方法存在明显不足，乃至未经验证的老旧理论仍被不加甄别地采信。研究针对日本地区的地震时间与震级记录，采用探索性数据分析（Exploratory Data Analysis，EDA）展开分析。EDA是一种基于数据特征的参数化统计方法，适配于数据驱动的研究场景。研究人员明确了各数据集的分布特征，并提取得到关键参数，借此实现了数据异常的识别与评估。在2011年东日本大地震发生前，主震前的群震以超出寻常的高频次发生；所有地震的发生频次与震级均有所上升，这两类变化均大幅提升了大震级地震的发生概率——甚至每两年就可能发生一次M9级地震。通过上述简单测算，EDA成功提取出了极具价值的有效信息。若能将该方法应用于各类数据集以完成异常检测与评估，将有望实现更为精准的地震预测。