Institute of Earthquake Forecasting, China Earthquake Administration, Beijing, China; School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, United Kingdom.
School of Informatics, University of Leicester, Leicester, United Kingdom.
Sci Total Environ. 2021 Jun 1;771:145256. doi: 10.1016/j.scitotenv.2021.145256. Epub 2021 Jan 28.
Earthquakes have become one of the leading causes of death from natural hazards in the last fifty years. Continuous efforts have been made to understand the physical characteristics of earthquakes and the interaction between the physical hazards and the environments so that appropriate warnings may be generated before earthquakes strike. However, earthquake forecasting is not trivial at all. Reliable forecastings should include the analysis and the signals indicating the coming of a significant quake. Unfortunately, these signals are rarely evident before earthquakes occur, and therefore it is challenging to detect such precursors in seismic analysis. Among the available technologies for earthquake research, remote sensing has been commonly used due to its unique features such as fast imaging and wide image-acquisition range. Nevertheless, early studies on pre-earthquake and remote-sensing anomalies are mostly oriented towards anomaly identification and analysis of a single physical parameter. Many analyses are based on singular events, which provide a lack of understanding of this complex natural phenomenon because usually, the earthquake signals are hidden in the environmental noise. The universality of such analysis still is not being demonstrated on a worldwide scale. In this paper, we investigate physical and dynamic changes of seismic data and thereby develop a novel machine learning method, namely Inverse Boosting Pruning Trees (IBPT), to issue short-term forecast based on the satellite data of 1371 earthquakes of magnitude six or above due to their impact on the environment. We have analyzed and compared our proposed framework against several states of the art machine learning methods using ten different infrared and hyperspectral measurements collected between 2006 and 2013. Our proposed method outperforms all the six selected baselines and shows a strong capability in improving the likelihood of earthquake forecasting across different earthquake databases.
在过去的五十年中，地震已成为自然灾害导致死亡的主要原因之一。人们一直在努力了解地震的物理特性以及物理灾害与环境之间的相互作用，以便在地震发生前发出适当的警报。然而，地震预测绝非易事。可靠的预测应该包括对即将发生的大地震的分析和信号。不幸的是，这些信号在地震发生前很少明显，因此在地震分析中很难检测到这些前兆。在现有的地震研究技术中，由于具有快速成像和宽图像采集范围等独特功能，遥感已被广泛应用。然而，早期的地震前和遥感异常研究大多侧重于异常识别和单个物理参数的分析。许多分析都是基于单一事件进行的，这使得人们对这种复杂的自然现象缺乏了解，因为通常情况下，地震信号隐藏在环境噪声中。这种分析的普遍性在全球范围内尚未得到证明。在本文中，我们研究了地震数据的物理和动态变化，并开发了一种新的机器学习方法，即反向提升修剪树（IBPT），以便根据 2006 年至 2013 年间收集的 1371 次六级或以上地震的卫星数据，对短期地震进行预测。我们分析并比较了我们提出的框架与几种最先进的机器学习方法，使用了十种不同的红外和高光谱测量。我们提出的方法优于所有六个选定的基线，并且在不同的地震数据库中表现出了强大的提高地震预测可能性的能力。
