基于量子漫步算法的地震震前异常挖掘

地震特别是大震前会产生一些异常，但这些异常信息难以识别，导致无法充分利用这些异常信息预测地震的发生时间，减少地震带来的灾害影响.针对这个问题，提出一种基于量子漫步算法的震前异常挖掘方法，提取汶川地震和芦山地震的震前射出长波辐射（Outgoing Long-wave Radiation，OLR）异常，进而计算地震前后的P值，异常值CD等数据，通过统计分析方法，探索OLR异常与地震的关系.并且通过实验将该算法扩展到最近十年左右全球发生的8.0级及以上地震，验证该算法的有效性.实验结果表明，该算法能够有效的反映在地震前后会出现OLR异常，而且越大的地震异常越明显.因此，该算法适用于震前异常挖掘.

Anomaly Mining before Earthquake Based on Quantum Walk Algorithm

There are some anomalies before the earthquake, especially the large earthquake. However, such abnormal information is too difficult to identify. Therefore, we cannot make full use of the abnormal information to predict the occurrence time of the earthquake in order to reduce the impact of the earthquake. To solve this problem, an anomaly mining method before earthquake based on the quantum walk algorithm is proposed to extract seismic Outgoing Long-wave Radiation (OLR) anomalies before the Wenchuan earthquake and the Lushan earthquake. Then, calculate the P value, anomaly value CD before and after the earthquake. Through statistical analysis method, the relationship between OLR anomalies and earthquake is explored. What is more, the algorithm is extended to the 8.0 magnitude and above earthquakes in the nearly last ten years. Through experiments, the effectiveness of the algorithm is verified. The experimental results show that the algorithm can effectively reflect the anomalies before and after the earthquake, and the larger the earthquake is, the more obvious anomaly is. Therefore, this algorithm is suitable for pre-earthquake anomaly excavation.