唐山台跨断层形变速率阶段性变化及预报效能评估

Evaluation of Earthquake Prediction Ability from Tangshan Cross-fault Deformation Data by Using the Bernaola-Galvan Algorithm

摘要:

采用启发式分割算法（BG算法）对唐山台跨断层形变观测时间序列进行均值突变检测。以此为基础，通过计算突变前后子序列均值变化量，探讨唐山台跨断层形变的阶段性变化特征。结合台站及其周围典型震例，分层次统计突变距离发震日的时间间隔，计算虚报率、漏报率和R值，并对唐山台跨断层形变的预报效能进行定量评估。结果显示，唐山台跨断层形变阶段性变化特征显著；各测项突变后对应发生地震的概率均不低于50%，突变距发震日的最短时间间隔自当天到2个月不等；水准的预报效能整体上略优于基线，而断层垂向分量和张压分量的预报效能整体上优于走滑分量。

Abstract:

The Bernaola-Galvan algorithm (BG algorithm) is used to detect the mean mutation of the across-fault movement time series at Tangshan station. Based on this,the characteristics of the stage changes of the cross-fault deformations at Tangshan station are analyzed by calculating the average changes of the subsequences before and after the mutations. Combing with the filtered typical earthquake cases in and around Tangshan station,we quantitatively evaluate the prediction ability on three levels by counting the minimum time interval between the mutations and the earthquakes,and calculating the false alarm rate,the missed alarm rate,and R. The results show that characteristics of the periodic change of the cross-fault deformation at Tangshan station are significant The probability of an earthquake corresponding to the mutations is 50% or more,and the minimum time interval is from the exact day to two months. In summary,we believe that in terms of earthquake prediction,the leveling measurement is better than the baseline observations slightly,while the lateral and the vertical deformation data is better than axial deformation data in the respect of prediction ability.