人工智能在拾取地震P波初至中的应用 ——以汶川地震余震序列为例

为了准确而迅速地拾取大量地震事件的P波初至, 将深度学习方法引入微地震P波初至到时拾取研究中, 对卷积神经网络的结构进行改造, 以便适应地震波形数据的特点 P波初至拾取的要求。该算法只需要输入10 s窗口的三分量地震波形数据, 就可以自动地判定P波初至时刻, 无需扫描连续波形, 运算时间远远小于长短窗、模板匹配等传统方法。使用该算法训练汶川地震主震后2008年7—8月7467条人工拾取的余震P波初至到时, 将得到的模型对测试集中 1867条数据的计算结果与人工拾取结果对比, 误差小于0.5 s者占比达到98.9%。在低信噪比条件下, 该方法仍能保持较好的拾取能力。

Using Artificial Intelligence to Pick P-Wave First-Arrival of the Microseisms:Taking the Aftershock Sequence of Wenchuan Earthquake as an Example

In order to accurately and quickly pick up P-wave first-arrival of a large number of seismic events,deep learning method is introduced into the micro seismic P-wave first-arrival picking problem. The structure ofconvolution neural network is adjusted to apply to the characteristics of the seismic waveform data and first-arrivalpicking problem. The algorithm takes a 10s-window three-component seismic waveform data as input instead ofscanning the continuous waveform. So the running time is far less than traditional methods such as STA/LTA andtemplate matching. The algorithm is applied to aftershocks of 2008 Wenchuan earthquake in July and August,using 7467 manual picked first-arrival data as training dataset. Among the 1867 testing data, 98.9% of the P arrivaltimes picked using this algorithm have an error less than 0.5 s compare to the results picked manually. This methodcan still maintain good pick-up capability under the condition of low signal-to-noise ratio.