Earthquake Phase Association Using a Bayesian Gaussian Mixture Model
Earthquake Phase Association Using a Bayesian Gaussian Mixture Model
复制标题
使用贝叶斯高斯混合模型的地震相位关联
DOI：
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
G. Beroza
中科院分区：
文献类型：
--
作者：
Weiqiang Zhu;I. McBrearty;S. Mousavi;W. Ellsworth;G. Beroza
地震震相关联算法将从地震仪网络中提取的地震震相聚合到单个地震事件中，在地震监测和研究中发挥着重要作用。密集的地震台网和改进的拾相方法产生了海量的地震震相数据集，特别是对于在时间和空间上密切发生的震群和余震，这使得震相关联成为一个具有挑战性的问题。我们提出了一种新的关联方法，即高斯混合模型关联(GAMMA)，它将高斯混合模型与地震位置、发震时间和震级估计相结合。在概率框架下，我们将地震震相组合看作一个无监督的聚类问题，其中每一次地震都对应于一组P和S震相，它们的到达时间是双曲的，振幅随距离衰减。我们使用多变量高斯分布来模拟事件的相位选择集合；多变量高斯分布的平均值是由预测的到达时间和来自起因事件的幅度给出的。在极大似然准则下，利用期望最大化算法对每一次地震进行分选，确定震源参数(即震源位置、发震时间和震级)。伽马方法不需要其他算法的典型关联步骤，例如网格搜索或监督训练。合成测试和2019年Ridgecrest地震序列的结果都表明，伽马有效地关联了时间和空间密集地震序列的相位，同时产生了对地震位置和震级的有用估计。
Earthquake phase association algorithms aggregate picked seismic phases from a network of seismometers into individual sesimic events and play an important role in earthquake monitoring and research. Dense seismic networks and improved phase picking methods produce massive seismic phase datasets, particularly for earthquake swarms and aftershocks occurring closely in time and space, making phase association a challenging problem. We present a new association method, the Gaussian Mixture Model Association (GaMMA), that combines the Gaussian mixture model with earthquake location, origin time, and magnitude estimation. We treat earthquake phase association as an unsupervised clustering problem in a probabilistic framework, where each earthquake corresponds to a cluster of P and S phases with a hyperbolic moveout of arrival times and a decay of amplitude with distance. We use the multivariate Gaussian distribution to model the collection of phase picks of an event; and the mean of the multivariate Gaussian distribution is given by the predicted arrival time and amplitude from the causative event. We carry out the pick assignment to each earthquake and determine earthquake source parameters (i.e., earthquake location, origin time, and magnitude) under the maximum likelihood criterion using the Expectation‐Maximization algorithm. The GaMMA method does not require typical association steps of other algorithms, such as grid‐search or supervised training. The results for both synthetic tests and for the 2019 Ridgecrest earthquake sequence show that GaMMA effectively associates phases from a temporally and spatially dense earthquake sequence while producing useful estimates of earthquake location and magnitude.
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