Bayesian Deconvolution of Signals Observed on Arrays
提出一种参数化指数调制自回归模型，结合伯努利正态混合随机振幅，对地震阵列信号进行贝叶斯反卷积，相比传统窄带滤波和波束形成方法有潜在优势，并应用于分析哈萨克斯坦阵列接收的中国云南地震数据。
Time series data collected from arrays of seismometers are traditionally used to solve the core problems of detecting and estimating the waveform of a nuclear explosion or earthquake signal that propagates across the array. We consider here a parametric exponentially modulated autoregressive model. The signal is assumed to be convolved with random amplitudes following a Bernoulli normal mixture. It is shown to be potentially superior to the usual combination of narrow band filtering and beam forming. The approach is applied to analyzing series observed from an earthquake from Yunnan Province in China received by a seismic array in Kazakhstan.