标题： 基于信号的贝叶斯地震监测 显示英文标题

标题： Signal-based Bayesian Seismic Monitoring

摘要： 从噪声传感器中检测微弱地震事件是一项困难的感知任务。 我们将此任务表述为贝叶斯推理，并提出了一种在空间分布的站点网络中地震事件和信号的生成模型。 我们的系统SIGVISA是第一个直接对地震波形进行建模的系统，使其能够包含信号生成过程背后的物理现象的丰富表示。 我们使用小波参数上的高斯过程，根据历史事件预测详细的波形波动，而在没有历史地震活动的区域则平滑降级为简单的参数包络线。 在西部美国的数据上进行评估，我们恢复的事件数量是以前工作的三倍，并将平均定位误差减少了四倍，同时大大提高了对低震级事件的敏感性。 显示英文摘要

摘要： Detecting weak seismic events from noisy sensors is a difficult perceptual task. We formulate this task as Bayesian inference and propose a generative model of seismic events and signals across a network of spatially distributed stations. Our system, SIGVISA, is the first to directly model seismic waveforms, allowing it to incorporate a rich representation of the physics underlying the signal generation process. We use Gaussian processes over wavelet parameters to predict detailed waveform fluctuations based on historical events, while degrading smoothly to simple parametric envelopes in regions with no historical seismicity. Evaluating on data from the western US, we recover three times as many events as previous work, and reduce mean location errors by a factor of four while greatly increasing sensitivity to low-magnitude events.