An enhancement in seismic measuring instrumentation has been proven to have implications in the quantity of observed earthquakes, since denser networks usually allow recording more events. However, phenomena such as strong earthquakes or even aseismic transients, as slow slip earthquakes, may alter the occurrence of earthquakes. In the field of seismology, it is a standard practice to model background seismicity as a Poisson process. Based on this idea, this work proposes a model that can incorporate the evolving spatial intensity of Poisson processes over time (i.e., we include temporal changes in the background seismicity when modeling). In recent years, novel methodologies have been developed for quantifying the uncertainty in the estimation of the background seismicity in homogeneous cases using Bayesian non-parametric techniques. This work proposes a novel methodology based on graphical Dirichlet processes for incorporating spatial and temporal inhomogeneities in background seismicity. The proposed model in this work is applied to study the seismicity in the southern Mexico, using recorded data from 2000 to 2015.

翻译：地震测量仪器的改进已被证实对观测到的地震数量具有影响，因为更密集的监测网络通常能记录更多事件。然而，诸如强震乃至无震瞬变（如慢滑移地震）等现象可能改变地震的发生模式。在地震学领域，将背景地震活动建模为泊松过程是一种标准做法。基于这一理念，本研究提出一种能够纳入泊松过程随时间演化的空间强度模型（即在建模时考虑了背景地震活动的时间变化）。近年来，已有研究利用贝叶斯非参数技术开发了量化均匀情况下背景地震活动估计不确定性的新方法。本文提出一种基于图狄利克雷过程的新方法，用于处理背景地震活动中的空间与时间非均匀性。该模型应用于研究墨西哥南部地区2000年至2015年记录数据的地震活动特征。