A deep Gaussian process model for seismicity background rates
A deep Gaussian process model for seismicity background rates
复制标题
地震活动背景率的深度高斯过程模型
DOI：
10.1093/gji/ggad074
复制
发表时间：
2023
影响因子：
2.8
通讯作者：
Ross, Zachary E.
中科院分区：
文献类型：
--
作者：
Muir, Jack B.;Ross, Zachary E.
地震活动的时空特征使我们对地壳的应力状态、演化和断裂结构有了深刻的了解。考虑到与物理模型相关的认知不确定性，基于自激点过程的经验模型继续提供分析地震活动的重要工具。特别是，流行型余震序列(ETAS)模型可作为研究地震活动目录的参考模型。传统的ETAS模型使用简单的参数定义来表示与触发无关的地震活动的背景速率。这降低了基本ETAS模型在模拟地震群中出现的时间上复杂的地震活动模式方面的有效性，这些地震群以非地震构造过程(如流体注入而不是余震触发)为主。为了更好地捕捉随时间变化的地震活动速率，我们引入了背景速率的深高斯过程(GP)公式作为ETAS的扩展。GPS是一种具有协方差结构的函数空间的稳健非参数模型。通过用另一个GP限制GP的长度-尺度结构，我们得到了一个深度GP：一个概率的、分层的模型，它自动调整其结构以匹配数据约束。我们展示了如何通过利用Gibbs中的Metropolis格式、利用ETAS的分支过程公式和Matérn GP的随机偏微分方程(SPDE)逼近来有效地抽样Depth-GP-ETAS模型。我们用合成的例子说明了我们的方法，并表明Depth-GP-ETAS模式成功地捕捉到了地震活动强迫速率背景下的多尺度时间行为。然后，我们将结果应用于两个实际数据目录：加州Ridgecrest，2019年7月5Mw7.1事件目录，表明Deep-GP-ETAS可以成功地刻画经典余震序列；以及2016-2019年加州Cahuilla地震群，它显示了与流体注入驱动的初始序列相一致的两个截然不同的无震强迫阶段，流体沿着物理屏障被阻止，并在该序列中最大的Mw4.4事件之后释放。
The spatio-temporal properties of seismicity give us incisive insight into the stress state evolution and fault structures of the crust. Empirical models based on self-exciting point processes continue to provide an important tool for analysing seismicity, given the epistemic uncertainty associated with physical models. In particular, the epidemic-type aftershock sequence (ETAS) model acts as a reference model for studying seismicity catalogues. The traditional ETAS model uses simple parametric definitions for the background rate of triggering-independent seismicity. This reduces the effectiveness of the basic ETAS model in modelling the temporally complex seismicity patterns seen in seismic swarms that are dominated by aseismic tectonic processes such as fluid injection rather than aftershock triggering. In order to robustly capture time-varying seismicity rates, we introduce a deep Gaussian process (GP) formulation for the background rate as an extension to ETAS. GPs are a robust non-parametric model for function spaces with covariance structure. By conditioning the length-scale structure of a GP with another GP, we have a deep-GP: a probabilistic, hierarchical model that automatically tunes its structure to match data constraints. We show how the deep-GP-ETAS model can be efficiently sampled by making use of a Metropolis-within-Gibbs scheme, taking advantage of the branching process formulation of ETAS and a stochastic partial differential equation (SPDE) approximation for Matérn GPs. We illustrate our method using synthetic examples, and show that the deep-GP-ETAS model successfully captures multiscale temporal behaviour in the background forcing rate of seismicity. We then apply the results to two real-data catalogues: the Ridgecrest, CA 2019 July 5Mw7.1 event catalogue, showing that deep-GP-ETAS can successfully characterize a classical aftershock sequence; and the 2016–2019 Cahuilla, CA earthquake swarm, which shows two distinct phases of aseismic forcing concordant with a fluid injection-driven initial sequence, arrest of the fluid along a physical barrier and release following the largestMw4.4 event of the sequence.