Microseismic event detection in large heterogeneous velocity models using Bayesian multimodal nested sampling

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Saptarshi Das, M. Hobson, F. Feroz, Xi Chen, S. Phadke, J. Goudswaard, D. Hohl
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引用次数: 3

Abstract

Abstract In passive seismic and microseismic monitoring, identifying and characterizing events in a strong noisy background is a challenging task. Most of the established methods for geophysical inversion are likely to yield many false event detections. The most advanced of these schemes require thousands of computationally demanding forward elastic-wave propagation simulations. Here we train and use an ensemble of Gaussian process surrogate meta-models, or proxy emulators, to accelerate the generation of accurate template seismograms from random microseismic event locations. In the presence of multiple microseismic events occurring at different spatial locations with arbitrary amplitude and origin time, and in the presence of noise, an inference algorithm needs to navigate an objective function or likelihood landscape of highly complex shape, perhaps with multiple modes and narrow curving degeneracies. This is a challenging computational task even for state-of-the-art Bayesian sampling algorithms. In this paper, we propose a novel method for detecting multiple microseismic events in a strong noise background using Bayesian inference, in particular, the Multimodal Nested Sampling (MultiNest) algorithm. The method not only provides the posterior samples for the 5D spatio-temporal-amplitude inference for the real microseismic events, by inverting the seismic traces in multiple surface receivers, but also computes the Bayesian evidence or the marginal likelihood that permits hypothesis testing for discriminating true vs. false event detection. Impact Statement Bayesian evidence-based reasoning is helpful in identifying real microseismic events as opposed to the environmental noise. The geophysical challenge here is the high-computational burden for simulating noiseless template seismic responses for explosive type events and combining them together having different amplitudes and origin times. We use Gaussian process based surrogate models as proxy for multi-receiver seismic responses to be used for the Bayesian detection of microseismic events in a heterogeneous marine velocity model. We used the MultiNest sampler for Bayesian inference since in the presence of multiple events, the likelihood surface becomes multimodal. From the sampled points, a density-based clustering algorithm is employed to filter out each microseismic event for improved mode separation and obtain the posterior distribution of each event in a joint 5D space of amplitude, origin time, and three spatial co-ordinates. Choice of the resolution parameter in MultiNest sampler (Nlive) is also crucial to obtain accurate inference within reasonable computational time and resources and have been compared for two different scenarios (Nlive = 300, 500). A data analytics pipeline is proposed in this paper, starting from GPU based simulation of microseismic events to training a surrogate model for cheaper likelihood calculation, followed by 5D posterior inference for simultaneous detection of multiple events.
基于贝叶斯多模态嵌套采样的大非均匀速度模型微震事件检测
在被动地震和微地震监测中,在强噪声背景下识别和表征地震事件是一项具有挑战性的任务。大多数已建立的地球物理反演方法都可能产生许多假事件检测。这些方案中最先进的需要成千上万的计算要求高的前向弹性波传播模拟。在这里,我们训练并使用高斯过程代理元模型或代理模拟器的集合,以加速从随机微地震事件位置生成准确的模板地震图。当多个微地震事件发生在不同的空间位置,具有任意振幅和起始时间,并且存在噪声时,推理算法需要导航高度复杂形状的目标函数或似然景观,可能具有多模态和窄曲线简并。即使对于最先进的贝叶斯抽样算法来说,这也是一项具有挑战性的计算任务。在本文中,我们提出了一种利用贝叶斯推理在强噪声背景下检测多个微地震事件的新方法,特别是多模态嵌套采样(MultiNest)算法。该方法不仅为真实微地震事件的5D时空振幅推断提供后验样本,通过反演多个地面接收器中的地震轨迹,而且还计算贝叶斯证据或边际似然,允许假设检验来区分真假事件检测。贝叶斯循证推理有助于识别真实的微地震事件,而不是环境噪声。这里的地球物理挑战是模拟爆炸型事件的无噪声模板地震反应并将它们组合在一起具有不同的振幅和起源时间,计算量很大。我们使用基于高斯过程的代理模型作为多接收器地震响应的代理,用于非均匀海洋速度模型中微地震事件的贝叶斯检测。我们使用multitest采样器进行贝叶斯推理,因为在存在多个事件的情况下,似然面变得多模态。从采样点中,采用基于密度的聚类算法对各微震事件进行过滤,提高模态分离,得到各微震事件在振幅、起始时间和三个空间坐标联合5D空间中的后验分布。在MultiNest sampler (Nlive)中,分辨率参数的选择对于在合理的计算时间和资源内获得准确的推断也是至关重要的,并且已经对两种不同的场景(Nlive = 300,500)进行了比较。本文提出了一种数据分析管道,从基于GPU的微地震事件模拟开始,到训练代理模型以进行更便宜的似然计算,然后进行5D后验推理以同时检测多个事件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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