Analysis and models of pre-injection surface seismic array noise recorded at the Aquistore carbon storage site
Analysis and models of pre-injection surface seismic array noise recorded at the Aquistore carbon storage site
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DOI：
10.1093/gji/ggw203
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发表时间：
2016-08-01
影响因子：
2.8
通讯作者：
Stork, Anna L.
中科院分区：
文献类型：
--
作者：
Birnie, Claire;Chambers, Kit;Stork, Anna L.
噪声是地震数据中的一个持久特征，因此在地震图像中提取更高精度和地下物理解释方面提出了挑战。本文分析了Aquistore碳捕获和封存试点项目永久地震阵列的被动地震数据，以识别、分类和建模地震噪声。我们对来自阵列的三个月被动地震数据子集进行了噪声分析，并提供了确凿的证据，证明噪声场不是白色、平稳或高斯的;这些特征在大多数传统噪声模型中常见但错误地假设。我们介绍了一种新的噪声建模方法，提供了一个显着更准确的表征真实的地震噪声相比，传统的方法，这是量化使用的曼-惠特尼-白色统计检验。该方法基于通过对单个噪声信号进行建模而创建的统计协方差建模方法。单个噪声信号的识别，大致分为平稳、伪平稳和非平稳，提供了建立适当的空间和时间噪声场模型的基础。此外，我们已经开发了一个工作流程，将现实的噪声模型合成地震数据集提供了一个机会，测试和分析现实的噪声条件下的检测和成像算法。
Noise is a persistent feature in seismic data and so poses challenges in extracting increased accuracy in seismic images and physical interpretation of the subsurface. In this paper, we analyse passive seismic data from the Aquistore carbon capture and storage pilot project permanent seismic array to characterise, classify and model seismic noise. We perform noise analysis for a three-month subset of passive seismic data from the array and provide conclusive evidence that the noise field is not white, stationary, or Gaussian; characteristics commonly yet erroneously assumed in most conventional noise models. We introduce a novel noise modelling method that provides a significantly more accurate characterisation of real seismic noise compared to conventional methods, which is quantified using the Mann-Whitney-White statistical test. This method is based on a statistical covariance modelling approach created through the modelling of individual noise signals. The identification of individual noise signals, broadly classified as stationary, pseudo-stationary and non-stationary, provides a basis on which to build an appropriate spatial and temporal noise field model. Furthermore, we have developed a workflow to incorporate realistic noise models within synthetic seismic data sets providing an opportunity to test and analyse detection and imaging algorithms under realistic noise conditions.