Automatic slowness vector measurements of seismic arrivals with uncertainty estimates using bootstrap sampling, array methods and unsupervised learning
Automatic slowness vector measurements of seismic arrivals with uncertainty estimates using bootstrap sampling, array methods and unsupervised learning
复制标题
使用引导采样、阵列方法和无监督学习对地震波峰进行自动慢度矢量测量，并进行不确定性估计
DOI：
10.1093/gji/ggab196
复制
发表时间：
2021
影响因子：
2.8
通讯作者：
Rost, S
中科院分区：
文献类型：
--
作者：
Ward, J;Thorne, M;Nowacki, A;Rost, S
使用阵列技术的水平慢度矢量测量已被用于分析从下地幔不均匀到气象事件位置的许多地球现象。虽然慢度向量分析提供了研究地球大部分地区所必需的观测，但它受限于对观测的必要的和主观的目视检查。此外，由于阵列处理的局限性，如阵列几何形状、局部结构、噪声及其对慢度矢量测量的影响，确定这些不确定因素是具有挑战性的。为了解决这些问题，我们提出了一种自动识别地震波到达并测量其具有不确定界的慢度矢量属性的方法。我们通过自举采样波形来实现这一点，因此还创建了随机子阵列，然后使用线性波束成形来测量在一定范围的慢度向量下的相干功率。对于每个Bootstrap样本，我们将来自每个功率分布的topN个峰值作为可能到达的慢度向量。收集所有Bootstrap样本的慢度向量，并使用基于密度的噪声应用空间聚类算法DBSCAN(Density-Based Space Cluping of Applications With Noise)将到达识别为慢度向量簇。每个簇中的慢度向量的平均值给出该到达的慢度向量测量，并且每个簇中的慢度向量的分布给出不确定性估计。我们使用2489个SKS和SKKS观测数据集在0.1到1 GHz的频带范围内调整了DBSCAN的参数。然后，我们在比调谐数据集更高的频率(0.5-2.0赫兹)上提供例子，识别PKP前兆，并通过识别面波中的多路径(0.04-0.06赫兹)来识别更低的频率。当我们使用线性波束形成过程时，该方法可以用任何波束形成过程来实现，例如互相关波束形成或相位加权叠加。这种方法允许在不对数据进行目视检查的情况下分析更大的数据集。可以在体波或面波中自动识别多路径、反射或散射等现象，并在不确定的情况下分析它们的特性。
Horizontal slowness vector measurements using array techniques have been used to analyse many Earth phenomena from lower mantle heterogeneity to meteorological event location. While providing observations essential for studying much of the Earth, slowness vector analysis is limited by the necessary and subjective visual inspection of observations. Furthermore, it is challenging to determine the uncertainties caused by limitations of array processing such as array geometry, local structure, noise and their effect on slowness vector measurements. To address these issues, we present a method to automatically identify seismic arrivals and measure their slowness vector properties with uncertainty bounds. We do this by bootstrap sampling waveforms, therefore also creating random sub arrays, then use linear beamforming to measure the coherent power at a range of slowness vectors. For each bootstrap sample, we take the topNpeaks from each power distribution as the slowness vectors of possible arrivals. The slowness vectors of all bootstrap samples are gathered and the clustering algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is used to identify arrivals as clusters of slowness vectors. The mean of slowness vectors in each cluster gives the slowness vector measurement for that arrival and the distribution of slowness vectors in each cluster gives the uncertainty estimate. We tuned the parameters of DBSCAN using a data set of 2489 SKS and SKKS observations at a range of frequency bands from 0.1 to 1 Hz. We then present examples at higher frequencies (0.5–2.0 Hz) than the tuning data set, identifying PKP precursors, and lower frequency by identifying multipathing in surface waves (0.04–0.06 Hz). While we use a linear beamforming process, this method can be implemented with any beamforming process such as cross correlation beamforming or phase weighted stacking. This method allows for much larger data sets to be analysed without visual inspection of data. Phenomena such as multipathing, reflections or scattering can be identified automatically in body or surface waves and their properties analysed with uncertainties.
登录
查看更多内容
挪威南部的上地幔结构，来自呈现多路径的瑞利波数据的波束形成
影响因子：
2.8
作者：
V. Maupin
通讯作者：
V. Maupin
确定错位向量以评估 GSETT-3 主站的偏差
影响因子：
1.6
作者：
K. Koch;Urs Kradolfer
通讯作者：
Urs Kradolfer
南加州波束成形短时环境噪声的瑞利相速度
DOI：
--
发表时间：
2017
期刊：
影响因子：
--
作者：
P. Roux;Y. Ben‐Zion
通讯作者：
Y. Ben‐Zion
亚洲北部的最下地幔——下地幔各向异性的证据
影响因子：
2.8
作者：
Ch. Thomas;J. Kendall
通讯作者：
J. Kendall
影响因子：
5.3
作者：
Cottaar, Sanne;Romanowicz, Barbara
通讯作者：
Romanowicz, Barbara