High-resolution seismic tomography of Long Beach, CA using machine learning
High-resolution seismic tomography of Long Beach, CA using machine learning
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使用机器学习对加利福尼亚州长滩进行高分辨率地震层析成像
作者：
Michael J. Bianco;P. Gerstoft;K. Olsen;F. Lin
我们使用基于机器学习的层析成像方法，从拥有5 204个地震检波器(约1350 百万走时)的大N阵列上记录的地震噪声中获得加利福尼亚州长滩的高分辨率地下地球物理结构。这种方法被称为局部稀疏走时层析成像(LST)，它使用无监督机器学习来利用通过对大阵列进行环境噪声处理而获得的密集采样。密集抽样允许LST方法直接从数据中学习局部或小规模地球物理特征的词典。这些特征是与给定层析成像场景最相关的地球结构的小尺度图案。利用LST得到了长滩高分辨率1 Hz瑞雷波相速度图。在地图上显示的地球物理特征中，重要的Silverado含水层与以前的面波层析成像研究相比被很好地隔离了。我们的结果表明，LST在旅行时间层析成像研究中获得详细的地球物理结构是有希望的。
We use a machine learning-based tomography method to obtain high-resolution subsurface geophysical structure in Long Beach, CA, from seismic noise recorded on a “large-N” array with 5204 geophones (~13.5 million travel times). This method, called locally sparse travel time tomography (LST) uses unsupervised machine learning to exploit the dense sampling obtained by ambient noise processing on large arrays. Dense sampling permits the LST method to learn directly from the data a dictionary of local, or small-scale, geophysical features. The features are the small scale patterns of Earth structure most relevant to the given tomographic imaging scenario. Using LST, we obtain a high-resolution 1 Hz Rayleigh wave phase speed map of Long Beach. Among the geophysical features shown in the map, the important Silverado aquifer is well isolated relative to previous surface wave tomography studies. Our results show promise for LST in obtaining detailed geophysical structure in travel time tomography studies.