Adaptive mesh seismic tomography based on tetrahedral and Voronoi diagrams: Application to Parkfield, California
Adaptive mesh seismic tomography based on tetrahedral and Voronoi diagrams: Application to Parkfield, California
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DOI：
10.1029/2004jb003186
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发表时间：
2005-04
影响因子：
--
通讯作者：
Haijiang Zhang;C. Thurber
中科院分区：
文献类型：
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作者：
Haijiang Zhang;C. Thurber
[1]我们已经开发了一种基于四面体和沃罗诺图的自适应网格地震断层扫描方法，以自动将反转网格与数据分布匹配。评估了两种不同的不规则网格插值方法，即线性和天然邻居。合成测试表明，在线性和天然邻居插值病例中，自适应网格地震层析成像方法都可以很好地恢复真正的合成模型。使用更准确的差分数据有助于删除由嘈杂的绝对数据产生的模型中的工件。与常规网格相比，适用于加利福尼亚州帕克菲尔德的自适应网状方案在加利福尼亚州的帕克菲尔德的应用导致模型的采样（按衍生物重量总和来测量）更加均匀。仅使用绝对数据或绝对数据和差异数据而产生的跨横截面截面显示在线性和自然邻居插值案例中San Andreas断层处的速度对比度明显。使用天然邻居插值获得的速度模型比线性插值的速度模型更光滑。反转中包括差分数据会产生更多聚类的事件位置，并将速度逆转到断层的西南部，这可能是由绝对数据中的噪声引起的。
[1] We have developed an adaptive mesh seismic tomography method based on tetrahedral and Voronoi diagrams to automatically match the inversion mesh to the data distribution. Two different irregular mesh interpolation methods, linear and natural neighbor, are evaluated. A synthetic test shows that the adaptive mesh seismic tomography method recovers the true synthetic model well in both the linear and natural neighbor interpolation cases. Use of more accurate differential data is helpful in removing artifacts in the model that result from the noisier absolute data. Application of the adaptive mesh scheme to Parkfield, California, results in much more uniform sampling of the model (as measured by the derivative weight sum) on the optimized irregular inversion mesh compared to a regular grid. The across-strike cross sections resulting from using either just the absolute data or both the absolute and differential data show a clear velocity contrast at the San Andreas fault in the linear and natural neighbor interpolation cases. The velocity model obtained using natural neighbor interpolation is smoother than that from linear interpolation. Including the differential data in the inversion yields more clustered event locations and removes a velocity reversal to the southwest of the fault, which is likely caused by noise in the absolute data.