Ambient noise surface wave tomography to determine the shallow shear velocity structure at Valhall: depth inversion with a Neighbourhood Algorithm
Ambient noise surface wave tomography to determine the shallow shear velocity structure at Valhall: depth inversion with a Neighbourhood Algorithm
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DOI：
10.1093/gji/ggu217
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发表时间：
2014-09-01
影响因子：
2.8
通讯作者：
Roux, P.
中科院分区：
文献类型：
--
作者：
Mordret, A.;Landes, M.;Roux, P.
本研究提出了一个深度反演Scholte波群和相速度图获得的互相关6.5小时的噪音数据从瓦尔霍尔寿命的现场地震网络。从2320个可用的传感器中计算出超过260万个垂直-垂直分量互相关，将每个传感器变成发射Scholte波的虚拟源。我们使用传统的直射线面波层析成像计算群速度图。已使用Eikonal层析成像方法计算的相速度图。这些地图的深度反演是用邻域算法完成的。为了减少自由参数反演的数量，地质先验信息被用来提出一个幂律1-D速度剖面参数化扩展高斯高速层在需要的地方。这些参数化使我们能够创建Valhall地下前600米的高分辨率三维S波模型，并精确定位地质结构的深度。这些结果将对剪切波静校正和石油开采引起的海底沉降监测具有重要意义。三维模型也可以是用于全波形反演的起始模型的良好候选者。
This study presents a depth inversion of Scholte wave group and phase velocity maps obtained from cross-correlation of 6.5 hr of noise data from the Valhall Life of Field Seismic network. More than 2 600 000 vertical-vertical component cross-correlations are computed from the 2320 available sensors, turning each sensor into a virtual source emitting Scholte waves. We used a traditional straight-ray surface wave tomography to compute the group velocity map. The phase velocity maps have been computed using the Eikonal tomography method. The inversion of these maps in depth are done with the Neighbourhood Algorithm. To reduce the number of free parameters to invert, geological a priori information are used to propose a power-law 1-D velocity profile parametrization extended with a gaussian high-velocity layer where needed. These parametrizations allowed us to create a high-resolution 3-D S-wave model of the first 600 m of the Valhall subsurface and to precise the locations of geological structures at depth. These results would have important implication for shear wave statics and monitoring of seafloor subsidence due to oil extraction. The 3-D model could also be a good candidate for a starting model used in full-waveform inversions.