Lithospheric Imaging Through Reverberant Layers: Sediments, Oceans, and Glaciers
Lithospheric Imaging Through Reverberant Layers: Sediments, Oceans, and Glaciers
复制标题
DOI：
10.1029/2022jb026348
复制
发表时间：
2023-05
期刊：
影响因子：
--
通讯作者：
Ziqi Zhang;T. Olugboji
中科院分区：
文献类型：
--
作者：
Ziqi Zhang;T. Olugboji
地球大部分被海洋、沉积物和冰川覆盖。在这种环境中的高分辨率体波成像通常会受到严重的混响，即反射层中捕获的入射散射波场的重复回波，使得岩石圈分层的解释变得困难。在这项研究中，我们提出了一个系统的数据驱动的方法，使用自相关和同态分析，以解决检测和消除混响的孪生问题，而无需混响层的弹性结构的先验知识。我们使用合成实验和数据实例证明，即使在记录地震阵列部署在复杂环境中的情况下，我们的方法也可以有效地识别混响的签名，例如，使用来自（a）位于松辽盆地的陆地台站，（B）阿拉斯加两栖社区地震实验的弧前环境中的海底台站，及（c）在南极洲冰川的冰沉积层上设立一个观测站。混响的消除是通过频域滤波器实现的，该滤波器的参数仅使用地震数据自动调谐。在冰川上，反射沉积层夹在岩石圈和上覆冰层之间，同态分析是更好地检测混响的签名。我们预计，我们的技术将在各种条件下广泛应用于高分辨率体波成像。
The Earth, in large portions, is covered in oceans, sediments, and glaciers. High‐resolution body wave imaging in such environments often suffers from severe reverberations, that is, repeating echoes of the incoming scattered wavefield trapped in the reverberant layer, making interpretation of lithospheric layering difficult. In this study, we propose a systematic data‐driven approach, using autocorrelation and homomorphic analysis, to solve the twin problem of detection and elimination of reverberations without a priori knowledge of the elastic structure of the reverberant layers. We demonstrate, using synthetic experiments and data examples, that our approach can effectively identify the signature of reverberations even in cases where the recording seismic array is deployed in complex settings, for example, using data from (a) a land station sitting on Songliao basin, (b) an ocean bottom station in the fore‐arc setting of the Alaska amphibious community seismic experiment, and (c) a station deployed on ice‐sediment strata in the glaciers of Antarctica. The elimination of the reverberation is implemented by a frequency domain filter whose parameters are automatically tuned using seismic data alone. On glaciers where the reverberating sediment layer is sandwiched between the lithosphere and an overlying ice layer, homomorphic analysis is preferable in detecting the signature of reverberation. We expect that our technique will see wide application for high‐resolution body wave imaging across a wide variety of conditions.