论文标题
相互差异的错误适合分布式声感应的功能，结合了来自被动和主动源的数据
Reciprocity-gap misfit functional for Distributed Acoustic Sensing, combining data from passive and active sources
论文作者
论文摘要
通过分布式的声感应数据对弹性介质中地下地球特性的定量成像进行。设计了一个基于互惠差的新型不合适的功能，设计了位移和应变的互相关，这些产品将观察结果与模拟相关联。与其他不合适的功能相比，该功能只需几乎需要关于令人兴奋的来源的A-Priori信息。特别是，不合适的标准可以使用区域地震中的数据（也可以包括远程宣传事件），然后进行勘探数据以执行多分辨率重建。来自区域地震的数据包含勘探含量中缺少的低频含量，即使很少有来源，也可以恢复长空间波长。这些数据用于从更高频率探索数据中为后续重建建立先前的模型。这给出了弹性全面互惠波形反演方法，我们通过用于弹性各向同性重建的试验实验证明了它的性能。
Quantitative imaging of sub-surface Earth's properties in elastic media is performed from Distributed Acoustic Sensing data. A new misfit functional based upon the reciprocity-gap is designed, taking cross-correlations of displacement and strain, and these products further associate an observation with a simulation. In comparison with other misfit functionals, this one has the advantage to only require little a-priori information on the exciting sources. In particular, the misfit criterion enables the use of data from regional earthquakes (teleseismic events can be included as well), followed by exploration data to perform a multi-resolution reconstruction. The data from regional earthquakes contain the low-frequency content which is missing in the exploration ones, allowing for the recovery of the long spatial wavelength, even with very few sources. These data are used to build prior models for the subsequent reconstruction from the higher-frequency exploration data. This gives the elastic Full Reciprocity-gap Waveform Inversion method, and we demonstrate its performance with a pilot experiment for elastic isotropic reconstruction.
