摘要
基于不同地球物理数据对地下结构不同的敏感性和数据分布,联合地球物理反演可以减少反演的非唯一性和提高反演模型的可靠性。在研究中,利用噪声成像得到的地震面波相速度,并结合当地的布格重力异常数据对四川地区的岩石圈速度结构进行了联合成像研究。地震面波频散数据主要对地下岩石的横波速度敏感,而重力数据对地下介质的岩石密度有很好的约束性,为了将两种数据归并统一到同一个联合反演系统中,利用了地震波速度和岩石密度之间的经验关系。基于面波和重力联合反演成像算法,得到了四川地区岩石圈的三维横波速度模型。该模型不仅与地表已观测到的地质特征有较好的吻合性,而且能够比较好地拟合面波数据和重力数据,新的模型对于研究四川地区的地震灾害有着重要的参考意义。
A simultaneous inversion of multiple,consistent,and complementary data sets is an effective way to reduce the non-uniqueness and improve the reliability of geophysical inversion.In this study,we obtain a self-consistent three-dimensional shear velocity-density model beneath Sichuan region from joint inversion of surface-wave velocity and gravity observations.It is well known that surface wave dispersion measurements are sensitive to seismic shear wave velocities,and the gravity measurements supply constraints on rock density variations.To combine surface wave dispersion and gravity observations into a single inversion,we use a relationship between seismic velocity and density constructed through the combination of two empirical relations.One determined by Nafe and Drake,most appropriate for sedimentary rocks,and a linear Birch's law,more applicable to denser rocks(the basement).We apply the method to investigate the structure of the crust and upper mantle beneath Sichuan basin region.The model obtained from joint inversion fits the surface-wave dispersion at least as well as when inverted individually,and the gravity anomaly are much better fit when included in the inversion.The new model has important reference significance for studying the earthquake disaster in Sichuan region.
作者
陈飞
于海英
高丽娜
CHEN Fei;YU Haiying;GAO Lina(Laboratory of Seismology and Physics of Earth's Interior,University of Science and Technology of China,Hefei 230026,China;Shanghai Seismological Bureau Earthquake Monitoring Center,Shanghai 201203,China;College of Geo exploration Science and Technology,Jilin University,Changchun 130026,China)
出处
《物探化探计算技术》
CAS
CSCD
2018年第3期277-289,共13页
Computing Techniques For Geophysical and Geochemical Exploration
基金
上海市科委科研计划项目(14231202600)