地球物理学进展 ›› 2019, Vol. 34 ›› Issue (5): 1818-1825.doi: 10.6038/pg2019CC0223

• 固体地球物理及空间物理学(大气、行星、地球动力学、重磁电及地震学、地热学) • 上一篇    下一篇

重震联合反演方法及其应用进展

张佩1,2,宋晓东3,4,熊奥林3   

  1. 1. 中国地震局地球物理研究所, 北京 100081
    2. 中国地震局第二监测中心, 西安 710054
    3. 武汉大学测绘学院, 武汉 430079
  • 收稿日期:2018-11-04 修回日期:2019-06-21 出版日期:2019-10-28 发布日期:2019-10-28
  • 作者简介:张佩,男,1989年生,博士研究生,主要从事地震学成像方法与地球内部结构方面的研究.(E-mail: zhpec15@163.com)
  • 基金资助:
    国家自然科学基金面上项目(41774056);国家重点研发计划“基于断层带行为监测的地球物理成像与地震物理过程研究”(2018YFC1503405)

Advancement of joint inversion of gravity and seismic data and its application

ZHANG Pei1,2,SONG Xiao-dong3,4,XIONG Ao-lin3   

  1. 1. Institute of Geophysics, China Earthquake Administration, Beijing 100081, China
    2. Second Monitoring and Application Center, China Earthquake Administration, Xi’an 710054, China;
    3. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
  • Received:2018-11-04 Revised:2019-06-21 Online:2019-10-28 Published:2019-10-28

摘要:

地球物理反演是探索地下结构的最佳途径之一.地震波可以穿透到地球深部进行直接采样,是探测地球深部的主要方法.重力是结构体密度分布与地表观测点之间距离的体积积分效应,重力异常随着源深度的增加衰减很快,其对浅部结构的灵敏度明显优于地震数据.地震和重力联合反演能够相互补充和约束,提高空间分辨率,使反演结果更加稳定可靠.本文首先介绍了联系地震和重力数据的速度-密度经验关系,随后分别介绍了重震联合反演的3种常用方法—顺序反演、同步反演和交叉梯度反演,简要阐述了各种方法在国内外的应用情况.分析认为顺序反演将两类数据分开独立进行计算,原理简单,易于操作实现.但是该方法依赖于先验模型和速度-密度经验关系,可能存在分辨率较低区域(如模型边界)的误差放大效应.同步反演采用将地震和重力数据放在同一方程组中同时反演的策略,减弱了单一数据先验模型对结果的影响,但两种数据的同时运用势必引入数据权重分配问题.交叉梯度寻求不同物理参数模型在结构上的相似性,对潜在的岩石物性关系做了最少的假设,一定程度上降低了反演的非唯一性,但强制性地匹配模型的结构不一定完全符合地下介质的物性分布.因此使用交叉梯度方法反演时应注意模型的推导需要遵循客观标准,以控制模型的结构相似性和数据拟合度.最后指出重震联合反演中的速度-密度经验关系和数据的权重分配仍是值得探究的问题.

关键词: 联合反演, 重力, 地震, 交叉梯度, 速度-密度经验关系

Abstract:

Geophysical inversion is one of the best ways to explore underground structures. As seismic waves can penetrate deep down to have local sampling, Seismology has been a primary method for human to probe the deep interior of the earth. While gravity is volumetric integral effect of density and distance, which is generally more sensitive to shallower structure than seismic data. Complemented and constrained with each other, joint inversion of seismic and gravity data can improve the spatial resolution, which make the inversion result more stable and reliable. This paper, firstly, introduces the velocity-density empirical relationship between seismic and gravimetric data. Then three commonly used methods for joint inversion are described separately, which are sequential inversion, synchronous inversion and cross-gradient inversion. A brief introduction to the application of the joint inversion methods worldwide is conducted subsequently. It is considered that sequential inversion can calculate different kinds of data separately and independently, which is simple in principle and easy to implement. However, this method depends on the prior model and the empirical relationship between velocity and density, and there may be an error amplification effect in lower resolution region (such as model boundaries). Synchronous inversion adopts the strategy of simultaneous inversion of seismic and gravity data in the same equation system, which reduces the impact of the single data prior model on the results, but the simultaneous application of two data is bound to introduce the problem of data weight allocation. While the cross-gradient method seeks structural similarity of different physical parameter models, and makes the least hypothesis about the potential rock physical relationship, which reduces the non-uniqueness of the inversion algorithm to some extent. However, the structure of the mandatory matching model does not necessarily conform to the physical parameters distribution of underground media. Therefore, when using cross-gradient joint inversion, it should be noted that the derivation of the model needs to follow objective criteria to control their structural similarity and data fit. Finally, as the empirical relationship between velocity and density and the weight distribution of the data have a significant impact on the inversion, more attention should be paid to them.

Key words: Joint inversion, Gravity, Seismology, Cross-gradient, Velocity-density empirical relationship

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