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Passive Surface-Wave Waveform Inversion for Source-Velocity Joint Imaging
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AI
DOI：10.1007/s10712-022-09691-7.png)
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摘要
En 中文
Reliable dispersion measurement between two seismic stations is an essential basis of surface wave imaging. Noise source directivity has become an inescapable obstacle and a main concern for passive seismic survey: It basically breaks the principle of Green's function retrieval in travel-time tomography; moreover, the azimuthal effect of heterogeneous ambient noise sources will inherently cause different levels of early arrival on cross-correlation functions, and the apparent velocity of surface waves can be overestimated by either multichannel slant stackings or interstation frequency-time analysis. Waveforms intrinsically contain the features of travel-time, energy and asymmetry in cross-correlation functions, and in return, they can be mapped into the causative noise sources and medium structures. Based on the theoretical framework of full waveform ambient noise inversion, we proposed a method to jointly invert noise source distributions and the corresponding unbiased surface wave velocities. The coupled dependencies of source distributions and path velocities in waveform misfit function show necessity of source-structure joint inversion. The decoupling strategy of partial derivatives is approved by the synthetic tests. Field experiments in the Hangzhou urban area further reveal the practicability of the theory. The inverted noise source models are comparable with the in situ noise distributions in urban environment, and the delineated surface wave velocities have been verified by local borehole datasets. Finally, we concluded that the developed waveform joint imaging algorithm can well relieve the dilemma of source induced velocity uncertainties.
Keyword:
Rayleigh wave
Cross-correlation function
Waveform inversion
期刊
IF:
7.1
论文数:
1.2K
被引数:
5.1K
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