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Near-surface model building is critical in exploration geophysics studies. Among various methods, first-arrival traveltime (FAT) tomography is among the most popular solutions, especially in land surveys over complex geologic structures in highly noisy environments. Though this is a relatively mature method, there are still many practical challenges that have motivated my study. The first part of my study focuses at finding a way to significantly improve the computational efficiency of FAT tomography, which has limited its usage for the increasingly massive data volumes in high density seismic surveys nowadays. Often, hundreds of millions of seismic picks are beyond the limit of standard FAT tomography methods in terms of computation memory and turnaround time, both are critical in practice. I have adopted an adjoint-state solution, which reduces the memory cost regardless of the quantity of input data. I have also devised a highly efficient FAT tomographic inversion scheme by combining the dimensionality reduction and the sparsity-promoting techniques based on a compressive sensing approach. The computation time cost is significantly reduced by taking randomly subsampled data for computation. The model update is regularized and many imaging artefacts induced by random subsampling are mitigated through exploiting its sparsity within learned dictionaries. My new inversion scheme enables to use just a small portion of a dataset to achieve results practically identical to those from standard FAT tomography methods using the full dataset. The second part of my study is an attempt to reconstruct an Q distribution model, in order to compensate for the near-surface loss in the amplitude of seismic data. This is a significant factor for seismic image quality. My approach is based on the impact of path attenuation factor (t*) to the amount of amplitude attenuation on seismic first arrival waveforms. To accurately estimate t*, I use a high-quality tomographic velocity model to guide the identification of the relevant first arrival waveforms. Then each t* is estimated through an adaptive correction method based on linear regression of the logarithmic spectral ratio. Based on t*, my adjoint-state Q tomographic inversion is able to reconstruct Q distribution model for the near-surface.



First arrival, travel tomography, near-surface velocity model, Q, attenuation tomography, sparsity-promoting, Stochastic process


Portions of this document appear in: Xinwei Huang, Zhenbo Guo, Huawei Zhou, Yubo Yue. First arrival Q tomography based on adjoint-state method. Journal of geophysics and engineering, 2020.