Joint seismic traveltime and waveform inversion for near surface imaging

2014 ◽  
Author(s):  
Jie Zhang* ◽  
Jing Chen
Keyword(s):  
Waveform Inversion ◽  
Surface Imaging ◽  
Near Surface

Introduction to this special section: Near-surface imaging and modeling

2020 ◽  
Vol 39 (5) ◽  
pp. 310-310
Author(s):  
Steve Sloan ◽  
Dan Feigenbaum
Keyword(s):  
Open Source Software ◽  
Special Section ◽  
Waveform Inversion ◽  
Radar Data ◽  
Surface Velocity ◽  
Surface Imaging ◽  
Near Surface ◽  
Velocity Models ◽  
Full Waveform ◽  
Ground Penetrating

This special section on near-surface imaging and modeling was intended originally to focus on improving deeper imaging for exploration purposes through more accurate representations of the near surface, the highly variable zone that energy must traverse through on the way down and back up again to be recorded at the surface. However, as proposed manuscript topics started coming in, it became clear that this section would cover a wider range, from kilometers down to meters. Papers in this section highlight a range of near-surface-related work that includes applying full-waveform inversion (FWI) to improve near-surface velocity models, identifying potential sinkhole hazards before they collapse, the potential of smartphones as geophysical sensors, and new open-source software for ground-penetrating radar data.

Near‐surface imaging with traveltime and waveform inversion

2008 ◽  
Author(s):  
Priyank Jaiswal ◽  
Colin A. Zelt ◽  
Rahul Dasgupta
Keyword(s):  
Waveform Inversion ◽  
Surface Imaging ◽  
Near Surface

Alternating first-arrival traveltime tomography and waveform inversion for near-surface imaging

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. R245-R257 ◽  
Author(s):  
Mengyao Sun ◽  
Jie Zhang ◽  
Wei Zhang
Keyword(s):  
High Efficiency ◽  
Joint Inversion ◽  
Waveform Inversion ◽  
Quality Data ◽  
Inversion Method ◽  
Multiscale Approach ◽  
Surface Imaging ◽  
Near Surface ◽  
Traveltime Tomography ◽  
First Arrival

Near-surface seismic imaging often plays a significant role in producing quality data processing results for the deep subsurface in land and shallow marine environments. First-arrival traveltime tomography is a common approach for near-surface imaging due to its high efficiency and simplicity. However, the method faces issues of missing hidden layers and resolving the structures with low resolution. On the other hand, waveform inversion should offer better solutions for dealing with these issues, but it may suffer from the cycle-skipping problem. We intend to use the advantages and reduce the disadvantages of the two methods by developing a new strategy of alternately applying traveltime tomography and waveform inversion through iterations. First-arrival traveltime tomography applies a wavefront raytracer and a nonlinear inversion approach. Waveform inversion is a multiscale approach in which a wavelet transform is applied in the data domain to better handle the cycle-skipping problem. By alternating the two inversions rather than performing a joint inversion, we reduce the memory requirements and avoid nonphysical scaling problems between the two approaches. Using one synthetic and two real data examples, we determine that alternating inversions minimize two separate objective functions at the same time and constrain the near-surface structures fairly well compared with the waveform inversion method alone. For the field examples, the new method avoids generating the obvious artifacts and provides results consistent with the geology analysis of those areas.

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