Near‐surface velocity estimation by weighted early‐arrival waveform inversion

2010 ◽  
Author(s):  
Xukai Shen
Keyword(s):  
Waveform Inversion ◽  
Velocity Estimation ◽  
Surface Velocity ◽  
Near Surface ◽  
Early Arrival

Near-surface velocity estimation using source-domain full traveltime inversion and early arrival waveform inversion

Geophysics ◽  
2018 ◽  
Vol 83 (4) ◽  
pp. R335-R344 ◽  
Author(s):  
Lu Liu ◽  
Yan Wu ◽  
Bowen Guo ◽  
Song Han ◽  
Yi Luo
Keyword(s):  
Plane Wave ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Surface Velocity ◽  
Accurate Estimation ◽  
Near Surface ◽  
Traveltime Inversion ◽  
Source Domain ◽  
Early Arrival

Accurate estimation of near-surface velocity is a key step for imaging deeper targets. We have developed a new workflow to invert complex early arrivals in land seismic data for near-surface velocities. This workflow is composed of two methods: source-domain full traveltime inversion (FTI) and early arrival waveform inversion (EWI). Source-domain FTI automatically generates the background velocity that kinematically matches the reconstructed plane-wave sources from early arrivals with true plane-wave sources. This method does not require picking first arrivals for inversion, which is one of the most challenging and labor-intensive steps in ray-based first-arrival traveltime tomography, especially when the subsurface medium contains low-velocity zones that cause shingled multivalue arrivals. Moreover, unlike the conventional Born-based method, source-domain FTI can determine if the initial velocity is slower or faster than the true one according to the gradient sign. In addition, the computational cost is reduced considerably by using the one-way wave equation to extrapolate the plane-wave Green’s function. The source-domain FTI tomogram is then used as the starting model for EWI to obtain the short-wavelength component associated with the velocity model. We tested the workflow on two synthetic and one onshore filed data sets. The results demonstrate that source-domain FTI generates reasonable background velocities for EWI even though the first arrivals are shingled, and that this workflow can produce a high-resolution near-surface velocity model.

Surface-Wave Simulation for the Continuously Moving Seismic Sources

2021 ◽  
Author(s):  
Yuefeng Yan ◽  
Chengyu Sun ◽  
Tengfei Lin ◽  
Jiao Wang ◽  
Jidong Yang ◽  
...  
Keyword(s):  
Rayleigh Waves ◽  
Frequency Dispersion ◽  
Dispersion Curves ◽  
Velocity Estimation ◽  
Surface Velocity ◽  
Seismic Sources ◽  
Near Surface ◽  
Moving Source ◽  
Source Data ◽  
Doppler Effects

Abstract In exploration and earthquake seismology, most sources used in subsurface structure imaging and rock property estimation are fixed in certain positions. Continuously moving seismic sources, such as vehicles and the metro, are one kind of important passive sources in ambient noise research. Commonly, seismic data acquisition and processing for moving sources are based on the assumption of simple point passive sources, and the dispersion curve inversion is applied to constrain near-surface velocity. This workflow neglects the Doppler effects. Considering the continuously moving properties of the sources, we first derive the analytical solution for the Rayleigh waves excited by heavy vehicles and then analyze their Doppler effects and dispersion curves. We observe that the moving source data have the Doppler effect when compared with the changes in the frequency of the source intensity, but this effect does not affect the frequency dispersion of Rayleigh waves. The dispersion curves computed for moving source records are consistent with the analytical dispersion solutions, which provide a theoretical foundation for velocity estimation using moving source data.

Full waveform inversion: early start of model building process to resolve the complex near surface model in the Exmouth sub-basin, North West Shelf Australia

2018 ◽  
Vol 58 (2) ◽  
pp. 884
Author(s):  
Lianping Zhang ◽  
Haryo Trihutomo ◽  
Yuelian Gong ◽  
Bee Jik Lim ◽  
Alexander Karvelas
Keyword(s):  
Model Building ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Surface Velocity ◽  
Image Point ◽  
Surface Model ◽  
Full Waveform Inversion ◽  
Near Surface ◽  
North West ◽  
Full Waveform

The Schlumberger Multiclient Exmouth 3D survey was acquired over the Exmouth sub-basin, North West Shelf Australia and covers 12 600 km2. One of the primary objectives of this survey was to produce a wide coverage of high quality imaging with advanced processing technology within an agreed turnaround time. The complexity of the overburden was one of the imaging challenges that impacted the structuration and image quality at the reservoir level. Unlike traditional full-waveform inversion (FWI) workflow, here, FWI was introduced early in the workflow in parallel with acquisition and preprocessing to produce a reliable near surface velocity model from a smooth starting model. FWI derived an accurate and detailed near surface model, which subsequently benefitted the common image point (CIP) tomography model updates through to the deeper intervals. The objective was to complete the FWI model update for the overburden concurrently with the demultiple stages hence reflection time CIP tomography could start with a reasonably good velocity model upon completion of the demultiple process.

Near-surface full-waveform inversion in a transmission surface-consistent scheme

Geophysics ◽  
2020 ◽  
pp. 1-57
Author(s):  
Daniele Colombo ◽  
Ernesto Sandoval ◽  
Diego Rovetta ◽  
Apostolos Kontakis
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Surface Velocity ◽  
Full Waveform Inversion ◽  
Velocity Mapping ◽  
Signal To Noise ◽  
Near Surface ◽  
Velocity Modeling ◽  
Full Waveform

Land seismic velocity modeling is a difficult task largely related to the description of the near surface complexities. Full waveform inversion is the method of choice for achieving high-resolution velocity mapping but its application to land seismic data faces difficulties related to complex physics, unknown and spatially varying source signatures, and low signal-to-noise ratio in the data. Large parameter variations occur in the near surface at various scales causing severe kinematic and dynamic distortions of the recorded wavefield. Some of the parameters can be incorporated in the inversion model while others, due to sub-resolution dimensions or unmodeled physics, need to be corrected through data preconditioning; a topic not well described for land data full waveform inversion applications. We have developed novel algorithms and workflows for surface-consistent data preconditioning utilizing the transmitted portion of the wavefield, signal-to-noise enhancement by generation of CMP-based virtual super shot gathers, and robust 1.5D Laplace-Fourier full waveform inversion. Our surface-consistent scheme solves residual kinematic corrections and amplitude anomalies via scalar compensation or deconvolution of the near surface response. Signal-to-noise enhancement is obtained through the statistical evaluation of volumetric prestack responses at the CMP position, or virtual super (shot) gathers. These are inverted via a novel 1.5D acoustic Laplace-Fourier full waveform inversion scheme using the Helmholtz wave equation and Hankel domain forward modeling. Inversion is performed with nonlinear conjugate gradients. The method is applied to a complex structure-controlled wadi area exhibiting faults, dissolution, collapse, and subsidence where the high resolution FWI velocity modeling helps clarifying the geological interpretation. The developed algorithms and automated workflows provide an effective solution for massive full waveform inversion of land seismic data that can be embedded in typical near surface velocity analysis procedures.

Transmission-based surface-consistent framework for residual statics, deconvolution, and FWI: A new paradigm for near-surface analysis

2020 ◽  
Vol 39 (6) ◽  
pp. 382-390
Author(s):  
Daniele Colombo ◽  
Diego Rovetta ◽  
Ernesto Sandoval ◽  
Apostolos Kontakis
Keyword(s):  
Surface Analysis ◽  
Reservoir Characterization ◽  
Waveform Inversion ◽  
Velocity Estimation ◽  
Time Shift ◽  
New Paradigm ◽  
Near Surface ◽  
Full Waveform ◽  
Minimal Data ◽  
Refracted Waves

We developed a novel surface-consistent processing framework that applies to transmitted wavefields (e.g., diving waves/refracted waves). Automatic velocity estimation from traveltime and full-waveform inversion, residual time shift corrections, and near-surface amplitude deconvolution are performed on the raw seismic gathers with minimal data preconditioning. In this implementation of the transmission-based surface-consistent analysis, we introduce a complete prestack and preprocessing workflow to fully analyze the near surface and enable highly specialized processes for what concerns land data imaging and reservoir characterization. Billions of traveltimes and traces can be analyzed automatically, providing enormous benefits in the execution time and in the accuracy and robustness of obtained results. The new paradigm for near-surface analysis opens the door to a better utilization of land seismic data for reservoir imaging and characterization.

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.

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