Can High Resolution Seismic Data Correct for the Near Surface Static's Issue in Saudi Arabia?

Author(s):  
T. A. Alkhalifah ◽  
M. AlMalki
Geophysics ◽  
2020 ◽  
pp. 1-57
Author(s):  
Daniele Colombo ◽  
Ernesto Sandoval ◽  
Diego Rovetta ◽  
Apostolos Kontakis

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.


2008 ◽  
Author(s):  
Tariq Alkhalifa ◽  
Ramzy AlZayer ◽  
and Majed AlMalki

2017 ◽  
Vol 5 (4) ◽  
pp. SR23-SR33 ◽  
Author(s):  
Xin Cheng ◽  
Kun Jiao ◽  
Dong Sun ◽  
Zhen Xu ◽  
Denes Vigh ◽  
...  

Over the past decade, acoustic full-waveform inversion (FWI) has become one of the standard methods in the industry to construct high-resolution velocity fields from the seismic data acquired. While most of the successful applications are for marine acquisition data with rich low-frequency diving or postcritical waves at large offsets, the application of acoustic FWI on land data remains a challenging topic. Land acoustic FWI application faces many severe difficulties, such as the presence of strong elastic effects, large near-surface velocity contrast, and heterogeneous, topography variations, etc. In addition, it is well-known that low-frequency transmitted seismic energy is crucial for the success of FWI to overcome sensitivity to starting velocity fields; unfortunately, those are the parts of the data that suffer the most from a low signal-to-noise ratio (S/N) in land acquisition. We have developed an acoustic FWI application on a land data set from North Kuwait, and demonstrated our solutions to mitigate some of the challenges posed by land data. More specifically, we have developed a semblance-based high-resolution Radon (HR-Radon) inversion approach to enhance the S/N of the low-frequency part of the FWI input data and to ultimately improve the convergence of the land FWI workflow. To mitigate the impact of elastic effects, we included only the diving and postcritical early arrivals in the waveform inversion. Our results show that, with the aid of HR-Radon preconditioning and a carefully designed workflow, acoustic FWI has the ability to derive a reliable high-resolution near-surface model that could not be otherwise recovered through traditional tomographic methods.


Author(s):  
C. Barajas-Olalde ◽  
A. A. Al-Shuhail ◽  
M. Aqel ◽  
A. Al-Shuhail ◽  
A. Nova ◽  
...  

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