3,368 research outputs found
Imaging subsurface scatterers across a dense geophone array in Long Beach using noise crosscorrelation and natural inversion
No full textWe use surface-wave scattering in ambient-noise cross-correlations to image near-surface scatterers and major faults under the populated area of Long Beach, California under a dense geophone array. Images are computed using empirical Green's functions from ambient-noise cross-correlation, and therefore, we eliminate the need for prior velocity models and the costly modeling of surface-waves propagation. The scattered waves are inverted in the least-squares sense to map scatterers in the subsurface. Our results show a number of faults in the area including faults in the Newport-Inglewood fault zone (NIFZ).This publication is based upon work supported by the KAUST Office of Competitive Research Funds (OCRF) under Award No.OCRF-2014-CRG3-62140387/ORS#2300. For computer time, this research used the resources of the Supercomputing Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia. We thank Nodal Seismic and SignalHill for provided access the field data. AlTheyab thanks Saudi Aramco for sponsoring his graduate studies
Imaging near-surface heterogeneities by natural migration of backscattered surface waves
No full textWe present a migration method that does not require a velocity model to migrate backscattered surface waves to their projected locations on the surface. This migration method, denoted as natural migration, uses recorded Green's functions along the surface instead of simulated Green's functions. The key assumptions are that the scattering bodies are within the depth interrogated by the surface waves, and the Green's functions are recorded with dense receiver sampling along the free surface. This natural migration takes into account all orders of multiples, mode conversions and non-linear effects of surface waves in the data. The natural imaging formulae are derived for both active source and ambient-noise data, and computer simulations show that natural migration can effectively image near-surface heterogeneities with typical ambient-noise sources and geophone distributions.This publication is based upon work supported by the KAUST
Office of Competitive Research Funds (OCRF) under award no.
OCRF-2014-CRG3-62140387/ORS#2300. We thank the sponsors for supporting the Consortium of Subsurface Imaging and Fluid Modeling (CSIM). AlTheyab is grateful to Saudi ARAMCO for sponsoring his graduate studies. For computer time, this research used the resources of the Supercomputing Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia
Wavefront picking for 3D tomography and full-waveform inversion
No full textWe have developed an efficient approach for picking firstbreak wavefronts on coarsely sampled time slices of 3D shot gathers. Our objective was to compute a smooth initial velocity model for multiscale full-waveform inversion (FWI). Using interactive software, first-break wavefronts were geometrically modeled on time slices with a minimal number of picks. We picked sparse time slices, performed traveltime tomography, and then compared the predicted traveltimes with the data in-between the picked slices. The picking interval was refined with iterations until the errors in traveltime predictions fell within the limits necessary to avoid cycle skipping in early arrivals FWI. This approach was applied to a 3D ocean-bottom-station data set. Our results indicate that wavefront picking has 28% fewer data slices to pick compared with picking traveltimes in shot gathers. In addition, by using sparse time samples for picking, data storage is reduced by 88%, and therefore allows for a faster visualization and quality control of the picks. Our final traveltime tomogram is sufficient as a starting model for early arrival FWI. © 2016 Society of Exploration Geophysicists.We sincerely thank PEMEX for providing the data used in this study. We thank the sponsors for supporting the Consortium of Subsurface Imaging and Fluid Modeling. A. AlTheyab is grateful to Saudi ARAMCO for sponsoring his graduate studies
Imaging near-surface heterogeneities by natural migration of backscattered surface waves: Field data test
No full textWe have developed a methodology for detecting the presence of near-surface heterogeneities by naturally migrating backscattered surface waves in controlled-source data. The near-surface heterogeneities must be located within a depth of approximately one-third the dominant wavelength λ of the strong surface-wave arrivals. This natural migration method does not require knowledge of the near-surface phase-velocity distribution because it uses the recorded data to approximate the Green’s functions for migration. Prior to migration, the backscattered data are separated from the original records, and the band-passed filtered data are migrated to give an estimate of the migration image at a depth of approximately one-third λ. Each band-passed data set gives a migration image at a different depth. Results with synthetic data and field data recorded over known faults validate the effectiveness of this method. Migrating the surface waves in recorded 2D and 3D data sets accurately reveals the locations of known faults. The limitation of this method is that it requires a dense array of receivers with a geophone interval less than approximately one-half λ.The research reported in this paper was supported by the King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia. We thank the sponsors of the Center for Subsurface Imaging and Fluid Modeling (CSIM) consortium for their support. We would also like to thank the high-performance computing center of KAUST for providing access to supercomputing facilities. We thank B. Guo and Z. Feng for editing the paper. A. AlTheyab thanks Saudi Aramco for sponsoring his graduate studies. We also thank the associate editor J. van der Neut and three anonymous reviewers whose reviews improved the quality of this manuscript
Inverting reflections using full-waveform inversion with inaccurate starting models
No full textWe present a method for inverting seismic reflections using full-waveform inversion (FWI) with inaccurate starting models. For a layered medium, near-offset reflections (with zero angle of incidence) are unlikely to be cycle-skipped regardless of the low-wavenumber velocity error in the initial models. Therefore, we use them as a starting point for FWI, and the subsurface velocity model is then updated during the FWI iterations using reflection wavepaths from varying offsets that are not cycle-skipped.
To enhance low-wavenumber updates and accelerate the convergence, we take several passes through the non-linear Gauss-Seidel iterations, where we invert traces from a narrow range of near offsets and finally end at the far offsets. Every pass is followed by applying smoothing to the cumulative slowness update. The smoothing is strong at the early stages and relaxed at later iterations to allow for a gradual reconstruction of the subsurface model in a multiscale manner. Applications to synthetic and field data, starting from inaccurate models, show significant low-wavenumber updates and flattening of common-image gathers after many iterations
Qademah Fault Artificial Ambient Noise Test
No full textThis data set was collected on 7 Dec. 2014 by Sherif and Abdullah. The receiver layout is the same as that of the passive data test at the same location, which is described as follow:
288 receivers are used and arranged as follow
- 12 lines, cross-line offset = 10 m
- 24 receiver in each line, inline offset = 5 m
- Additional 24 receivers are placed at line # 6, where the receiver interval is decreased to 2.5 m.
Data Recording:
We start recording at 10:10 am and stop recording at 11:25 am.
Each record has total of 20 s, with time interval of 0.004 ms and around 2 s overlap between each two successive files.
Source:
We used a piece of wood attached to a pick-up truck to create the noise; we drove around the array of receivers in a rectangle-shape route during the recording time
Controlled Noise Seismology
No full textWe use controlled noise seismology (CNS) to generate surface waves, where we continuously record seismic data while generating artificial noise along the profile line. To generate the CNS data we drove a vehicle around the geophone line and continuously recorded the generated noise. The recorded data set is then correlated over different time windows and the correlograms are stacked together to generate the surface waves. The virtual shot gathers reveal surface waves with moveout velocities that closely approximate those from active source shot gathers
3D super-virtual refraction interferometry
No full textSuper-virtual refraction interferometry enhances the signal-to-noise ratio of far-offset refractions. However, when applied to 3D cases, traditional 2D SVI suffers because the stationary positions of the source-receiver pairs might be any place along the recording plane, not just along a receiver line. Moreover, the effect of enhancing the SNR can be limited because of the limitations in the number of survey lines, irregular line geometries, and azimuthal range of arrivals. We have developed a 3D SVI method to overcome these problems. By integrating along the source or receiver lines, the cross-correlation or the convolution result of a trace pair with the source or receiver at the stationary position can be calculated without the requirement of knowing the stationary locations. In addition, the amplitudes of the cross-correlation and convolution results are largely strengthened by integration, which is helpful to further enhance the SNR. In this paper, both synthetic and field data examples are presented, demonstrating that the super-virtual refractions generated by our method have accurate traveltimes and much improved SNR
Imaging of Scattered Wavefields in Passive and Controlled-source Seismology
No full textSeismic waves are used to study the Earth, exploit its hydrocarbon resources, and understand its hazards. Extracting information from seismic waves about the Earth’s subsurface, however, is becoming more challenging as our questions become more complex and our demands for higher resolution increase. This dissertation introduces two new methods that use scattered waves for improving the resolution of subsurface images: natural migration of passive seismic data and convergent full-waveform inversion.
In the first part of this dissertation, I describe a method where the recorded seismic data are used to image subsurface heterogeneities like fault planes. This method, denoted as natural migration of backscattered surface waves, provides higher resolution images for near-surface faults that is complementary to surface-wave tomography images. Our proposed method differ from contemporary methods in that it does not (1) require a velocity model of the earth, (2) assumes weak scattering, or (3) have a high computational cost. This method is applied to ambient noise recorded by the US-Array to map regional faults across the American continent. Natural migration can be formulated as a least-squares inversion to furtherer enhance the resolution and the quality of the fault images. This inversion is applied to ambient noise recorded in Long Beach, California to reveal a matrix of shallow subsurface faults.
The second part of this dissertation describes a convergent full waveform inversion method for controlled source data. A controlled source excites waves that scatter from subsurface reflectors. The scattered waves are recorded by a large array of geophones. These recorded waves can be inverted for a high-resolution image of the
subsurface by FWI, which is typically convergent for transmitted arrivals but often does not converge for deep reflected events. I propose a preconditioning approach that extends the ability of FWI to image deep parts of the velocity model, which significantly improves the chances for finding hydrocarbon deposits
Imaging near-surface heterogeneities by natural migration of surface waves
No full textWe demonstrate that near-surface heterogeneities can be imaged by natural migration of backscattered surface waves in common shot gathers. No velocity model is required because the data are migrated onto surface points with the virtual Green's functions computed from the shot gathers. Migrating shot gathers recorded by 2D and 3D land surveys validates the effectiveness of detecting nearsurface heterogeneities by natural migration. The implication is that more accurate hazard maps can be created by migrating surface waves in land surveys.The research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia. We thank the sponsors of the CSIM consortium for their support. We would also like to thank the high performance computing (HPC) center of KAUST for providing access to super computing facilities
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