scholarly journals Ray-tracing traveltime tomography versus wave-equation traveltime inversion for near-surface seismic land data

2017 ◽  
Vol 5 (3) ◽  
pp. SO11-SO19
Author(s):  
Lei Fu ◽  
Sherif M. Hanafy
Keyword(s):  
Wave Equation ◽  
Ray Tracing ◽  
Water Table ◽  
Seismic Data ◽  
Velocity Model ◽  
Water Table Depth ◽  
Seismic Survey ◽  
Near Surface ◽  
Traveltime Tomography ◽  
Initial Starting

Full-waveform inversion of land seismic data tends to get stuck in a local minimum associated with the waveform misfit function. This problem can be partly mitigated by using an initial velocity model that is close to the true velocity model. This initial starting model can be obtained by inverting traveltimes with ray-tracing traveltime tomography (RT) or wave-equation traveltime (WT) inversion. We have found that WT can provide a more accurate tomogram than RT by inverting the first-arrival traveltimes, and empirical tests suggest that RT is more sensitive to the additive noise in the input data than WT. We present two examples of applying WT and RT to land seismic data acquired in western Saudi Arabia. One of the seismic experiments investigated the water-table depth, and the other one attempted to detect the location of a buried fault. The seismic land data were inverted by WT and RT to generate the P-velocity tomograms, from which we can clearly identify the water table depth along the seismic survey line in the first example and the fault location in the second example.

3D seismic traveltime tomography imaging of the shallow subsurface at the C O2 SINK project site, Ketzin, Germany

Geophysics ◽  
2009 ◽  
Vol 74 (1) ◽  
pp. G1-G15 ◽  
Author(s):  
Sawasdee Yordkayhun ◽  
Ari Tryggvason ◽  
Ben Norden ◽  
Christopher Juhlin ◽  
Björn Bergman
Keyword(s):  
Velocity Structure ◽  
Velocity Model ◽  
Geological Structure ◽  
Seismic Survey ◽  
Storage Site ◽  
Near Surface ◽  
Shallow Subsurface ◽  
Traveltime Tomography ◽  
Reflection Seismic ◽  
Reflection Data

A 3D reflection seismic survey was performed in 2005 at the Ketzin carbon dioxide [Formula: see text] pilot geological-storage site (the [Formula: see text] project) near Berlin, Germany, to image the geological structure of the site to depths of about [Formula: see text]. Because of the acquisition geometry, frequency limitations of the source, and artefacts of the data processing, detailed structures shallower than about [Formula: see text] were unclear. To obtain structural images of the shallow subsurface, we applied 3D traveltime tomography to data near the top of the Ketzin anticline, where faulting is present. Understanding the shallow subsurface structure is important for long-term monitoring aspects of the project after [Formula: see text] has been injected into a saline aquifer at about [Formula: see text] depth. We used a 3D traveltime tomography algorithm based on a combination ofsolving for 3D velocity structure and static corrections in the inversion process to account for artefacts in the velocity structure because of smearing effects from the unconsolidated cover. The resulting velocity model shows low velocities of [Formula: see text] in the uppermost shallow subsurface of the study area. The velocity reaches about [Formula: see text] at a depth of [Formula: see text]. This coincides approximately with the boundary between Quaternary units, which contain the near-surface freshwater reservoir and the Tertiary clay aquitard. Correlation of tomographic images with a similarity attribute slice at [Formula: see text] (about [Formula: see text] depth) indicates that at least one east-west striking fault zone observed in the reflection data might extend into the Tertiary unit. The more detailed images of the shallow subsurface from this study provided valuable information on this potentially risky area.

Tomographic modeling of a cross‐borehole data set

Geophysics ◽  
1989 ◽  
Vol 54 (10) ◽  
pp. 1249-1257 ◽  
Author(s):  
Larry R. Lines ◽  
Edward D. LaFehr
Keyword(s):  
Wave Equation ◽  
Ray Tracing ◽  
Velocity Model ◽  
Velocity Estimation ◽  
P Wave ◽  
Seismic Survey ◽  
Equation Modeling ◽  
Data Set ◽  
True Solution ◽  
Velocity Models

In this paper we describe a methodology for estimating P‐wave velocities from a cross‐borehole seismic survey that uses straight‐ray tomography, ray tracing, and finite‐difference wave‐equation modeling to produce velocity models that fit the first‐break traveltimes. After a starting model is established by straight‐ray tomography, the velocity model is checked by ray tracing and wave‐equation modeling. Since the models for each procedure show consistent results and the modeled traveltimes closely match those traveltimes from the actual data, we felt our interpretation was confirmed. However, the fitting of cross‐well first break traveltimes is only a necessary validity check and is not sufficient to guarantee that the true solution has been found. Two wells were drilled through the areas that were anomalous on the derived tomogram and check‐shot velocity surveys were run. Due primarily to a lateral ambiguity in velocity estimation caused by too few near‐vertical raypaths, the check‐shot surveys did not agree with the tomogram velocities. However, subsequently the check‐shot traveltimes were used to place bounds on velocity in a constrained least‐squares procedure; the combined modeling of uphole and cross‐well rays produced an optimum velocity model which satisfies all available data.

A reality check on full-wave inversion applied to land seismic data for near-surface modeling

2022 ◽  
Vol 41 (1) ◽  
pp. 40-46
Author(s):  
Öz Yilmaz ◽  
Kai Gao ◽  
Milos Delic ◽  
Jianghai Xia ◽  
Lianjie Huang ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
Surface Modeling ◽  
P Wave ◽  
Borehole Data ◽  
Near Surface ◽  
Traveltime Tomography ◽  
Wave Inversion ◽  
Full Wave ◽  
Elastic Inversion

We evaluate the performance of traveltime tomography and full-wave inversion (FWI) for near-surface modeling using the data from a shallow seismic field experiment. Eight boreholes up to 20-m depth have been drilled along the seismic line traverse to verify the accuracy of the P-wave velocity-depth model estimated by seismic inversion. The velocity-depth model of the soil column estimated by traveltime tomography is in good agreement with the borehole data. We used the traveltime tomography model as an initial model and performed FWI. Full-wave acoustic and elastic inversions, however, have failed to converge to a velocity-depth model that desirably should be a high-resolution version of the model estimated by traveltime tomography. Moreover, there are significant discrepancies between the estimated models and the borehole data. It is understandable why full-wave acoustic inversion would fail — land seismic data inherently are elastic wavefields. The question is: Why does full-wave elastic inversion also fail? The strategy to prevent full-wave elastic inversion of vertical-component geophone data trapped in a local minimum that results in a physically implausible near-surface model may be cascaded inversion. Specifically, we perform traveltime tomography to estimate a P-wave velocity-depth model for the near-surface and Rayleigh-wave inversion to estimate an S-wave velocity-depth model for the near-surface, then use the resulting pairs of models as the initial models for the subsequent full-wave elastic inversion. Nonetheless, as demonstrated by the field data example here, the elastic-wave inversion yields a near-surface solution that still is not in agreement with the borehole data. Here, we investigate the limitations of FWI applied to land seismic data for near-surface modeling.

Traveltime information-based wave-equation inversion

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. WCC27-WCC36 ◽  
Author(s):  
Yu Zhang ◽  
Daoliu Wang
Keyword(s):  
Wave Equation ◽  
Conventional Method ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Back Propagation ◽  
Velocity Model ◽  
Data Fitting ◽  
Inversion Method ◽  
New Wave ◽  
Seismic Record

We propose a new wave-equation inversion method that mainly depends on the traveltime information of the recorded seismic data. Unlike the conventional method, we first apply a [Formula: see text] transform to the seismic data to form the delayed-shot seismic record, back propagate the transformed data, and then invert the velocity model by maximizing the wavefield energy around the shooting time at the source locations. Data fitting is not enforced during the inversion, so the optimized velocity model is obtained by best focusing the source energy after a back propagation. Therefore, inversion accuracy depends only on the traveltime information embedded in the seismic data. This method may overcome some practical issues of waveform inversion; in particular, it relaxes the dependency of the seismic data amplitudes and the source wavelet.

Two robust imaging methodologies for challenging environments: Wave-equation dispersion inversion of surface waves and guided waves and supervirtual interferometry + tomography for far-offset refractions

2018 ◽  
Vol 6 (4) ◽  
pp. SM27-SM37 ◽  
Author(s):  
Jing Li ◽  
Kai Lu ◽  
Sherif Hanafy ◽  
Gerard Schuster
Keyword(s):  
Wave Equation ◽  
Velocity Distribution ◽  
Surface Waves ◽  
Guided Waves ◽  
Velocity Model ◽  
Dispersion Curves ◽  
P Wave ◽  
Head Wave ◽  
Near Surface ◽  
Velocity Models

Two robust imaging technologies are reviewed that provide subsurface geologic information in challenging environments. The first one is wave-equation dispersion (WD) inversion of surface waves and guided waves (GW) for the shear-velocity (S-wave) and compressional-velocity (P-wave) models, respectively. The other method is traveltime inversion for the velocity model, in which supervirtual refraction interferometry (SVI) is used to enhance the signal-to-noise ratio of far-offset refractions. We have determined the benefits and liabilities of both methods with synthetic seismograms and field data. The benefits of WD are that (1) there is no layered-medium assumption, as there is in conventional inversion of dispersion curves. This means that 2D or 3D velocity models can be accurately estimated from data recorded by seismic surveys over rugged topography, and (2) WD mostly avoids getting stuck in local minima. The liability is that WD for surface waves is almost as expensive as full-waveform inversion (FWI) and, for Rayleigh waves, only recovers the S-velocity distribution to a depth no deeper than approximately 1/2 to 1/3 wavelength of the lowest-frequency surface wave. The limitation for GW is that, for now, it can estimate the P-velocity model by inverting the dispersion curves from GW propagating in near-surface low-velocity zones. Also, WD often requires user intervention to pick reliable dispersion curves. For SVI, the offset of usable refractions can be more than doubled, so that traveltime tomography can be used to estimate a much deeper model of the P-velocity distribution. This can provide a more effective starting velocity model for FWI. The liability is that SVI assumes head-wave first arrivals, not those from strong diving waves.

Target-oriented wave-equation inversion

Geophysics ◽  
2006 ◽  
Vol 71 (4) ◽  
pp. A35-A38 ◽  
Author(s):  
Alejandro A. Valenciano ◽  
Biondo Biondi ◽  
Antoine Guitton
Keyword(s):  
Wave Equation ◽  
Least Squares ◽  
Seismic Data ◽  
Hessian Matrix ◽  
Velocity Model ◽  
Complex Model ◽  
Inverse Filtering ◽  
Band Limited ◽  
Filtering Problem ◽  
Number Of Iterations

A target-oriented strategy can be applied to estimate a wave-equation least-squares inverse (LSI) image. By explicitly computing the wave-equation Hessian, the LSI image is obtained as the solution of a nonstationary least-squares inverse filtering problem. The rows of the Hessian are the nonstationary filters containing information about the acquisition geometry, the velocity model, and the band-limited characteristics of the seismic data. By exploiting the sparsity and the structure of the Hessian matrix, a large number of iterations, necessary to achieve convergence, can be computed cheaply. The results on a structurally complex model show the improvements of the LSI image versus the migrated image.

3‐D tomographic static correction

Geophysics ◽  
2002 ◽  
Vol 67 (4) ◽  
pp. 1275-1285 ◽  
Author(s):  
Xu Chang ◽  
Yike Liu ◽  
Hui Wang ◽  
Fuzhong Li ◽  
Jing Chen
Keyword(s):  
Computation Time ◽  
Matrix Decomposition ◽  
Velocity Model ◽  
Seismic Survey ◽  
Surface Model ◽  
Low Velocity ◽  
Near Surface ◽  
Model Inversion ◽  
Static Corrections ◽  
Refracted Waves

A 3‐D tomographic inversion approach based on a surface‐consistent model for static corrections is presented in this paper. Direct, reflected, and refracted waves are used simultaneously to update the near‐surface model. We analyze the characteristics of the first‐break traveltime in complicated low‐velocity layers. To improve the accuracy for the velocity model, the various first‐break times from direct, reflected, and refracted waves are considered for model inversion. A fractal algorithm which overcomes the error caused by wavelet shape differences is applied to pick first breaks. It also overcomes the leg jump of refractions. The method can pick a large number of first breaks automatically. The raypaths and traveltimes are calculated with a 3‐D ray tracer that does not increase computation time for complicated geological models. Our method can determine the raypath associated with minimum traveltimes regardless of wave mode (direct, refracted, or reflected). We use a least‐squares approach in conjunction with a matrix decomposition to reconstruct a 3‐D velocity model from the actual first‐break times obtained from 3‐D data. Finally, long‐ and short‐wavelength static corrections are calculated concurrently, based on the reconstructed velocity profile. The method can be applied to wide‐line profiles, crooked lines, and 2‐D and 3‐D seismic survey geometries. The results applied to a real 3‐D data example indicate that the 3‐D tomographic static corrections are effective for field data.

Case Study: Improving the quality of the seismic reflection image for a Fujian mineral exploration data set with offset-domain common-image gathers

Geophysics ◽  
2021 ◽  
pp. 1-45
Author(s):  
Guofeng Liu ◽  
Xiaohong Meng ◽  
Johanes Gedo Sea
Keyword(s):  
Wave Equation ◽  
Image Quality ◽  
Seismic Data ◽  
Seismic Reflection ◽  
Mineral Exploration ◽  
Velocity Model ◽  
Image Point ◽  
Single Shot ◽  
Data Set ◽  
Depth Migration

Seismic reflection is a proven and effective method commonly used during the exploration of deep mineral deposits in Fujian, China. In seismic data processing, rugged depth migration based on wave-equation migration can play a key role in handling surface fluctuations and complex underground structures. Because wave-equation migration in the shot domain cannot output offset-domain common-image gathers in a straightforward way, the use of traditional tools for updating the velocity model and improving image quality can be quite challenging. To overcome this problem, we employed the attribute migration method. This worked by sorting the migrated stack results for every single-shot gather into the offset gathers. The value of the offset that corresponded to each image point was obtained from the ratio of the original migration results to the offset-modulated shot-data migration results. A Gaussian function was proposed to map every image point to a certain range of offsets. This helped improve the signal-to-noise ratio, which was especially important in handing low quality seismic data obtained during mineral exploration. Residual velocity analysis was applied to these gathers to update the velocity model and improve image quality. The offset-domain common-image gathers were also used directly for real mineral exploration seismic data with rugged depth migration. After several iterations of migration and updating the velocity, the proposed procedure achieved an image quality better than the one obtained with the initial velocity model. The results can help with the interpretation of thrust faults and deep deposit exploration.

The Role of Legacy Seismic in Enhancing the Image in the Current Seismic Data – Abu Dhabi: Case Study

2021 ◽  
Author(s):  
Ramy Elasrag ◽  
Thuraya Al Ghafri ◽  
Faaeza Al Katheer ◽  
Yousuf Al-Aufi ◽  
Ivica Mihaljevic ◽  
...  
Keyword(s):  
Seismic Data ◽  
Seismic Survey ◽  
Surface Model ◽  
Noise Attenuation ◽  
Near Surface ◽  
Velocity Models ◽  
Multiple Attenuation ◽  
Seismic Image ◽  
Seismic Acquisition

Abstract Acquiring surface seismic data can be challenging in areas of intense human activities, due to presence of infrastructures (roads, houses, rigs), often leaving large gaps in the fold of coverage that can span over several kilometers. Modern interpolation algorithms can interpolate up to a certain extent, but quality of reconstructed seismic data diminishes as the acquisition gap increases. This is where vintage seismic acquisition can aid processing and imaging, especially if previous acquisition did not face the same surface obstacles. In this paper we will present how the legacy seismic survey has helped to fill in the data gaps of the new acquisition and produced improved seismic image. The new acquisition survey is part of the Mega 3D onshore effort undertaken by ADNOC, characterized by dense shot and receiver spacing with focus on full azimuth and broadband. Due to surface infrastructures, data could not be completely acquired leaving sizable gap in the target area. However, a legacy seismic acquisition undertaken in 2014 had access to such gap zones, as infrastructures were not present at the time. Legacy seismic data has been previously processed and imaged, however simple post-imaging merge would not be adequate as two datasets were processed using different workflows and imaging was done using different velocity models. In order to synchronize the two datasets, we have processed them in parallel. Data matching and merging were done before regularization. It has been regularized to radial geometry using 5D Matching Pursuit with Fourier Interpolation (MPFI). This has provided 12 well sampled azimuth sectors that went through surface consistent processing, multiple attenuation, and residual noise attenuation. Near surface model was built using data-driven image-based static (DIBS) while reflection tomography was used to build the anisotropic velocity model. Imaging was done using Pre-Stack Kirchhoff Depth Migration. Processing legacy survey from the beginning has helped to improve signal to noise ratio which assisted with data merging to not degrade the quality of the end image. Building one near surface model allowed both datasets to match well in time domain. Bringing datasets to the same level was an important condition before matching and merging. Amplitude and phase analysis have shown that both surveys are aligned quite well with minimal difference. Only the portion of the legacy survey that covers the gap was used in the regularization, allowing MPFI to reconstruct missing data. Regularized data went through surface multiple attenuation and further noise attenuation as preconditioning for migration. Final image that is created using both datasets has allowed target to be imaged better.

scholarly journals Mitigation of defocusing by statics and near-surface velocity errors by interferometric least-squares migration with a reference datum

Geophysics ◽  
2016 ◽  
Vol 81 (4) ◽  
pp. S195-S206 ◽  
Author(s):  
Mrinal Sinha ◽  
Gerard T. Schuster
Keyword(s):  
Least Squares ◽  
Prior Knowledge ◽  
Seismic Data ◽  
Field Data ◽  
Velocity Model ◽  
Well Logs ◽  
Surface Velocity ◽  
Near Surface ◽  
Reference Layer ◽  
Least Squares Migration

Imaging seismic data with an erroneous migration velocity can lead to defocused migration images. To mitigate this problem, we first choose a reference reflector whose topography is well-known from the well logs, for example. Reflections from this reference layer are correlated with the traces associated with reflections from deeper interfaces to get crosscorrelograms. Interferometric least-squares migration (ILSM) is then used to get the migration image that maximizes the crosscorrelation between the observed and the predicted crosscorrelograms. Deeper reference reflectors are used to image deeper parts of the subsurface with a greater accuracy. Results on synthetic and field data show that defocusing caused by velocity errors is largely suppressed by ILSM. We have also determined that ILSM can be used for 4D surveys in which environmental conditions and acquisition parameters are significantly different from one survey to the next. The limitations of ILSM are that it requires prior knowledge of a reference reflector in the subsurface and the velocity model below the reference reflector should be accurate.

Sign in / Sign up

Export Citation Format

Share Document