Turning-ray tomography using a low-spatial-frequency model parameterization

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. VE93-VE100 ◽  
Author(s):  
James L. Simmons
Keyword(s):  
Velocity Field ◽  
Spatial Frequency ◽  
Field Data ◽  
Velocity Model ◽  
Surface Velocity ◽  
Low Velocity ◽  
Arrival Times ◽  
Model Parameterization ◽  
First Arrival ◽  
Velocity Anomalies

A linear least-squares inversion is applied to the turning-ray first-arrival times of a shallow-marine seismic reflection data set to estimate the slowly varying (laterally and vertically) components of the near-surface velocity field. The velocity model is represented with a low-spatial-frequency parameterization (2D cubic B-splines) designed specifically for the predicted components of the data. This model parameterization effectively decouples the slowly varying background from the higher spatial-frequency component of the velocity field produced by shallow, low-velocity, gas-charged sands and allows the solution to be obtained in a single iteration. The observed first-arrival times (background and shallow anomaly-induced perturbations) and the slowly varying first-arrival times related to the background velocity are inverted separately. Similar velocity-model estimates result, demonstrating the decoupling imposed by the B-spline model parameterization. The background velocity and the low-velocity anomalies are best treated as separate inverse problems using very different model parameterizations. Ray tracing a synthetic model containing local low-velocity anomalies embedded in a smooth background does not accurately predict the anomaly-induced first-arrival time perturbations seen in the field data. Acoustic finite-difference waveform modeling shows that reflections and diffractions from the anomalies interfere with the diving-wave first arrivals. First-arrival times picked from the full-waveform synthetics more accurately predict the field data first-arrival times.

Resolving near-seabed velocity anomalies: Deep water offshore eastern India

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. VE235-VE241 ◽  
Author(s):  
Juergen Fruehn ◽  
Ian F. Jones ◽  
Victoria Valler ◽  
Pranaya Sangvai ◽  
Ajoy Biswal ◽  
...  
Keyword(s):  
Velocity Field ◽  
Deep Water ◽  
Model Building ◽  
Velocity Model ◽  
Narrow Channel ◽  
Small Scale ◽  
Low Velocity ◽  
Near Surface ◽  
Velocity Model Building ◽  
Velocity Anomalies

Imaging in deep-water environments poses a specific set of challenges, both in data preconditioning and velocity model building. These challenges include scattered, complex 3D multiples, aliased noise, and low-velocity shallow anomalies associated with channel fills and gas hydrates. We describe an approach to tackling such problems for data from deep water off the east coast of India, concentrating our attention on iterative velocity model building, and more specifically the resolution of near-surface and other velocity anomalies. In the region under investigation, the velocity field is complicated by narrow buried canyons ([Formula: see text] wide) filled with low-velocity sediments, which give rise to severe pull-down effects; possible free-gas accumulation below an extensive gas-hydrate cap, causing dimming of the image below (perhaps as a result of absorption); and thin-channel bodies with low-velocity fill. Hybrid gridded tomography using a conjugate gradient solver (with [Formula: see text] vertical cell size) was applied to resolve small-scale velocity anomalies (with thicknesses of about [Formula: see text]). Manual picking of narrow-channel features was used to define bodies too small for the tomography to resolve. Prestack depth migration, using a velocity model built with a combination of these techniques, could resolve pull-down and other image distortion effects in the final image. The resulting velocity field shows high-resolution detail useful in identifying anomalous geobodies of potential exploration interest.

Use of refraction, reflection, and wave-equation-based tomography for imaging beneath shallow gas: A Trinidad field data example

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. VE281-VE289 ◽  
Author(s):  
Nurul Kabir ◽  
Uwe Albertin ◽  
Min Zhou ◽  
Vishal Nagassar ◽  
Einar Kjos ◽  
...  
Keyword(s):  
Wave Equation ◽  
Velocity Field ◽  
Velocity Model ◽  
Surface Velocity ◽  
Low Velocity ◽  
Shallow Gas ◽  
Near Surface ◽  
Velocity Anomaly ◽  
Refraction Tomography ◽  
Reflection Tomography

Shallow localized gas pockets cause challenging problems in seismic imaging because of sags and wipe-out zones they produce on imaged reflectors deep in the section. In addition, the presence of shallow gas generates strong surface-related and interbed multiples, making velocity updating very difficult. When localized gas pockets are very shallow, we have limited information to build a near-surface velocity model using ray-based reflection tomography alone. Diving-wave refraction tomography successfully builds a starting model for the very shallow part. Usual ray-based reflection tomography using single-parameter hyperbolic moveout might need many iterations to update the deeper part of the velocity model. In addition, the method generates a low-velocity anomaly in the deeper part of the model. We present an innovative method for building the final velocity model by combining refraction, reflection, and wave-equation-based tomography. Wave-equation-based tomography effectively generates a detailed update of a shallow velocity field, resolving the gas-sag problem. When applied as the last step, following the refraction and reflection tomography, it resolves the gas-sag problem but fails to remove the low-velocity anomaly generated by the reflection tomography in the deeper part of the model. To improve the methodology, we update the shallow velocity field using refraction tomography followed by wave-equation tomography before updating the deeper part of the model. This step avoids generating the low-velocity anomaly. Refraction and wave-equation-based tomography followed by reflection tomography generates a simpler velocity model, giving better focusing in the deeper part of the image. We illustrate how the methodology successfully improves resolution of gas anomalies and significantly reduces gas sag from an imaged section in the Greater Cassia area, Trinidad.

Robust refraction statics solution and near-surface velocity model building using feedback from reflection data

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. U63-U77
Author(s):  
Bernard K. Law ◽  
Daniel Trad
Keyword(s):  
Model Building ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Surface Velocity ◽  
Near Surface ◽  
Arrival Times ◽  
Reflection Data ◽  
Velocity Model Building ◽  
First Arrival ◽  
Refraction Data

An accurate near-surface velocity model is critical for weathering statics correction and initial model building for depth migration and full-waveform inversion. However, near-surface models from refraction inversion often suffer from errors in refraction data, insufficient sampling, and over-simplified assumptions used in refraction algorithms. Errors in refraction data can be caused by picking errors resulting from surface noise, attenuation, and dispersion of the first-arrival energy with offset. These errors are partially compensated later in the data flow by reflection residual statics. Therefore, surface-consistent residual statics contain information that can be used to improve the near-surface velocity model. We have developed a new dataflow to automatically include median and long-wavelength components of surface-consistent reflection residual statics. This technique can work with any model-based refraction solution, including grid-based tomography methods and layer-based methods. We modify the cost function of the refraction inversion by adding model and data weights computed from the smoothed surface-consistent residual statics. By using an iterative inversion, these weights allow us to update the near-surface velocity model and to reject first-arrival picks that do not fit the updated model. In this nonlinear optimization workflow, the refraction model is derived from maximizing the coherence of the reflection energy and minimizing the misfit between model arrival times and the recorded first-arrival times. This approach can alleviate inherent limitations in shallow refraction data by using coherent reflection data.

Tomographic velocity model building of the near surface with velocity-inversion interfaces: A test using the Yilmaz model

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. U39-U47 ◽  
Author(s):  
Hui Liu ◽  
Hua-wei Zhou ◽  
Wenge Liu ◽  
Peiming Li ◽  
Zhihui Zou
Keyword(s):  
Model Building ◽  
Velocity Model ◽  
Surface Velocity ◽  
Low Velocity ◽  
Near Surface ◽  
Velocity Inversion ◽  
Velocity Models ◽  
Velocity Model Building ◽  
First Arrival ◽  
The Impact

First-arrival traveltime tomography is a popular approach to building the near-surface velocity models for oil and gas exploration, mining, geoengineering, and environmental studies. However, the presence of velocity-inversion interfaces (VIIs), across which the overlying velocity is higher than the underlying velocity, might corrupt the tomographic solutions. This is because most first-arrival raypaths will not traverse along any VII, such as the top of a low-velocity zone. We have examined the impact of VIIs on first-arrival tomographic velocity model building of the near surface using a synthetic near-surface velocity model. This examination confirms the severe impact of VIIs on first-arrival tomography. When the source-to-receiver offset is greater than the lateral extent of the VIIs, good near-surface velocity models can still be established using a multiscale deformable-layer tomography (DLT), which uses a layer-based model parameterization and a multiscale scheme as regularization. Compared with the results from a commercial grid-based tomography, the DLT delivers much better near-surface statics solutions and less error in the images of deep reflectors.

scholarly journals Imaging of the Upper Mantle Beneath Southeast Asia: Constrained by Teleseismic P-Wave Tomography

2020 ◽  
Vol 12 (18) ◽  
pp. 2975
Author(s):  
Huiyan Shi ◽  
Tonglin Li ◽  
Rongzhe Zhang ◽  
Gongcheng Zhang ◽  
Hetian Yang
Keyword(s):  
South China Sea ◽  
Southeast Asia ◽  
Travel Time ◽  
South China ◽  
High Velocity ◽  
Velocity Model ◽  
P Wave ◽  
Deep Part ◽  
Low Velocity ◽  
Velocity Anomalies

It is of great significance to construct a three-dimensional underground velocity model for the study of geodynamics and tectonic evolution. Southeast Asia has attracted much attention due to its complex structural features. In this paper, we collected relative travel time residuals data for 394 stations distributed in Southeast Asia from 2006 to 2019, and 14,011 seismic events were obtained. Then, teleseismic tomography was applied by using relative travel time residuals data to invert the velocity where the fast marching method (FMM) and subspace method were used for every iteration. A novel 3D P-wave velocity model beneath Southeast Asia down to 720 km was obtained using this approach. The tomographic results suggest that the southeastern Tibetan Plateau, the Philippines, Sumatra, and Java, and the deep part of Borneo exhibit high velocity anomalies, while low velocity anomalies were found in the deep part of the South China Sea (SCS) basin and in the shallow part of Borneo and areas near the subduction zone. High velocity anomalies can be correlated to subduction plates and stable land masses, while low velocity anomalies can be correlated to island arcs and upwelling of mantle material caused by subduction plates. We found a southward subducting high velocity body in the Nansha Trough, which was presumed to be a remnant of the subduction of the Dangerous Grounds into Borneo. It is further inferred that the Nansha Trough and the Dangerous Grounds belong to the same tectonic unit. According to the tomographic images, a high velocity body is located in the deep underground of Indochina–Natuna Island–Borneo–Palawan, depth range from 240 km to 660 km. The location of the high velocity body is consistent with the distribution range of the ophiolite belt, so we speculate that the high velocity body is the remnant of thee Proto-South China Sea (PSCS) and Paleo-Tethys. This paper conjectures that the PSCS was the southern branch of Paleo-Tethys and the gateway between Paleo-Tethys and the Paleo-Pacific Ocean. Due to the squeeze of the Australian plate, PSCS closed from west to east in a scissor style, and was eventually extinct under Borneo.

Model–parameterization scaling for improving accuracy of seismic tomography in reconstructing near–surface velocity model

2020 ◽  
Author(s):  
Gleb S. Chernyshov ◽  
◽  
Anton A. Duchkov ◽  
Aleksander A. Nikitin ◽  
Ivan Yu. Kulakov ◽  
...  
Keyword(s):  
Model Building ◽  
Regularization Parameter ◽  
Velocity Model ◽  
Surface Velocity ◽  
Surface Model ◽  
Tomographic Inversion ◽  
Near Surface ◽  
Model Parameterization ◽  
Regularization Parameters ◽  
Improving Accuracy

The problem of tomographic inversion is non–unique and requires regularization to solve it in a stable manner. It is highly non–trivial to choose between various regularization approaches or tune the regularization parameters themselves. We study the influence of one particular regularization parameter on the resolution and accuracy the tomographic inversion for the near–surface model building. We propose another regularization parameter, which allows to increase the accuracy of model building.

A robust first-arrival picking workflow using convolutional and recurrent neural networks

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. U109-U119
Author(s):  
Pengyu Yuan ◽  
Shirui Wang ◽  
Wenyi Hu ◽  
Xuqing Wu ◽  
Jiefu Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Model Building ◽  
Signal To Noise Ratio ◽  
Synthetic Data ◽  
Velocity Model ◽  
Surface Velocity ◽  
Data Sets ◽  
Near Surface ◽  
Segmentation Boundary ◽  
First Arrival

A deep-learning-based workflow is proposed in this paper to solve the first-arrival picking problem for near-surface velocity model building. Traditional methods, such as the short-term average/long-term average method, perform poorly when the signal-to-noise ratio is low or near-surface geologic structures are complex. This challenging task is formulated as a segmentation problem accompanied by a novel postprocessing approach to identify pickings along the segmentation boundary. The workflow includes three parts: a deep U-net for segmentation, a recurrent neural network (RNN) for picking, and a weight adaptation approach to be generalized for new data sets. In particular, we have evaluated the importance of selecting a proper loss function for training the network. Instead of taking an end-to-end approach to solve the picking problem, we emphasize the performance gain obtained by using an RNN to optimize the picking. Finally, we adopt a simple transfer learning scheme and test its robustness via a weight adaptation approach to maintain the picking performance on new data sets. Our tests on synthetic data sets reveal the advantage of our workflow compared with existing deep-learning methods that focus only on segmentation performance. Our tests on field data sets illustrate that a good postprocessing picking step is essential for correcting the segmentation errors and that the overall workflow is efficient in minimizing human interventions for the first-arrival picking task.

Application of waveform tomography to marine seismic reflection data from the Queen Charlotte Basin of western Canada

Geophysics ◽  
2011 ◽  
Vol 76 (2) ◽  
pp. B55-B70 ◽  
Author(s):  
E. M. Takam Takougang ◽  
A. J. Calvert
Keyword(s):  
Seismic Reflection ◽  
Field Data ◽  
Velocity Model ◽  
P Wave ◽  
Low Velocity ◽  
Seismic Line ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Sonic Log ◽  
Waveform Tomography

To obtain a higher resolution quantitative P-wave velocity model, 2D waveform tomography was applied to seismic reflection data from the Queen Charlotte sedimentary basin off the west coast of Canada. The forward modeling and inversion were implemented in the frequency domain using the visco-acoustic wave equation. Field data preconditioning consisted of f-k filtering, 2D amplitude scaling, shot-to-shot amplitude balancing, and time windowing. The field data were inverted between 7 and 13.66 Hz, with attenuation introduced for frequencies ≥ 10.5 Hz to improve the final velocity model; two different approaches to sampling the frequencies were evaluated. The limited maximum offset of the marine data (3770 m) and the relatively high starting frequency (7 Hz) were the main challenges encountered during the inversion. An inversion strategy that successively recovered shallow-to-deep structures was designed to mitigate these issues. The inclusion of later arrivals in the waveform tomography resulted in a velocity model that extends to a depth of approximately 1200 m, twice the maximum depth of ray coverage in the ray-based tomography. Overall, there is a good agreement between the velocity model and a sonic log from a well on the seismic line, as well as between modeled shot gathers and field data. Anomalous zones of low velocity in the model correspond to previously identified faults or their upward continuation into the shallow Pliocene section where they are not readily identifiable in the conventional migration.

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.

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