Three-dimensional data-domain full traveltime inversion using a practical workflow of early arrival selection

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. U77-U86
Author(s):  
Lu Liu ◽  
Xudong Duan ◽  
Yi Luo
Keyword(s):  
Optimization Problem ◽  
Three Dimensional ◽  
Velocity Model ◽  
Surface Velocity ◽  
Data Sets ◽  
Near Surface ◽  
Arrival Times ◽  
Traveltime Inversion ◽  
Early Arrival ◽  
Crosscorrelation Function

A new method of data-domain full traveltime inversion (FTI) is proposed to estimate the near-surface velocity model using early arrivals in seismic shot gathers. Data-domain FTI is capable of generating a background velocity model from which the predicted early arrivals can kinematically match the observed ones. Such a match is measured and quantified in terms of the crosscorrelation function between the computed and observed traces. Our method aims to find an optimal estimated velocity model that minimizes the crosscorrelation computed from the selected early arrivals. The early arrivals are isolated via a sequence of operations, including the [Formula: see text]-[Formula: see text] scan, autopicking, multidomain quality control, and guide interpolation. Because windows, rather than exact arrival times, are constructed, the difficulties encountered while picking precise arrivals are reduced. In addition, the gradient of data-domain FTI is derived based on an amplitude-constrained optimization problem, which makes the gradient essentially different from that derived with the Born approximation in which no constraint is used. The constraint requires the inversion to honor traveltime information only, and it thus ignores any amplitude changes caused by velocity variations. This method is validated using 3D synthetic as well as field data sets. The results show that data-domain FTI, combined with the early arrival selection workflow, is able to generate reasonable background velocities that kinematically match the predicted early arrivals with the observed ones, and the associated depth-domain images are clearly improved.

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.

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.

A layer-stripping method for 3D near-surface velocity model building using seismic first-arrival times

2015 ◽  
Vol 26 (4) ◽  
pp. 502-507 ◽  
Author(s):  
Taikun Shi ◽  
Jianzhong Zhang ◽  
Zhonglai Huang ◽  
Changkun Jin
Keyword(s):  
Model Building ◽  
Velocity Model ◽  
Surface Velocity ◽  
Near Surface ◽  
Arrival Times ◽  
Velocity Model Building ◽  
Stripping Method ◽  
First Arrival

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.

Interpretation of 3-D effects in long‐offset transient electromagnetic (LOTEM) soundings in the Münsterland area/Germany

Geophysics ◽  
1992 ◽  
Vol 57 (9) ◽  
pp. 1127-1137 ◽  
Author(s):  
Andreas Hördt ◽  
Vladimir L. Druskin ◽  
Leonid A. Knizhnerman ◽  
Kurt‐Martin Strack
Keyword(s):  
Integral Equation ◽  
Thin Sheet ◽  
Three Dimensional ◽  
Equation Modeling ◽  
Data Sets ◽  
Sign Reversal ◽  
Near Surface ◽  
Transient Electromagnetic ◽  
Long Offset ◽  
Geological Situation

The interpretation of long‐offset transient electromagnetic (LOTEM) data is usually based on layered earth models. Effects of lateral conductivity variations are commonly explained qualitatively, because three‐dimensional (3-D) numerical modeling is not readily available for complex geology. One of the first quantitative 3-D interpretations of LOTEM data is carried out using measurements from the Münsterland basin in northern Germany. In this survey area, four data sets show effects of lateral variations including a sign reversal in the measured voltage curve at one site. This sign reversal is a clear indicator of two‐dimensional (2-D) or 3-D conductivity structure, and can be caused by current channeling in a near‐surface conductive body. Our interpretation strategy involves three different 3-D forward modeling programs. A thin‐sheet integral equation modeling routine used with inversion gives a first guess about the location and strike of the anomaly. A volume integral equation program allows models that may be considered possible geological explanations for the conductivity anomaly. A new finite‐difference algorithm permits modeling of much more complex conductivity structures for simulating a realistic geological situation. The final model has the zone of anomalous conductivity aligned below a creek system at the surface. Since the creeks flow along weak zones in this area, the interpretation seems geologically reasonable. The interpreted model also yields a good fit to the 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.

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.

Generalization of traveltime inversion

Geophysics ◽  
1992 ◽  
Vol 57 (1) ◽  
pp. 9-14 ◽  
Author(s):  
Gérard C. Herman
Keyword(s):  
Time Windows ◽  
Velocity Model ◽  
Inversion Method ◽  
Nonlinear Inversion ◽  
Error Norm ◽  
Arrival Times ◽  
Traveltime Inversion ◽  
Velocity Models ◽  
Use Of Data

A nonlinear inversion method is presented, especially suited for the determination of global velocity models. In a certain sense, it can be considered as a generalization of methods based on traveltimes of reflections, with the requirement of accurately having to determine traveltimes replaced by the (less stringent and less subjective) requirement of having to define time windows around main reflections (or composite reflections) of interest. It is based on an error norm, related to the phase of the wavefield, which is directly computed from wavefield measurements. Therefore, the cumbersome step of interpreting arrivals and measuring arrival times is avoided. The method is applied to the reconstruction of a depth‐dependent global velocity model from a set of plane‐wave responses and is compared to other methods. Despite the fact that the new error norm only makes use of data having a temporal bandwidth of a few Hz, its behavior is very similar to the behavior of the error norm used in traveltime inversion.

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