Integrated turning-ray and reflection tomography for velocity model building in foothill areas

2018 ◽  
Vol 6 (4) ◽  
pp. SM63-SM70 ◽  
Author(s):  
Tian Jun ◽  
Peng Gengxin ◽  
Junru Jiao ◽  
Grace (Yan) Yan ◽  
Xianhuai Zhu
Keyword(s):  
Model Building ◽  
Synthetic Data ◽  
Velocity Model ◽  
Surface Velocity ◽  
Seismic Exploration ◽  
Surface Model ◽  
Near Surface ◽  
Velocity Model Building ◽  
Tomographic Method ◽  
Reflection Tomography

A special challenge for land seismic exploration is estimating velocities, in part due to complex near-surface structures, and in some instances because of rugose topography over foothills. We have developed an integrated turning-ray and reflection-tomographic method to face this challenge. First, turning-ray tomography is performed to derive a near-surface velocity-depth model. Then, we combine the near-surface model with the initial-subsurface model. Taking the combined model as starting model, we go through a reflection tomographic process to build the model for imaging. During reflection tomography, the near-surface model and subsurface models are jointly updated. Our method has been successfully applied to a 2D complex synthetic data example and a 3D field data example. The results demonstrate that our method derives a very decent model even when there is no reflection information available in a few hundred meters underneath the surface. Joint tomography can lead to geologic plausible models and produce subsurface images with high fidelity.

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.

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.

Multi-Wave Inversion for Near-Surface Velocity Model Building on a Broadband Land Survey in North Kuwait

2020 ◽  
Author(s):  
O. Bouhdiche ◽  
L. Vivin ◽  
P. Plasterie ◽  
T. Rebert ◽  
M. Retailleau ◽  
...  
Keyword(s):  
Model Building ◽  
Velocity Model ◽  
Surface Velocity ◽  
Near Surface ◽  
Wave Inversion ◽  
Velocity Model Building ◽  
Land Survey
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