Full-waveform inversion and joint migration inversion with an automatic directional total variation constraint

Qu, Shaobo; Verschuur, Eric; Chen, Yangkang

doi:10.1190/geo2018-0085.1

Cited by 32 publications

(7 citation statements)

References 25 publications

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“…We solve the directional total variation using the split‐Bregman iterative algorithm (Goldstein and Osher ; Qu et al . ).…”

Section: Theory Of High‐resolution Simultaneous Joint Migration Invermentioning

confidence: 97%

“…The second penalty function in equation denotes a directional total variation regularization (Qu, Verschuur and Chen ) on the baseline velocity and velocity difference. This constraint smooths the model by enhancing the sparsity of the spatial gradient of the velocity, thereby, preserving its edges and meanwhile it optimally includes geologic information.…”

Section: Theory Of High‐resolution Simultaneous Joint Migration Invermentioning

confidence: 99%

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Simultaneous joint migration inversion for high‐resolution imaging/inversion of time‐lapse seismic datasets

Verschuur

2020

Geophysical Prospecting

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A B S T R A C TThe current time-lapse practice is to exactly repeat well-sampled acquisition geometries to mitigate acquisition effects on the time-lapse differences. In order to relax the rigid requirements on acquisition effects, we propose simultaneous joint migration inversion as an effective time-lapse tool for reservoir monitoring, which combines a joint time-lapse data processing strategy with the joint migration inversion method. Joint migration inversion is a full-wavefield inversion method that explains the measured reflection data using a parameterization in terms of reflectivity and propagation velocity. Both the inversion process inside the imaging/inversion scheme and the extra illumination provided by including multiples in joint migration inversion makes the obtained velocity and reflectivity operator largely independent of the utilized acquisition geometry and, thereby, relaxes the strong requirement of non-repeatability during the monitoring. Because simultaneous joint migration inversion inverts for all datasets simultaneously and utilizes various constraints on the estimated reflectivities and velocity, the obtained time-lapse differences have much higher accuracy compared to inverting each dataset separately. It allows the baseline and monitor parameters to communicate with each other dynamically during inversion via a userdefined spatial weighting operator. In order to get more localized time-lapse velocity differences, we further extend the regular simultaneous joint migration inversion to a robust high-resolution simultaneous joint migration inversion process using the timelapse reflectivity difference as an extra constraint for the velocity estimation during inversion. This constraint makes a link between the reflectivity-and the velocity difference by exploiting the relationship between them. We demonstrate the feasibility of the proposed method with a highly realistic synthetic model based on the Grane field offshore Norway and a time-lapse field dataset from the Troll Field.

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“…We solve the directional total variation using the split‐Bregman iterative algorithm (Goldstein and Osher ; Qu et al . ).…”

Section: Theory Of High‐resolution Simultaneous Joint Migration Invermentioning

confidence: 97%

Section: Theory Of High‐resolution Simultaneous Joint Migration Invermentioning

confidence: 99%

Simultaneous joint migration inversion for high‐resolution imaging/inversion of time‐lapse seismic datasets

Verschuur

2020

Geophysical Prospecting

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“…The fifth penalty term in equation () denotes a directional total variation regularization Qu et al . (2018) on the baseline velocity and velocity difference. This penalty term smooths the model by enhancing the sparsity of the spatial gradient of the velocity, thereby, preserving its edges and meanwhile it optimally includes geologic information.…”

Section: Review Of High‐resolution Simultaneous Joint Migration Inversion As An Effective Time‐lapse Imaging Toolmentioning

confidence: 99%

“…Qu et al . (2018) provide more details on the directional total variation regularization. Note that the optimal values of the weighting parameters

λ_{1}

λ_{2}

and

λ_{3}

are usually data dependent.…”

Section: Review Of High‐resolution Simultaneous Joint Migration Inversion As An Effective Time‐lapse Imaging Toolmentioning

confidence: 99%

Simultaneous joint migration inversion as a high‐resolution time‐lapse imaging method: Feasibility and robustness study

Verschuur

2021

Geophysical Prospecting

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The conventional time‐lapse processing workflow is usually sensitive to the non‐repeatable uncertainties between different vintages caused by noise, acquisition designs and independent processing. Therefore, in order to reduce these non‐repeatable uncertainties, all the datasets are usually acquired from well‐sampled and well‐repeated acquisition surveys, and the independent processing is always carefully tailored to maximally reduce the non‐repeatable uncertainties during processing. Moreover, the conventional time‐lapse analysis method, based on a time‐shift map, is not always a good indicator of the actual velocity differences due to its local one‐dimensional subsurface assumption. In order to relax these rigid requirements and have a better velocity change indicator, a robust high‐resolution simultaneous joint migration inversion was proposed as an effective time‐lapse tool for reservoir monitoring. The method combines a simultaneous data‐processing strategy with the joint migration inversion method. Joint migration inversion is a full wavefield inversion method with a parameterization in terms of reflectivity and propagation velocity. The simultaneous strategy allows the baseline and monitor parameters to communicate and compensate with each other dynamically during inversion, thus, suppressing the non‐repeatable uncertainties during the time‐lapse processing. To investigate the feasibility of using high‐resolution simultaneous joint migration inversion in practice, some numerical experiments are conducted to test the dependence of high‐resolution simultaneous joint migration inversion on the quality of the time‐lapse datasets including the following aspects: random noise; sparse surveys; ocean bottom node versus streamer (different types of monitoring surveys); non‐repeated sources, including source positioning errors and non‐repeated source wavelets; spatial weighting operators in the L2‐norm penalty terms; and sensitivity to weak time‐lapse effects. All the experiments are carried on the basis of a realistic synthetic time‐lapse model based on the Grane field offshore Norway. These experiments show that high‐resolution simultaneous joint migration inversion is robust to random noise, survey sparsity, survey non‐repeatability, source positioning errors and source wavelet discrepancies. Furthermore, high‐resolution simultaneous joint migration inversion remains effective when the spatial weighting operators in the L2‐norm penalty terms are largely relaxed, and high‐resolution simultaneous joint migration inversion is capable of detecting weak time‐lapse changes (e.g. velocity changes down to ±35 m/s).

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“…To solve the problem of normal L1 norm inversion, the main methods include the iterative shrinkage thresholding/ shaping method [28][29][30], the preconditioned conjugate gradient (PCG) method [31,32], and split-Bregman method [33]. Among them, the PCG method has the advantages of solving the convenient inverse matrix and accelerating the convergence rate [34].…”

Section: E Ant Colony Optimizationmentioning

confidence: 99%

Ant Colony Optimization Inversion Using the L1 Norm in Advanced Tunnel Detection

Zhou

Deng

et al. 2021

Mathematical Problems in Engineering

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During the construction of the tunnel, there may be water-bearing anomalous structures such as fault fracture zone. In order to ensure the safety of the tunnel, it is necessary to carry out advanced tunnel detection. The traditional linear inversion method is highly dependent on the initial model in the tunnel resistivity inversion, which makes the inversion results falling into the local optimal optimum rather than the global one. Therefore, an inversion method for tunnel resistivity advanced detection based on ant colony algorithm is proposed in this paper. In order to improve the accuracy of tunnel advanced detection of deep anomalous bodies, an ant colony optimization (ACO) inversion is used by integrating depth weighting into the inversion function. At the same time, in view of the high efficiency and low cost of one-dimension inversion and the advantages of L1 norm in boundary characterization, a one-dimensional ant colony algorithm is adopted in this paper. In order to evaluate the performance of the algorithm, two sets of numerical simulations were carried out. Finally, the application of the actual tunnel water-bearing anomalous structure was carried out in a real example to evaluate the application effect, and it was verified by excavation exposure.

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Full-waveform inversion and joint migration inversion with an automatic directional total variation constraint

Cited by 32 publications

References 25 publications

Simultaneous joint migration inversion for high‐resolution imaging/inversion of time‐lapse seismic datasets

Simultaneous joint migration inversion for high‐resolution imaging/inversion of time‐lapse seismic datasets

Simultaneous joint migration inversion as a high‐resolution time‐lapse imaging method: Feasibility and robustness study

Ant Colony Optimization Inversion Using the L1 Norm in Advanced Tunnel Detection

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