• Geophysics and Geophysical Exploration
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• v.8 no.2
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• pp.163-169
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• 2005

Recently, the velocity stack domain is having an attention as a very useful domain for various processing in seismic data processing. In order to be used in many applications, the velocity stack should be obtained through an inversion method and the used inversion should have properties like the robustness to noise and the parsimony of velocity stack result. Iteratively Reweighted Least-Squares (IRLS) method is the one of the inversion methods that have such properties. This paper describes the theoretical background, implementation of the method, and examines the characteristics and limits of the IRLS method.

• Geophysics and Geophysical Exploration
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• v.8 no.2
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• pp.170-176
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• 2005

Recently the velocity stack inversion is having many attentions as an useful way to perform various seismic data processing. In order to be used in various seismic data processing, the inversion method used should have properties such as robustness to noise and parsimony of the velocity stack result. The IRLS (Iteratively Reweighted Least-Squares) method that minimizes ${L_1}-norm$ is the one used mostly. This paper introduce another method, CGG (Conjugate Guided Gradient) method, which can be used to achieve the same goal as the IRLS method does. The CGG method is a modified CG (Conjugate Gradient) method that minimizes ${L_1}-norm$. This paper explains the CGG method and compares the result of it with the one of IRSL methods. Testing on synthetic and real data demonstrates that CGG method can be used as an inversion method f3r minimizing various residual/model norms like IRLS methods.

• Economic and Environmental Geology
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• v.48 no.2
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• pp.115-130
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• 2015

Extended elastic impedance (EEI) is an extension of elastic impedance (EI) which is a generalization of acoustic impedance (AI) for nonzero angles of incidence and can be tuned to be proportional to reservoir properties. In this study, we evaluated EEI inversion by estimating the P-($V_p$) and S-wave velocities ($V_s$), P-wave to S-wave velocity ratio ($V_p/V_s$), and Poisson's ratio of the Second Wall Creek Sand of the Teapot Dome field, Wyoming, USA. We also applied the EEI inversion technique to estimate porosity, gamma-ray values, and density of the Second Wall Creek Sand. Data used in the study include 3-D pre-stack seismic data from the southern part of the field and four wells, selected from a large well database. The $V_s$ logs at the wells were constructed from the $V_p$ logs using the empirical relationships. The percent prediction errors for the four velocity properties are less than about 5% except for Poisson's ratio at one well, supporting that the EEI inversion can be used in the prediction of rock properties. However, the results from the EEI inversion analysis of porosity, gamma-ray values, and density at the wells were unsatisfactory and thus these properties, which are not directly computed from velocities, may not be suitable for EEI inversion.

• Geophysics and Geophysical Exploration
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• v.10 no.2
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• pp.124-130
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• 2007

Least squares ($l^2$ norm) solutions of seismic inversion tend to be very sensitive to data points with large errors. The $l^1$ norm minimization gives more robust solutions, but usually with higher computational cost. Iteratively reweighted least squares (IRLS) method gives efficient approximate solutions of these $l^1$ norm problems. I propose an efficient implementation of the IRLS method for a hybrid $l^1/l^2$ minimization problem that behaves as $l^2$ norm fit for small residual and $l^1$ norm fit for large residuals. The proposed algorithm shows more robust characteristics to the decision of the threshold value than the l1 norm IRLS inversion does with respect to the threshold value to avoid singularity.

• Geophysics and Geophysical Exploration
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• v.17 no.2
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• pp.66-73
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• 2014

To estimate the shear-velocity ($v_s$) structure beneath the WIZ station on White Island in New Zealand, we applied receiver function (RF) inversion and H-${\kappa}$ stacking methods to 362 teleseismic events (Mw > 5.5) recorded during April 20, 2007 to September 6, 2013. Using 71 RFs with errors less than 20% after 200 iterative computations, we determined that the depth to Moho of $v_s$ = 4.35 km/s is $24{\pm}1km$ within a 15 km radius of the station. In an 1-d $v_s$ model derived by RF inversions, a 4-km thick low-velocity layer (LVL) at depths of 18 ~ 22 km was identified in the lower crust. This LVL, which is 0.15 km/s slower than the rocks above and below it, may indicate the presence of a deep magma reservoir. The H-${\kappa}$ stacking method yielded an estimate of the depth to the Moho of 24.5 km, which agrees well with the depth determined by RF inversions. The low $v_p/v_s$ ratio of 1.64 may be due to the presence of gas-filled rock or hot crystallizing magma.

• Geophysics and Geophysical Exploration
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• v.22 no.4
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• pp.225-238
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• 2019

Migration velocity analysis (MVA) for creating optimum depth-domain velocities in seismic imaging was applied to marine long-offset multi-channel data, and the effectiveness of the MVA approach was demonstrated by the combinations of conventional data processing procedures. The time-domain images generated by conventional time-processing scheme has been considered to be sufficient so far for the seismic stratigraphic interpretation. However, when the purpose of the seismic imaging moves to the hydrocarbon exploration, especially in the geologic modeling of the oil and gas play or lead area, drilling prognosis, in-place hydrocarbon volume estimation, the seismic images should be converted into depth domain or depth processing should be applied in the processing phase. CMP-based velocity analysis, which is mainly based on several approximations in the data domain, inherently contains errors and thus has high uncertainties. On the other hand, the MVA provides efficient and somewhat real-scale (in depth) images even if there are no logging data available. In this study, marine long-offset multi-channel seismic data were optimally processed in time domain to establish the most qualified dataset for the usage of the iterative MVA. Then, the depth-domain velocity profile was updated several times and the final velocity-in-depth was used for generating depth images (CRP gather and stack) and compared with the images obtained from the velocity-in-time. From the results, we were able to confirm the depth-domain results are more reasonable than the time-domain results. The spurious local minima, which can be occurred during the implementation of full waveform inversion, can be reduced when the result of MVA is used as an initial velocity model.