• The Journal of Engineering Geology
• /
• v.16 no.4 s.50
• /
• pp.327-335
• /
• 2006

Three processing strategies of seismic refraction data are tested in terms of velocity and depth profiles or structures for mapping of geological discontinuities: GRM(generalized reciprocal method), GLI(generalized linear inversion), Tomography. The test data used in this study are the shot gathers reconstructed by numerical modeling for the structures of 3 planar layers(horizontal, inclined), the buried vertical fracture zones, and vertical fault zones. Tomography is shown to be very efficient for mapping of more complicated tone such as vertical fault and buried fracture zones, whereas GRM and GLI can be useful for horizontal and/or inclined layers, probably on their bases of analysis of first arrivals in travel time curves.

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

• The Journal of the Acoustical Society of Korea
• /
• v.42 no.4
• /
• pp.305-312
• /
• 2023

Physics-Informed Neural Network (PINN) is used to invert bubble size distributions from attenuation losses. By considering a linear system for the bubble population inversion, Adaptive Learned Iterative Shrinkage Thresholding Algorithm (Ada-LISTA), which has been solved linear systems in image processing, is used as a neural network architecture in PINN. Furthermore, a regularization based on the linear system is added to a loss function of PINN and it makes a PINN have better generalization by a solution satisfying the bubble physics. To evaluate an uncertainty of bubble estimation, deep ensemble is adopted. 20 Ada-LISTAs with different initial values are trained using the same training dataset. During test with attenuation losses different from those in the training dataset, the bubble size distribution and corresponding uncertainty are indicated by average and variance of 20 estimations, respectively. Deep ensemble Ada-LISTA demonstrate superior performance in inverting bubble size distributions than the conventional convex optimization solver of CVX.

• Geophysics and Geophysical Exploration
• /
• v.19 no.3
• /
• pp.136-144
• /
• 2016

Since seismic inversion is based on the wave equation, it is important to calculate the solution of wave equation exactly. In particular, full waveform inversion would produce reliable results only when the forward modeling is accurately performed because it uses full waveform. When we use finite-difference or finite-element method to solve the wave equation, the convergence of numerical scheme should be guaranteed. Although the general proof of convergence is provided theoretically, the consistency and stability of numerical schemes should be verified for practical applications. The implementation of source function is the most crucial factor for the consistency of modeling schemes. While we have to use the sinc function normalized by grid spacing to correctly describe the Dirac delta function in the finite-difference method, we can simply use the value of basis function, regardless of grid spacing, to implement the Dirac delta function in the finite-element method. If we use frequency-domain wave equation, we need to use a conservative criterion to determine both sampling interval and maximum frequency for the source wavelet generation. In addition, the source wavelet should be attenuated before applying it for modeling in order to make it obey damped wave equation in case of using complex angular frequency. With these conditions satisfied, we can develop reliable inversion algorithms.

• Journal of the Korean earth science society
• /
• v.25 no.8
• /
• pp.774-783
• /
• 2004

The S wave velocity and Q$s^{-1}$ structure of the uppermost part of the soil in Nakdong Delta area have been obtained to determine the characteristics of the forementioned soil. The phase and attenuation coefficients of multichannel seismic records were inverted to obtain the S wave velocity and Q$s^{-1}$ structure of the soil. The inversion results have been compared with the borehole measurements of the area. The seismic signal of the nearest geophone from a seismic source was used as the source signal to obtain the attenuation coefficients. Amplitude ratios of the signal at each geophone to the source signal wave plotted as a function of distance for the frequency range between 10 Hz and 45 Hz. The slope of a linear regression line which fits amplitude ratio-distance relationship best for a given frequency was used as the attenuation coefficients for the frequency. The dispersion curve of Rayleigh waves and the attenuation coefficients were inverted to obtain the S-wave velocity and Q$s^{-1}$, respectively, in the uppermost 8 meter of soil layer. The borehole measurements of the area show that are two distinct layers; the upper 4 meter of silty-sand and the lower 4 meter of silty-clay. The inversion results indicate that the shear wave velocity of the upper layer is 80 m/sec and 40m/sec in the lower silty-clay layer. The spacial resolution of the shear wave velocity structure is very good down to a depth of 8 meter. The Q$s^{-1}$ in the upper silty-sand layer is 0.02 and increase to 0.03 in the lower silty-sand layer. The spacial resolution of quality factor is relatively good down to a depth of 5 meter, but very poor below the depth. In this study, the S-wave velocity is higher in the silty-clay and the Q$s^{-1}$ is smaller silty-sand than in the silty-clay. However, much more data should be analyzed and accumulated before making any generalization on the shear wave velocity and Q$s^{-1}$ of the sediments.