Dynamic smoothing in crosswell traveltime tomography

Abstract

Variable-size (dynamic) smoothing operator constraints are applied in crosswell traveltime tomography to reconstruct both the smooth-and fine-scale details of the tomogram. In mixed and underdetermined problems a large number of iterations may be necessary to introduce the slowly varying slowness features into the tomogram. To speed up convergence, the dynamic smoothing operator applies adaptive regularization to the traveltime prediction error function with the help of the model covariance matrix. By so doing, the regularization term has a larger weight at initial iterations and the prediction error term dominates the final iterations with a small regularization term weight. In addition, it is shown that adaptive regularization acts by reweighting the adjoint modeling operator (preconditioning) and by providing additional damping.Comparisons of two dynamic smoothing operators, the low-pass filter smoothing and the multigrid technique, with the fixed-size (static) smoothing operators show that the dynamic smoothing operator yields more accurate velocity distributions with greater stability for larger velocity contrasts. Consequently, it is a preferred choice for regularization.

Enhanced refractor imaging by supervirtual interferometry

“…Th e picked fi rst arrival traveltimes for both the fi ltered and supervirtual traces are inverted by the tomographic procedure described in Nemeth et al (1997); the resulting tomograms are shown in Figure 5a and 5b. Because the actual velocity model is not known, it is not possible to say with certainty which result is correct, but the tomogram from the supervirtual data appear to show much improved resolution of the soil-bedrock interface near the left side of the image and does not contain the low-velocity zone which appears in the raw data tomogram.…”

Pressure and fluid saturation prediction in a multicomponent reservoir using combined seismic and electromagnetic imaging

“…The majority of effort, as measured by the topics of published and presented work, has concentrated on developing and improving algorithms for estimating the geophysical parameters themselves (Newman, 1995;Lazaratos et al, 1995;Wilt et al, 1995;Nemeth et al, 1997;Goudswaard et al 1998 to list but a few). In most applications where nongeophysical parameters, such as temperature during a steam flood (Lee et al, 1995) or CO 2 saturations during CO 2 flood Wang et al, 1998) are the object of the crosswell survey, correlations between the geophysical parameters, e.g., velocity or electrical conductivity, and the desired reservoir parameter are derived and used to infer the distribution of reservoir parameters from the distribution of the geophysical parameters.…”

Anthropogenic-Induced Changes in the Mechanism of Drylands Ephemeral Stream Recharge, Western Saudi Arabia

“…The first arrival travel times of the recorded data were picked and then inverted to find the subsurface velocity model shown in Figure 13 [25,26]. The travel time tomogram represents the variation in seismic wave propagation in both the horizontal and vertical directions.…”