We formulated two coherency measures, based on the bootstrapped differential semblance (BDS) estimator, that offered higher resolution in parameter tracking than did standard normalized differential semblance. Bootstrapping is a statistical resampling procedure used to infer estimates of standard errors and confidence intervals from data samples for which the statistical properties are unattainable via simple means, or when the probability density function is unkown or difficult to estimate. The first proposed estimator was based on a deterministic sorting of original offset traces by alternating near and far offsets to achieve maximized time shifts between adjacent traces. The near offsets were indexed with odd integers, while the even integers were used to index far offsets that were located at a constant index increment from the previous trace. The second was the product of several BDS terms, with the first term being the deterministic BDS defined above. The other terms were generated by random sorting of traces that alternated near and far offsets in an unpredictible manner. The proposed estimators could be applied in building velocity (and anellipticity) spectra for time-domain velocity analysis, depth-domain residual velocity update, or to any parameter-fitting algorithm involving discrete multichannel data. The gain in resolution provided by the suggested estimators over the differential semblance coefficient was illustrated on a number of synthetic and field data examples.
Accuracy Evaluation and Modification Method for Estimating Point Light Spot Positions with Gaussian Fitting Algorithm
