PREDICTIVE PROCESSING OF ARRAY ACOUSTIC WAVE-FORM DATA

Estimation of wave velocity (or slowness) from array waveform data is a basic and very important process in acoustic logging and seismic pro cessing. A predictive method is developed to process array waveform da ta containing multiple wave modes. These wave modes may overlap in bot h time and frequency and are inseparable using conventional techniques . In this new technique, the waveform at a receiver is modeled by a co mbination of wave data at other receivers using a time-domain predicti on theory. It is assumed that the array data contain a number of propa gating modes. A minimization procedure is formulated to optimize the m atch between the predicted and measured waveforms. yielding slowness e stimates of the wave modes across the array. Most important, the optim ization is performed directly in the time domain using the entire arra y wave data set, including all possible data combinations. This strate gy effectively reduces the noise effects and enhances the robustness o f the estimation. Furthermore, the estimated slowness values can be us ed in formulating a procedure to split the array data into individual wave modes, allowing their behavior to be analyzed. Examples are shown to demonstrate the ability of the technique to extract wave slowness from multiple wave-mode data.