PROJECT TITLE :
Robust Detection and Estimation for Logging While Drilling Monopole Acoustic Data
In this paper, we present ways and methods for strong detection and slowness estimation of weak compressional (P) and shear (S) waves in borehole sonic logging in noisy environments like whereas logging whereas drilling, by proposing methods for enhancing the appearance utilized in the slowness time coherence methodology used in the logging. In this direction our contributions are 2 fold. Initial, we propose a novel class of shrinkage estimators within the discrete Radon remodel domain derived from data semblance, for waveform de-noising to combat random additive noise and reflections. In this context we conjointly determine necessary and sufficient conditions for the optimality of the proposed estimator. Our second contribution lies in developing a unique method to cancel energy propagating at the slowness of borehole modes, chiefly Stoneley, and to a lesser extent, shear, that significantly lowers the semblance of the weaker head wave arrivals. This approach is based on representing the info using time-frequency compact area-time propagators. Finally, we tend to present an algorithm that combines the house time shrinkage in the discrete Radon transform domain and also the proposed interference cancelation methods for enhanced compressional and shear detection via semblance processing. The algorithm is validated on artificial information and demonstrated to boost performance on real logging whereas drilling field information sets.
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