Knowledge of Q is desirable for improving seismic resolution through inverse Q filtering, facilitating amplitude analysis and seismic interpretation, and understanding the subsurface environment better. However, Q is rarely measured since estimates are mainly made from VSP data, which is usually not available. In addition, the question of reliable Q estimation remains, especially in case of unfavorable signal to noise ratio (SNR). Estimating Q from VSP or even reflection data in presence of moderate noise with sufficient accuracy is still under investigation. To address this problem, a match-filter method for Q estimation is proposed in this paper, and evaluated using synthetic 1D, 2D data and field data. Our method takes two local amplitude spectra from different times in a seismic record and estimates the interval Q between them. First we compute minimum-phase equivalent wavelets from each amplitude spectra, and then we find the best forward Q filter that best matches the shallow wavelet to the deeper wavelet. Our method is theoretically similar to the spectral-ratio method because the inverse Fourier transform of a spectral ratio is a matching filter. However, computing the match filter in the time domain is more robust in the presence of noise than direct spectral division in the frequency domain. Testing results show that the proposed method is, compared to the spectral-ratio method, more robust to noise and more suitable for the Q estimation from reflection, and has the potential to indentify a localized low Q zone of the subsurface, which can be used as a gas indicator.
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