Time-frequency analysis of seismic data by time-reassigned multi-synchrosqueezing transform to detect low frequency shadows

Identification of low-frequency shadows is very important in the sense that they are related to gas reservoirs. These low-frequency shadows, produced by gas attenuation on seismic waves, cause the low frequencies under the gas reservoirs to have stronger amplitudes compared to the amplitudes of the high frequencies. Therefore, if proper time accuracy is considered in the identification of this indicator, the gas reservoir, and consequently, its position will be identified with considerable accuracy. One of the methods of identifying low-frequency shadows is timefrequency transforms. Therefore, those time-frequency transforms that have good time and frequency resolution, can be of great help in identifying low-frequency shadows. In this research, a method called time-reassigned multisynchrosqueezing transform (TMSST) is used that acts better than common time-frequency transforms such as shorttime Fourier transform (STFT), reassignment method (RM), synchrosqueezing transform (SST) and multisynchrosqueezing transform (MSST) in terms of time and frequency resolution. Therefore, by applying this transform on a synthetic dataset and a real dataset, its performance has been demonstrated. As a seismic application, singlefrequency sections obtained from a hydrocarbon field were prepared in MATLAB environment and low-frequency shadow anomalies were detected using this time-frequency method with high resolution. In addition, in this study, the Rennie parameter, which is directly related to the sparsity, has been used to evaluate the energy concentration. The number obtained for the Rennie parameter using the method proposed in this paper is another reason for proving the remarkable performance of this method in obtaining time-frequency representation with high time and high frequency resolution at the same time.