Abstract：Time-frequency analysis is one of the important methods for thin layer identification with seismic data. Conventional time-frequency analysis methods are affected by fixed time window, window function and other factors. To this end, continuous wavelet transform(CWT), based on tunable factor Gabor wavelet(TFGW), TFGW-CWT for short, is used to process seismic data. In the TFGW-CWT method, TFGW transform is performed, and then the minimum absolute value projection method is employed to combine the continuous wavelet coefficients of each tunable factor to reduce the cross interference of adjacent frequencies and improve the local time-frequency resolution. Furthermore, non-negative matrix factorization(NMF) is conducted to reduce the dimensionality of the time-frequency data body to obtain a low-rank characteristic data structure relation volume. This reduces the data redundancy of high-dimensional space, highlights the high-frequency information, and achieves the purpose of improving the resolution of seismic data. The results of simulation record and actual data processing confirm the validity of the proposed method, which can provide a new technique for thin layer detection.
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