اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدRobust and high-resolution seismic complex trace analysis
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M. Kazemnia Kakhki
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Seismic attributes calculated by conventional methods are susceptible to noise. Conventional filtering reduces the noise in the cost of losing the spectral bandwidth. The challenge of having a high-resolution and robust signal processing tool motivated us to propose a sparse time-frequency decomposition while is stabilized for random noise. The procedure initiates by using Sparsity-based adaptive S-transform to regularize abrupt variations in frequency content of the nonstationary signals. Then, considering the fact that a higher amplitude of a frequency component results in a higher signal to noise ratio, an adaptive filter is applied to the time-frequency spectrum which is sparcified previously. The proposed zero adaptive filter enhances the high amplitude frequency components while suppresses the lower ones. The performance of the proposed method is compared to the sparse S-transform and the robust window Hilbert transform in estimation of instantaneous attributes by applying on synthetic and real data sets. Seismic attributes estimated by the proposed method is superior to the conventional ones in terms of its robustness and high resolution image. The proposed approach has a vast application in interpretation and identification of geological structures.
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Squared Coherence Canonical Correlation Analysis (MSC-CCA). Yet, my analysis did not require such two parameters. In fact: 1) using the MSC-CCA reduced-rank option neither changes the result nor was needed since Scafetta (2016) statistically evaluated the significance of the coherence spectral peaks; 2) the analysis algorithm neither contains nor needed the regularization term. Herein, I show that Holm could not replicate Scafetta (2016) because he used different analysis algorithms. In fact, although Holm claimed to be using MSC-CCA, for his figures 2-4 he used a MatLab code labeled gcs_cca_1D.m (see paragraph 2 of his Section 3), which Holm also modified, that implements a different methodology known as the Generalized Coherence Spectrum using the Canonical Correlation Analysis (GCS-CCA). This code is herein demonstrated to be unreliable under specific statistical circumstances such as those required to replicate Scafetta (2016). On the contrary, the MSC-CCA method is stable and reliable. Moreover, Holm could not replicate my result also in his figure 5 because there he used the basic Welch MSC algorithm by erroneously equating it to MSC-CCA. Herein I clarify step-by-step how to proceed with the correct analysis, and I fully confirm the 95% significance of my results. I add data and codes to easily replicate my results.
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