High-resolution characterization of subsurface hydrocarbon reservoirs critically depends on the integration of seismic and well log data. However, the precision of well-to-seismic tie procedures is often compromised by inaccuracies in time-depth conversion curves, stemming from imprecise migration velocity errors, scale discrepancy and the inherent domain disparity between seismic data (time-domain) and well log data (depth-domain). Traditional methods rely on iterative wavelet estimation and manual adjustments, leading to wavelet-depth curve coupling problems, reduced accuracy, and significant time consumption. To address these limitations, we propose a novel automated time-depth conversion curve correction method. This approach leverages key feature matching in the time-frequency domain of well log reflection coefficients and borehole-side seismic traces, incorporating dual constraints: stratigraphic constraints in the time domain and spectral notch points in frequency domain. By directly aligning with well log reflection coefficients, this time-frequency joint analysis eliminates the need for wavelet estimation as well as iterative and interactive processes. Experiments performed using both synthetic and field data demonstrate the validity and effectiveness of the proposed method compared to traditional iterative matching method and interactive commercial software.

Dynamic wavelet amplitude spectra extraction (DWASE), which is an ill-posed problem, is of great importance for nonstationary seismic data processing. The most difficult challenge is how to decouple the dynamic wavelets and reflection coefficients. The traditional DWASE methods solve the ill-posed problem depending on some prior information, such as the piecewise stationary hypothesis or estimation of the attenuation factor$Q$. In this article, we propose a multitask learning-based DWASE method and apply the method for$Q$estimation. Our proposed method can reduce the multiplicity of the ill-posed problem by estimating the logarithmic time–frequency amplitude spectrum (logarithmic TFAS) of both reflection coefficients and dynamic seismic wavelets, simultaneously. In our method, a parameter-sharing U-net is used to extract the logarithmic TFAS of the reflection coefficients and dynamic wavelets from the logarithmic TFAS of the nonstationary seismic data. To verify the accuracy of the DWASE results of our method, we make a quantitative analysis of the synthetic seismic data, which are obtained by our method and some traditional methods. We also apply the DWASE results of our method for$Q$estimation and attenuation compensation in both synthetic and field seismic data, to prove the effectiveness of the method. Also, comparisons with some traditional methods are given.

Deghosting is a critical step to improve the resolution of marine seismic data. For the frequency wavenumber (f-k) spectra of the original seismic data with ghosts, there always exist some low-SNR frequency components around the centers of the frequency notch bands, which are caused by the ghosts. In general, the traditional inverse filtering-based deghosting methods lack the ability to recover these low-SNR frequency components without amplifying the additive noise. In this letter, we propose a postprocessing method to enhance the deghosted seismic data by reconstructing these low-SNR frequency components. The proposed method includes two key steps. First, the low-SNR frequency components of the deghosted seismic data in the f-k domain are located adaptively by using both the original seismic data and its deghosted result. Second, the projection onto convex sets algorithm is introduced to reconstruct these low-SNR frequency components located in the previous step. To illustrate this, we use the proposed method to improve the deghosted results obtained by the non-Gaussianity maximization based f-k deghosting method. Applications on synthetic and real field marine data sets demonstrate the validity of the proposed method which can enhance the deghosted results significantly.