Time-frequency analysis is able to reveal the useful information hidden in the seismic data. The high resolution of the time-frequency representation is of great importance to depict geological structures. In this letter, we propose a novel seismic time-frequency analysis approach using the newly developed empirical wavelet transform (EWT). It is the first time that EWT is applied in analyzing multichannel seismic data for the purpose of seismic exploration. EWT is a fully adaptive signal-analysis approach, which is similar to the empirical mode decomposition but has a consolidated mathematical background. EWT first estimates the frequency components presented in the seismic signal, then computes the boundaries, and extracts oscillatory components based on the boundaries computed. Synthetic, 2-D, and 3-D real seismic data are used to comprehensively demonstrate the effectiveness of the proposed seismic time-frequency analysis approach. Results show that the EWT can provide a much higher resolution than the traditional continuous wavelet transform and offers the potential in precisely highlighting geological and stratigraphic information.

We have introduced a novel time-frequency decomposition approach for analyzing seismic data. This method is inspired by the newly developed variational mode decomposition (VMD). The principle of VMD is to look for an ensemble of modes with their respective center frequencies, such that the modes collectively reproduce the input signal and each mode is smooth after demodulation into baseband. The advantage of VMD is that there is no residual noise in the modes and it can further decrease redundant modes compared with the complete ensemble empirical mode decomposition (CEEMD) and improved CEEMD (ICEEMD). Moreover, VMD is an adaptive signal decomposition technique, which can nonrecursively decompose a multicomponent signal into several quasi-orthogonal intrinsic mode functions. This new tool, in contrast to empirical mode decomposition (EMD) and its variations, such as EEMD, CEEMD, and ICEEMD, is based on a solid mathematical foundation and can obtain a time-frequency representation that is less sensitive to noise. Two tests on synthetic data showed the effectiveness of our VMD-based time-frequency analysis method. Application on field data showed the potential of the proposed approach in highlighting geologic characteristics and stratigraphic information effectively. All the performances of the VMD-based approach were compared with those from the CEEMD- and ICEEMD-based approaches.

Random noise attenuation is an important step in seismic data processing. In this paper, we propose a novel denoising approach based on compressive se

Hydrocarbons can cause anomalies in the energy density of seismic signals when seismic waves pass through them. Teager-Kaiser energy (TKE) is an important attribute that can be utilized to depict the energy density of a seismic signal and the energy distribution of a seismic wavefield. In this letter, a novel spectral decomposition-based approach for hydrocarbon detection is proposed that applies the variational mode decomposition (VMD) associated with TKE to seismic data, which is called the VMDTKE algorithm. The proposed method not only possesses the better performance of TKE in focusing instantaneous energy, but also inherits the merit of high time-frequency resolution from VMD. The Marmousi2 example is used to demonstrate that the VMDTKE approach is capable of depicting the location and extent of strong anomalies which correlate to hydrocarbons more clearly. We compare the spectral decomposition results with that from the conventional VMD-based method. Application on field data further confirms the potential of the VMDTKE algorithm in delineating strong amplitude anomalies that are associated with hydrocarbon reservoirs.

Seismic random noise attenuation is a key step in seismic data processing. The random seismic data recorded by the detector tends to have strong noise, and this noisy seismic ratio can be seen as a low signal-to-noise ratio (SNR). Low SNR data can seriously affect the subsequent processing of seismic data, such as migration and imaging. Therefore, it is crucial to eliminate random noise in seismic data. In this paper, we aimed to improve the SNR of seismic data, and proposed an intelligent convolutional neural network noise reduction framework to adaptively capture seismic signals from seismic data with noise. The eponential linear unit (ELU) activation function and the Adam optimization algorithm were used to train the network, which increased the effective information extraction of the network in the negative interval. In order to speed up network training, we added residual learning and batch normalization methods to the network. In addition, three datasets were used to train and test the network. The experimental results verified the feasibility and effectiveness of the model.The experimental results show that the method proposed in this paper is better than feed-forward denoising convolutional neural networks (DnCNNs) and other contrast methods in denoising performance. More importantly, a well-trained network not only preserves weak features in learning, but also removes spatially random noise. First, the proposed method is fully trained to extract random noise from the training data, then we retain the data features learned in the training, and estimate the waveform characteristics in the test set by reconstructing the recorded seismic data. Secondly, the characteristics of seismic data in the field record are quite different from those of the training set. However, the proposed adaptive denoising framework can still capture the connection between prediction and reality from the difference. The processing results of theoretical pure record, common-shot-point record, and field data showed great potential in random noise attenuation applications.