Fast Matching Pursuit Algorithm Research Articles

Seismic resolution enhancement with variational modal-based fast-matching pursuit decomposition

Enhancing vertical resolution and signal-to-noise ratio (S/N) is a key objective in seismic data processing. Considering the underground medium is inhomogeneous and incompletely elastic, seismic wave energy attenuation occurs during underground propagation, which has a significant impact on seismic data resolution and S/N. Traditional fast-matching pursuit (FMP) algorithms make it difficult to separate valid signals and noise effectively while reconstructing the noisy signals. Therefore, an improved FMP algorithm that combines the variational-mode decomposition (VMD) strategy is developed. The VMD algorithm is used to obtain intrinsic mode functions with varying amplitudes, frequencies, and center times. It can achieve a multiscale decomposition of nonstationary seismic data. Based on the intrinsic mode functions of different scales, the FMP algorithm can reconstruct prior information of the amplitude, frequency, and center time of valid signals and noise signals in the mode functions. Thus, the high-resolution sparse representation of intrinsic mode functions is achieved. The numerical results indicate that our method not only separates the effective signal and noise but also preserves the valid signal as much as possible. In addition, the feasibility of the method is further verified by field exploration data. The results indicate that this strategy can enhance the resolution of seismic data while restoring the attenuated energy using multiscale seismic data.

Two-Stage Fast Matching Pursuit Algorithm for Multi-Target Localization

Two-Stage Fast Matching Pursuit Algorithm for Multi-Target Localization

Generalized Orthogonal Matching Pursuit With Singular Value Decomposition

Matching pursuit (MP) is an algorithm that can represent signal sparsely, and this advantage makes MP popular in signal processing. However, MP algorithm is a greedy algorithm which means it cannot deal with a large family of signals like seismic data which becomes larger and larger with development of data acquisition technologies. Generalized orthogonal MP (GOMP) is an improved algorithm which helps to reduce the cost of the calculation greatly. Fast MP algorithm is a method that can build dynamic dictionary by making full use of the characteristics of the original signal. In this study, singular value decomposition (SVD) is involved into the GOMP algorithm with dynamic dictionary to improve its efficiency. Compared with conventional MP, the proposed method picks multiatoms at each iteration. It has advantage in calculation speed and can reconstruct the original signal more exactly. Synthetic and field data examples are utilized to demonstrate the feasibility, computational efficiency, and precision of the proposed method.