Large-scale geologic structures with strong contrasts present difficulties for seismic imaging and inversion. Previously, direct envelope inversion (DEI) with ultralow frequency has been proposed to retrieve strong-contrast velocity models. However, the envelope signals lose the instantaneous phase of the original wavefield, resulting in erroneous seismic inversion results. To solve this problem, the instantaneous phase of the wavefield is encoded into envelopes for DEI. In this way, a polarized envelope is created to contain both abundant low frequencies and the instantaneous phase information of the original seismic waveform. Furthermore, to improve the resolution of deep regions underneath the strong reflections, we propose a phase-amplitude-based polarized direct envelope inversion (PA-PDEI) misfit in the time-frequency domain that uses an amplitude factor to emphasize the phase information. This makes it possible to adjust the amplitude influence and boost the low-amplitude seismic signals for deep targets. Tests on a model with a salt body and on field data show that combining the PA-PDEI with the phase-amplitude-based full-waveform inversion (PA-FWI) make it possible to reliably reconstruct velocity models, especially for deep regions, despite large and strong perturbations.

Full Waveform Inversion (FWI) is based on the least squares algorithm to minimize the difference between the synthetic and observed data, which is a promising technique for high-resolution velocity inversion. However, the FWI method is characterized by strong model dependence, because the ultra-low-frequency components in the field seismic data are usually not available. In this work, to reduce the model dependence of the FWI method, we introduce a Weighted Local Correlation-phase based FWI method (WLCFWI), which emphasizes the correlation phase between the synthetic and observed data in the time-frequency domain. The local correlation-phase misfit function combines the advantages of phase and normalized correlation function, and has an enormous potential for reducing the model dependence and improving FWI results. Besides, in the correlation-phase misfit function, the amplitude information is treated as a weighting factor, which emphasizes the phase similarity between synthetic and observed data. Numerical examples and the analysis of the misfit function show that the WLCFWI method has a strong ability to reduce model dependence, even if the seismic data are devoid of low-frequency components and contain strong Gaussian noise.

Full waveform inversion (FWI) is, as an optimization problem, strongly nonconvex and is influenced intensely by the cycle skipping issue, especially for multiparameter inversions like the elastic case. When there are strong scattering heterogeneities in the target media, there will be more challenges for the inversion problem. The direct envelope inversion strategy uses the envelope Fréchet derivative to tackle the cycle skipping problem for strong scattering inversion and has been effectively used for the acoustic case. We extend the direct envelope inversion method to the elastic situation in this letter. We derive the elastic envelope Fréchet derivative and show how the strong scattering multiparameter elastic inversion is accomplished under the direct envelope inversion framework. Numerical tests with the SEG/EAGE salt velocity model proved the effectiveness of this method for the strong scattering elastic medium.

Audio magnetotelluric (AMT) is commonly used in mineral resource exploration. However, the weak energy of AMT signals makes them susceptible to being overwhelmed by noise, leading to erroneous geophysical interpretations. In recent years, deep learning has been applied to AMT denoising and has shown better denoising performance compared to traditional methods. However, current deep learning denoising methods overlook the characteristics of AMT signals, resulting in reduced denoising accuracy. To enhance the denoising performance of deep learning by better matching the features of AMT signals, we propose a CBAM-based (Convolutional Block Attention Module) method for AMT denoising. This method focuses on the features of AMT signals and improves the process from three aspects: (1) In the establishment of the sample set, we adopt a multi-component form based on the correlation of noise to enable the neural network to explore the potential connections among the components of AMT during the training process, thus constructing a stronger network mapping relationship. (2) In the construction of the neural network, we have introduced the CBAM structure into the residual blocks of the ResNet to enhance the network’s feature learning capability by focusing on the characteristics of noise. (3) In the design of the denoising procedure, we adopt a process of identification before denoising to protect the noise-free data segments from being compromised during the denoising process. Finally, through synthetic, field data experiments, and comparative tests, we demonstrate that our proposed method achieves higher denoising accuracy than some traditional methods and conventional deep learning methods.

Least-squares reverse time migration (LSRTM) aims to produce a high-quality migration image of complex geological structures. However, the weaker deep seismic reflections are often masked by the overlying strata in migration images. Therefore, it is difficult for the LSRTM to image the deeper structures. This letter proposes a phase-amplitude LSRTM (PA-LSRTM) in the time-frequency domain to improve the migration image of deep seismic reflections and subsalt structures. The PA-LSRTM is formulated as an inverse problem that minimizes the time-frequency phase-amplitude difference between the predicted and observed data. The seismic data was initially transformed into the time-frequency domain to establish a PA-LSRTM misfit. An amplitude factor was then introduced in the time-frequency misfit to weaken the weights of amplitude components. In this case, it emphasized the similarity of phase components. Furthermore, the sparsity promotion method was combined with a simultaneous source technique to increase the computational efficiency and reduce the crosstalk noise. The PA-LSRTM with sparsity promotion (SPA-LSRTM) misfit can finally be solved using a Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). The numerical and marine field data tests demonstrate that the SPA-LSRTM can effectively produce a high-resolution image of deep structures.