We designed an improved pulse sequence combined with the hybrid saturation recovery and inversion recovery and the multiple echo spaced Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence (HSIR-ME-CPMG) to measure the longitudinal relaxation time (T1) and the transverse relaxation time (T2) simultaneously, to overcome the shortcomings of conventional IR-CPMG and SR-CPMG pulse train acquisition time and low image resolution. The longitudinal relaxation time (T1) is encoded using the combination of the saturation recovery and the inversion recovery pulse sequence to improve the resolution between different relaxation components. The transverse relaxation time (T2) are encoded by the CPMG pulse sequence. To reduce the energy consumption and the data storage space, the echo spacing is varied in different windows. Numerical simulations show that the proposed pulse sequence can capture the contrast between different components with similar relaxation times, even in low signal-to-noise ratio (SNR). The proposed pulse sequence can be popularized for better characterizing relaxation components of unconventional reservoirs such as the shale oil.

Reverse time migration based on the two-way wave equation using cross correlation imaging can effectively yield migrated sections. However, when extrapolating the wavefield, reflected waves produce high-amplitude, low-frequency artifacts, so the imaging quality is a serious concern. Applying a Laplace filter to the migrated image can effectively suppress noise. However, the results of applying this method are often polluted by discontinuous events and residual noise. To avoid these limitations, this article proposes a Laplace filter by seeking the dip angle of underground structure. Based on an analysis of the mechanism responsible for generating low-frequency noise, this article analyses the basic principle and application of Laplace filter, and the low-frequency and high-amplitude characteristics of existing problems when extrapolating the wavefield. The low-frequency noise of the dipping strata and horizontal layer is different in imaging results, so this article proposes establishing a Laplace operator based on dip angle of formation that is approximately normal to implement the filter. The results of experiments on models containing simple and complex structures show that the Laplace filter related to dip angle is effective and can better eliminate the low-frequency noise generated by back-reflected waves while maintaining the continuity of the events and protecting the effective signal.

Wavefield Reconstruction Inversion (WRI) is a novel seismic inversion method with a more expansive search space and reduced non-linearity and is more robust than other conventional techniques (e.g., full waveform inversion, FWI) in terms of local minima. Most of the modifications and applications of WRI are generally employed in the frequency domain, while the current time-domain solutions for WRI are either based on rough approximations, which lead to inaccurate inversion results, or are computationally expensive due to the data-domain Hessian. Here, we will provide another perspective to address time-domain WRI and maintain its effectiveness, namely reducing non-linearity and the effects of “cycle-skipping” while ignoring the data-domain Hessian. On the basis of iteratively solving the inversion problem, the time-domain augmented wave equation is solved by approximating the reconstructed wavefield on the right as the current wavefield. Due to this approximation and the traditionalL2measurement, the reconstructed wavefield based on the poor model will be inaccurate away from the receivers, which will affect the subsequent model inversion. To alleviate this problem, we incorporate the unbalanced optimal transmission (UOT) distance measurement into the inversion. This distance measurement method can accurately obtain the best misfit between two unbalanced signals. Moreover, the accurately measured misfit with low-frequency and long-wavelength acts as an extended source of the subsequent wavefield reconstruction, ensuring the reconstructed wavefield’s accuracy based on the defective initial model without excessive additional computation. The numerical results demonstrate the accuracy and applicability of the proposed method.

Full Waveform Inversion (FWI) methods evaluate subsurface media properties by minimizing the misfit between synthetic and observed data. However, its derivation process omits measurement errors and physical assumptions in modeling, resulting in many problems in practical utilizations. Wavefield Reconstruction Inversion (WRI) can handle non-physical data by combining theoretical and measurement errors but is computationally expensive in large-scale or 3D cases. An alternative formula for WRI in the form of traditional FWI (WRI in the FWI form, FWRI) was developed. The new form includes a medium-dependent weight function or a data domain Hessian matrix. Based on the data domain Hessian matrix, a feasible accurate time-domain solution is provided for 2D and 3D FWRI. Specifically, the point spread function (PSF) method and an-isotropic TV regularization were used to calculate the weighted residuals. Furthermore, an approximate solution based on the forward operator is provided, considering the efficient application in 3D. Numerical tests using the homogeneous model show that accurately computed residuals mitigate the effects of "cycle-skipping" and non-physical data. Sensitivity kernel analysis demonstrates the low wavenumber update mechanism of FWRI and verifies the robustness of the data domain Hessian to "cycle-skipping". The robustness of the proposed algorithm is demonstrated using a 2D Marmousi model with partially elastic perturbations and a visco-acoustic Red Sea model. Furthermore, the 3D Overthrust model is solved with an approximate solution to demonstrate the feasibility of the proposed method in 3D applications.

High-precision time-frequency (TF) analysis (TFA) based on the synchrosqueezing wavelet transform can improve the TF resolution and sharpen TF representations (TFRs). However, this method works only for time-varying signals and cannot characterize spatially varying signals with unique spatial properties. Herein, we propose extending the time-domain synchrosqueezing wavelet transform (TSWT) to the spatial domain, yielding the spatial-domain synchrosqueezing wavelet transform (SSWT), and we introduce a velocity parameter. First, we apply the proposed SSWT to several synthetic signals to test its feasibility. From such experiments, we observe that the SSWT is capable of characterizing non-stationary, spatially varying signals with high resolution and robustness, characteristics that are inherited from the TSWT. The SSWT can also maintain its signal reconstruction ability. Furthermore, we apply the SSWT to suppress ground roll in seismic data processing. Through examples of both synthetic datasets and field datasets, we conclude that the SSWT can accurately characterize spatially varying signals and could have many potential applications in the field of signal analysis and processing.