Distributed acoustic sensing (DAS) has emerged as a transformative technology for high-resolution seismic exploration. However, compared to conventional geophysics, the vertical seismic profile (VSP) data obtained by DAS has a relatively low signal-to-noise ratio (SNR). This limitation stems primarily from the transient fiber deployment in casing operations, which leads to suboptimal fiber-ground coupling and creates characteristic zigzag noise patterns that degrade signal fidelity. This study presents a feature picking principal component analysis (FPPCA) framework for adaptive zigzag noise suppression. The method includes four stages: power spectral density estimation with multilevel spectral smoothing to preserve critical mid-low frequency components, design of frequency-adaptive hanning windows targeting harmonic sidelobe suppression, bandwidth parameter optimization guided by dominant frequency localization to prevent spectral aliasing during feature extraction, and PCA based noise separation using cumulative variance thresholds derived from localized zigzag features. Validation dataset comprising one synthetic and one field DAS-VSP datasets demonstrates the framework’s ability to maintain broadband signal integrity while achieving spectrally consistent noise attenuation.

B-factor is a measure of ray attenuation or scattering caused by atomic thermal motion during X-ray diffraction of protein crystal structure. B-factor reflects the vibration of atoms; hence, it is the most common experimental descriptor of protein flexibility and has been extensively applied in the studies of protein dynamics, screening of bioactive small molecules, and protein engineering. The prediction of B-factor profiles has considerable significance for analyzing the dynamic properties of unknown proteins. Deep learning technology has developed rapidly in recent years and has been widely implemented in many research fields, especially structural biology. In this paper, a deep neural network model based on bidirectional long short-term memory (biLSTM) network is proposed to predict the B-factor profile of a protein by combining its sequence-based features and structure-based features. Based on a large dataset of high-resolution proteins, our method predicts the B-factor profiles with an average Pearson correlation coefficient (PCC) of 0.71, and 85% of the B-factor profiles have a PCC greater than 0.6, which indicates a strong correlation between predicted and experimental values. In addition, our method remarkably outperforms the existing methods on four test datasets with different protein sizes.

Seismic reflection coefficient equation for the fluid-saturated porous reservoirs under the effect of in situ stress is of great importance to broad fields such as geofluid discrimination, in situ stress prediction, and safe production. However, the stress effect on seismic reflection coefficient in porous reservoirs is poorly understood. To fill this knowledge gap, an approximate seismic reflection coefficient equation for fluid-saturated porous reservoirs under horizontal stress was proposed to model the natural effect of horizontal stress on seismic reflection response. We first revisited the acoustoelasticity (AE) theory and used it to characterize the impact of horizontal stress on skeleton anisotropy. Then, the effective elastic stiffness tensor and the corresponding spatial perturbation in the stressed fluid-saturated porous reservoirs were established under the assumption of fluid incompressibility, which were further employed to derive the approximate seismic reflection coefficient equation based on the elastic inverse scattering theory. By comparing our equation to the exact one, we confirmed its validity within the moderate incidence angles and stresses. The effects of horizontal stress on the P-wave amplitude variation with angle and azimuth (AVAZ) characteristics and seismic response were thoroughly investigated. It was shown that the horizontal stress significantly influenced the amplitude magnitude and seismic phases. Furthermore, the derived reflection coefficient equation was inserted into the Bayesian inversion scheme to estimate the geofluid indicator and other elastic parameters. Synthetic test and filed application showed a reasonable agreement between the inverted result and drilling data, which illustrated the feasibility and stability of our inversion method.