Traditional pre-stack depth migration can only provide subsurface structural information. However, simple structure information is insuffi cient for petroleum exploration which also needs amplitude information proportional to reflection coefficients. In recent years, pre-stack depth migration algorithms which preserve amplitudes and based on the oneway wave equation have been developed. Using the method in the shot domain requires a deconvolution imaging condition which produces some instability in areas with complicated structure and dramatic lateral variation in velocity. Depth migration with preserved amplitude based on the angle domain can overcome the instability of the one-way wave migration imaging condition with preserved amplitude. It can also offer provide velocity analysis in the angle domain of common imaging point gathers. In this paper, based on the foundation of the one-way wave continuation operator with preserved amplitude, we realized the preserved amplitude prestack depth migration in the angle domain. Models and real data validate the accuracy of the method.

It is very important for systems biologists to predict the state of the multi-omics time series for disease occurrence and health detection. However, it is difficult to make the prediction due to the high-dimensional, nonlinear and noisy characteristics of the multi-omics time series data. For this reason, this study innovatively proposes an Embedding, Koopman and Autoencoder technologies-based multi-omics time series predictive model (EKATP) to predict the future state of a high-dimensional nonlinear multi-omics time series. We evaluate this EKATP by using a genomics time series with chaotic behavior, a proteomics time series with oscillating behavior and a metabolomics time series with flow behavior. The computational experiments demonstrate that our proposed EKATP can substantially improve the accuracy, robustness and generalizability to predict the future state of a time series for multi-omics data.

Oil and gas exploration is turning to the areas with irregular topography and complex geologic structures. Prestack depth migration turns out to be a valid way to deal with irregular topography and complex geologic structures. "Wave field downward continuation" based on accumulating step by step is a valid way to solve the problem of irregular surface migration. Xwfd pre-stack migration based on wave equation has strong adaptability to the medium which has strongly variable transverse velocity and it can be used for migration with dual-complexity. Similar to other conventional migration, it makes just continuation of phase and does nothing to the amplitude. We derived the preserved-amplitude Xwfd one-way wave equation operator and added the error compensation caused when solving the wave equation. Based on the Xwfd error compensation preservedamplitude operator, we use the method of "wave field downward continuation" to process the dual-complexity model and real seismic data. The impulse response test and results of the prestack depth migration for model and real seismic data show that the method is an effective tool for dual-complexity. The error compensation can be done during several continuation steps, compared to conventional Finite Difference Fourier (ffd) it has better migration quality.

In this research, we present a seismic trace interpolation method which uses seismic data with surface-related multiples. It is different from conventional seismic data interpolation using information transformation or extrapolation of adjacent channels for reconstruction of missing seismic data. In this method there are two steps, first, we construct pseudo-primaries by cross-correlation of surface multiple data to extract the missing nearoffset information in multiples, which are not displayed in the acquired seismic record. Second, we correct the pseudo-primaries by applying a Least-squares Matching Filter (LMF) and RMS amplitude correction method in time and space sliding windows. Then the corrected pseudo-primaries can be used to fill the data gaps. The method is easy to implement, without the need to separate multiples and primaries. It extracts the seismic information contained by multiples for filling missing traces. The method is suitable for seismic data with surfacerelated multiples.