Closed-loop surface-related multiple estimation with full-wavefield migration-reconstructed near-offsets for shallow water

Reliably separating primary and multiple reflections in a shallow water environment (i.e., 50 m to 200 m water depth) still remains a challenge. The success of previously published closed-loop surface-related multiple estimation (CL-SRME) depends heavily on the data coverage, i.e., the near-offset reconstruction. Therefore, we propose the integrated framework of CL-SRME and full-wavefield migration (FWM). Multiples recorded in the data are capable of helping infill the acquisition imprint of the FWM image. With this image as a strong constraint, we are able to reconstruct the data at near-offsets, which is essential for better primary and multiple estimation during CL-SRME. FWM applied in a non-linear way can avoid the negative influences from the missing data, and at the same time bring in more physics between primaries and multiples. The FWM image of the top part of the subsurface is also used to back-project the information from multiples to primaries with the physical constraint of all this information belongs to the same earth model, provided that a good description of the source wavefield and a reasonable velocity model are available. The proposed integrated framework first reconstructs near-offsets via the closed-loop imaging process of FWM and then feeds the complete reconstructed data to CL-SRME for better primary and multiple estimation. A good performance is demonstrated on both 2D synthetic and field data examples in a challenging shallow water environment.

Training deep networks with only synthetic data: Deep-learning-based near-offset reconstruction for closed-loop surface-related multiple estimation on shallow-water field data

Accurate removal of surface-related multiples remains a challenge in shallow-water cases. One reason is that the success of the surface-related multiple estimation (SRME) related algorithms is sensitive to the quality of the near-offset reconstruction. When it comes to a larger missing gap and a shallower water-bottom, the state-of-the-art near-offset gap construction method — parabolic Radon transform (PRT) — fails to provide a reliable recovery of the shallow reflections due to the limited information from the data and highly curved events at near offsets with strong lateral amplitude variations. Therefore, we propose a novel workflow, which first deploys a deep-learning(DL)-based reconstruction of the shallow reflections and then uses the reconstructed data as the input for the subsequent surface multiple removal. In particular, we use a convolutional neural network architecture --- U-net, which was developed from convolutional autoencoders with extra direct skip connections between different levels of encoders and the corresponding decoders. Instead of using field data directly in network training, the training set is carefully synthesized based on the prior water-layer information of the field data; thus, a fully sampled field dataset, which is hard to obtain, is not needed for training in the proposed workflow. An inversion-based approach — closed-loop surface-related multiple estimation (CL-SRME) -- is used for the surface multiple removal, in which the primaries are directly estimated via full waveform inversion in a data-driven manner. Finally, the effectiveness of the proposed workflow is demonstrated based on a 2D North Sea field data in a shallow-water scenario (92.5 m water depth) with a relatively large minimum offset (150 m).

Functional Comparison of Human and Zebra Fish FKBP52 Confirms the Importance of the Proline-Rich Loop for Regulation of Steroid Hormone Receptor Activity

Previous studies demonstrated that the 52-kDa FK506-binding protein (FKBP52) proline-rich loop is functionally relevant in the regulation of steroid hormone receptor activity. While zebra fish (Danio rerio; Dr) FKBP52 contains all of the analogous domains and residues previously identified as critical for FKBP52 potentiation of receptor activity, it fails to potentiate activity. Thus, we used a cross-species comparative approach to assess the residues that are functionally critical for FKBP52 function. Random selection of gain-of-function DrFKBP52 mutants in Saccharomyces cerevisiae identified two critical residues, alanine 111 (A111) and threonine 157 (T157), for activation of receptor potentiation by DrFKBP52. In silico homology modeling suggests that alanine to valine substitution at position 111 in DrFKBP52 induces an open conformation of the proline-rich loop surface similar to that observed on human FKBP52, which may allow for sufficient surface area and increased hydrophobicity for interactions within the receptor–chaperone complex. A second mutation in the FKBP12-like domain 2 (FK2), threonine 157 to arginine (T157R), also enhanced potentiation, and the DrFKBP52-A111V/T157R double mutant potentiated receptor activity similar to human FKBP52. Collectively, these results confirm the functional importance of the FKBP52 proline-rich loop, suggest that an open conformation on the proline-rich loop surface is a predictor of activity, and highlight the importance of an additional residue within the FK2 domain.

Flat Knitting Loop Deformation Simulation Based on Interlacing Point Model

In order to create realistic loop primitives suitable for the faster CAD of the flat-knitted fabric, we have performed research on the model of the loop as well as the variation of the loop surface. This paper proposes an interlacing point-based model for the loop center curve, and uses the cubic Bezier curve to fit the central curve of the regular loop, elongated loop, transfer loop, and irregular deformed loop. In this way, a general model for the central curve of the deformed loop is obtained. The obtained model is then utilized to perform texture mapping, texture interpolation, and brightness processing, simulating a clearly structured and lifelike deformed loop. The computer program LOOP is developed by using the algorithm. The deformed loop is simulated with different yarns, and the deformed loop is applied to design of a cable stitch, demonstrating feasibility of the proposed algorithm. This paper provides a loop primitive simulation method characterized by lifelikeness, yarn material variability, and deformation flexibility, and facilitates the loop-based fast computer-aided design (CAD) of the knitted fabric.

Integrated receiver deghosting and closed-loop surface-multiple elimination

Accurate surface-related multiple removal is an important step in conventional seismic processing, and more recently, primaries and surface multiples are separated such that each of them is available for imaging algorithms. Current developments in the field of surface-multiple removal aim at estimating primaries in a large-scale inversion process. Using such a so-called closed-loop process, in each iteration primaries and surface multiples will be updated until they fit the measured data. The advantage of redefining surface-multiple removal as a closed-loop process is that certain preprocessing steps can be included, which can lead to an improved multiple removal. In principle, the surface-related multiple elimination process requires deghosted data as input; thus, the source and receiver ghost must be removed. We have focused on the receiver ghost effect and assume that the source is towed close to the sea surface, such that the source ghost effect is well-represented by a dipole source. The receiver ghost effect is integrated within the closed-loop primary estimation process. Thus, primaries are directly estimated without the receiver ghost effect. After receiver deghosting, the upgoing wavefield is defined at zero depth, which is the surface. We have successfully validated our method on a 2D simulated data and on a 2D subset from 3D broadband field data with a slanted cable.

Rapid Simulation of Flat Knitting Loops Based On the Yarn Texture and Loop Geometrical Model

In order to create realistic loop primitives suitable for the fast computer-aided design (CAD) of the flat knitted fabric, we have a research on the geometric model of the loop as well as the variation of the loop surface. Establish the texture variation model based on the changing process from the normal yarn to loop that provides the realistic texture of the simulative loop. Then optimize the simulative loop based on illumination variation. This paper develops the computer program with the optimization algorithm and achieves the loop simulation of different yarns to verify the feasibility of the proposed algorithm. Our work provides a fast CAD of the flat knitted fabric with loop simulation, and it is not only more realistic but also material adjustable. Meanwhile it also provides theoretical value for the flat knitted fabric computer simulation.