We introduce a concept of generalized blending and deblending, develop its models and accordingly establish a method of deblended-data reconstruction using these models. The generalized models can handle real situations by including random encoding into the generalized operators both in the space and time domain, and both at the source and receiver side. We consider an iterative optimization scheme using a closed-loop approach with the generalized blending and deblending models, in which the former works for the forward modelling and the latter for the inverse modelling in the closed loop. We applied our method to existing real data acquired in Abu Dhabi. The results show that our method succeeded to fully reconstruct deblended data even from the fully generalized, thus quite complicated blended data. We discuss the complexity of blending properties on the deblending performance. In addition, we discuss the applicability to time-lapse seismic monitoring as it ensures high repeatability of the surveys. Conclusively, we should acquire blended data and reconstruct deblended data without serious problems but with the benefit of blended acquisition.@en

Oxidative dehydrogenation of ethane (ODHE) is an essential reaction in modern society to produce ethylene. The orthorhombic Mo3VOx catalyst (MoVO) was reported as one of the best catalysts for this reaction after a particular redox treatment to generate lattice oxygen defects. This study elucidates the location and nature of the defect sites as well as how molecular oxygen is activated toward the ODHE. The insights into the active site formation and guidelines for its design were gained through structural characterization, quantification of oxygen defects, and experimental and theoretical investigation of the reaction mechanisms. Peroxo formed at the defect sites of the bridging oxygen facing the heptagonal channel was found to drive the reaction efficiently.@en

The application of blended acquisition has drawn considerable attention owing to its ability to improve the operational efficiency as well as the data quality and health, safety and environment performance. Furthermore, the acquisition of less data contributes to the business aspect, while the desired data density is still realizable via subsequent data reconstruction. The use of fewer detectors and sources also minimizes operational risks in the field. Therefore, a combined implementation of these technologies potentially enhances the value of a seismic survey further. One way to encourage this is to minimize any imperfection in deblending and data reconstruction during processing. In addition, one may derive survey parameters that enable a further improvement in these processes as introduced in this study. The proposed survey design workflow iteratively performs the following steps to derive the survey parameters responsible for source blending as well as the spatial sampling of detectors and sources. The first step is the application of blending and sampling operators to unblended and well-sampled data. We then apply closed-loop deblending and data reconstruction. The residue for a given design from this step is evaluated and subsequently used by genetic algorithms to simultaneously update the survey parameters related to both blending and spatial sampling. The updated parameters are fed into the next iteration until they satisfy the given termination criteria. We also propose a repeated encoding sequence to form a parameter sequence in genetic algorithms, making the size of problem space manageable. The results of the proposed workflow are outlined using blended dispersed source array data incorporating different scenarios that represent acquisition in marine, transition zone and land environments. Clear differences attributed solely to the parameter design are easily recognizable. Additionally, a comparison among different optimization schemes illustrates the ability of genetic algorithms along with a repeated encoding sequence to find better solutions within a computationally affordable time. The optimized parameters yield a notable enhancement in the deblending and data reconstruction quality and consequently provide optimal acquisition scenarios.@en

We introduce a generalized concept of blending and deblending, establish its models, and accordingly establish a method of deblended-data reconstruction using these models. The generalized models can handle real-life situations by including random encoding into the generalized operators both in the space and time domain, and both at the source and receiver side. We consider an iterative optimization scheme using a closed-loop approach with the generalized-blending and -deblending models, in which the former works for the forward modelling and the latter for the inverse modelling in the closed loop. We established and applied this method to existing real datasets acquired in Abu Dhabi. The results show that our method succeeded to fully reconstruct deblended data even from the fully generalized, thus quite complicated blended data@en

The application of 3D symmetric and dense sampling is technically desirable when designing a seismic survey with wide-azimuth sampling. However, budgetary and operational constraints often hinder us from pursuing this ideal particularly under the current economic circumstances in the industry. The combined implementation of blending and efficient acquisition geometries enables us to acquire seismic data in a cost-effective manner. Subsequent deblending and data reconstruction allow for the retrieval of deblended and well-sampled data from blended and irregularly sampled data such that the desired data quality is also attainable. We propose a survey-design workflow that involves an iterative scheme to update survey parameters responsible for source blending as well as spatial sampling of detectors and sources. The optimized parameters subsequently yield satisfactory deblending and data reconstruction results. Numerical examples demonstrate the results of the proposed survey-design workflow for wide azimuth 3D geometries. Differences attributable solely to the parameter design are clearly recognizable. The optimized parameters lead to the improvement of deblending and data reconstruction quality and subsequently provide optimal acquisition designs.@en