A B S T R A C TSeismic data reconstruction, as a preconditioning process, is critical to the performance of subsequent data and imaging processing tasks. Often, seismic data are sparsely and non-uniformly sampled due to limitations of economic costs and field conditions. However, most reconstruction processing algorithms are designed for the ideal case of uniformly sampled data. In this paper, we propose the non-equispaced fast discrete curvelet transform-based three-dimensional reconstruction method that can handle and interpolate non-uniformly sampled data effectively along two spatial coordinates. In the procedure, the three-dimensional seismic data sets are organized in a sequence of two-dimensional time slices along the source-receiver domain. By introducing the two-dimensional non-equispaced fast Fourier transform in the conventional fast discrete curvelet transform, we formulate an L1 sparsity regularized problem to invert for the uniformly sampled curvelet coefficients from the non-uniformly sampled data. In order to improve the inversion algorithm efficiency, we employ the linearized Bregman method to solve the L1-norm minimization problem. Once the uniform curvelet coefficients are obtained, uniformly sampled three-dimensional seismic data can be reconstructed via the conventional inverse curvelet transform. The reconstructed results using both synthetic and real data demonstrate that the proposed method can reconstruct not only non-uniformly sampled and aliased data with missing traces, but also the subset of observed data on a non-uniform grid to a specified uniform grid along two spatial coordinates. Also, the results show that the simple linearized Bregman method is superior to the complex spectral projected gradient for L1 norm method in terms of reconstruction accuracy.
The uranium deposits in the Tuanyushan area of northern Qaidam Basin commonly occur in coal‐bearing series. To decipher the U‐enrichment mechanism and controlling factors in this area, a database of 72 drill cores, including 56 well‐logs and 3 sampling wells, was examined for sedimentology and geochemistry in relation to uranium concentrations. The results show that coal‐bearing series can influence uranium mineralization from two aspects, i.e., spatial distribution and dynamic control. Five types of uranium‐bearing rocks are recognized, mainly occurring in the braided river and braided delta sedimentary facies, among which sandstones near the coals are the most important. The lithological associations of sandstone‐type uranium deposits can be classified into three subtypes, termed as U‐coal type, coal‐U‐coal type, and coal‐U type, respectively. The coal and fine siliciclastic rocks in the coal‐bearing series confined the U‐rich fluid flow and uranium accumulation in the sandstone near them. Thus, the coal‐bearing series can provide good accommodations for uranium mineralization. Coals and organic matters in the coal‐bearing series may have served as reducing agents and absorbing barriers. Methane is deemed to be the main acidolysis hydrocarbon in the U‐bearing beds, which shows a positive correlation with U‐content in the sandstones in the coal‐bearing series. Additionally, the δ13C in the carbonate cements of the U‐bearing sandstones indicates that the organic matters, associated with the coal around the sandstones, were involved in the carbonation, one important component of alteration in the Tuanyushan area. Recognition of the dual control of coal‐bearing series on the uranium mineralization is significant for the development of coal circular economy, environmental protection during coal utilization and the security of national rare metal resources.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.