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With the emergence of spot electricity trading, market-oriented trading has been intensively carried out, forming a multi-cycle trading system. In this process, a large amount of fine-grained electricity trading data circulates on the trading platform. Trading data is an important basis for decision-making in the electricity spot market, which directly affects the trading profits of market entities, proper disclosure of this information is very important for market enterprises. Information disclosure must ensure the validity and security. However, it is hard to judge the security demand for trading data, and there is no suitable evaluation system for determining the security requirement of data, which will limit the electricity market’s further development. In this paper, we first design an indicator system for security requirements classification, which manages data risks from three aspects: data classification, data risk, and entity demand. This system will guide us in determining data security requirements and further help identify differentiated data security protection schemes. Then, based on this system, we propose the classification method of data security requirements through a hierarchical fuzzy Petri net. The lower network realizes a reasonable assessment of data risk with reference to the index system, and the upper network finally determines the level of security requirements through the fuzzy rule base. Last, two types of data are selected to judge their security requirements. The results show that our method can provide a reference for privacy protection in electricity market data.

Collecting fine-grained data on electricity usage can reduce the user’s energy cost by accurate demand response and improve the services from utilities. However, non-intrusive power load monitoring (NILM) technology shows that these data will reveal the user’s privacy. In this paper, we first design a price-sensitive BLH scheme which achieves differential privacy. Then regarding such issues above, we propose a price-sensitive cost-friendly differential privacy (PCDP) scheme through a contextual multi-armed bandit framework. Last, realistic electricity consumption data is extracted to validate the proposed method. The results show that our method provides effective privacy protection and achieve considerable cost saving.

Deep dolomite in cratonic basins is an essential target for future natural gas exploration in China. Statistical analyses of major cratonic basins in China, such as the Sichuan, Tarim, and Ordos basins, indicate that the major types of deep dolomite (including granular dolomite, microbial dolomite, and crystalline dolomite) where fracture-vug and fracture-pore dominate the pore types. It is proposed that the favorable sedimentary microfacies is the base, whereas early dissolution is a necessary condition to form pores. Early dolomitization in penecontemporaneous and shallow burial periods is helpful to preserving the pores that developed during the penecontemporaneous period. Additionally, supergene karstification and structural fractures improve reservoir physical properties. It is pointed that sedimentary facies still control the referred high-quality deep dolomite reservoirs; they inherited more than being transformed in structure. The early sedimentary cycles, rather than depth, control their vertical distribution. Meanwhile, their lateral distribution is controlled by high-energy sedimentary facies belt rather than karstification. It is suggested that future exploration of high-quality deep dolomite reservoirs should target “both sides of an intra-platform rift” and low-middle parts of carbonate slopes. Keywords: Cratonic basin, Deep dolostone, Dolomite reservoir, Sichuan basin, Tarim Basin, Ordos Basin

The seismic sedimentology characteristics of sub-clinoformal shallow-water meandering river delta can be comprehensively analyzed by core, well logging and 3-D seismic data. This paper summarizes the sedimentary pattern of sub-clinoformal shallow-water meandering river delta of Suning area in Raoyang sag, Jizhong depression of Bohai Bay Basin. In Suning area, there are eight fourth-order sequences recognized from the upper part of Sha 1 Member (Es1s) to Dong 3 Member (Ed3), reflecting periodical change of lake level. From the central lake to the shore, and from the transgressive to highstand systems tracts, the color of mudstones varies from grey to red, showing a drop of lake-level. The seismic reflection pattern indicates the drop of lake level: the shingled reflections in Es1s (SQ?3) indicate water depth of 40−60 m in the lake center, followed by sub-parallel (SQ?4-SQ?6) with lower continuity reflections, and disappearance of shingled reflections, showing water depth no more than 30 m. Analysis of core, well logging, and seismic amplitude strata slices reveal that the sequences SQ?4-SQ?6 of Es1s are sub-clinoformal shallow-water meandering river delta, different from the shingled reflection shallow-water meandering river delta in SQV3 of Es1s. The main seismic sedimentology characteristics are channel forms and lobate landforms advancing to the central lake by multiple zones, indicating numerous distributary channel and deltaic lobes depositions. Key words: shallow-water meandering river delta, sub-clinoformal seismic reflection, seismic sedimentology, Raoyang sag, Bohai Bay Basin

According to the similarity of speech data of the same type and characteristic that data of different types has different geometrical distance, the paper proposed a speaker recognition system based on SVM-GMM. The system combines advantages of GMM and SVM, solves problems that GMM cannot distinguish differences between the voice data while the data is small, and recognition rate of SVM drops while handling large amounts of data. The improved K-Means algorithm is used for initialization of model parameters to improve accuracy. The experiment results show that speaker recognition system based on SVM-GMM has better recognition rate and robustness than the system using GMM or SVM alone.

The exploration of beach and bar sandstone reservoirs is a tricky issue. In this study, under the guidance of seismic sedimentary theory, the 90° phase shift technique, stratal slicing, and quantitative seismic sedimentology were used to identify and characterize the distribution of beach and bar sandbodies and reflect the evolutionary history in the Chepaizi area, Junggar Basin. Four third-order sequences in the Lower-Cretaceous and relevant systems tracts in the study area were studied using seismic sedimentology, and with the assistance of the comprehensive analysis of core, lithology and well-logging data, stratal slices were interpreted and calibrated, and the distribution and evolution of beach bar sandbodies were predicted. Based on the detailed and extensive analyses on sedimentary characteristics and seismic sedimentary quantitative results, this study established two typical beach and bar sedimentary models in the Chepaizi area, namely normal bedrock-beach bar and fan delta-beach bar. In early sedimentary period of SQ I, beach and bar sandbodies were reformed by coastal, wave and onshore currents, and developed around the lateral margin of the fan delta front. During sedimentary periods of SQ II to SQ IV, the northern provenance stopped supplying sediments, and the wave and coastal currents eroded the bedrock of the western Chepaizi uplift and transported sediments to the study area, forming beach and bar sandbodies parallel to or obliquely intersecting with coastal line. Key words: seismic sedimentology, phase shift, stratal slice, beach and bar, Chepaizi area, Junggar Basin

Satellite synthetic aperture radar (SAR) interferometry (InSAR) is a powerful technology to monitor slow ground surface movements. However, the extraction and interpretation of information from big sets of InSAR measurements is a complex and demanding task. In this paper, a new method is presented for automatically detecting potential instability risks affecting buildings and infrastructures, by searching for anomalies in the persistent scatterer (PS) deformations, either in the spatial or in the temporal dimensions. In the spatial dimension, in order to reduce the dataset size and improve data reliability, we utilize a hierarchical clustering method to obtain convergence points that are more trustworthy. Then, we detect deformations characterized by large values and spatial inhomogeneity. In the temporal dimension, we use a signal processing method to decompose the input into two main components: regular periodic deformations and piecewise linear deformations. After removing the periodic component, the velocity variation in each identified temporal partition is analyzed to detect anomalous velocity trends and accelerations. The method has been tested on different sites in China, based on InSAR measurements from COSMO-SkyMed data. The results, verified with in-field surveys, confirm the potential of the method for the automatic detection of deformation anomalies that could cause building or infrastructure stability problems.

The localized surface plasmon resonances (LSPRs) of gold particles assembled on a crystal plate are a powerful tool for biological sensors. Here, we prepare gold colloids in different pH solutions. We monitor the effects of the particle radius and particle coverage on the absorption spectra of AT-cut (r-face dihedral angle of about 3°) crystal plates supporting gold nanoparticles. The surface morphologies were monitored on silicon dioxide substrates using ultraviolet and visible (UV-vis) spectroscopy, and atomic force microscopy (AFM). The results showed that the gold particle coverage decreases with increasing pH value of the gold colloid solution. This phenomenon demonstrates that self-assembled gold surfaces were formed via the electrostatic adsorption of gold particles on the positively charged, ionized amino groups on the crystal plates in the acidic solution. The spectrum of gold nanoparticles with different coverage degree on the crystal plates showed that the LSPR properties are highly dependent on pH.