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Temperature cross-sensitivity, which especially performs complicatedly and severely in fiber-optic sensors composed of various materials, will significantly affect the stability and the accuracy of the parameter to be measured. Consequently, the polarization-multiplexed technology is introduced to separate the temperature and strain parameters, while a piecewise uniform compensation method is further proposed and employed to get rid of the nonlinear temperature effects in the fiber-optic vibration sensing unit (FOVSU). In the sensitive fiber coil (SFC) of the FOVSU, the solution interval of temperature coefficient is subdivided and employed for decoupling in different temperature ranges; then, the data are processed by splicing finally. The experimental results show that a suppression effect of temperature drift noise of 43 dB at 1 mHz is achieved under unidirectional self-cooling temperature conditions and 33 dB at 1 mHz under continuous temperature rise and fall conditions by using the piecewise method, which demonstrates that the performances in both situations are better than the original method using only one set of temperature coefficients. Therefore, the proposed new method could significantly reduce the temperature noise, leading to the testing accuracy and stability enhancement of FOVSUs.

Seismic resolution is a key indicator in seismic exploration technology and directly affects the reliability of seismic data interpretation. Seismic wavelet extraction plays a vital role in high-resolution seismic processing. Traditional seismic wavelet extraction methods, such as autocorrelation and homomorphic deconvolution, are limited to the minimum phase assumption. The wavelet extraction method that combines multitrace statistical wavelet extraction and logging data requires the reflection coefficients and wavelets of adjacent traces to be consistent. In view of these limitations, this study proposes a wavelet joint extraction method based on seismic velocity and acceleration signals. This method does not require assumptions regarding the phase of the wavelet, and an acceleration signal is introduced to avoid an excessive gap between adjacent traces. Independent component analysis (ICA) was used to reduce the influence of noise on the higher order cumulants. This method first performs ICA on the velocity and acceleration signals to obtain the preprocessed data and the corresponding high-order cumulants. The amplitude and phase spectra of the wavelet were then reconstructed using the amplitude and phase spectra of the high-order cumulant. Finally, a seismic wavelet was reconstructed based on the obtained amplitude and phase spectra. The theoretical model and actual seismic data were processed using the proposed method. The test results show that this method can extract a more accurate seismic wavelet and has feasibility and application potential for improving the resolution of seismic data.

Abstract: A stepping scheme-based modeling and calculating methodology for predicting the elastic properties of composite materials that contain arbitrarily shaped particles is presented. This study involved different disciplinary approaches and proved it by mathematical. A multiple parameter microstructure model is constructed using inverse Fourier transforms and constraint conditions. A stepping scheme is used to calculate elastic properties and relationships between the microstructure and effective performance are constructed. Finally, this stepping scheme calculation methodology is used to predict the effective elastic moduli of CuCr alloys. Graphical abstract:

The efficient integration of satellite and terrestrial networks has become an important component for 6G wireless architectures to provide highly reliable and secure connectivity over a wide geographical area. As the satellite and cellular networks are developed separately these years, the integrated network should synergize the communication, storage, computation capabilities of both sides towards an intelligent system more than mere consideration of coexistence. This has motivated us to develop double-edge intelligent integrated satellite and terrestrial networks (DILIGENT). Leveraging the boost development of multi-access edge computing (MEC) technology and artificial intelligence (AI), the framework is entitled with the systematic learning and adaptive network management of satellite and cellular networks. In this article, we provide a brief review of the state-of-art contributions from the perspective of academic research and standardization. Then we present the overall design of the proposed DILIGENT architecture, where the advantages are discussed and summarized. Strategies of task offloading, content caching and distribution are presented. Numerical results show that the proposed network architecture outperforms the existing integrated networks.

This paper addresses the problem of flattening the events on pre-stack seismic gathers. Undesirable non-flat events induced by inaccurate normal moveout and static correction inevitably degrade the reliability of reservoir prediction. The typical methods based on dynamic time warping (DTW) are sample-by-sample procedures, distorting the seismic waveforms resulting from the locally non-sensible matchings. We observe that the multichannel reflectivity gathers are inherently temporally sparse and spatially correlated impulse sequences and can be aligned precisely. Hence, we propose an inversion-based gather flattening method based on sparse representation (SR) and DTW, which can substantially reduce waveform distortions and pathological alignments. A novel SR algorithm, iteratively reweighted L 1−2 -L 1 -norm minimization (IR-L 1−2 -L 1), is proposed for improved robustness to outliers and better handling of highly coherent dictionaries. Given a dictionary constructed with a seismic wavelet as the basis function, we can invert the reflectivity patterns of seismic gathers via IR-L 1−2 -L 1. The obtained multichannel offset-dependent reflectivity traces, rather than the seismic seismograms, are flattened by an improved alignment algorithm: smooth shape dynamic time warping (SSDTW), which enhances DTW by a few structural modifications to the prototype. Finally, the flat gathers can be reconstructed by the matrix multiplication of the dictionary and reflectivity gathers. Both synthetic and practical applications indicate that our algorithm yields more flat events and improved lateral continuity while separating random noise and outliers. [ABSTRACT FROM AUTHOR]