We designed an improved pulse sequence combined with the hybrid saturation recovery and inversion recovery and the multiple echo spaced Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence (HSIR-ME-CPMG) to measure the longitudinal relaxation time (T1) and the transverse relaxation time (T2) simultaneously, to overcome the shortcomings of conventional IR-CPMG and SR-CPMG pulse train acquisition time and low image resolution. The longitudinal relaxation time (T1) is encoded using the combination of the saturation recovery and the inversion recovery pulse sequence to improve the resolution between different relaxation components. The transverse relaxation time (T2) are encoded by the CPMG pulse sequence. To reduce the energy consumption and the data storage space, the echo spacing is varied in different windows. Numerical simulations show that the proposed pulse sequence can capture the contrast between different components with similar relaxation times, even in low signal-to-noise ratio (SNR). The proposed pulse sequence can be popularized for better characterizing relaxation components of unconventional reservoirs such as the shale oil.

Hyperspectral image (HSI) classification is a common task in remote sensing that often faces challenges due to limited samples and cross-domain discrepancies between training and test data. This particular problem is termed as HSI Cross-domain Few-Shot Classification (HSI-CFSC). To solve this problem, we propose a Self-motivated Ensemble Learning Model (SELM). Building upon source pre-trained representations, our end-to-end approach comprises a self-training paradigm to iteratively refine target representations independent of direct source supervision. Moreover, an ensemble classifier suite leveraging diverse decision boundaries is optimized to excavate comprehensive classification cues from limited labeled target data. The OA, AA and Kappa of SELM in UP, PC and Salinas data sets are respectively 86.55%, 82.27%, 82.10%, 98.07%, 94.20%, 97.30% and 91.33%, 94.96%, 90.37%, which achieve the state-of-art performance compared with other classical methods.

In this paper, we reported a critical improvement on the measurement method for the dielectric dispersion characteristics of hydrate-bearing sediment in a frequency range between 1MHz and 3GHz, and, with the use of the method, a systematic evaluation of the microwave dielectric dispersion of the hydrate-bearing artificial sediment and influence factors. Firstly, the optimized and verified procedure of the dielectric detection method was introduced, followed with the results of complex permittivity (CP) measurements obtained by carrying out a series of experiments with different dielectric configurations. To balance the frequency coverage of the dispersion characteristic of both hydrate and solution among the dielectric measurement, the inversion means of the open-ended coaxial (OEC) probe was improved so that the lower frequency limit of the method has been extended as low as 1.0MHz. Furthermore, error analysis based on the experimental results among the overlapping frequency range with cylindrical capacitance verified the detecting accuracy of the OEC probes below 10MHz. The microwave dielectric dispersion of hydrate-bearing artificial sediment as a function of sediment particle size, temperature, and clay content was determined. It was found that the dielectric spectrum is not only routinely dependent on the porosity and temperature, but more importantly, the introduction of clay content has a distinct dynamic influence on the dielectric spectrum and the formation and dissociation process.

A good knowledge of the electromagnetic (EM) wave propagation behaviors in dielectric logging (DL) and borehole radar (BHR) surveying is critically important for the optimization of tool design and implementation, and interpretation of the acquired logging data, as well as understanding the influences of the dielectric permittivity and conductivity of the formation on the EM waves. This letter reported a novel method for the numerical simulation and analysis of the guided wave (GW) propagating along a metallic pipe in a typical DL configuration. A numerical simulation with the 3-D finite-difference time-domain (FDTD) method was applied to the broadband DL tool to obtain the wavefield and responses of the receiver. By monitoring the wave attenuation along the metallic drill collar, the intensity of the GW and loss factor can be determined. The coupling efficiency of the GW can be obtained when the total power emitted from the transmitting antenna is known. Simulation results revealed that the coupling efficiency of the GW changes with the water saturation of the formation and frequency. The simulation also suggest, by installing a slope structure adjacent to the transmitting antenna, the energy coupled into the GW could be reduced at different levels. Finally, the relationship between the received signals’ amplitude and GW’s coupling efficiency showed the quantified contribution of the GW to the received signal.

In the field of remote sensing, it is infeasible to collect a large number of labeled samples due to imaging equipment and the imaging environment. Few-Shot Learning (FSL) is the dominant method to alleviate this problem, which pursues quickly adapting to novel categories from a limited number of labeled samples. The few-shot Remote Sensing Scene Classification (RSSC) generally includes the pre-training and meta-test phases. However, a “negative transfer” problem exists that data categories in both phases are different. It causes the pre-trained feature extractor to be unable well-adapted to the novel data category. This paper proposes Class Shared Dictionary Learning for Few-Shot Remote Sensing Scene Classification (CSDL) to address this issue. Specifically, this paper designs the Mirror-based Feature Extractor (MFE) in the pre-training phase, constructing a self-supervised classification task to improve the feature extractor robustness. Furthermore, this paper proposes a Class Shared Dictionary classifier (CSD) based on dictionary learning. The CSD projects the novel data feature in meta-test into subspace to reconstruct more discriminative features and complete the classification task. Extensive experiments on remote sensing datasets have demonstrated that the proposed CSDL achieves the advanced classification performance.

This article reports a novel dielectric model developed for estimating the complex permittivity of unconsolidated porous media containing gas hydrate. The complex permittivity spectra were experimentally obtained by using open-ended-coaxial-probes over a frequency range between 1 MHz and 3 GHz, in which the dielectric dispersion of both hydrate and liquid solution are covered. With tetrahydrofuran used as the hydrate former, reflection coefficients were recorded during the hydrate formation and dissociation processes in quartz sands and the complex permittivity spectra were inversed. Volumetric fractions estimated from the X-ray-tomography were used as the referenced values. Experimental data showed that the Maxwell-Wagner effect, surface conductance, and phase configuration can affect the bulk permittivity. The discrepancy was found to be unacceptable when the fitting was conducted with pre-existing models such as complex-refractive-index-method (CRIM) and Maxwell-Wagner-Bruggeman-Hanai (MWBH). In this study, by modifying the nested Wagner's theory, a shell-coated model was applied, and the surface of the solid sphere was assumed to possess surface conductance when it was humidified by the liquid solution. In contrast to CRIM and MWBH, the proposed model allows more accurate estimation of the volumetric fraction. By adopting this model with a range of dielectric measurements with different phase configurations, temperature, particle size, surface conductivity, and frequency, the contents of components and their influences onto the bulk permittivity can be physically estimated. The proposed model provides an essential tool for the interpretation of dielectric dispersion curves and the prediction of the volumetric fractions, which can be useful for both the field and laboratory applications.