Compressing the memory variables in constant-Q viscoelastic wave propagation via an improved sum-of-exponentials approximation

Earth introduces strong attenuation and dispersion to propagating waves. The time-fractional wave equation with very small fractional exponent, based on Kjartansson's constant-Q theory, is widely recognized in the field of geophysics as a reliable model for frequency-independent Q anelastic behavior. Nonetheless, the numerical resolution of this equation poses considerable challenges due to the requirement of storing a complete time history of wavefields. To address this computational challenge, we present a novel approach: a nearly optimal sum-of-exponentials (SOE) approximation to the Caputo fractional derivative with very small fractional exponent, utilizing the machinery of generalized Gaussian quadrature. This method minimizes the number of memory variables needed to approximate the power attenuation law within a specified error tolerance. We establish a mathematical equivalence between this SOE approximation and the continuous fractional stress-strain relationship, relating it to the generalized Maxwell body model. Furthermore, we prove an improved SOE approximation error bound to thoroughly assess the ability of rheological models to replicate the power attenuation law. Numerical simulations on constant-Q viscoacoustic equation in 3D homogeneous media and variable-order P- and S- viscoelastic wave equations in 3D inhomogeneous media are performed. These simulations demonstrate that our proposed technique accurately captures changes in amplitude and phase resulting from material anelasticity. This advancement provides a significant step towards the practical usage of the time-fractional wave equation in seismic inversion.Comment: 28 pages, 52 figures, 6 table

Phase transition of a one-dimensional Ising model with distance-dependent connections

The critical behavior of Ising model on a one-dimensional network, which has long-range connections at distances $l>1$ with the probability $\Theta(l)\sim l^{-m}$, is studied by using Monte Carlo simulations. Through studying the Ising model on networks with different $m$ values, this paper discusses the impact of the global correlation, which decays with the increase of $m$, on the phase transition of the Ising model. Adding the analysis of the finite-size scaling of the order parameter $[]$, it is observed that in the whole range of $0<m<2$, a finite-temperature transition exists, and the critical exponents show consistence with mean-field values, which indicates a mean-field nature of the phase transition.Comment: 5 pages,8 figure

Privacy Preserving K-means Clustering with Chaotic Distortion

Randomized data distortion is a popular method used to mask the data for preserving the privacy. But the appropriateness of this method was questioned because of its possibility of disclosing original data. In this paper, the chaos system, with its unique characteristics of sensitivity on initial condition and unpredictability, is advocated to distort the original data with sensitive information for privacy preserving k-means clustering. The chaotic distortion procedure is proposed and three performance metrics specifically for k-means clustering are developed. We use a large scale experiment (with 4 real world data sets and corresponding reproduced 40 data sets) to evaluate its performance. Our study shows that the proposed approach is effective; it not only can protect individual privacy but also maintain original information of cluster cente

Tin-graphene tubes as anodes for lithium-ion batteries with high volumetric and gravimetric energy densities.

Limited by the size of microelectronics, as well as the space of electrical vehicles, there are tremendous demands for lithium-ion batteries with high volumetric energy densities. Current lithium-ion batteries, however, adopt graphite-based anodes with low tap density and gravimetric capacity, resulting in poor volumetric performance metric. Here, by encapsulating nanoparticles of metallic tin in mechanically robust graphene tubes, we show tin anodes with high volumetric and gravimetric capacities, high rate performance, and long cycling life. Pairing with a commercial cathode material LiNi0.6Mn0.2Co0.2O2, full cells exhibit a gravimetric and volumetric energy density of 590 W h Kg-1 and 1,252 W h L-1, respectively, the latter of which doubles that of the cell based on graphite anodes. This work provides an effective route towards lithium-ion batteries with high energy density for a broad range of applications

A general physics-based data-driven framework for numerical simulation and history matching of reservoirs

This paper proposed a general physics-based data-driven framework for numerical mod-eling and history matching of reservoirs that achieves a good balance of flow physics and actual field data. Underground reservoir is easily discretized in this framework as a flow network composed of one-dimensional connection elements, each of which is defined by two flow characteristic parameters. Each one-dimensional connection element is divided into some grids, and the cross-sectional area and permeability of the grids on the same connection element are equal. The fully implicit scheme of flow equations and the Newton iteration nonlinear solver concurrently solve all unknown quantities. Then, using actual field data, the simultaneous perturbation stochastic approximation algorithm is used to invert flow characteristic parameters of each connection element, and the unequal constraint that the volume of connection elements should not exceed the total reservoir volume is added to control the data-driven process. To demonstrate the unequal constraint is physical, a test case of a waterflooding reservoir with a high permeability zone is given. A waterflooding reservoir example with five injectors and four producers is used to demonstrate that this framework outperforms earlier techniques, and another case with single-phase depletion development is used to demonstrate that this framework has a high generalization for flow models. In addition, this data-driven framework based on physics is expected to serve as a reference for other fields of science and engineering.Cited as: Rao, X., Xu, Y., Liu, D., Liu, Y., Hu, Y. A general physics-based data-driven framework for numerical simulation and history matching of reservoirs. Advances in Geo-Energy Research, 2021, 5(4): 422-436, doi: 10.46690/ager.2021.04.0

Cooperative Multi-Cell Massive Access with Temporally Correlated Activity

This paper investigates the problem of activity detection and channel estimation in cooperative multi-cell massive access systems with temporally correlated activity, where all access points (APs) are connected to a central unit via fronthaul links. We propose to perform user-centric AP cooperation for computation burden alleviation and introduce a generalized sliding-window detection strategy for fully exploiting the temporal correlation in activity. By establishing the probabilistic model associated with the factor graph representation, we propose a scalable Dynamic Compressed Sensing-based Multiple Measurement Vector Generalized Approximate Message Passing (DCS-MMV-GAMP) algorithm from the perspective of Bayesian inference. Therein, the activity likelihood is refined by performing standard message passing among the activities in the spatial-temporal domain and GAMP is employed for efficient channel estimation. Furthermore, we develop two schemes of quantize-and-forward (QF) and detect-and-forward (DF) based on DCS-MMV-GAMP for the finite-fronthaul-capacity scenario, which are extensively evaluated under various system limits. Numerical results verify the significant superiority of the proposed approach over the benchmarks. Moreover, it is revealed that QF can usually realize superior performance when the antenna number is small, whereas DF shifts to be preferable with limited fronthaul capacity if the large-scale antenna arrays are equipped.Comment: 16 pages, 17 figures, minor revisio