Defining the 3D geometry of thin shale units in the Sleipner reservoir using seismic attributes

Acknowledgments The seismic interpretation and image processing was carried out in the SeisLab facility at the University of Aberdeen (sponsored by BG BP and Chevron). Seismic imaging analysis was performed using GeoTeric (ffA), and analysis of seismic amplitudes was performed in Petrel 2015 (Schlumberger). We would like to thank the NDDC (RG11766-10) for funding this research and Statoil for the release of the Sleipner field seismic dataset utilized in this research paper and also Anne-Kari Furre and her colleagues for their assistance. We also thank the editor, Alejandro Escalona and the two anonymous reviewers for their constructive and in depth comments that improved the paper.Peer reviewedPostprin

Ultraviolet and soft X--ray photon--photon elastic scattering in an electron gas

We have considered the processes which lead to elastic scattering between two far ultraviolet or X--ray photons while they propagate inside a solid, modeled as a simple electron gas. The new ingredient, with respect to the standard theory of photon--photon scattering in vacuum, is the presence of low--energy, nonrelativistic electron--hole excitations. Owing to the existence of two--photon vertices, the scattering processes in the metal are predominantly of second order, as opposed to fourth order for the vacuum case. The main processes in second order are dominated by exchange of virtual plasmons between the two photons. For two photons of similar energy $\hbar \Omega$, this gives rise to a cross section rising like $\Omega^2$ up to maximum of around $10^{-32}$~cm$^2$, and then decreasing like $\Omega^{-6}$. The maximal cross section is found for the photon wavevector $k \sim k_{F}$, the Fermi surface size, which typically means a photon energy $\hbar \Omega$ in the keV range. Possible experiments aimed at checking the existence of these rare but seemingly measurable elastic photon--photon scattering processes are discussed, using in particular intense synchrotron sources.Comment: 33 pages, TeX, Version 3.1, S.I.S.S.A. preprint 35/93/C

Bayesian nonparametric graphical models for time-varying parameters VAR

Over the last decade, big data have poured into econometrics, demanding new statistical methods for analysing high-dimensional data and complex non-linear relationships. A common approach for addressing dimensionality issues relies on the use of static graphical structures for extracting the most significant dependence interrelationships between the variables of interest. Recently, Bayesian nonparametric techniques have become popular for modelling complex phenomena in a flexible and efficient manner, but only few attempts have been made in econometrics. In this paper, we provide an innovative Bayesian nonparametric (BNP) time-varying graphical framework for making inference in high-dimensional time series. We include a Bayesian nonparametric dependent prior specification on the matrix of coefficients and the covariance matrix by mean of a Time-Series DPP as in Nieto-Barajas et al. (2012). Following Billio et al. (2019), our hierarchical prior overcomes over-parametrization and over-fitting issues by clustering the vector autoregressive (VAR) coefficients into groups and by shrinking the coefficients of each group toward a common location. Our BNP timevarying VAR model is based on a spike-and-slab construction coupled with dependent Dirichlet Process prior (DPP) and allows to: (i) infer time-varying Granger causality networks from time series; (ii) flexibly model and cluster non-zero time-varying coefficients; (iii) accommodate for potential non-linearities. In order to assess the performance of the model, we study the merits of our approach by considering a well-known macroeconomic dataset. Moreover, we check the robustness of the method by comparing two alternative specifications, with Dirac and diffuse spike prior distributions

Bayesian Markov-Switching Tensor Regression for Time-Varying Networks

Modeling time series of multilayer network data is challenging due to the peculiar characteristics of real-world networks, such as sparsity and abrupt structural changes. Moreover, the impact of external factors on the network edges is highly heterogeneous due to edge- and time-specific effects. Capturing all these features results in a very high-dimensional inference problem. A novel tensor-on-tensor regression model is proposed, which integrates zero-inflated logistic regression to deal with the sparsity, and Markov-switching coefficients to account for structural changes. A tensor representation and decomposition of the regression coefficients are used to tackle the high-dimensionality and account for the heterogeneous impact of the covariate tensor across the response variables. The inference is performed following a Bayesian approach, and an efficient Gibbs sampler is developed for posterior approximation. Our methodology applied to financial and email networks detects different connectivity regimes and uncovers the role of covariates in the edge-formation process, which are relevant in risk and resource management. Code is available on GitHub. Supplementary materials for this article are available online

Bayesian SAR model with stochastic volatility and multiple time-varying weights

A novel spatial autoregressive model for panel data is introduced, which incorporates multilayer networks and accounts for time-varying relationships. Moreover, the proposed approach allows the structural variance to evolve smoothly over time and enables the analysis of shock propagation in terms of time-varying spillover effects. The framework is applied to analyse the dynamics of international relationships among the G7 economies and their impact on stock market returns and volatilities. The findings underscore the substantial impact of cooperative interactions and highlight discernible disparities in network exposure across G7 nations, along with nuanced patterns in direct and indirect spillover effects

Static and Dynamic BART for Rank-Order Data

Ranking lists are often provided at regular time intervals by one or multiple rankers in a range of applications, including sports, marketing, and politics. Most popular methods for rank-order data postulate a linear specification for the latent scores, which determine the observed ranks, and ignore the temporal dependence of the ranking lists. To address these issues, novel nonparametric static (ROBART) and autoregressive (ARROBART) models are introduced, with latent scores defined as nonlinear Bayesian additive regression tree functions of covariates. To make inferences in the dynamic ARROBART model, closed-form filtering, predictive, and smoothing distributions for the latent time-varying scores are derived. These results are applied in a Gibbs sampler with data augmentation for posterior inference. The proposed methods are shown to outperform existing competitors in simulation studies, and the advantages of the dynamic model are demonstrated by forecasts of weekly pollster rankings of NCAA football teams.Comment: The Supplementary Material is available upon request to the author

Quantifying human performance in chess

From sports to science, the recent availability of large-scale data has allowed to gain insights on the drivers of human innovation and success in a variety of domains. Here we quantify human performance in the popular game of chess by leveraging a very large dataset comprising of over 120 million games between almost 1 million players. We find that individuals encounter hot streaks of repeated success, longer for beginners than for expert players, and even longer cold streaks of unsatisfying performance. Skilled players can be distinguished from the others based on their gaming behaviour. Differences appear from the very first moves of the game, with experts tending to specialize and repeat the same openings while beginners explore and diversify more. However, experts experience a broader response repertoire, and display a deeper understanding of different variations within the same line. Over time, the opening diversity of a player tends to decrease, hinting at the development of individual playing styles. Nevertheless, we find that players are often not able to recognize their most successful openings. Overall, our work contributes to quantifying human performance in competitive settings, providing a first large-scale quantitative analysis of individual careers in chess, helping unveil the determinants separating elite from beginner performance.Comment: 8 pages, 5 figure

A Spatiotemporal Gamma Shot Noise Cox Process

A new discrete-time shot noise Cox process for spatiotemporal data is proposed. The random intensity is driven by a dependent sequence of latent gamma random measures. Some properties of the latent process are derived, such as an autoregressive representation and the Laplace functional. Moreover, these results are used to derive the moment, predictive, and pair correlation measures of the proposed shot noise Cox process. The model is flexible but still tractable and allows for capturing persistence, global trends, and latent spatial and temporal factors. A Bayesian inference approach is adopted, and an efficient Markov Chain Monte Carlo procedure based on conditional Sequential Monte Carlo is proposed. An application to georeferenced wildfire data illustrates the properties of the model and inference