Yagnashree Velanki, Pouya Hosseinzadeh, Soukaina Filali Boubrahimi
et al.
Solar flares are significant occurrences in solar physics, impacting space weather and terrestrial technologies. Accurate classification of solar flares is essential for predicting space weather and minimizing potential disruptions to communication, navigation, and power systems. This study addresses the challenge of selecting the most relevant features from multivariate time-series data, specifically focusing on solar flares. We employ methods such as Mutual Information (MI), Minimum Redundancy Maximum Relevance (mRMR), and Euclidean Distance to identify key features for classification. Recognizing the performance variability of different feature selection techniques, we introduce an ensemble approach to compute feature weights. By combining outputs from multiple methods, our ensemble method provides a more comprehensive understanding of the importance of features. Our results show that the ensemble approach significantly improves classification performance, achieving values 0.15 higher in True Skill Statistic (TSS) values compared to individual feature selection methods. Additionally, our method offers valuable insights into the underlying physical processes of solar flares, leading to more effective space weather forecasting and enhanced mitigation strategies for communication, navigation, and power system disruptions.
Accurate and high-resolution Earth system model (ESM) simulations are essential to assess the ecological and socio-economic impacts of anthropogenic climate change, but are computationally too expensive to be run at sufficiently high spatial resolution. Recent machine learning approaches have shown promising results in downscaling ESM simulations, outperforming state-of-the-art statistical approaches. However, existing methods require computationally costly retraining for each ESM and extrapolate poorly to climates unseen during training. We address these shortcomings by learning a consistency model (CM) that efficiently and accurately downscales arbitrary ESM simulations without retraining in a zero-shot manner. Our approach yields probabilistic downscaled fields at a resolution only limited by the observational reference data. We show that the CM outperforms state-of-the-art diffusion models at a fraction of computational cost while maintaining high controllability on the downscaling task. Further, our method generalizes to climate states unseen during training without explicitly formulated physical constraints.
Dirk-Jan van Manen, Johannes Aichele, Jonas Müller
et al.
We derive time reflection and transmission coefficients for 1D acoustic waves encountering a time boundary at which the properties of the medium change instantaneously. The time reflection and transmission coefficients are shown to be identical to so-called reverse-space reflection and transmission coefficients which appear in the recursive computation of focusing wavefields used in seismology. We establish a bijectivity between the focusing wavefields and the wavefields produced by time scattering and show how this can be used to construct a space-time medium where the time scattering anticipates the space scattering and "computes" the exact inverse for the space scattering. The construction is shown to be independent of the boundary conditions chosen to compute the reflection and transmission coefficients. We demonstrate the construction with a simple numerical example of a single pulse encountering a series of time boundaries before reaching a spatial inhomogeneity. The time boundaries scatter the single pulse into a focusing wavefield that subsequently focuses through the spatial inhomogeneity. Under certain conditions, the transmitted wave has both the same wave shape and amplitude as the original pulse, yielding a transmission coefficient of unity. The reflection coefficient of the space-time medium is always non-zero however.
Ori Saporta-Katz, Nadav Mantel, Rotem Liran
et al.
The Mediterranean Sea releases approximately 1Sv of water into the North Atlantic through the Gibraltar Straits, forming the saline Mediterranean Outflow Water (MOW). Its impact on large-scale flow and specifically its northbound Lagrangian pathways are widely debated, yet a comprehensive overview of MOW pathways over recent decades is lacking. We calculate and analyze synthetic Lagrangian trajectories in 1980-2020 reanalysis velocity data. 16\% of the MOW follow a direct northbound path to the sub-polar gyre, reaching a 1000m depth crossing window at the southern tip of Rockall Ridge in about 10 years. Surprisingly, time-dependent chaotic advection, not steady currents, drives over half of the northbound transport. Our results suggest a potential 15-20yr predictability in the direct northbound transport, which points to an upcoming decrease of MOW northbound transport in the next couple of decades. Additionally, monthly variability appears more significant than inter-annual variability in mixing and spreading the MOW.
Nominally anhydrous minerals (NAMs) are the primary carriers of water in rocky planet mantles. Therefore, studying water solubilities of major NAMs in the mantle can help us estimate the water storage capacities of rocky planet mantles and indirectly constrain the actual water contents of their interiors. By using data science methods such as statistics and statistical learning algorithms, in this paper, current modeling studies on the mantle water storage capacities of Earth, Mars, and exoplanets have been introduced and summarized. Firstly, the thermodynamic model for mantle water storage capacity has been reviewed. Then, based on the two case studies on Earth and Mars, how to translate atomic-scale experimental data of water solubility and their measurement errors into planetary-scale models of mantle water storage capacity has been explored by using robust regression, Monte Carlo methods, and bootstrap aggregation algorithms. Thirdly, how the large sample data from the exoplanet observational campaigns can help us understand the statistical properties of the mantle water storage capacities of rocky exoplanets has been introduced. Finally, the application limitations of data science methods in mineral physics research have been discussed, and how to better combine statistics and statistical algorithms with mineral physics data research has been prospected.
There has been an increasing interest in integrating physics knowledge and machine learning for modeling dynamical systems. However, very limited studies have been conducted on seismic wave modeling tasks. A critical challenge is that these geophysical problems are typically defined in large domains (i.e., semi-infinite), which leads to high computational cost. In this paper, we present a novel physics-informed neural network (PINN) model for seismic wave modeling in semi-infinite domain without the nedd of labeled data. In specific, the absorbing boundary condition is introduced into the network as a soft regularizer for handling truncated boundaries. In terms of computational efficiency, we consider a sequential training strategy via temporal domain decomposition to improve the scalability of the network and solution accuracy. Moreover, we design a novel surrogate modeling strategy for parametric loading, which estimates the wave propagation in semin-infinite domain given the seismic loading at different locations. Various numerical experiments have been implemented to evaluate the performance of the proposed PINN model in the context of forward modeling of seismic wave propagation. In particular, we define diverse material distributions to test the versatility of this approach. The results demonstrate excellent solution accuracy under distinctive scenarios.
Current techniques for predicting climate change are mainly based on "massive" deterministic numerical modeling. However, the ocean-atmosphere system is a so-called "complex system", made up of a large number of interacting elements. We show that, in such systems, owing to the particularly large sensitivity to initial conditions, the approach of a possible tipping over a critical point cannot be evidenced "by construction" using numerical modeling, due to the divergence of computation time in the vicinity of the tipping point. On the other hand, the increasing amplitudes of observed climatic instabilities seem to be an obvious sign of the approach of such a tipping point, easily interpreted as a "critical softening", well known in the theory of dynamical systems, that would bring us irreversibly into a new and totally unexplored equilibrium state, except for a significantly higher temperature and in a much closer time than expected from numerical modeling extrapolations. Thus, maintaining climate warming around 1.5$^o$C or 2$^o$C by 2030 or 2050 appears fairly unrealistic unless worldwide drastic green house gases reduction measures are immediately taken and applied.
Time-domain seismic forward and inverse modeling for a dissipative medium is a vital research topic to investigate the attenuation structure of the Earth. Constant Q, also called frequency independence of the quality factor, is a common assumption for seismic Q inversion. We propose the first- and second-order nearly constant Q dissipative models of the generalized standard linear solid type, using a novel Q-independent weighting function approach. The two new models, which originate from the Kolsky model (a nearly constant Q model) and the Kjartansson model (an exactly constant Q model), result in the corresponding wave equations in differential form. Even for extremely strong attenuation (e.g., Q = 5), the quality factor and phase velocity for the two new models are close to those for the Kolsky and Kjartansson models, in a frequency range of interest. The wave equations for the two new models involve explicitly a specified Q parameter and have compact and simple forms. We provide a novel perspective on how to build a nearly constant Q dissipative model which is beneficial for time-domain large scale wavefield forward and inverse modeling. This perspective could also help obtain other dissipative models with similar advantages. We also discuss the extension beyond viscoacousticity and other related issues, for example, extending the two new models to viscoelastic anisotropy.
André Rafael Cunha, Celso Peres Fernandes, Luís Orlando Emerich dos Santos
et al.
The interconnectivity of the porous space is an important characteristic in the study of porous media and their transport properties. Hence we propose a way to quantify it and relate it with the intrinsic permeability of rocks. We propose a measure of connectivity based on geometric and topological information of pore-throat network, which are models built from microtomographic images, and we obtain an analytical method to compute that property. The method is expanded to handle rocks that present a higher degree of heterogenity in the porous space, which characterization requires images from different resolutions (multiscale analysis). Trying to expand the methodology beyond the scope of images, we also propose a new interpretation for the experiment that generates the mercury intrusion curve and calculate the permeability. The methodology was applied to images of 11 rocks, 3 sandstone and 8 carbonate rock samples, and to the experimental mercury intrusion curve of 4 tight gas sand rock samples. We observe as result the existence of a correlation between the experimental and the predicted values. The notions of connectivity developed in this work seek above all to characterize a porous material before a typical macroscopic phenomenology.
In recent years researchers in oil-gas industry have established that the contribution of memory is significant for the modeling of fluid flow in unconventional reservoirs. Mathematically, a memory-based fluid flow model can be described by the system of integro-differential equations. Despite the fact that a large number of journal articles are devoted to numerical methods for the forward solution of such equations, the problems of optimization and optimal control of these systems are actual and insufficiently studied. We consider the one-dimensional model of gas filtration and diffusion as a model with memory. The system includes a partial differential equation for filtration in fractures and weakly singular Volterra integral equation of the second kind, which describes the diffusion of gas from blocks with closed nanopores. Numerical simulation, obtained using a Navot-trapezoidal algorithm, shows that the effect of memory influences on the distribution and the time evolution of pressure and density in comparison with the classical double porosity model. The pressure-constrained maximization of discounted cumulative gas production was chosen as a basic optimization problem. The appearance of memory in the model makes the standard adjoint-based approach not applicable since it was developed only for conventional systems of partial differential equations. The novel adjoint model for media with memory was obtained from the necessary conditions of optimality using the classical theory of calculus of variations and efficiently applied to production optimization problem. In conclusion we compare optimal control scenarios for the model with memory and for the classical double porosity model. Analysis has shown the importance of memory accounting in reservoir optimization problems.
We present a simple and efficient implementation of a viscous creep rheology based on diffusion creep, dislocation creep and the Peierls mechanism in conjunction with an elasto-plastic rheology model into a shock-physics code, the iSALE open-source impact code. Our approach is based on the calculation of an effective viscosity which is then used as a reference viscosity for any underlying viscoelastic (or even visco-elasto-plastic) model. Here we use a Maxwell-model which best describes stress relaxation and is therefore likely most important for the formation of large meteorite impact basins. While common viscoelastic behavior during mantle convection or other slow geodynamic or geological processes is mostly controlled by diffusion and dislocation creep, we showed that the Peierls mechanism dominates at the large strain rates that typically occur during meteorite impacts. Thus, the resulting visco-elasto-plastic rheology allows implementation of a more realistic mantle behavior in computer simulations, especially for those dealing with large meteorite impacts. The approach shown here opens the way to more faithful simulations of large impact basin formation, especially in elucidating the physics behind the formation of the external fault rings characteristic of large lunar basins.
In this paper we treat a particular class of planar Filippov systems which consist of two smooth systems that are separated by a discontinuity boundary. In such systems one vector field undergoes a saddle-node bifurcation while the other vector field is transversal to the boundary. The boundary-saddle-node (BSN) bifurcation occurs at a critical value when the saddle-node point is located on the discontinuity boundary. We derive a local topological normal form for the BSN bifurcation and study its local dynamics by applying the classical Filippov’s convex method and a novel regularization approach. In fact, by the regularization approach a given Filippov system is approximated by a piecewise-smooth continuous system. Moreover, the regularization process produces a singular perturbation problem where the original discontinuous set becomes a center manifold. Thus, the regularization enables us to make use of the established theories for continuous systems and slow-fast systems to study the local behavior around the BSN bifurcation.
The hypothesis of the conserved vector current, relating the vector weak and isovector electromagnetic currents, plays a fundamental role in quantitative description of neutrino interactions. Despite being experimentally confirmed with great precision, it is not fully implemented in existing calculations of the cross section for inverse beta decay, the dominant mechanism of antineutrino scattering at energies below a few tens of MeV. In this article, I estimate the corresponding cross section and its uncertainty, ensuring conservation of the vector current. While converging to previous calculations at energies of several MeV, the obtained result is appreciably lower and predicts more directional positron production near the reaction threshold. These findings suggest that in the current estimate of the flux of geologically produced antineutrinos the 232Th and 238U components may be underestimated by 6.1 and 3.7%, respectively. The proposed search for light sterile neutrinos using a 144Ce--144Pr source is predicted to collect the total event rate lower by 3% than previously estimated and to observe a spectral distortion that could be misinterpreted as an oscillation signal. In reactor-antineutrino experiments, together with a re-evaluation of the positron spectra, the predicted event rate should be reduced by 0.9%, diminishing the size of the reported anomaly.
Volumetric sediment concentrations computed by phase-resolving swash morphodynamic models are shown to exceed unity minus porosity (i.e. the maximal physically possible concentration value) by up to factor of $10^5$ when using standard expressions to compute the sediment transport rate. An ad hoc limit of sediment concentration is introduced as a means to evaluate consequences of exceeding physically realistic concentration by standard expressions. We find that implementation of this ad hoc limit strongly changes the quantitative and qualitative predictions of phase-resolving swash morphodynamic models, suggesting that existing swash predictions are unreliable. This is because standard expressions inappropriately consider or ignore the fact that the shallow swash water depth limits the storage capacity of transported sediment.