Predicting permeability from Nuclear Magnetic Resonance (NMR) data is a fundamental yet challenging task in reservoir characterization, primarily due to the uncertainty associated with surface relaxivity ( ρ ) parameters. In this work, we investigate the feasibility of using Machine Learning (ML) to estimate permeability from T_2 distributions and quantify how ρ uncertainty affects predictive accuracy. To address this, we generated a dataset of 15,000 synthetic 3D porous media representing granular sedimentary rock samples. We employed efficient in-house implementations of a Random Walk algorithm (governed by Bloch-Torrey physics) to simulate magnetization decay and obtain T_2 distributions, alongside a Finite Element Method (FEM) solver for the Stokes equations to compute absolute permeability, assuming 100 ρ . In the first experiment, we simulated T_2 distributions by assigning a constant ρ value for all synthetic porous media. In the second experiment, we applied a different ρ value to each synthetic porous medium to emulate real-world uncertainty, representing the scenario where ρ is unknown. The third experiment extends the second by converting the T_2 distributions into surface-to-volume ratio distributions using the specific ρ value assigned in the second experiment to each medium. We systematically compared the Multilayer Perceptron (MLP) performance against the industry-standard Schlumberger-Doll-Research (SDR) model. Overall, the MLP yielded strong predictive performance. The second experiment presented a performance drop for both models, confirming the impact of ρ uncertainty. The main contribution of this work is the systematic quantification of the sensitivity of predictive permeability models to ρ , establishing a controlled benchmark that addresses and reduces existing uncertainties. Additionally, these findings demonstrate that the MLP provides a robust and competitive alternative for permeability estimation in scenarios where ρ is unknown.
The Ribeira Orogen in southeastern Brazil preserves the Neoproterozoic-Cambrian processes involved in the final assembly of West Gondwana. To investigate its largely uncharacterized deep lithospheric structure, this study integrates long-period magnetotelluric (MT) data with seismic tomography and geothermal heat-flow information. The MT dataset includes 61 stations along five NW-SE profiles spaced 15-25 km apart, with periods from 10 to 10,000 s. A 3D MT inversion produced a detailed resistivity model examined jointly with the complementary geophysical datasets. The results reveal three high-resistivity lithospheric blocks, bounded by major conductive zones, interpreted as fragments of Rodinia reassembled during the Brasiliano Orogeny. The western block represents the eastward extension of the S & atilde;o Francisco paleocontinent beneath the Ribeira Orogen. The central block, underlying the Paraiba do Sul Domain, corresponds to a preserved microcontinent (Paraiba do Sul-Embu), characterized by high P-wave velocities and low surface heat flow. The eastern block, beneath the Cabo Frio Terrane, is interpreted as a fragment of the Angola-Congo paleocontinent that remained attached to South America after the opening of the South Atlantic. Two main conductive anomalies correspond to distinct tectonic episodes. The older, beneath the Occidental Terrane, records eastward subduction of the S & atilde;o Francisco lithosphere during the Cryogenian-Ediacaran. The younger, located at the boundary with the Cabo Frio Terrane, is associated with Tonian-Cryogenian intra-oceanic magmatic arcs later accreted to the orogen during Cambrian collision. These findings provide robust geophysical evidence that southeastern Brazil evolved through subduction-related accretionary processes, rather than intracontinental deformation.
In this paper, we obtain new measurements of the angular homogeneity scale ( θ _H ) from the BOSS DR12 and eBOSS DR16 catalogs of Luminous Red Galaxies of the Sloan Digital Sky Survey. Considering the flat Λ CDM model, we use the θ _H(z) data to constrain the matter density parameter ( Ω _m0 ) and the Hubble constant ( H_0 ). We find H_0 = 65^+10_-7 km s ^-1 Mpc ^-1 and Ω _m0>0.296 . By combining the θ _H measurements with current Baryon Acoustic Oscillations (BAO) and Type Ia Supernova (SN) data, we obtain H_0= 66.8 ± 5.0 km s ^-1 Mpc ^-1 and Ω _m0 = 0.292^+0.013_-0.015 ( θ _H + BAO) and H_0=66.8 ± 5.4 km s ^-1 Mpc ^-1 and Ω _m0=0.331 ± 0.018 ( θ _H + SN). We show that θ _H measurements help break the BAO and SN degeneracies concerning H_0 , as they do not depend on the sound horizon scale at the drag epoch or the SN absolute magnitude value obtained from the distance ladder method. Hence, despite those constraints are less stringent compared to other probes, θ _H data may provide an independent cosmological probe of H_0 in light of the Hubble tension. For completeness, we also forecast the constraining power of future θ _H data via Monte Carlo simulations. Considering a relative error of the order of 1 % , we obtain competitive constraints on Ω _m0 and H_0 ( ≈ 5% error) from the joint analysis with current SN and BAO measurements.
The assumption of a flat Universe that follows the cosmological principle, i.e., that the universe is statistically homogeneous and isotropic at large scales, comprises one of the core foundations of the standard cosmological model – namely, the $$\Lambda $$ Λ CDM paradigm. Nevertheless, it has been rarely tested in the literature. In this work, we assess the validity of this hypothesis by reconstructing the cosmic curvature with currently available observations, such as Type Ia Supernova and Cosmic Chronometers. We do so by means of null tests, given by consistency relations within the standard model scenario, using a non-parametric method – which allows us to circumvent prior assumptions on the underlying cosmology. We find no statistically significant departure from the cosmological principle and null curvature in our analysis. In addition, we show that future cosmological observations, specifically those expected from Hubble parameter measurements from redshift surveys, along with gravitational wave observations as standard sirens, will be able to significantly reduce the uncertainties of current reconstructions.
A compelling way to address the inflationary period is via the warm inflation scenario, where the interaction of the inflaton field with other degrees of freedom affects its dynamics in such a way that slow-roll inflation is maintained by dissipative effects in a thermal bath. In this context, if a dark matter particle is coupled to the bath due to non-renormalizable interactions, the observed dark matter abundance may be produced during warm inflation via ultra-violet freeze-in. In this work, we propose applying this scenario in the framework of a U(1)B−L gauge extension of the Standard Model of Particle Physics, where we also employ the seesaw mechanism for generating neutrino masses.
