Geophysical Journal International

SummaryThe conductive and capacitive properties of rocks are influenced by the type and concentration of the electrolyte present in the pore water. Sodium (Na⁺) and potassium (K⁺) are common pore water cations in saturated sedimentary rocks. Their distinct physicochemical properties are expected to produce different frequency-dependent electrical dispersion when adsorbed onto mineral surfaces. We tested this expectation by using spectral induced polarization (SIP), a method sensitive to interfacial processes. Complex conductivity spectra (10–2 to 105 Hz) were measured on two clayey, opal-A-rich diatomite samples, saturated with either NaCl or KCl solutions. One sample was tested over a stepwise increase in molar concentration (5.4–53 mM), while the other was tested over a stepwise increase in bulk water conductivity (0.050–0.48 S/m). At equivalent molar concentration, the in-phase conductivity of a sample was ~20 per cent higher when KCl saturated than when NaCl saturated, reflecting the greater molar conductivity of K⁺. At matched bulk water conductivity, which required a ~20 per cent higher NaCl molarity than KCl molarity, in-phase conductivity was ~10 per cent higher when NaCl saturated. In both tests, the quadrature conductivity and normalized chargeability followed a lower trend in the KCl-saturated state than in the NaCl-saturated state. This relatively low polarization for the K+ saturated state can be attributed to a weaker hydration and more compact adsorption of K⁺ within the inner layer of the electrical double layer. Additionally, time-lapse monitoring of complex conductivity spectra indicates that chemical equilibration via diffusion is achieved within 72 hours for both electrolyte types. This relatively rapid ionic diffusion is consistent with estimates based on the intrinsic formation factor and probably reflects the high porosity of the diatomite (~0.7). These findings establish that pore-water cation identity (Na⁺ vs. K⁺) is a primary control on SIP-derived polarization parameters, and cation identity must therefore be incorporated into petrophysical models to avoid biased estimates of surface area, permeability, and hydrogeochemical state.

SummaryImages of the Earth’s interior can provide us with insight into the underlying properties of the Earth, such as how seismic activity might emerge and the interplay between seismic and volcanic activity. Understanding these systems requires reliable high-resolution images to understand mechanisms and estimate physical quantities. However, reliable images are often difficult to obtain due to the non-linear nature of seismic wave propagation and the ill-posedness of the related inverse problem. Reconstructions rely on good initial estimates as well as hand-crafted priors, which can ultimately bias solutions. In our work, we present a 3D reconstruction of Kilauea’s magmatic system at a previously unattained resolution. Our eikonal tomography procedure improves upon prior imaging results of Kilauea through increased resolution and per-pixel uncertainties estimated through variational inference. In particular, solving eikonal imaging using variational inference with stochastic gradient descent enables stable inversion and uncertainty quantification in the absence of strong prior knowledge of the velocity structure. Our work makes two key contributions: developing a stochastic eikonal tomography scheme with uncertainty quantification and illuminating the structure and melt quantity of the magmatic system that underlies Kilauea.

SummaryThe global energy transition has created an urgent need for expanded critical mineral supply. Projected production from existing deposits and current discovery rates remains insufficient to meet this demand. More efficient exploration strategies are therefore required, particularly in optimizing costly and low-success-rate data acquisition campaigns. To address this challenge, we introduce the concept of sequential Efficacy of Information (sequential EOI), a decision-making metric that quantifies the uncertainty reduction of target variables under proposed exploration action sequences. Unlike Value of Information (VOI), sequential EOI operates in the domain of uncertainty reduction, removing the need for an economic model that is rarely available in early-stage exploration. We demonstrate the framework using synthetic 2D and more realistic 3D porphyry copper systems, evaluating sequential combinations of exploration plans including ambient noise tomography (ANT) surveys and borehole drilling campaigns. In both cases, sequential EOI identified exploration plans that maximized the uncertainty reduction to the target variables. These results demonstrate that sequential EOI offers a principled framework for multi-physics, multi-step, and uncertainty-driven plan optimization in mineral exploration, providing exploration teams with a practical and scalable decision-analytic tool for rational campaign design without requiring economic assumptions.