Abstract The estimation of petrophysical properties, such as porosity and water saturation, from geophysical data through rock physics–based inversion is crucial for understanding groundwater and weathering processes in the critical zone (CZ). A unified rock physics model is developed to map porosity and water saturation to P- and S-wave velocities obtained from seismic data. The rock physics model is based on an exponential function whose parameters control the velocity values at the minimum and maximum porosity, as well as the rate at which velocity decreases with increasing porosity. The model incorporates Gassmann’s equation to account for partial saturation conditions. Unlike traditional rock physics models that apply only to specific geological settings, the new formulation accurately captures velocity changes in the near surface as materials transition from disaggregated sediments to fractured zones and ultimately to more coherent bedrock with depth, under fully saturated, partially saturated, and unsaturated conditions. By introducing Archie’s equation, the framework also models resistivity, allowing the integration of elastic and electrical rock physics relationships for an improved petrophysical characterization of the critical zone. Based on the proposed rock physics model, a Bayesian inversion workflow is developed to estimate porosity and water saturation from seismic velocity and electrical resistivity data. The inversion method provides probabilistic estimates of the petrophysical properties, accounting for measurement uncertainty and prior information. The rock physics model and the Bayesian inversion are applied to a field dataset from the Laramie Range, predicting porosity and water saturation from geophysical measurements. The method provides accurate spatial models of petrophysical data to inform hydrological analysis in the CZ and it enables data-driven characterization of weathered and fractured near-surface rocks.

We propose using different geophysical methods to locate uranium roll fronts. We implement two methods: Induced Polarization (IP) and Self-Potential (SP). This research focuses on IP and SP analyses of the geochemical reactions involved in sedimentary uranium deposit formation and movement. If observable, we can more directly describe and determine the location of roll-front uranium deposits and potentially monitor their movement during in situ recovery (ISR) extraction. We hypothesize that the redox geochemical reaction at uranium roll fronts produces measurable and distinct IP and SP responses.

To decrease the generation of greenhouse gas emissions, it is necessary to depart from oil-and-gas in favor of alternative energy sources such as uranium. Sandstone-hosted uranium deposits (SU) are the most economically valuable uranium sources but are typically evaluated using traditional radiometric and drilling methods exclusively. The Shirley Basin of southeastern Wyoming harbors an SU roll-front that has been previously assessed geologically but has only been sparsely surveyed using modern techniques. This study seeks to evaluate which ground-based geophysical methods best delineate the SU roll-front in Shirley Basin, WY using electrical, electromagnetic, and seismic methods. In doing so, resistivity and seismic velocity properties are acquired that prompt the geophysical characterization of the reduced, ore, and oxidized zones. To evaluate confidence in the ground-based methods, findings are correlated with borehole and InSAR data.

Quantification of fluid distribution and flow in the Earth’s near-surface benefits from precise estimation of electrical properties of fluid-saturated rocks, such as resistivity estimated from inversion of electrical resistivity tomography (ERT) data. The predicted resistivity values are often uncertain due to two main types of uncertainties: epistemic uncertainty in the inversion process (e.g., inaccuracy in the physical models) and aleatoric uncertainty in the data (e.g., measurement errors). This work focuses on the quantification of aleatoric variability in the ERT measurements and its effect on the inverted resistivity models. We first investigate how measurement uncertainty, in the form of reciprocal error, correlates with the measured electrical contact resistance of electrodes with ground. Next, we apply a statistical approach based on the stochastic perturbation and inversion of multiple realizations of resistance data to study the uncertainty in the predicted resistivity tomograms. We then study the effect of data uncertainty on the inverted resistivity model for individual data sets. We finally quantify the effect of variation in data quality over time on the inverted time-lapse resistivity results. The results from 20 campaigns and two time-lapse ERT data sets show that reciprocal error is positively correlated with both contact resistance and ground’s apparent resistivity, confirming the significance of practicing lowering electrode contact resistance during ERT field campaigns. Additionally, our results show that uncertainty in the estimated resistivity model depends on both the ground’s resistivity and measurement error of the input data. The time-lapse results provide additional insight that model uncertainty is the highest at the driest and coldest months of the year, corresponding to the highest measured contact resistance and reciprocal error.

Little is known about the local plumbing systems that fuel Yellowstone's famous hot springs, geysers and mud pots. A multi-method, multi-scale geophysical investigation was carried out in the Obsidian Pool Thermal Area (OPTA) to: (a) delineate the lateral extent of the hydrothermal area and associated surface features; (b) estimate the dimensions of the upflow zone and identify its main controlling structures; (c) assess fluids circulation pathways from depth to surface. Ground and airborne geophysical data were acquired to connect local and regional scales, from shallow to large depths. Maps of surface electrical resistivity show a strong correlation with hydrothermal features. At intermediate depths, electrical resistivity permits delineating the upper limit of the upflow zone, while Poisson's ratio highlights differences in subsurface fluid content. Combining these results with surface observations and topographic information, we speculate that differential mixing of hydrothermal and fresh water could explain the wide diversity of features observed at OPTA. Low electrical resistivity observed at large depths also suggest that a vast upflow zone, controlled by rhyolite flows and conjugate faults, underlies the OPTA. We speculate that hydrothermal fluids rise along fractures and reach the surface in topographic lows to form hydrothermal features. Our results show that synoptic, multi-scale geophysical measurements provide a roadmap for understanding where and how geologic heterogeneity, topography, fluid-gas separation, and the mixing of thermal and meteoric waters conspire to produce the wide variety of Yellowstone's renowned hydrothermal features.

Physics-based catchment models of mountain environments can suffer from equifinality when solely calibrated against streamfiow data. Inclusion of intra-catchment data such as soil moisture or groundwater levels in model calibration can reduce equifinality problems, though physical demands of installation and remote field sites can limit their availability. Non-invasive geophysical surveys such as electromagnetic (EM) induction have become practical alternative sources of information on the subsurface. As such, we are interested in addressing the applicability of EM data to directly calibrate hydraulic parameters in physics-based catchment models in hydrogeophysical inversions. This study explores the interrelationships between calibration data, hydraulic parameters, and calibrated model dynamics for a headwater catchment in the Reynolds Creek Experimental Watershed, Idaho, USA. Five calibration scenarios and a global sensitivity analysis are performed to quantify the ability of different combinations of hydrological (streamfiow, groundwater levels, soil moisture) and EM data (airborne and ground-based surveys) to predict both streamfiow and intra-catchment dynamics. Results indicate that calibrating against streamfiow data alone yields accurate streamfiow but inconsistent intra-catchment predictions (streamfiow, groundwater level, and soil moisture average Kling-Gupta efficiency values of KGE = 0.89,-0.53, and 0.44, respectively). Calibrating against all hydrological data yields reasonable predictions of hydrological dynamics (streamfiow, groundwater level, and soil moisture average KGE = 0.91, 0.23, and 0.62, respectively), though some calibrated parameter values do not match expectations from literature values. Reasonably accurate hydrological predictions were obtained when including EM data with either streamfiow data alone (streamfiow, groundwater level, and soil moisture average KGE = 0.83, 0.29, and 0.52, respectively) or all hydrological data (streamfiow, groundwater level, and soil moisture average KGE = 0.87, 0.39, and 0.51, respectively) during calibration. However, EM data alone yields hydraulic parameters that overpredict saturation throughout the catchment (streamfiow, groundwater level, and soil moisture average KGE = 0.09,-0.57, and 0.37, respectively). These results highlight potential advantages of collecting EM data in catchments with existing streamfiow data but poor coverage of intra-catchment hydrological data sets. Additional work regarding petrophysical model parameterizations, objective function definitions, and data set weighting schemes is needed to ensure that the contribution of EM data to hydraulic parameter identification is maximized.

Quantification of fluid distribution and flow in the earth's near surface benefits from precise estimation of electrical properties of fluid-saturated rocks, such as resistivity estimated from inversion of electrical resistivity tomography (ERT) data. The predicted resistivity values are often uncertain due to two main types of uncertainties: epistemic uncertainty in the inversion process (e.g., inaccuracy in the physical models) and aleatoric uncertainty in the data (e.g., measurement errors). This work focuses on the quantification of aleatoric variability in the ERT measurements and its effect on the inverted resistivity models. We first investigate how measurement uncertainty, in the form of reciprocal error, correlates with the measured electrical contact resistance of electrodes with ground. Next, we apply a statistical approach based on the stochastic perturbation and inversion of multiple realizations of resistance data to study the uncertainty in the predicted resistivity tomograms. We then study the effect of data uncertainty on the inverted resistivity model for individual data sets. We finally quantify the effect of variation in data quality over time on the inverted time-lapse resistivity results. The results from 20 campaigns and two timelapse ERT data sets show that reciprocal error is positively correlated with contact resistance and ground's apparent resistivity, confirming the significance of practicing lowering electrode contact resistance during ERT field campaigns. In addition, our results show that uncertainty in the estimated resistivity model depends on the ground's resistivity and measurement error of the input data. The time-lapse results provide additional insight that model uncertainty is the highest in the driest and coldest months of the year, corresponding to the highest measured contact resistance and reciprocal error.

Within Earth's critical zone, weathering processes influence landscape evolution and hillslope hydrology by creating porosity in bedrock, transforming it into saprolite and eventually soil. In situ weathering processes drive much of this transformation while preserving the rock fabric of the parent material. Inherited rock fabric in regolith makes the critical zone anisotropic, affecting its mechanical and hydrological properties. Therefore, quantifying and studying anisotropy is an important part of characterising the critical zone, yet doing so remains challenging. Seismic methods can be used to detect rock fabric and infer mechanical and hydrologic conductivity anisotropy across landscapes. We present a novel way of measuring seismic anisotropy in the critical zone using Rayleigh and Love surface waves. This method leverages multi‐component surface seismic data to create a high‐resolution model of seismic anisotropy, which we compare with a nuclear magnetic resonance log measured in a nearby borehole. The two geophysical data sets show that seismic anisotropy and porosity develop at similar depths in weathered bedrock and both reach their maximum values in saprolite, implying that in situ weathering enhances anisotropy while concurrently generating porosity in the critical zone. We bolster our findings with in situ measurements of seismic and hydrologic conductivity anisotropy made in a 3 m deep soil excavation. Our study offers a fresh perspective on the importance of rock fabric in the development and function of the critical zone and sheds new insights into how weathering processes operate.

Controls on the physical and chemical architecture of the subsurface critical zone are somewhat controversial, with multiple hypotheses proposed to account for variations in the depth of weathering between sites, and with landscape position at a site. In the Piedmont region of the Mid-Atlantic US weathering of crystalline bedrock has been observed to extend tens of meters below the surface and groundwater in a'bow-tie' shape - i.e. weathering extends to lower elevations below ridges than below channels. The chemical and physical structure of a hillslope transect in the Maryland Piedmont was explored with a 45 m borehole in the ridge, as well as shallow bedrock boreholes at the toe of the slope and valley. Chemical weathering fronts were characterized using elemental abundances and mineralogical analysis. The ridge borehole did not extend deeper than the chemically and physically weathered rock. Surface and borehole geophysics and density measurements were used to characterize the weathered rock and saprolite. Na and Ca results suggest that plagioclase feldspar weathering is similar between samples collected from 45 m under the ridge and 2.2 m under the valley bottom. A narrow Fe oxidation garnet weathering front co-insides with the transition from weathered bedrock to saprolite, suggesting that this reaction may generate initial saprolite porosity. Muscovite weathering co-occurs with complete depletion of plagioclase a few meters above the Fe oxidation front. These nested weathering fronts in the saprolite appear to follow a subdued version of the surface topography. The location and shape of the nested saprolite weathering fronts may be controlled by the feedback between the transport of reactants and solutes and reaction-generated porosity, consistent with the conceptual "valve" hypothesis. Differing dominant control mechanisms on deep bedrock weathering and saprolite initiating reactions may explain the thickness and structure of the critical zone at our site.

Poisson's ratio for earth materials is usually assumed to be positive (V-p/V-s > 1.4). However, this assumption may not be valid in the critical zone because near Earth's surface effective pressures are low (<1 MPa), porosity has a wide range (0%-60%), there are significant texture changes (e.g., unconsolidated vs. fractured media), and saturation ranges from 0% to 100%. We present P-wave (V-p) and S-wave (V-s) velocities from seismic refraction profiles collected in weathered crystalline environments in South Carolina and Wyoming. Our data show that similar to 20% of the subsurface has negative Poisson's ratios (V-p/V-s values < 1.4), a conclusion supported by borehole sonic logs. The low V-p/V-s values are confined to the fractured bedrock and saprolite. Our data support the hypothesis that weathering-generated microcracks can produce a negative Poisson's ratio and that V-p/V-s values can thus provide insight into important critical zone weathering processes.

In this study, we introduce a novel field-based method to estimate specific yield (S-y) in fractured, low-porosity granite aquifers using borehole nuclear magnetic resonance (bNMR). This method requires collecting a bNMR survey immediately following a pump test, which dewaters the near-borehole fractures. The residual water content measured from bNMR is interpreted as "bound" and represents the specific retention (S-r) while the water drained by the pump is the S-y. The transverse relaxation cutoff time (T-2C) is the length of time that partitions the total porosity measured by bNMR into S-r and S-y. When applying a calibrated T-2C, S-y equals the bNMR total porosity minus S-r; thus, a calibrated T-2C is required to determine S-y directly from NMR results. Based on laboratory experiments on sandstone cores, the default T-2C is 33 ms; however, its applicability to fractured granite aquifers is uncertain. The optimal T-2C based on our pumping test is 110 +/- 25 ms. Applying this calibrated T-2C on a saturated, A-type granite at our field site, we estimate the S-y to be 0.012 +/- 0.005 m(3) m(-3) which is significantly different from the S-y (0.021 +/- 0.005 m(3) m(-3)) estimate using the default T-2C of 33 ms. This S-y estimate falls within a range determined using traditional hydraulic testing at the same site. Using the conventional T-2C (33 ms) for fractured granite leads to an inaccurate S-y; therefore, it is essential to calibrate the bNMR T-2C for the local site conditions prior to estimating S-y.

Understanding the near-surface structure of the Earth requires accurate prediction of physical properties of the subsurface, such as velocity estimated from tomographic inversion of seismic refraction data. The predicted velocity values are often uncertain due to epistemic uncertainty in the inversion process (i.e., imperfectly known underlying physics) and aleatoric variability in the data (i.e., inherent noise in observations). Although seismic refraction is widely used in near-surface applications, the associated uncertainty is rarely quantified and presented alongside the inverted velocity tomograms. In this study, the effect of epistemic uncertainty due to local variability in the initial model and aleatoric variability due to first-arrival picking error on the velocity prediction uncertainty are investigated. A stochastic framework is implemented based on a statistical approach where multiple realizations of stochastically perturbed initial models and travel time picks are generated and the uncertainty in the predicted velocity models is quantified. The two sources of uncertainty are first studied independently and then the combined effect is investigated. The results show that both sources affect the posterior uncertainty, but the uncertainty in the initial model has a greater effect than picking error on the uncertainty of the posterior velocity model. In addition, joint analysis of both sources of uncertainty shows that the uncertainty in the inverted model depends on predicted velocity values, depths, velocity gradients and ray coverages.

An accepted paradigm of hydrothermal systems is the process of phase separation, or boiling, of a deep, homogeneous hydrothermal fluid as it ascends through the subsurface resulting in gas rich and gas poor fluids. While phase separation helps to explain first-order patterns in the chemistry and biology of a hot spring’s surficial expression, we know little about the subsurface architecture beneath “phase-separated” pools and the timescales over which phase separation processes occur. Essentially, we have a two-dimensional understanding of a four-dimensional process. By combining geophysical, geochemical, isotopic, and microbiological measurements of two adjacent phase-separated hot springs in Norris Geyser Basin, Yellowstone National Park, we provide a four-dimensional assessment of phase separation processes and their biological manifestation. We uniquely show that Yellowstone’s hydrothermal waters originate from a deep sedimentary aquifer and that both meteoric recharge and shallow reactive transport processes are required to establish the geobiological feedbacks that drive bimodal distributions in the geochemical and microbial composition of hot springs. Specifically, over periods of tens of years, gas-enriched fluids containing volcanic sulfide mix with meteoric waters resulting in microbially-mediated production of sulfuric acid by thermoacidophilic Archaea in the near subsurface. In contrast, over periods of hundreds of years, anoxic residual liquid rises to the surface where it is infused with atmospheric gas fostering Archaea and Bacteria that are largely dependent on oxygen. As such, our results provide formative insight into the causative links between subsurface geological processes, the development of geochemical fluids, and the assembly and diversification of thermophilic microbial communities in hydrothermal systems.

Yellowstone National Park (YNP) is home to roughly 500 geysers, making it the most concentrated geyser field in the world. Recent studies exploring the mechanics of geyser eruptions utilizing laboratory models were limited by a lack of knowledge of the subsurface geometry of the geyser features. This study presents results from active hydrogeophysical surveys, including ground penetrating radar, nuclear magnetic resonance (NMR), seismic refraction, electrical resistivity tomography, and transient electromagnetics to image subsurface geyser structure and constrain the geophysical response of the reservoir structure of Spouter Geyser, Yellowstone National Park. Previous geophysical studies on similar geyser systems in Yellowstone characterize the hydrothermal reservoir that supplies the geyser with hydrothermal fluids and vapors as high porosity, low density structures. Whereas our imaging highlights the hydrothermal conduits and reservoir as high resistivity and high velocity structures, interpreted as silica precipitate decreasing the porosity and increasing the bulk modulus (i.e., from unconsolidated media to semi/fully consolidated). Ground penetrating radar identifies sinter thickness around the geyser, NMR provides porosity measurements of the geyser reservoir, and transient electromagnetics maps the depth to the Biscuit Basin Rhyolite bedrock throughout the field site. Electrical resistivity tomography and seismic refraction image the shallow geyser conduit structures to ∼15 m depth and establish the extent of the geyser hydrothermal reservoir structure. Furthermore, a spatial correlation of the electrical resistivity and seismic velocity results establishes the interpreted hydrothermal geyser reservoir as a high resistivity, high velocity structure located to the northeast of Spouter Geyser at depths greater than 15 m.

Physical, chemical, and biological processes create and maintain the critical zone (CZ). In weathered and crystalline rocks, these processes occur over 10–100 s of meters and transform bedrock into soil. The CZ provides pore space and flow paths for groundwater, supplies nutrients for ecosystems, and provides the foundation for life. Vegetation in the aboveground CZ depends on these components and actively mediates Earth system processes like evapotranspiration, nutrient and water cycling, and hill slope erosion. Therefore, the vertical and lateral extent of the CZ can provide insight into the important chemical and physical processes that link life on the surface with geology 10–100 s meters below. In this study, we present 3.9 km of seismic refraction data in a weathered and crystalline granite in the Laramie Range, Wyoming. The refraction data were collected to investigate two ridges with clear contrasts in vegetation and slope. Given the large contrasts in slope, aspect, and vegetation cover, we expected large differences in CZ structure. However, our results suggest no significant differences in large-scale (>10 s of m) CZ structure as a function of slope or aspect. Our data appears to suggest a relationship between LiDAR-derived canopy height and depth to fractured bedrock where the tallest trees are located over regions with the shallowest depth to fractured bedrock. After separating our data by the presence or lack of vegetation, higher P-wave velocities under vegetation is likely a result of higher saturation.

Geysers are unique hydrothermal features, requiring specific geometry, fluid, and vapor input, and heat to produce eruptions. In a geyser eruption, the decompression of superheated hydrothermal fluids results in a conversion of thermal to kinetic energy. Recent studies conclude that a laterally offset cavity structure (bubble trap) plays an integral role in the geyser eruption process. In this second study of Spouter Geyser, Yellowstone National Park (YNP), we build on the geyser structure developed in the first publication and explore how structural components, such as the bubble trap, control eruption dynamics. We utilize time‐lapse electrical resistivity tomography (ERT) and transient electromagnetics (TEM) geophysical methods to track changes in the saturation of conductive hydrothermal fluid and resistive vapor through the eruption cycle. Additionally, we use pressure‐temperature transducer data to measure eruption and recharge durations over the past 23 years at Spouter Geyser, identifying long‐period trends in geyser behavior. The geophysics results support the bubble trap model, capturing an increase in resistivity in the bubble trap structure during the recharge phase interpreted as a vapor saturation increase. The TEM also captures a resistivity increase in the 20–30 m depth interval during the eruption phase, interpreted as a vapor‐dominated “flash eruption zone” where the superheated hydrothermal fluids flash into steam and drive fluid out of this zone during the eruption. From the temperature time‐series detailing 23 years of eruption and recharge durations, we find that eruption durations have remained constant at ∼2 hr while recharge durations have decreased linearly at 6.6 min/year.

Geophysical data (EMI, GTEM, Seismic, Resistivity) collected over Obsidian Pool Thermal Area, Yellowstone National Park, in 2015 and 2016.

Bedrock property quantification is critical for predicting the hydrological response of watersheds to climate disturbances. Estimating bedrock hydraulic properties over watershed scales is inherently difficult, particularly in fracture-dominated regions. Our analysis tests the covariability of above- and belowground features on a watershed scale, by linking borehole geophysical data, near-surface geophysics, and remote sensing data. We use machine learning to quantify the relationships between bedrock geophysical/hydrological properties and geomorphological/vegetation indices and show that machine learning relationships can estimate most of their covariability. Although we can predict the electrical resistivity variation across the watershed, regions of lower variability in the input parameters are shown to provide better estimates, indicating a limitation of commonly applied geomorphological models. Our results emphasize that such an integrated approach can be used to derive detailed bedrock characteristics, allowing for identification of small-scale variations across an entire watershed that may be critical to assess the impact of disturbances on hydrological systems.

The Oregon Trail is an ephemeral artifact of great importance to the history of the United States. While there is a great deal of historical documentation in the way of journals, travel itineraries, and cargo manifests, there is much less documentation in terms of the physical trail itself. That lack of research on the condition of the roadbed, then and now, and of alternate paths taken off the trail are having a deleterious effect on the understanding of this time in history, as the trail itself is disappearing. Using ground-penetrating radar, we have begun digital documentation of the trail and its unique characteristics in a way that has not been widely published. With these new methodologies and the theoretical approach of conflict-event theory, we propose a new process capable of creating a "digital signature" of the Oregon Trail.