Geology topics

Ryan Anderson

Publications and source records attributed to Ryan Anderson.

6 recordsLinked to original sources

USGS2024

Manganese-rich sandstones as an indicator of ancient oxic lake water conditions in Gale crater, Mars

Manganese has been observed on Mars by the NASA Curiosity rover in a variety of contexts and is an important indicator of redox processes in hydrologic systems on Earth. Within the Murray formation, an ancient primarily fine-grained lacustrine sedimentary deposit in Gale crater, Mars, have observed up to 45× enrichment in manganese and up to 1.5× enrichment in iron within coarser grained bedrock targets compared to the mean Murray sediment composition. This enrichment in manganese coincides with the transition between two stratigraphic units within the Murray: Sutton Island, interpreted as a lake margin environment, and Blunts Point, interpreted as a lake environment. On Earth, lacustrine environments are common locations of manganese precipitation due to highly oxidizing conditions in the lakes. Here, we explore three mechanisms for ferromanganese oxide precipitation at this location: authigenic precipitation from lake water along a lake shore, authigenic precipitation from reduced groundwater discharging through porous sands along a lake shore, and early diagenetic precipitation from groundwater through porous sands. All three scenarios require highly oxidizing conditions and we discuss oxidants that may be responsible for the oxidation and precipitation of manganese oxides. This work has important implications for the habitability of Mars to microbes that could have used Mn redox reactions, owing to its multiple redox states, as an energy source for metabolism.

JGR Planets↗

USGS2022

In situ recording of Mars soundscape

Prior to the Perseverance rover landing, the acoustic environment of Mars was unknown. Models predicted that: (i) atmospheric turbulence changes at centimeter scales or smaller at the point where molecular viscosity converts kinetic energy into heat 1 , (ii) the speed of sound varies at the surface with frequency 2,3 , and (iii) high frequency waves are strongly attenuated with distance in CO 2 2–4 . However, theoretical models were uncertain because of a lack of experimental data at low pressure, and the difficulty to characterize turbulence or attenuation in a closed environment. Here using Perseverance microphone recordings, we present the first characterization of Mars’ acoustic environment and pressure fluctuations in the audible range and beyond, from 20 Hz to 50 kHz. We find that atmospheric sounds extend measurements of pressure variations down to 1,000 times smaller scales than ever observed before, revealing a dissipative regime extending over 5 orders of magnitude in energy. Using point sources of sound (Ingenuity rotorcraft, laser-induced sparks), we highlight two distinct values for the speed of sound that are ~10 m/s apart below and above 240 Hz, a unique characteristic of low-pressure CO 2 -dominated atmosphere. We also provide the acoustic attenuation with distance above 2 kHz, allowing us to elucidate the large contribution of the CO 2 vibrational relaxation in the audible range. These results establish a ground truth for modelling of acoustic processes, which is critical for studies in atmospheres like Mars and Venus ones.

Nature↗

USGS2016

Integrating remote sensing with species distribution models; Mapping tamarisk invasions using the Software for Assisted Habitat Modeling (SAHM)

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk ( Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

Journal of Visualized Experiments↗

USGS2014

Trace element geochemistry (Li, Ba, Sr, and Rb) using Curiosity's ChemCam: early results for Gale crater from Bradbury Landing Site to Rocknest

The ChemCam instrument package on the Mars rover, Curiosity, provides new capabilities to probe the abundances of certain trace elements in the rocks and soils on Mars using the laser-induced breakdown spectroscopy technique. We focus on detecting and quantifying Li, Ba, Rb, and Sr in targets analyzed during the first 100 sols, from Bradbury Landing Site to Rocknest. Univariate peak area models and multivariate partial least squares models are presented. Li, detected for the first time directly on Mars, is generally low (<15 ppm). The lack of soil enrichment in Li, which is highly fluid mobile, is consistent with limited influx of subsurface waters contributing to the upper soils. Localized enrichments of up to ~60 ppm Li have been observed in several rocks but the host mineral for Li is unclear. Bathurst_Inlet is a fine-grained bedrock unit in which several analysis locations show a decrease in Li and other alkalis with depth, which may imply that the unit has undergone low-level aqueous alteration that has preferentially drawn the alkalis to the surface. Ba (~1000 ppm) was detected in a buried pebble in the Akaitcho sand ripple and it appears to correlate with Si, Al, Na, and K, indicating a possible feldspathic composition. Rb and Sr are in the conglomerate Link at abundances >100 ppm and >1000 ppm, respectively. These analysis locations tend to have high Si and alkali abundances, consistent with a feldspar composition. Together, these trace element observations provide possible evidence of magma differentiation and aqueous alteration.

Journal of Geophysical Research E: Planets↗

USGS2011

Characterizing the performance of ecosystem models across time scales: A spectral analysis of the North American Carbon Program site‐level synthesis

[1] Ecosystem models are important tools for diagnosing the carbon cycle and projecting its behavior across space and time. Despite the fact that ecosystems respond to drivers at multiple time scales, most assessments of model performance do not discriminate different time scales. Spectral methods, such as wavelet analyses, present an alternative approach that enables the identification of the dominant time scales contributing to model performance in the frequency domain. In this study we used wavelet analyses to synthesize the performance of 21 ecosystem models at 9 eddy covariance towers as part of the North American Carbon Program's site‐level intercomparison. This study expands upon previous single‐site and single‐model analyses to determine what patterns of model error are consistent across a diverse range of models and sites. To assess the significance of model error at different time scales, a novel Monte Carlo approach was developed to incorporate flux observation error. Failing to account for observation error leads to a misidentification of the time scales that dominate model error. These analyses show that model error (1) is largest at the annual and 20–120 day scales, (2) has a clear peak at the diurnal scale, and (3) shows large variability among models in the 2–20 day scales. Errors at the annual scale were consistent across time, diurnal errors were predominantly during the growing season, and intermediate‐scale errors were largely event driven. Breaking spectra into discrete temporal bands revealed a significant model‐by‐band effect but also a nonsignificant model‐by‐site effect, which together suggest that individual models show consistency in their error patterns. Differences among models were related to model time step, soil hydrology, and the representation of photosynthesis and phenology but not the soil carbon or nitrogen cycles. These factors had the greatest impact on diurnal errors, were less important at annual scales, and had the least impact at intermediate time scales.

Journal of Geophysical Research: Biogeosciences↗

USGS2010

A model‐data intercomparison of CO2 exchange across North America: Results from the North American Carbon Program site synthesis

Our current understanding of terrestrial carbon processes is represented in various models used to integrate and scale measurements of CO 2 exchange from remote sensing and other spatiotemporal data. Yet assessments are rarely conducted to determine how well models simulate carbon processes across vegetation types and environmental conditions. Using standardized data from the North American Carbon Program we compare observed and simulated monthly CO 2 exchange from 44 eddy covariance flux towers in North America and 22 terrestrial biosphere models. The analysis period spans ∼220 site‐years, 10 biomes, and includes two large‐scale drought events, providing a natural experiment to evaluate model skill as a function of drought and seasonality. We evaluate models' ability to simulate the seasonal cycle of CO 2 exchange using multiple model skill metrics and analyze links between model characteristics, site history, and model skill. Overall model performance was poor; the difference between observations and simulations was ∼10 times observational uncertainty, with forested ecosystems better predicted than nonforested. Model‐data agreement was highest in summer and in temperate evergreen forests. In contrast, model performance declined in spring and fall, especially in ecosystems with large deciduous components, and in dry periods during the growing season. Models used across multiple biomes and sites, the mean model ensemble, and a model using assimilated parameter values showed high consistency with observations. Models with the highest skill across all biomes all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step.

Journal of Geophysical Research: Biogeosciences↗