14 research outputs found
Microbial-Induced Heterogeneity in the Acoustic Properties of Porous Media
Abstract It is not known how biofilms affect seismic wave propagation in porous media. Such knowledge is critical for assessing the utility of seismic techniques for imaging biofilm development and their effects in field settings. Acoustic wave data were acquired over a two-dimensional region of a microbial-stimulated sand column and an unstimulated sand column. The acoustic signals from the unstimulated column were relatively uniform over the 2D scan region. The data from the microbial-stimulated column exhibited a high degree of spatial heterogeneity in the acoustic wave amplitude, with some regions exhibiting significant increases in attenuation while others exhibited decreases. Environmental scanning electron microscopy showed differences in the structure of the biofilm between regions of increased and decreased acoustic wave amplitude. We conclude from these observations that variations in microbial growth and biofilm structure cause heterogeneity in the elastic properties of porous media with implications for the validation of bioclogging models. INDEX TERMS: 5102 Acoustic properties, 0416 Biogeophysics, 0463 Microbe/mineral interactions
Hydrogeochemical controls on brook trout spawning habitats in a coastal stream
Brook trout (Salvelinus fontinalis) spawn in fall and
overwintering egg development can benefit from stable, relatively warm
temperatures in groundwater-seepage zones. However, eggs are also sensitive
to dissolved oxygen concentration, which may be reduced in discharging
groundwater (i.e., seepage). We investigated a 2 km reach of the coastal
Quashnet River in Cape Cod, Massachusetts, USA, to relate preferred fish
spawning habitats to geology, geomorphology, and discharging groundwater
geochemistry. Thermal reconnaissance methods were used to locate zones of
rapid groundwater discharge, which were predominantly found along the central
channel of a wider stream valley section. Pore-water chemistry and temporal
vertical groundwater flux were measured at a subset of these zones during
field campaigns over several seasons. Seepage zones in open-valley
sub-reaches generally showed suboxic conditions and higher dissolved solutes
compared to the underlying glacial outwash aquifer. These discharge zones
were cross-referenced with preferred brook trout redds and evaluated during
10 years of observation, all of which were associated with discrete alcove
features in steep cutbanks, where stream meander bends intersect the glacial
valley walls. Seepage in these repeat spawning zones was generally stronger
and more variable than in open-valley sites, with higher dissolved oxygen and
reduced solute concentrations. The combined evidence indicates that regional
groundwater discharge along the broader valley bottom is predominantly
suboxic due to the influence of near-stream organic deposits; trout show no
obvious preference for these zones when spawning. However, the meander bends
that cut into sandy deposits near the valley walls generate strong oxic
seepage zones that are utilized routinely for redd construction and the
overwintering of trout eggs. Stable water isotopic data support the
conclusion that repeat spawning zones are located directly on preferential
discharges of more localized groundwater. In similar coastal systems with
extensive valley peat deposits, the specific use of groundwater-discharge points
by brook trout may be limited to morphologies such as cutbanks, where
groundwater flow paths do not encounter substantial buried organic material
and remain oxygen-rich.</p
Mu2e Technical Design Report
The Mu2e experiment at Fermilab will search for charged lepton flavor
violation via the coherent conversion process mu- N --> e- N with a sensitivity
approximately four orders of magnitude better than the current world's best
limits for this process. The experiment's sensitivity offers discovery
potential over a wide array of new physics models and probes mass scales well
beyond the reach of the LHC. We describe herein the preliminary design of the
proposed Mu2e experiment. This document was created in partial fulfillment of
the requirements necessary to obtain DOE CD-2 approval.Comment: compressed file, 888 pages, 621 figures, 126 tables; full resolution
available at http://mu2e.fnal.gov; corrected typo in background summary,
Table 3.
Field evaluation of semi‐automated moisture estimation from geophysics using machine learning
Abstract Geophysical methods can provide three‐dimensional (3D), spatially continuous estimates of soil moisture. However, point‐to‐point comparisons of geophysical properties to measure soil moisture data are frequently unsatisfactory, resulting in geophysics being used for qualitative purposes only. This is because (1) geophysics requires models that relate geophysical signals to soil moisture, (2) geophysical methods have potential uncertainties resulting from smoothing and artifacts introduced from processing and inversion, and (3) results from multiple geophysical methods are not easily combined within a single soil moisture estimation framework. To investigate these potential limitations, an irrigation experiment was performed wherein soil moisture was monitored through time, and several surface geophysical datasets indirectly sensitive to soil moisture were collected before and after irrigation: ground penetrating radar, electrical resistivity tomography (ERT), and frequency domain electromagnetics (FDEM). Data were exported in both raw and processed form, and then snapped to a common 3D grid to facilitate moisture prediction by standard calibration techniques, multivariate regression, and machine learning. A combination of inverted ERT data, raw FDEM, and inverted FDEM data was most informative for predicting soil moisture using a random regression forest model (one‐thousand 60/40 training/test cross‐validation folds produced root mean squared errors ranging from 0.025–0.046 cm3/cm3). This cross‐validated model was further supported by a separate evaluation using a test set from a physically separate portion of the study area. Machine learning was conducive to a semi‐automated model‐selection process that could be used for other sites and datasets to locally improve accuracy
Scenario Evaluator for Electrical Resistivity Survey Pre-modeling Tool
Geophysical tools have much to offer users in environmental, water resource, and geotechnical fields; however, techniques such as electrical resistivity imaging (ERI) are often oversold and/or overinterpreted due to a lack of understanding of the limitations of the techniques, such as the appropriate depth intervals or resolution of the methods. The relationship between ERI data and resistivity is nonlinear; therefore, these limitations depend on site conditions and survey design and are best assessed through forward and inverse modeling exercises prior to field investigations. In this approach, proposed field surveys are first numerically simulated given the expected electrical properties of the site, and the resulting hypothetical data are then analyzed using inverse models. Performing ERI forward/inverse modeling, however, requires substantial expertise and can take many hours to implement. We present a new spreadsheet-based tool, the Scenario Evaluator for Electrical Resistivity (SEER), which features a graphical user interface that allows users to manipulate a resistivity model and instantly view how that model would likely be interpreted by an ERI survey. The SEER tool is intended for use by those who wish to determine the value of including ERI to achieve project goals, and is designed to have broad utility in industry, teaching, and research