Geology topics

Dale Werkema

Publications and source records attributed to Dale Werkema.

3 recordsLinked to original sources

USGS2024

Geophysical characterization of mine influenced groundwater and surface water in the Mayflower section of the Animas River, Bonita Peak Mining District, Silverton Colorado

This report details findings from geophysical investigations to identify possible groundwater - surface water interactions near the Mayflower Section of the Animas River in Silverton, Colorado. The Mayflower Section is a mine influenced Superfund Site in Colorado. This investigation utilized electromagnetic induction (EM or EMI), magnetic, and fiber optic distributed temperature system (FODTS) geophysical methods to measure the bulk earth electrical conductivity, magnetic susceptibility, and temperature of specific surveyed volumes of the earth near the Mayflower Section of the Animas River. These physical parameters are used to understand the groundwater – surface water interactions, which can guide decision makers in their assessment of mine-impacted surface water. This report details the results from characterization and monitoring technologies to provide high data density and continuous monitoring of bulk earth electrical conductivity and temperature in the Mayflower section of the Animas River to identify zones of groundwater – surface water interactions and potential metal loading from mine influenced water. The investigation separated right and left bank characterization for each method and indicates more groundwater is entering from the right bank than the left bank and these predominantly right bank discharges potentially contain metal-rich water compared to the left bank. Results also indicate mineral veins facilitate preferential groundwater discharge to the river due to possible jointing, fractures, and permeability differences sometimes occurring along veins relative to host rock. For example, Boulder Gulch is likely groundwater dominated and may be receive water impacted by Mayflower Impoundments #1 and #2. Additionally, the beaver ponds near Blair Gulch may influence groundwater discharge and Mayflower Impoundment #4 is possibly impacting groundwater and surface water and may be connected to the wetlands to the west of the impoundment. These data could be further analyzed for smaller spatial scale analysis within areas of interest. The identification of these locations along sections of the river likely impacted by mine influenced groundwater potentially entering the Animas River and can be used by site investigators, decision makers, and stakeholders in mitigation decisions and strategies.

Colorado↗

USGS2023

Field evaluation of semi-automated moisture estimation from geophysics using machine learning

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 cm 3 /cm 3 ). 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.

Connecticut↗

USGS2022

Application of recursive estimation to heat tracing for groundwater/surface-water exchange

We present and demonstrate a recursive-estimation framework to infer groundwater/surface-water exchange based on temperature time series collected at different vertical depths below the sediment/water interface. We formulate the heat-transport problem as a state-space model (SSM), in which the spatial derivatives in the convection/conduction equation are approximated using finite differences. The SSM is calibrated to estimate time-varying specific discharge using the Extended Kalman Filter (EKF) and Extended Rauch-Tung-Striebel Smoother (ERTSS). Whereas the EKF is suited to real-time (“online”) applications and uses only the past and current measurements for estimation (filtering), the ERTSS is intended for near-real time or batch-processing (“offline”) applications and uses a window of data for batch estimation (smoothing). The two algorithms are demonstrated with synthetic and field-experimental data and are shown to be efficient and rapid for the estimation of time-varying flux over seasonal periods; further, the recursive approaches are effective in the presence of rapidly changing flux and (or) nonperiodic thermal boundary conditions, both of which are problematic for existing approaches to heat tracing of time-varying groundwater/surface-water exchange.

Water Resources Research↗