Map of DAS, nodal, vibroseis and Reftek stations during March 2016 deployment. The plot on the left has nodal stations labeled; the plot on the right has vibroseis observations labeled. Stations are shown in map-view using Brady's rotated X-Y coordinates with side plots denoting elevation with respect to the WGS84 ellipsoid. Blue circles denote vibroseis data, x symbols denote DAS (cyan for horizontal and magenta for vertical), black asterisks denote Reftek data, and red plus signs denote nodal data. This map can be found on UW-Madison's askja server at /PoroTomo/DATA/MAPS/Deployment_Stations.pdf

Under the Federal Technology Transfer Act (FTTA), Federal Agencies can patent inventions developed during the course of research. These technologies can then be licensed to businesses or individuals for further development and sale in the marketplace. These technologies primarily relate to contaminant removal in the environment.

Under the Federal Technology Transfer Act (FTTA), Federal Agencies can patent inventions developed during the course of research. These technologies can then be licensed to businesses or individuals for further development and sale in the marketplace. These technologies relate to water monitoring and treatment technologies.

The goal of work reported here was to assess the potential for early detection of acid gas in case of leakage into overlying aquifers at Zama. Breakthroughs of acid gas at producing wells at other acid gas disposal sites in Alberta indicated that CO2 will arrive first at a producing well, ahead of H2S, although the two gases are injected together. From a monitoring point of view, the implication is that the earlier arrival of CO2, even if not accompanied by H2S, should signal leakage of acid gas from the storage reservoir. Accordingly, an experimental program was set up under contract at Hycal Energy Research Laboratories Ltd. in Calgary to test the hypothesis that the partitioning of the two gases and different arrival time at the producing well are due to different solubility of the two gases in water.

Effective enhanced geothermal systems (EGS) require optimal fracture networks for efficient heat transfer between hot rock and fluid. Microseismic mapping is a key tool used to infer the subsurface fracture geometry. Traditional earthquake detection and location techniques are often employed to identify microearthquakes in geothermal regions. However, most commonly used algorithms may miss events if the seismic signal of an earthquake is small relative to the background noise level or if a microearthquake occurs within the coda of a larger event. Consequently, we have developed a set of algorithms that provide improved microearthquake detection. Our objective is to investigate the microseismicity at the DOE Newberry EGS site to better image the active regions of the underground fracture network during and immediately after the EGS stimulation. Detection of more microearthquakes during EGS stimulations will allow for better seismic delineation of the active regions of the underground fracture system. This improved knowledge of the reservoir network will improve our understanding of subsurface conditions, and allow improvement of the stimulation strategy that will optimize heat extraction and maximize economic return.

These datasets can be used to evaluate and benchmark the performance accuracy of Fault Detection and Diagnostics (FDD) algorithms or tools. It contains operational data from simulation, laboratory experiments, and field measurements from real buildings for seven HVAC systems/equipment (rooftop unit, single-duct air handler unit, dual-duct air handler unit, variable air volume box, fan coil unit, chiller plant, and boiler plant). Each dataset includes a .pdf file to document key information necessary to understand the content and scope, multiple csv files containing all the time-series data for faults at different severity levels and one fault-free case, and a ttl file to visualize the data according to BRICK schema. The dataset was created by LBNL, PNNL, NREL, ORNL and Drexel University.

EPA Method 1615 measures enteroviruses and noroviruses present in environmental and drinking waters. The viral ribonucleic acid (RNA) from water sample concentrates is extracted and tested for enterovirus and norovirus RNA using reverse transcription-quantitative PCR (RT-qPCR). Virus concentrations for the molecular assay are calculated in terms of genomic copies of viral RNA per liter based upon a standard curve. The method uses a number of quality controls to increase data quality and to reduce interlaboratory and intralaboratory variation. The method has been evaluated by examining virus recovery from ground and reagent grade waters seeded with poliovirus type 3 and murine norovirus as a surrogate for human noroviruses. Mean poliovirus recoveries were 20% in groundwaters and 44% in reagent grade water. Mean murine norovirus recoveries with the RT-qPCR assay were 31% in groundwaters and 4% in reagent grade water. This dataset is associated with the following publication: Fout , S., J. Cashdollar , S. Griffin , N. Brinkman , E. Varughese , and S. Parshionikar. EPA Method 1615. Measurement of Enterovirus and Norovirus Occurrence in Water by Culture and RT-qPCR. Part III. Virus Detection by RT-qPCR. Journal of Visualized Experiments. JoVE, Somerville, MA, USA, 107: e52646, (2016).

The Channel Migration Potential (CHAMP) layer contains stream networks of Western Washington (and much of Western Oregon) with associated data and information important for assessing channel migration activity. It also features information on channel characteristics such as stream flow and physical dimensions. This data layer’s main feature is a classification of channel migration potential based on channel confinement and erosion potential. The layer was derived from existing statewide geospatial datasets and classified according to channel migration measurements by the High Resolution Change Detection (HRCD) project for the Puget Sound Region (Washington Department of Fish and Wildlife, 2014). While the layer identifies the potential for channel migration, it does not predict channel migration rates. Thus, this data layer should be used to screen and prioritize stream reaches for further channel migration evaluation. The tool helps plan and prioritize floodplain management actions such as Channel Migration Zone mapping, erosion risk reduction, and floodplain restoration. The background, use, and development of the CHAMP layer are fully described in Ecology Publication 15-06-003 (full report citation and URL below). That report also describes visual assessment techniques that should be used along with the CHAMP layer to assess channel migration potential. Legg, N.T. and Olson, P.L., 2015, Screening Tools for Identifying Migrating Stream Channels in Western Washington: Geospatial Data Layers and Visual Assessments: Washington State Department of Ecology Publication 15-06-003, 40 p. https://fortress.wa.gov/ecy/publications/SummaryPages/1506003.htmlThe tool developers would like to thank the following people for their contribution to this work: • Brian D. Collins (University of Washington) • Jerry Franklin (Washington Department of Ecology) • Christina Kellum (Washington Department of Ecology) • Matt Muller (Washington Department of Fish and Wildlife) • Hugh Shipman (Washington Department of Ecology) • Terry Swanson (Washington Department of Ecology) This project has been funded wholly or in part by the United States Environmental Protection Agency under Puget Sound Ecosystem Restoration and Protection Cooperative Agreement Grant PC-00J27601 with Washington Department of Ecology. The contents of this document do not necessarily reflect the views and policies of the Environmental Protection Agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.Generally, this data layer should be used to screen and prioritize stream reaches for further channel migration evaluation. The data resolution does not allow one to predict channel migration. The classification identifies stream segments for further examination, and those that likely require limited attention or analysis. The potential uncertainty involved in the classification approach is a reason for the visual assessment techniques (described below in Ecology Publication 15-06-003) being described along with the CHAMP data layer.

This is the Utah FORGE well 16A(78)-32 stimulation microseismic detection and location report from Silixa LLC. The stimulation was accomplished during April, 2022.