The Neversink River watershed (above the Neversink Reservoir) has been a focus of U.S. Geological Survey (USGS) research regarding stream geochemistry, acidification, and ecology dynamics for decades. In 2019, the Water Mission Area Next Generation Water Observing Systems Program augmented the existing stream gage network there, including instrumentation to specifically characterize various aspects of groundwater discharge to streams. An important control on the spatiotemporal dynamics of groundwater discharge can be stream valley corridor depth to bedrock, otherwise conceptualized as the thickness of unconsolidated sediments sediments over the contiguous bedrock interface. In June 2019, and November 2020, passive seismic recordings were acquired at locations directly along stream banks in the Neversink River watershed, using MOHO Tromino Model TEP-3C (MOHO, S.R.L.) three-component seismometers to assess depth to bedrock using the horizontal-to-vertical spectral-ratio (HVSR) method. Resonance frequencies were derived from the raw data using the GRILLA software (MOHO, S.R.L.) and converted to inferred depths to the bedrock contact. This method requires a value for seismic shear wave velocity, which depends on the unconsolidated sediment composition and density, for the conversion of HVSR measured resonance frequency to a depth to bedrock. Possible shear wave velocities were estimated for Neversink River watershed sediment based on previous research in the glacial terrain of the Northeast USA, providing a range of possible data interpretations as shown in the ‘Processed_Data’ folder of this data release. We expect to update the release in the future as additional HVSR data are collected.

Using the horizontal-to-vertical spectral-ratio (HVSR) method, we infer regolith thickness (i.e., depth to bedrock) throughout the Farmington River Watershed, CT, USA. Between Nov. 2019 and Nov. 2020, MOHO Tromino Model TEP-3C (MOHO, S.R.L.) three-component seismometers collected passive seismic recordings along the Farmington River and the upstream West Branch of Salmon Brook. From these recordings, we derived resonance frequencies using the GRILLA software (MOHO, S.R.L.), and then inferred potential regolith thicknesses based on likely shear wave velocities, Vs, intrinsic to the underlying sediment. Three potential shear wave velocities (Vs = 300m/s, 337m/s, 362 m/s) were considered for Farmington River watershed sediments, providing a range of potential depth estimates along the Farmington. This release contains raw passive seismic recording data, processed resonance frequency data, and the resulting inferred depth estimates displayed in both tabular and vector form. This dataset currently contains 3 zipped files: 1) ‘Processed.zip’ is a zipped directory containing .asc text files of processed passive seismic data, individual processed reports, tabulated results, and an associated summary text file, 'readme_Processed.txt'; 2) 'Raw.zip' contains .saf text files of passive seismic recordings and an associated 'readme_Raw.txt;' and 3) ‘XYLegacyN_HVSR.zip'’ contains ESRI shapefile of HVSR point locations with attribute data & a map image offering a visualization of the depth results (where, Vs = 300m/s). Additionally, the main folder contains LegacyN_HVSR_readme.txt which describes these sub-directories in further detail

Using the horizontal-to-vertical spectral-ratio (HVSR) method, we infer regolith thickness (i.e., depth to bedrock) throughout the Farmington River Watershed, CT, USA. Between Nov. 2019 and Nov. 2020, MOHO Tromino Model TEP-3C (MOHO, S.R.L.) three-component seismometers collected passive seismic recordings along the Farmington River and the upstream West Branch of Salmon Brook. From these recordings, we derived resonance frequencies using the GRILLA software (MOHO, S.R.L.), and then inferred potential regolith thicknesses based on likely shear wave velocities, Vs, intrinsic to the underlying sediment. Three potential shear wave velocities (Vs = 300m/s, 337m/s, 362 m/s) were considered for Farmington River watershed sediments, providing a range of potential depth estimates along the Farmington. This release contains raw passive seismic recording data, processed resonance frequency data, and the resulting inferred depth estimates displayed in both tabular and vector form. This dataset currently contains 3 zipped files: 1) ‘Processed.zip’ is a zipped directory containing .asc text files of processed passive seismic data, individual processed reports, tabulated results, and an associated summary text file, 'readme_Processed.txt'; 2) 'Raw.zip' contains .saf text files of passive seismic recordings and an associated 'readme_Raw.txt;' and 3) ‘XYLegacyN_HVSR.zip'’ contains ESRI shapefile of HVSR point locations with attribute data & a map image offering a visualization of the depth results (where, Vs = 300m/s). Additionally, the main folder contains LegacyN_HVSR_readme.txt which describes these sub-directories in further detail

Using the horizontal-to-vertical spectral-ratio (HVSR) method, we inferred the depth to bedrock at the Slate River Floodplain, CO, USA. The point-scale passive seismic data were collected using Model TEP-3C Tromino seismometers over 20 min or less intervals with the instruments coupled directly to the floodplain ground surface at 42 non-flooded locations during June 2021. The ratio of horizontal-to-vertical Fourier spectra (HVSR), determined using Grilla software (MOHO, S.R.L.), along with the estimated sediment shear-wave velocity, was used to calculate the depth to the bedrock contact. This passive seismic dataset indicates that the deepest bedrock is 16 m below the surface, while the bedrock reaches the surface at the hillslope. This release contains the inferred bedrock depths based on likely shear wave velocities (Vs) intrinsic to the underlying sediment, ranging from 300 m/s to 400 m/s, listed in the processed_data subdirectory in the file 'SLAC_HVSR_June2021.csv.' The range of possible depth to bedrock interpretations is included for demonstration purposes only.

Digital hydrogeologic datasets were developed for the Rondout-Neversink study area in upstate New York in cooperation with the New York State Department of Environmental Conservation. These datasets define the hydrogeologic framework of the valley-fill aquifer and surrounding till-covered uplands within the study area. Datasets include: bedrock elevation raster, lacustrine silt and clay top and bottom elevation rasters, LIDAR minimum elevation raster, lacustrine extent polygon, valley-fill extent polygon, and surficial geology polygons. Elevation layers were interpolated at 125-foot discretization to match the model grid cell size.

Digital hydrogeologic datasets were developed for the Rondout-Neversink study area in upstate New York in cooperation with the New York State Department of Environmental Conservation. These datasets define the hydrogeologic framework of the valley-fill aquifer and surrounding till-covered uplands within the study area. Datasets include: bedrock elevation raster, lacustrine silt and clay top and bottom elevation rasters, LIDAR minimum elevation raster, lacustrine extent polygon, valley-fill extent polygon, and surficial geology polygons. Elevation layers were interpolated at 125-foot discretization to match the model grid cell size.

Digital hydrogeologic datasets were developed for the Rondout-Neversink study area in upstate New York in cooperation with the New York State Department of Environmental Conservation. These datasets define the hydrogeologic framework of the valley-fill aquifer and surrounding till-covered uplands within the study area. Datasets include: bedrock elevation raster, lacustrine silt and clay top and bottom elevation rasters, LIDAR minimum elevation raster, lacustrine extent polygon, valley-fill extent polygon, and surficial geology polygons. Elevation layers were interpolated at 125-foot discretization to match the model grid cell size.

This dataset consists of a raster and surficial shapefiles for the Neversink River watershed, NY, that were generated using ArcGIS Pro's deep learning functionality. The shapefiles contain polygons that show the locations of shallow-to-exposed bedrock and alluvium-filled valleys, while the raster provides estimated minimum sediment thicknesses in the areas between the shallow/exposed bedrock and alluvium-filled valleys. The deep learning model that generated shallow/exposed bedrock and alluvium polygons was trained on existing geologic maps of New York and Pennsylvania. Minimum sediment thicknesses were estimated by applying an inverse distance weighting interpolation to measurements of stream incision depth and edges of shallow/exposed bedrock zones (where sediment thickness ≈ 0 m). In this area, stream incision through bedrock was considered negligible. The interpolation used variable numbers of points, a fixed interpolation distance of 250 m, and an output resolution of 30 m/pixel. Given the possibility that streams may have not completely incised through surficial materials down to bedrock, these estimates are minimum constraints on the true thicknesses of surficial sediments.

This dataset consists of a raster and surficial shapefiles for the Neversink River watershed, NY, that were generated using ArcGIS Pro's deep learning functionality. The shapefiles contain polygons that show the locations of shallow-to-exposed bedrock and alluvium-filled valleys, while the raster provides estimated minimum sediment thicknesses in the areas between the shallow/exposed bedrock and alluvium-filled valleys. The deep learning model that generated shallow/exposed bedrock and alluvium polygons was trained on existing geologic maps of New York and Pennsylvania. Minimum sediment thicknesses were estimated by applying an inverse distance weighting interpolation to measurements of stream incision depth and edges of shallow/exposed bedrock zones (where sediment thickness ≈ 0 m). In this area, stream incision through bedrock was considered negligible. The interpolation used variable numbers of points, a fixed interpolation distance of 250 m, and an output resolution of 30 m/pixel. Given the possibility that streams may have not completely incised through surficial materials down to bedrock, these estimates are minimum constraints on the true thicknesses of surficial sediments.

From April 2013 to August 2015, the U.S. Geological Survey, in cooperation with the Town of Enfield and the Tompkins County Planning Department, collected horizontal-to-vertical seismic soundings at 69 locations in the Enfield Creek valley to help determine thickness of the unconsolidated deposits and depth to bedrock. The HVSR technique, commonly referred to as the passive-seismic method, is used to estimate the thickness of unconsolidated sediments and the depth to bedrock (Lane and others, 2008). The passive-seismic method uses a single, broad-band three-component (two horizontal and one vertical) seismometer to record ambient seismic noise. In areas that have a strong acoustic contrast between the bedrock and overlying sediments, the seismic noise induces resonance at frequencies that range from about 0.3 to 40 Hz. The ratio of the average horizontal-to-vertical spectrums produces a spectral-ratio curve with peaks at fundamental and higher-order resonance frequencies. The spectral ratio curve (the ratio of the averaged horizontal-to-vertical component spectrums) is used to determine the fundamental resonance frequency that can be used along with an average shear-wave velocity or a power-law regression equation to estimate sediment thickness and depth to bedrock (Lane and others, 2008; Brown and others, 2013; Fairchild and others, 2013; Chandler and others, 2014; and Johnson and Lane, 2016). The HVSR data presented in this data release were collected at each site for 30 minutes using a Tromino Model TEP-3C three-component seismometer. The data were processed with Grilla 2012 version. 6.2 software to 1) remove anthropogenic noise, 2) convert the time-domain data to frequency domain, 3) compute and plot the spectral ratio curve, and 4) determine the resonance frequency. This data release presents the resonance frequency peaks identified from the HVSR measurements. Also presented are reported depth-to-bedrock data for wells located at or near HVSR data-collection sites in the Town of Enfield for use in comparison of HVSR forward model depths to reported well depths. Raw and processed HVSR data for each HVSR measurement are presented in the attached. The HVSR data-collection sites are designated by a county sequential numbering system (TMHVSR79, TMHVSR80, etc. where TM indicates Tompkins County). References Brown, C.J., Voytek, E.B., Lane, J.W., Jr., and Stone, J.R., 2013, Mapping bedrock surface contours using the horizontal-to-vertical spectral ratio (HVSR) method near the middle quarter area, Woodbury, Connecticut: U.S. Geological Survey Open-File Report 2013–1028, 4 p., available at http://pubs.usgs.gov/of/2013/1028. Chandler, V. W., and Lively, R. S., 2014, Evaluation of the horizontal-to-vertical spectral ratio (HVSR) passive seismic method for estimating the thickness of Quaternary deposits in Minnesota and adjacent parts of Wisconsin: Minnesota Geological Survey Open File Report 14-01, 52 p. Fairchild, G.M., Lane, J.W., Jr., Voytek, E.B., and LeBlanc, D.R., 2013, Bedrock topography of western Cape Cod, Massachusetts, based on bedrock altitudes from geologic borings and analysis of ambient seismic noise by the horizontal-to-vertical spectral-ratio method: U.S. Geological Survey Scientific Investigations Map 3233, 1 sheet, maps variously scaled, 17-p. pamphlet, on one CD–ROM. (Also available at http://pubs.usgs.gov/sim/3233.) Johnson, C. D. and Lane, J. W., 2016, Statistical comparison of methods for estimating sediment thickness from horizontal-to-vertical spectral ratio (HVSR) seismic methods: An example from Tylerville, Connecticut, USA, in Symposium on the Application of Geophysics to Engineering and Environmental Problems Proceedings: Denver, Colorado, Environmental and Engineering Geophysical Society, pp. 317-323. https://doi.org/10.4133/SAGEEP.29-057. Lane, J.W., Jr., White, E.A., Steele, G.V., and Cannia, J.C., 2008, Estimation of bedrock depth using the horizontal-to-vertical (H/V) ambient-noise seismic method,