The top of bedrock is an important 3D surface in geology, hydrology, hydrogeology, geomorphology, soil science, and engineering; however, its elevation is very poorly known where it is buried. Prior to the advent of lidar, the top of rock at or near the land surface was difficult to map accurately in forested, previously glaciated regions like New England. Consequently, a modern high-resolution database of the altitude of the buried bedrock surface is lacking. A national scale map of the bedrock topography exists at 1:5,000,000. In New England, surficial geologic maps exist at multiple scales ranging from 1:24,000 to 1:500,000. Large-scale top of rock mapping is available for only a small percentage of the region usually as contours of bedrock topography but not in vector format. The goal of the USGS New England Top of Rock (NETOR) task is to develop an inventory of available surface and subsurface data and establish a model of the bedrock topography for six northeast states, with a targeted scale of 1:100,000. The objectives include creating a seamless GIS database for the elevation of the top of bedrock beneath unconsolidated glacial deposits, mapping the 2D boundary between exposed bedrock and surficial deposits regionwide, and designing a robust spatial database for 3D modeling. Long term objectives include developing 3D models and depicting the thickness of surficial deposits regionwide in cooperation with the New England States Geologic Mapping Coalition.
Currently, the NETOR task has assembled a database of approximately 475,000 borehole and water well locations with top of rock information. USGS data releases in southern New England provide approximately 10,000 boreholes converted from scanned analog reports to digital databases. Ongoing work includes converting additional analog borehole data and developing GeMS (Geologic Map Schema)-compliant map products of surficial materials for Rhode Island and Connecticut. Additionally, lidar-based revisions of surficial geologic data sets are being created from a combination of best available data, deep-learning methods (pixel classification), and geomorphic tools such as surface roughness.