Geology topics

William E. Odom

Publications and source records attributed to William E. Odom.

11 recordsLinked to original sources

USGS2026

Springs of the Catskill Mountains: A conduit flow system in predominantly siliciclastic strata

The Catskill Mountains of New York state are composed of thick sequences of interbedded mudstone, sandstone, and siltstone that are intercalated with thin beds of paludal limestone and calcareous mudstone. Sandstones often contain rip-up clasts of calcareous material at their bases and contain minor amounts of carbonate cement throughout their mass. This minor amount of calcareous material weathers out to produce thin zones of solutional porosity and permeability integrated within perched aquifers that feed perennial hillslope springs. Solutional features are evident within thin carbonate lenses and along fractures in sandstones, occasionally resulting in explorable cave-sized conduits. This study focuses on the behavior of bedrock springs and seeps in the headwaters of the Neversink River basin above the Neversink Reservoir. Bedrock springs are numerous throughout the Catskill Mountains and serve to provide baseflow to the headwater streams that tends to be more persistent during dry times than other baseflow sources. We observed dissolution-influenced conduits within the bedrock, and suffosional conduits within unconsolidated materials. Continuous water temperature at six springs was monitored at hourly intervals from May to October 2022 to investigate the thermal response of groundwater to daily and seasonal changes in air temperature along with event-based meteorological events. Temperature monitoring was augmented at some springs with time-lapse photography to evaluate persistence of flow. The thermal responses of springs to precipitation events were highly variable across sites, with some springs exhibiting flashy responses indicating a strong connection to the surface, while other springs showed muted or no thermal response to events. The seasonal thermal response also varied across sites having lag times of 23-48 days between peak air temperature and peak spring water temperature. The water temperatures and geochemical compositions of these bedrock springs support a conceptual model of multiple residence times, showing both event and seasonal groundwater storage within the conduit flow system that feeds the springs of the Catskill Mountains.

New York↗

USGS2026

Deep learning models for mapping surficial geology in selected physiographic regions of New York

Surficial geologic mapping is required for decisions including infrastructure and water-resource development and management. Deep learning, a type of machine learning that uses training data to self-learn and perform tasks, is explored as a tool to help increase efficiency of the labour- and time-intensive mapping process. Deep learning models were trained, and their potential to aid in mapping surficial geology was explored for two physiographic regions in New York: the high-relief Allegheny Plateau and the low-relief Erie-Ontario Lowlands. Key to the development of deep learning models is the availability of highly detailed surficial geologic maps in each of these regions from which the models can learn. Through exploring different groupings of surficial deposits and their corresponding spatial data, deep learning models were iteratively developed to reproduce published mapping for greater than 79% of the training areas, and with similar accuracy in test areas within the same physiographic region. These models were trained using only two inputs: high-resolution lidar data and previously published surficial geologic maps. This straightforward approach is intended to make the methods and models created easily reproducible using widely accessible datasets. This research describes the strengths and limitations of lidar-derived surficial geologic models and how broadly grouping surficial types, characteristics of different physiographic regions, and testing models in physiographic regions outside of their training areas affect model creation and performance. In addition, the models created could be used as a tool for mapping surficial geology in similar physiographic regions, in addition to establishing a framework for creating similar models elsewhere.

New York↗

USGS2026

Preliminary geologic map of the Sparta East, Sparta West, and parts of the Glade Valley and Whitehead 7.5-minute quadrangles, North Carolina and Virginia, and the epicentral area of the August 9, 2020, Mw 5.1 earthquake near Sparta, North Carolina

Introduction New bedrock and surficial geologic mapping in the Sparta East, Sparta West, and parts of the Glade Valley and Whitehead 7.5-minute quadrangles, North Carolina and Virginia, investigates the geologic framework and causative mechanisms of the August 9, 2020, Mw 5.1 earthquake near Sparta, North Carolina. The mapping documents (1) the coseismic surface rupture from the 2020 earthquake and related brittle structures in the bedrock; (2) the fault contact between the western Blue Ridge and eastern Blue Ridge; (3) lithostratigraphy in the Lynchburg Group, Ashe Metamorphic Suite, and Alligator Back Metamorphic Suite; (4) the nature of the contact between the Lynchburg Group, Ashe Metamorphic Suite, and Alligator Back Metamorphic Suite; and (5) surficial deposits.

North Carolina, Virginia↗

USGS2025

Measurement of in situ-produced cosmogenic nuclides in fine-grained quartz from shale

In situ -produced 10 Be in quartz is widely used to constrain exposure ages and denudation rates, traditionally measured in sand-sized grains. Here we report a new method for isolating fine-grained quartz from shale and demonstrate its reliability for grain sizes down to single microns. Sequential dissolution tests and analyses of grain size separates show that meteoric 10 Be is eliminated and that recoil losses during cosmogenic nuclide production are balanced by implantation from other mineral grains. High-temperature combustion leads to diffusion of meteoric 10 Be into fine-grained quartz and must be used with caution.

Nuclear Instruments and Methods in Physics Researc↗

USGS2025

Pysochron: A Python-based solution for calculating cosmogenic 26Al/10Be isochron burial ages

Cosmogenic 26 Al/ 10 Be isochron burial dating is a powerful tool for dating sediment burial over the past several million years. By measuring in-situ 26 Al and 10 Be in a suite of samples from the same depth in a buried deposit, it is possible to quantify the inventory of cosmogenic nuclides produced after burial, date the burial of shallow sediments, identify sediment reworking, and calculate paleo-erosion rates. While this approach has been used to date materials around the world for over a decade, few published codes exist for performing 26 Al/ 10 Be isochron calculations. The isochron calculation options that are available typically rely on numerous files and libraries, rendering modification and troubleshooting difficult. Moreover, the widespread use of proprietary programming languages – and their associated addon packages – can place an additional financial burden on an already costly endeavor. Pysochron ( https://code.usgs.gov/recon/pysochron ) provides a solution to these issues. In its base form, it exists as a single script that can be easily modified, upgraded, and shared. Because it was developed in an open-source environment, all required computational packages are available free of charge. A user-friendly interface allows rapid modification of calculation parameters, and an automated commentary on isochron results provides insights and recommendations. Pysochron has been validated with 40 published cosmogenic 26 Al/ 10 Be burial isochrons around the world, with burial ages ranging from ∼5 Ma to ∼180 ka. As such, it is a promising option for members of the cosmogenic nuclide community seeking a straightforward, cost-effective, and flexible solution to isochron burial dating challenges.

Quaternary Geochronology↗

USGS2025

Stratigraphy, structure, and geomorphology of the central Appalachians across the North Mountain fault zone near Harrisonburg, Virginia, USA

This field trip focuses on the geology of the central Appalachian Valley and Ridge province near Harrisonburg, Virginia, USA. Recent geologic mapping utilizing 1-m resolution lidar data has revealed new insights into the Paleozoic stratigraphy, structural geology, and Neogene landscape evolution of the region. The detailed mapping reveals the presence of the Big Spring Station Member and multiple thrombolite zones in the Cambrian Conococheague Formation extending as far south as the Briery Branch 7.5 min quadrangle, providing insights into Late Cambrian sea-level fluctuations. Multiple outcrop exposures in the study area of this guidebook confirm recent work in Pennsylvania, USA, showing that the Ordovician Reedsville Shale overlies the Martinsburg Formation and that the two are distinct and mappable as separate formations rather than laterally equivalent units as previously interpreted. Our work extends the Silurian Williamsport Sandstone into Shenandoah County, Virginia, and describes its facies relationships with the Bloomsburg Formation along strike and across the Adams Run anticline. Mapping within the thick Devonian siliciclastic sequence reveals the presence of the Mahantango Formation on the western limb of Supin Lick syncline and illustrates its complex facies relationship with the Millboro Shale. In addition, we highlight new mapping criteria for the Brallier and Foreknobs Formations and demonstrate how the specific changes to the placement of the contact between them addresses previous challenges in their differentiation. We present cosmogenic burial ages of broad alluvial fan sediments in the Shenandoah Valley near Timberville and Briery Branch, Virginia, and erosion rates estimated for the Briery Branch stream basin. Both analyses provide new constraints on the timing of landscape evolution and karst development since the middle Pliocene. This field guide also highlights some significant structural features within the North Mountain fault zone, such as evidence of imbricated thrust sheets cut by cross-strike faults that have been exploited by Eocene igneous intrusions. Map-scale horses of Silurian and Ordovician rocks hold up ridges that are oblique to the regional strike. Deformation internal to one of these horse blocks is shown to be non-coaxial with respect to the main regional northwest directed transport.

Virginia↗

USGS2025

10Be/9Be and 26Al/10Be support a late Miocene burial age for basal Gray Fossil Site sediments

We provide 2 independent radioisotopic age estimates for cored basal sediments of the Gray Fossil Site using cosmogenic nuclides. The first estimate uses meteoric 10 Be/ 9 Be from the bottom of the GFS-1 core, as well as from modern local grasses, to constrain the deposition of basal GFS sinkhole complex sediments to 6.60 ± 0.85 Ma. We corroborated this age estimate using in-situ 10 Be and 26 Al in quartz sands from the GFS-1 core. This estimate provided a looser constraint than the 10 Be met / 9 Be approach, yielding a minimum burial age for the basal sediments of 4.43 ± 0.34 Ma. These independent geochronometers provide evidence that the deepest GFS sediments are at least early Pliocene in age, and likely date to the late Miocene.

Tennessee↗

USGS2024

Mesoproterozoic to Paleozoic tectonics, Pleistocene landforms, and Holocene seismicity in the Blue Ridge: Results from integrated studies of the 9 August 2020, Mw 5.1 earthquake area near Sparta, North Carolina, USA

This field trip examines the results of integrated geologic studies of the 9 August 2020, M w 5.1 earthquake near Sparta, North Carolina, USA. The earthquake generated ~4 km of coseismic surface rupture of the Little River fault and uplifted a surface area of ~11 km 2 . The Little River fault is a thrust fault oriented 110–130°/45–70°SW, and mapped fault segments are en echelon with scarp heights from <5–30 cm. The epicenter is in polydeformed rocks of the Ashe and Alligator Back Metamorphic Suites in the eastern Blue Ridge. Bedrock structure formed during multiple Paleozoic orogenies; the regional foliation strikes NE-SW and dips SE (mean orientation 063°/52°SE). Mapping identified late Paleozoic veins and shear zones, a regional joint set striking 330–340° and 250–240°, and brittle faults that cut the Paleozoic foliation. Brittle faults oriented similar to the Little River fault are mapped up to 4 km along strike from the coseismic rupture along Bledsoe Creek valley, and the combined length of the Little River fault system is ~8 km. Paleoseismic trenches across the Little River fault corroborate the reactivation of an older fault by the 2020 earthquake and reveal two events during late Pleistocene (<50 ka). Surficial mapping identified several terrace deposits, including a deposit along Bledsoe Creek that yielded a 26 Al/ 10 Be isochron burial age of 0.46 ± 0.13 Ma and overlies a brittle fault, thus constraining the timing of movement of the fault at that location. Paleoliquefaction studies document soft-sediment deformation features in alluvium that may represent paleoseismic events. Collectively, these results highlight long-lived paleoseismicity of the Blue Ridge and that the 9 August 2020 earthquake reactivated an older, suitably oriented brittle fault in the bedrock. The Little River fault is an example of a previously unknown but active fault lying outside of known seismic zones with demonstrated recurrence of paleo-ruptures, raising questions about the assumption that damaging earthquakes are limited to areas of ongoing background seismicity, which is counter to seismic hazard assessments in the eastern United States. Bedrock mapping separates eastern Blue Ridge lithostratigraphy of the Lynchburg Group and Ashe and Alligator Back Metamorphic Suites into separate fault-bound packages juxtaposed over various 1.3–1.0 Ga basement rocks of the northern French Broad massif by the Gossan Lead fault.

North Carolina↗

USGS2023

Exploring the influence of input feature space on CNN-based geomorphic feature extraction from digital terrain data

Many studies of Earth surface processes and landscape evolution rely on having accurate and extensive data sets of surficial geologic units and landforms. Automated extraction of geomorphic features using deep learning provides an objective way to consistently map landforms over large spatial extents. However, there is no consensus on the optimal input feature space for such analyses. We explore the impact of input feature space for extracting geomorphic features from land surface parameters (LSPs) derived from digital terrain models (DTMs) using convolutional neural network (CNN)-based semantic segmentation deep learning. We compare four input feature space configurations: (a) a three-layer composite consisting of a topographic position index (TPI) calculated using a 50 m radius circular window, square root of topographic slope, and TPI calculated using an annulus with a 2 m inner radius and 10 m outer radius, (b) a single illuminating position hillshade, (c) a multidirectional hillshade, and (d) a slopeshade. We test each feature space input using three deep learning algorithms and four use cases: two with natural features and two with anthropogenic features. The three-layer composite generally provided lower overall losses for the training samples, a higher F1-score for the withheld validation data, and better performance for generalizing to withheld testing data from a new geographic extent. Results suggest that CNN-based deep learning for mapping geomorphic features or landforms from LSPs is sensitive to input feature space. Given the large number of LSPs that can be derived from DTM data and the variety of geomorphic mapping tasks that can be undertaken using CNN-based methods, we argue that additional research focused on feature space considerations is needed and suggest future research directions. We also suggest that the three-layer composite implemented here can offer better performance in comparison to using hillshades or other common terrain visualization surfaces and is, thus, worth considering for different mapping and feature extraction tasks.

Earth and Space Science↗

USGS2023

Rapid estimation of minimum depth-to-bedrock from lidar leveraging deep-learning-derived surficial material maps

Previously glaciated landscapes often share similar surficial characteristics, including large areas of exposed bedrock, blankets of till deposits, and alluvium-floored valleys. These materials play significant roles in geologic and hydrologic resources, geohazards, and landscape evolution; however, the vast extents of many previously glaciated landscapes have rendered comprehensive, detailed field mapping difficult. While recent advances in remote sensing have facilitated mapping of surficial materials and landforms, manual map creation has remained a time-intensive task. The development of convolutional neural networks (CNNs) for image classification has provided a new opportunity for rapid characterization of digital elevation models, thus enabling efficient mapping of surficial materials and landforms. We have developed a methodology that leverages existing geologic maps and high-resolution (1–3 m) lidar data to train a U-Net CNN to classify alluvium and exposed bedrock in previously glaciated regions. Coupled with U.S. Geological Survey-developed geomorphometry tools capable of approximating stream incision depths, these classifications can be used to estimate the minimum thicknesses of stream-proximal hillslope sediments in areas where streams have undergone minimal incision into bedrock. We validate this approach in the context of the Neversink River watershed, a subbasin of the Delaware River Basin and significant water source for New York City. Evaluation of deep learning model performance demonstrates substantial agreement with manually drawn maps of alluvium and exposed bedrock. Validation of the minimum sediment thickness map using borehole data and passive seismic measurements shows the greatest performance for shallow materials and decreased performance in deep sediments, as well as in areas where bedrock exposures were too small to be resolved by lidar. To resolve these issues and create more accurate surficial maps, we are training new CNNs with additional geologic data and exploring advanced approaches for estimating depths of stream incision.

Delaware, New Jersey, New York, Pennsylvania↗

USGS2022

The Pliocene-to-present course of the Tennessee River

The Tennessee River, a primary drainage of the southern Appalachians and significant sediment source for the Gulf of Mexico, is generally considered to be the product of captures that rerouted the river from a more direct gulfward course. Sedimentary and genetic evidence indicates that a paleo-Tennessee flowed into the Mobile Basin through the late Miocene, although alternate models propose other redirections of the river. We constrain the river course’s age by dating terraces near Pickwick, Tennessee, with cosmogenic 26 Al/ 10 Be isochron burial dating. We find that the river’s present path dates to at least the early Pliocene.

Tennessee↗