Search USGSSearch

USGS · sir20255020

Paleomagnetic correlation of surface and subsurface basalt flows in the central and southwestern part of the Idaho National Laboratory, Idaho

Abstract

The U.S. Geological Survey, in cooperation with the U.S. Department of Energy, used paleomagnetic data from 22 coreholes to construct 3 fence diagrams of subsurface basalt flows in the southern part of the Idaho National Laboratory. These diagrams provide comprehensive descriptions of the horizontal and vertical distribution of basalt flows and sediment layers beneath the surface, aiding geological studies and contributing valuable data to numerical models of groundwater flow and contaminant transport. The correlations established though these diagrams include spatial correlations between basalt flows found in multiple coreholes. Correlations were identified by matching average paleomagnetic inclinations and confirming or denying these correlations using petrology, geochemistry and radiometric ages. The fence diagrams aid in identifying potential locations of subsurface vents, volcanic vents that have been buried by more recent volcanic activity, associated to subsurface basalt flows. By tracing the subsurface flows and analyzing where the greatest thickness occurs, the locations of buried vents can be inferred. Some subsurface flows exhibit correlations across several coreholes and may indicate yet unidentified surface or buried vents, thereby enhancing our understanding of the volcanic history and subsurface geology of the region.

Explore related subjects

Keep this discovery

BibTeXRIS

Mary Hodges, Allison R. Trcka, Duane E. Champion. 2025-06-05. Paleomagnetic correlation of surface and subsurface basalt flows in the central and southwestern part of the Idaho National Laboratory, Idaho. https://doi.org/10.3133/sir20255020

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related USGS reports

Methodology for quantitative spatial sensitivity analysis of volcanic geodetic networks

Introduction This report introduces a methodology for assessing the state of the U.S. Geological Survey Volcano Observatories’ geodetic monitoring networks that measure how volcanoes deform or change shape. This new method uses a model-based approach that considers the uniqueness of the instrument environments at each volcano. This report focuses on simplified volcanic sources, is independent of the shape or size of the volcano, or the network geometry, and thus highlights the strengths and potential vulnerabilities of each volcano’s geodetic network in an actionable visual format. This analysis can help observatories to make informed decisions about whether volcanoes have an adequate level of geodetic monitoring and indicate where improvements are needed.

Lassen Peak, Mount Shasta

2023 Earthquake Ground-Motion Workshop for the Central and Eastern United States, with a focus on the Gulf and Atlantic Coastal Plains—Agenda and abstracts

The U.S. Geological Survey held a virtual workshop December 7–8, 2023, to share research and ideas about earthquake ground motions in the Central and Eastern United States, with a focus on the Atlantic and Gulf Coastal Plains. The workshop was organized to learn about potential regionalization of ground-motion characteristics (source, path, and site), consider new explanatory variables for site response, and hear and discuss updates on ground-motion research on the Atlantic and Gulf Coastal Plains. The workshop was organized into a series of contributed presentations and three panel discussions held during 2 days. This report documents the agenda, contributed abstracts, and panel summaries.

Scientific Investigations Report

A model uncertainty quantification protocol for evaluating the value of observation data

The history-matching approach to parameter estimation with models enables a powerful offshoot analysis of data worth—using the uncertainty of a model forecast as a metric for the worth of data. Adding observation data will either have no impact on forecast uncertainty or will reduce it. Removing existing data will either have no impact on forecast uncertainty or will increase it. The history-matching framework makes it possible to perform this quantitative analysis leveraging the connections among observations, model parameters, and model forecasts. We show this behavior on a specific groundwater flow model of the Mississippi Alluvial Plain and show where the analysis can be informative for considering the potential design of an observation network based on existing or potential observations.

Scientific Investigations Report