Search USGS⌕ Search
Home/Archive/USGS

USGS · sir20055205

Two-dimensional resistivity investigation of the North Cavalcade Street site, Houston, Texas, August 2003

Wade H. Kress·Andrew Teeple
View original source ↗Read source document ↗Export metadata ↓

Abstract

The North Cavalcade Street site was first developed for wood treating in 1946. By 1955, pentachlorophenol wood preservation services and other support facilities, such as creosote ponds, pentachlorophenol and creosote storage structures, various tanks, lumber sheds, a treatment facility, and other buildings had been added. In 1961, the property was closed. To protect public health and welfare and the environment from release or threatened releases of hazardous substances, the U.S. Environmental Protection Agency added the North Cavalcade Street site to the National Priorities List on October 5, 1984. Between September 1985 and November 1987, the U.S. Environmental Protection Agency conducted a remedial investigation which, through exploratory drilling, determined the locations of two contaminated source areas and a normal fault. During August 2003, the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency, conducted a two-dimensional (2D) resistivity investigation at the North Cavalcade Street site to provide additional characterization of the dense non-aqueous phase liquids and the lithologies that can influence contaminant migration. The 2D resistivity investigation used a capacitively coupled (CC) resistivity method as a reconnaissance tool to locate geophysical anomalies that could be associated with possible areas of creosote contamination. The inversion results of the CC resistivity survey identified resistive anomalies in the subsurface near the eastern and western contaminated source areas. A direct-current (DC) resistivity survey conducted near the CC resistivity survey confirmed the occurrence of subsurface resistive anomalies. The inversion results of the DC resistivity survey were used to define the subsurface distribution of resistivity at each line. Forward modeling was used as an interpretative tool to relate the subsurface distribution of resistivity from four DC resistivity lines to known, assumed, and hypothetical information on subsurface lithologies. The final forward models were used as an estimate of the true resistivity structure for the field data. The forward models and the inversion results of the forward models show the depth, thickness, and extent of strata as well as the resistive anomalies occurring along the four lines and the displacement of strata resulting from the Pecore Fault along two of the four DC resistivity lines. Ten additional DC resistivity lines show similarly distributed shallow subsurface lithologies of silty sand and clay strata. Eight priority areas of resistive anomalies were identified for evaluation in future studies. The interpreted DC resistivity data allowed subsurface stratigraphy to be extrapolated between existing boreholes resulting in an improved understanding of lithologies that can influence contaminant migration.

Explore related subjects

Scientific Investigations ReportReport

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wade H. Kress, Andrew Teeple. 2005. Two-dimensional resistivity investigation of the North Cavalcade Street site, Houston, Texas, August 2003. https://doi.org/10.3133/sir20055205

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

KEEP EXPLORING

Related USGS reports

USGS2026

Evaluating the number of realizations (train-test splits) necessary to ensure robust estimates of conventional hydrothermal resource favorability using Monte Carlo cross-validation, the Great Basin, Western USA

Supervised machine learning approaches used to produce favorability maps in resource assessments commonly partition an input dataset into training and testing subsets (that is, through train-test splits). However, if a single split is not representative of underlying resource conditions, the resulting favorability map may be biased, particularly with sparse or heterogeneous datasets. To mitigate the potential for unrepresentative favorability maps, recent works by U.S. Geological Survey researchers have used Monte Carlo cross-validation to implement a large number ( n) of randomized train-test splits to produce n realizations. The realizations are then aggregated using cell-wise median predictions to produce the resulting final favorability map, recognizing that the median will remove extreme values that might have been estimated from a comparatively few unrepresentative realizations. Theoretically, the median prediction at every cell converges to a single estimate as n approaches infinity, and the question addressed herein is: How many realizations are necessary to get a robust estimate (that is, an estimate unaffected by additional realizations)? Therefore, convergence metrics are described to identify the number of train-test splits and realizations needed to make a reliable map using the data and supervised machine learning workflow for conventional hydrothermal resource assessment of the Great Basin of the Western United States. For this study, robust prediction occurs with n approximately equal to 600 realizations, and smaller ensembles (for example, n is approximately equal to 200 or even 100) may be suitable during preliminary or exploratory analyses. Although the resulting threshold numbers will vary depending on the data and modeling approach used, the convergence metrics proposed and applied herein are applicable to other supervised machine learning studies that use Monte Carlo cross-validation.

Scientific Investigations Report↗
USGS2026

Groundwater budget for the Portneuf River Basin, southeastern Idaho

The U.S. Geological Survey, in cooperation with the Idaho Department of Water Resources, developed the first groundwater budget for the upper Portneuf River Basin in southeastern Idaho, a tributary of the Snake River. A groundwater budget for the Portneuf River Basin supports water resource and administrative management decisions within the basin and for downstream users of the eastern Snake River Plain aquifer. The analysis quantifies inflows, outflows, and residuals for an aquifer system defined by the Idaho Department of Water Resources under three recent precipitation conditions: an average year (2016), a dry year (2021), and a wet year (2023). The budget was calculated for the basin and for two subbasins delineated by the U.S. Geological Survey streamgage Portneuf River at Topaz, Idaho (13073000): the above Topaz subbasin, which includes the Portneuf-Gem valley, and the below Topaz subbasin, which includes Marsh Valley. Aquifer inflows include mountain-front recharge; areal recharge; recharge from applied water, including recharge from irrigation, canal seepage, and effluent returns; groundwater flow into the basin; and reservoir seepage. Outflows include groundwater pumping for agricultural, municipal, and domestic uses; subsurface outflow through the Portneuf gap; and discharge to streams as base flow. Estimated annual inflows for the full basin ranged from 116,880 acre-feet in 2021 to 154,880 acre-feet in 2023, and outflows ranged from 132,190 acre-feet in 2016 to 158,490 acre-feet in 2023. Basinwide residuals, the change in storage plus uncertainty, were −2,410 acre-feet in 2016, −18,410 acre-feet in 2021, and −3,610 acre-feet in 2023, and each subbasin had a mixture of negative and positive residuals. These basinwide results, interpreted as change in storage plus water-budget uncertainty, indicate minor net losses that increase with dry conditions, consistent with well depth-to-water measurements indicating gradual multi-decadal declines. Patterns in subbasin residuals are driven by irrigation practices and surface-water management; for example, the above Topaz subbasin relies more on groundwater for irrigation, resulting in more negative residuals during dry years. In contrast, the below Topaz subbasin depends more on surface water, producing positive residuals during dry years because of irrigation losses and canal seepage. The analyses for both subbasins indicate decades-long groundwater declines, which agree with the overall negative residuals calculated. The largest sources of uncertainty include climate inputs (precipitation and evapotranspiration) and poorly constrained components such as groundwater inflow and tributary stream infiltration. These findings provide insight into how climatic variables and irrigation practices affect groundwater inflows and outflows within the Portneuf River Basin.

Idaho↗
USGS2026

World minerals outlook—Antimony, copper, graphite, niobium, potash, silicon, silver, and tungsten through 2030

Demand for mineral commodities continues to increase owing to rapid technological advancements and global economic growth. To support this increase in demand, information about current and near-term supplies of these mineral commodities is essential. Such data help evaluate the health and stability of global supply chains and protect against potential supply risks. Current production and production capacity data coupled with information regarding near-term expected changes (such as facility closures, planned expansions, or the completion of new mineral production projects) can provide insight into expected future market developments and expected available supply of mineral commodities. This report within the “World Minerals Outlook” series analyzes data for antimony, copper, natural graphite, niobium, potash, silicon metal, silver, and tungsten—eight mineral commodities listed in the 2025 List of Critical Minerals. World production data from 2019 through 2025, as well as estimated world capacity data from 2023 through 2025 were analyzed. Using these data, near-term (2026–2030) expected world capacities were estimated. For antimony, copper, niobium, silicon metal, silver, and tungsten, modest increases in production capacity of 4 to 10 percent were expected between 2025 and 2030, whereas capacities for natural graphite and potash were expected to increase by about 18 to 20 percent.

Scientific Investigations Report↗