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At least 109 records · Page 6Linked to original sources
USGS2002

Identification of lithofacies using Kohonen self-organizing maps

Lithofacies identification is a primary task in reservoir characterization. Traditional techniques of lithofacies identification from core data are costly, and it is difficult to extrapolate to non-cored wells. We present a low-cost automated technique using Kohonen self-organizing maps (SOMs) to identify systematically and objectively lithofacies from well log data. SOMs are unsupervised artificial neural networks that map the input space into clusters in a topological form whose organization is related to trends in the input data. A case study used five wells located in Appleton Field, Escambia County, Alabama (Smackover Formation, limestone and dolomite, Oxfordian, Jurassic). A five-input, one-dimensional output approach is employed, assuming the lithofacies are in ascending/descending order with respect to paleoenvironmental energy levels. To consider the possible appearance of new logfacies not seen in training mode, which may potentially appear in test wells, the maximum number of outputs is set to 20 instead of four, the designated number of lithosfacies in the study area. This study found eleven major clusters. The clusters were compared to depositional lithofacies identified by manual core examination. The clusters were ordered by the SOM in a pattern consistent with environmental gradients inferred from core examination: bind/boundstone, grainstone, packstone, and wackestone. This new approach predicted lithofacies identity from well log data with 78.8% accuracy which is more accurate than using a backpropagation neural network (57.3%). The clusters produced by the SOM are ordered with respect to paleoenvironmental energy levels. This energy-related clustering provides geologists and petroleum engineers with valuable geologic information about the logfacies and their interrelationships. This advantage is not obtained in backpropagation neural networks and adaptive resonance theory neural networks. ?? 2002 Elsevier Science Ltd. All rights reserved.

Computers & Geosciences↗
USGS2002

VOLATILECALC: A silicate melt-H2O-CO2 solution model written in Visual Basic for excel

We present solution models for the rhyolite-H2O-CO2 and basalt-H2O-CO2 systems at magmatic temperatures and pressures below ~ 5000 bar. The models are coded as macros written in Visual Basic for Applications, for use within MicrosoftR Excel (Office'98 and 2000). The series of macros, entitled VOLATILECALC, can calculate the following: (1) Saturation pressures for silicate melt of known dissolved H2O and CO2 concentrations and the corresponding equilibrium vapor composition; (2) open- and closed-system degassing paths (melt and vapor composition) for depressurizing rhyolitic and basaltic melts; (3) isobaric solubility curves for rhyolitic and basaltic melts; (4) isoplethic solubility curves (constant vapor composition) for rhyolitic and basaltic melts; (5) polybaric solubility curves for the two end members and (6) end member fugacities of H2O and CO2 vapors at magmatic temperatures. The basalt-H2O-CO2 macros in VOLATILECALC are capable of calculating melt-vapor solubility over a range of silicate-melt compositions by using the relationships provided by Dixon (American Mineralogist 82 (1997) 368). The output agrees well with the published solution models and experimental data for silicate melt-vapor systems for pressures below 5000 bar. ?? 2002 Elsevier Science Ltd. All rights reserved.

Computers & Geosciences↗
USGS2003

Comparison of approaches for simulating reactive solute transport involving organic degradation reactions by multiple terminal electron acceptors

Reactive solute transport models are useful tools for analyzing complex geochemical behavior resulting from biodegradation of organic compounds by multiple terminal electron acceptors (TEAPs). The usual approach of simulating the reactions of multiple TEAPs by an irreversible Monod rate law was compared with simulations that assumed a partial local equilibrium or kinetically controlled reactions subject to the requirement that the Gibbs free energy of reaction (Δ G) was either less than zero or less than a threshold value. Simulations were performed using a single organic substrate and O 2 , FeOOH, SO 4 -2 and CO 2 as the terminal electron acceptors. It was assumed that the organic substrate was slowly and completely fermented to CO 2 and H 2 and the H 2 was oxidized by the TEAPs. Simulations using the Monod approach showed that this irreversible rate law forced the reduction of both FeOOH and CO 2 to proceed even when Δ G was positive. This resulted in an over prediction in amount of FeOOH reduced to Fe(II) in parts of the domain and it resulted in large errors in pH. Simulations using mass action kinetics agreed with equilibrium simulations for the case of large rate constants. The extent of reductive dissolution of FeOOH was strongly dependent on the thermodynamic stability of the FeOOH phase. Transport simulations performed assuming that the reactions of the TEAPs stopped when Δ G exceeded a threshold value showed that only simulated H 2 concentrations were affected if the threshold value was the same for each TEAP. Simulated H2 concentrations were controlled by the fastest reaction of the TEAP, but it was common for reactions to occur concomitantly rather than sequentially.

Computers & Geosciences↗
USGS2003

Modeling Np and Pu transport with a surface complexation model and spatially variant sorption capacities: Implications for reactive transport modeling and performance assessments of nuclear waste disposal sites

One-dimensional (1D) geochemical transport modeling is used to demonstrate the effects of speciation and sorption reactions on the ground-water transport of Np and Pu, two redox-sensitive elements. Earlier 1D simulations (Reardon, 1981) considered the kinetically limited dissolution of calcite and its effect on ion-exchange reactions (involving 90 Sr, Ca, Na, Mg and K), and documented the spatial variation of a 90 Sr partition coefficient under both transient and steady-state chemical conditions. In contrast, the simulations presented here assume local equilibrium for all reactions, and consider sorption on constant potential, rather than constant charge, surfaces. Reardon's (1981) seminal findings on the spatial and temporal variability of partitioning (of 90 Sr) are reexamined and found partially caused by his assumption of a kinetically limited reaction. In the present work, sorption is assumed the predominant retardation process controlling Pu and Np transport, and is simulated using a diffuse-double-layer-surface-complexation (DDLSC) model. Transport simulations consider the infiltration of Np- and Pu-contaminated waters into an initially uncontaminated environment, followed by the cleanup of the resultant contamination with uncontaminated water. Simulations are conducted using different spatial distributions of sorption capacities (with the same total potential sorption capacity, but with different variances and spatial correlation structures). Results obtained differ markedly from those that would be obtained in transport simulations using constant K d , Langmuir or Freundlich sorption models. When possible, simulation results (breakthrough curves) are fitted to a constant K d advection–dispersion transport model and compared. Functional differences often are great enough that they prevent a meaningful fit of the simulation results with a constant K d (or even a Langmuir or Freundlich) model, even in the case of Np, a weakly sorbed radionuclide under the simulation conditions. Functional behaviors that cannot be fit include concentration trend reversals and radionuclide desorption spikes. Other simulation results are fit successfully but the fitted parameters ( K d and dispersivity) vary significantly depending on simulation conditions (e.g. “infiltration” vs. “cleanup” conditions). Notably, an increase in the variance of the specified sorption capacities results in a marked increase in the dispersion of the radionuclides. The results presented have implications for the simulation of radionuclide migration in performance assessments of nuclear waste-disposal sites, for the future monitoring of those sites, and more generally for modeling contaminant transport in ground-water environments.

Computers & Geosciences↗
USGS2003

A Visual Basic program to classify sediments based on gravel-sand-silt-clay ratios

Nomenclature describing size distributions is important to geologists because grain size is the most basic attribute of sediments. Traditionally, geologists have divided sediments into four size fractions that include gravel, sand, silt, and clay, and classified these sediments based on ratios of the various proportions of the fractions. Definitions of these fractions have long been standardized to the grade scale described by Wentworth (1922), and two main classification schemes have been adopted to describe the approximate relationship between the size fractions. Specifically, according to the Wentworth grade scale gravel-sized particles have a nominal diameter of ⩾2.0 mm; sand-sized particles have nominal diameters from <2.0 mm to ⩾62.5 μm; silt-sized particles have nominal diameters from <62.5 to ⩾4.0 μm; and clay is <4.0 μm. As for sediment classification, most sedimentologists use one of the systems described either by Shepard (1954) or Folk (1954, 1974). The original scheme devised by Shepard (1954) utilized a single ternary diagram with sand, silt, and clay in the corners to graphically show the relative proportions among these three grades within a sample. This scheme, however, does not allow for sediments with significant amounts of gravel. Therefore, Shepard's classification scheme (Fig. 1) was subsequently modified by the addition of a second ternary diagram to account for the gravel fraction (Schlee, 1973). The system devised by Folk (1954, 1974) is also based on two triangular diagrams (Fig. 2), but it has 23 major categories, and uses the term mud (defined as silt plus clay). The patterns within the triangles of both systems differ, as does the emphasis placed on gravel. For example, in the system described by Shepard, gravelly sediments have more than 10% gravel; in Folk's system, slightly gravelly sediments have as little as 0.01% gravel. Folk's classification scheme stresses gravel because its concentration is a function of the highest current velocity at the time of deposition, together with the maximum grain size of the detritus that is available; Shepard's classification scheme emphasizes the ratios of sand, silt, and clay because they reflect sorting and reworking (Poppe et al., 2000).

Computers & Geosciences↗
USGS1996

A model of Precambrian geology of Kansas derived from gravity and magnetic data

The fabric of the Precambrian geology of Kansas is revealed through inversion of gravity and magnetic data to pseudo-lithology. There are five main steps in the inversion process: (1) reduction of potential-field data to a horizontal plane in the wavenumber domain; (2) separation of the residual anomaly of interest from the regional background, where an assumption is made that the regional anomaly could be represented by some order of polynomial; (3) subtraction of the signal due to the known topography on the Phanerozoic/Precambrian boundary from the residual anomaly (we assume what is left at this stage are the signals due to lateral variation in the Precambrian lithology); (4) inversion of the residual anomaly in the wavenumber domain to density and magnetization distribution in the top part of the Precambrian constrained by the known geologic information; (5) derivation of pseudo-lithology by characterization of density and magnetization. The boundary between the older Central Plains Province to the north and the Southern Granite-Rhyolite Province to the south is clearly delineated. The Midcontinent Rift System appears to widen in central Kansas and involve a considerable portion of southern Kansas. Lithologies in southwestern Kansas appear to change over fairly small areas and include mafic rocks which have not been encountered in drill holes. The texture of the potential field data from southwestern Kansas suggests a history of continental growth by broad extension. Copyright ?? 1996 Elsevier Science Ltd.

Computers & Geosciences↗
USGS1997

An interactive program for computer-aided map design, display, and query: EMAPKGS2

EMAPKGS2 is a user-friendly, PC-based electronic mapping tool for use in hydrogeologic exploration and appraisal. EMAPKGS2 allows the analyst to construct maps interactively from data stored in a relational database, perform point-oriented spatial queries such as locating all wells within a specified radius, perform geographic overlays, and export the data to other programs for further analysis. EMAPKGS2 runs under Microsoft?? Windows??? 3.1 and compatible operating systems. EMAPKGS2 is a public domain program available from the Kansas Geological Survey. EMAPKGS2 is the centerpiece of WHEAT, the Windows-based Hydrogeologic Exploration and Appraisal Toolkit, a suite of user-friendly Microsoft?? Windows??? programs for natural resource exploration and management. The principal goals in development of WHEAT have been ease of use, hardware independence, low cost, and end-user extensibility. WHEAT'S native data format is a Microsoft?? Access?? database. WHEAT stores a feature's geographic coordinates as attributes so they can be accessed easily by the user. The WHEAT programs are designed to be used in conjunction with other Microsoft?? Windows??? software to allow the natural resource scientist to perform work easily and effectively. WHEAT and EMAPKGS have been used at several of Kansas' Groundwater Management Districts and the Kansas Geological Survey on groundwater management operations, groundwater modeling projects, and geologic exploration projects. ?? 1997 Elsevier Science Ltd.

Computers & Geosciences↗
USGS1999

Multivariate analyses of Erzgebirge granite and rhyolite composition: Implications for classification of granites and their genetic relations

High-precision major, minor and trace element analyses for 44 elements have been made of 329 Late Variscan granitic and rhyolitic rocks from the Erzgebirge metallogenic province of Germany. The intrusive histories of some of these granites are not completely understood and exposures of rock are not adequate to resolve relationships between what apparently are different plutons. Therefore, it is necessary to turn to chemical analyses to decipher the evolution of the plutons and their relationships. A new classification of Erzgebirge plutons into five major groups of granites, based on petrologic interpretations of geochemical and mineralogical relationships (low-F biotite granites; low-F two-mica granites; high-F, high-P2O5 Li-mica granites; high-F, low-P2O5 Li-mica granites; high-F, low-P2O5 biotite granites) was tested by multivariate techniques. Canonical analyses of major elements, minor elements, trace elements and ratio variables all distinguish the groups with differing amounts of success. Univariate ANOVA's, in combination with forward-stepwise and backward-elimination canonical analyses, were used to select ten variables which were most effective in distinguishing groups. In a biplot, groups form distinct clusters roughly arranged along a quadratic path. Within groups, individual plutons tend to be arranged in patterns possibly reflecting granitic evolution. Canonical functions were used to classify samples of rhyolites of unknown association into the five groups. Another canonical analysis was based on ten elements traditionally used in petrology and which were important in the new classification of granites. Their biplot pattern is similar to that from statistically chosen variables but less effective at distinguishing the five groups of granites. This study shows that multivariate statistical techniques can provide significant insight into problems of granitic petrogenesis and may be superior to conventional procedures for petrological interpretation.

Computers & Geosciences↗
USGS2000

Integration of a numerical model and remotely sensed data to study urban/rural land surface climate processes

Simulation of urban/rural land surface climate processes using boundary layer climate models requires accurate input data with regard to surface thermal and radiative properties. The research reported here resulted in development of a procedure to integrate the satellite-derived surface biophysical parameters with a boundary layer climate model for simulating spatial surface energy exchange. The procedure was tested through spatial surface energy balance simulation of an urban/rural landscape in eastern Nebraska. The modeled surface temperature and net radiation were compared to those derived from the concurrent satellite data. The errors of the modeled surface temperature were small, and were mainly attributed to uncertainties in the estimation of surface moisture availability and satellite-derived surface radiant temperature. Modeled net radiation was also in agreement with the values calculated from satellite data. Modeled turbulent heat fluxes were in general agreement as compared to those reported in the literature, but the model tended to overestimate the latent heat flux for most rural land cover types. It was concluded that by incorporation of satellite-derived surface physical parameters into a boundary layer model, simulation of spatial land surface climate processes was much improved. The method and procedures developed from this study can be utilized in other boundary layer climate models.

Nebraska↗
USGS2002

Shape functions for velocity interpolation in general hexahedral cells

Numerical methods for grids with irregular cells require discrete shape functions to approximate the distribution of quantities across cells. For control-volume mixed finite-element (CVMFE) methods, vector shape functions approximate velocities and vector test functions enforce a discrete form of Darcy's law. In this paper, a new vector shape function is developed for use with irregular, hexahedral cells (trilinear images of cubes). It interpolates velocities and fluxes quadratically, because as shown here, the usual Piola-transformed shape functions, which interpolate linearly, cannot match uniform flow on general hexahedral cells. Truncation-error estimates for the shape function are demonstrated. CVMFE simulations of uniform and non-uniform flow with irregular meshes show first- and second-order convergence of fluxes in the L2 norm in the presence and absence of singularities, respectively.

Computational Geosciences↗
USGS1983

The role of digital cartographic data in the geosciences

The increasing demand of the Nation's natural resource developers for the manipulation, analysis, and display of large quantities of earth-science data has necessitated the use of computers and the building of geoscience information systems. These systems require, in digital form, the spatial data on map products. The basic cartographic data shown on quadrangle maps provide a foundation for the addition of geological and geophysical data. If geoscience information systems are to realize their full potential, large amounts of digital cartographic base data must be available. A major goal of the U.S. Geological Survey is to create, maintain, manage, and distribute a national cartographic and geographic digital database. This unified database will contain numerous categories (hydrography, hypsography, land use, etc.) that, through the use of standardized data-element definitions and formats, can be used easily and flexibly to prepare cartographic products and perform geoscience analysis. ?? 1983.

Computers & Geosciences↗
USGS2004

NADM Conceptual Model 1.0 -- A Conceptual Model for Geologic Map Information

Executive Summary -- The NADM Data Model Design Team was established in 1999 by the North American Geologic Map Data Model Steering Committee (NADMSC) with the purpose of drafting a geologic map data model for consideration as a standard for developing interoperable geologic map-centered databases by state, provincial, and federal geological surveys. The model is designed to be a technology-neutral conceptual model that can form the basis for a web-based interchange format using evolving information technology (e.g., XML, RDF, OWL), and guide implementation of geoscience databases in a common conceptual framework. The intended purpose is to allow geologic information sharing between geologic map data providers and users, independent of local information system implementation. The model emphasizes geoscience concepts and relationships related to information presented on geologic maps. Design has been guided by an informal requirements analysis, documentation of existing databases, technology developments, and other standardization efforts in the geoscience and computer-science communities. A key aspect of the model is the notion that representation of the conceptual framework (ontology) that underlies geologic map data must be part of the model, because this framework changes with time and understanding, and varies between information providers. The top level of the model distinguishes geologic concepts, geologic representation concepts, and metadata. The geologic representation part of the model provides a framework for representing the ontology that underlies geologic map data through a controlled vocabulary, and for establishing the relationships between this vocabulary and a geologic map visualization or portrayal. Top-level geologic classes in the model are Earth material (substance), geologic unit (parts of the Earth), geologic age, geologic structure, fossil, geologic process, geologic relation, and geologic event.

Open-File Report↗
USGS2025

lasertram: A Python library for time resolved analysis of laser ablation inductively coupled plasma mass spectrometry data

Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) data has a wide variety of uses in the geosciences for in-situ chemical analysis of complex natural materials. Improvements to instrument capabilities and operating software have drastically reduced the time required to generate large volumes of data relative to previous methodologies. Raw data from LA-ICP-MS, however, is in counts per unit time (typically counts per second), not elemental concentrations and converting these count ratesto concentrations requires additional processing. For complex materials where the ablated volume may contain a range of material compositions, a moderate amount of user input is also required if appropriate concentrations are to be accurately calculated. In geologic materials such as glasses and minerals that potentially have numerous heterogeneities (e.g., microlites or other inclusions) within them, this is typically determiningwhether the total ablation signal should be filtered to remove these heterogeneities. This necessitates that the LA-ICP-MS data processing pipeline is one that is not automated, but is also designed to enable rapid and efficient processing of large volumes of data. Here we introduce , a Python library for the time resolved analysis of LA-ICP-MS data. We outline its mathematical theory, code structure, and provide an example of how it can be used to provide the time resolved analysis necessitated by LA-ICP-MS data of complex geologic materials. Throughout the pipeline we show how metadata and data are incrementally added to the objects created such that virtually any aspect of an experiment may be interrogated and its quality assessed. We also show, that when combined with other Python libraries for building graphical user interfaces, it can be utilized outside of a pure scripting environment. can be found at https://doi.org/10.5066/P1DZUR3Z

Applied Computing and Geosciences↗
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↗
USGS2023

Near surface sediments introduce low frequency noise into gravity models

3D geologic modeling and mapping often relies on gravity modeling to identify key geologic structures, such as basin depth, fault offset, or fault dip. Such gravity models generally assume either homogeneous or spatially uncorrelated densities within modeled rock bodies and overlying sediments, with average densities typically derived from surface and drill-hole sampling. The noise contributed to the gravity anomaly by these density assumptions is zero in the homogeneous case and typically <200 μGal in the uncorrelated case. Rock bodies and sediments, however, show both a range of densities and spatial correlation of these densities, in both surface and drill-hole samples, and this correlation causes an increase in power in the low frequency content of the resulting gravity anomaly. Spatial correlation of densities can be modeled as a Gaussian random field (GRF), with the random field parameters derived from drill-hole and geologic map data. Data from alluvial fan sediments in southern Nevada indicate correlation lengths of up to 300 m in the vertical dimension and kilometers in the horizontal dimension. The resulting GRF density models show that the noise contributed to the measured gravity anomaly is of low frequency and can be several mGal in amplitude, contradicting the common attribution of lower frequencies to deeper sources. This low-frequency noise increases in power with an increase in sediment thickness. Its presence increases the ambiguity of interpretations of subsurface geologic body shape, such as basin analyses that attempt to quantify concealed basement fault depths, offsets, and dip angles. In the southwestern United States, where basin analyses are important for natural resource applications, such ambiguity increases the uncertainty of subsequent process modeling.

Applied Computing and Geosciences↗
USGS2025

Predictive regressive models of recent marsh sediment thickness improve the quantification of coastal marsh sediment budgets

Coastal marsh wetlands experience variations in vertical gains and losses through time, which have allowed them to infill relict topography and record variations in drivers. The stratigraphic unit associated with the development of the marsh also reflects the long-term importance of key ecosystem services supplied by the marsh environment, including carbon storage and storm mitigation. Mapping these coastal wetland sediments and the marsh unit thickness is challenging as traditional coastal geophysical tools are not easily deployable (acoustic methods) or are unreliable in saline-soil environments (e.g., ground-penetrating radar), leaving core-based methods the most viable mapping method. In the present study, we utilized prior information on the geologic architecture of the region to select spatial and physical metrics that likely persisted throughout evolution of the marsh during the late Holocene. We then assessed the individual and collective power of these metrics to predict marsh thickness observed from cores. Employing regressive predictive models powered by these data, we improve the quantification of marsh thickness for a coastal fringing marsh within the Grand Bay estuary in Mississippi and Alabama (USA). The information gained from this approach yields improved estimates of the carbon stocks in this environment. Additionally, the stored sediment masses reflect the past, and potential future, persistence of the Grand Bay marsh under historical and present marsh-estuarine sediment exchange fluxes. Such improvements to both the sediment budget of recent marsh stratigraphic units and the spatial extent provide new resources for comparison with large-scale landscape models, the latter of which may be used, when validated, to predict future change and ecosystem transformations.

Alabama. Mississippi↗