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At least 19 recordsLinked to original sources
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↗
USGS2025

Statistical approaches for modeling correlated grade and tonnage distributions and applications for mineral resource assessments

Correlations between grade and tonnage exist in mineral resource data compiled from published reports, but they are not always addressed during quantitative assessment of undiscovered mineral resources. Failure to account for correlated grade and tonnage distributions can result in geologically unrealistic assessment results. Current software tools simulate univariate ore tonnage and multivariate resource grades of undiscovered deposits independently. As a result, analysts are forced to rely on ad-hoc solutions to minimize the correlation issues by: 1) creating subsets of data with restricted criteria; 2) truncating grade and tonnage distributions; and 3) testing model robustness using exploratory data analysis. While these methods represent pragmatic solutions, the statistical solutions presented here provide additional options to address real correlations in grade and tonnage data used for mineral resource assessments. We present a modified version of the MapMark4 package in R that introduces two alternatives for modeling grade and tonnage distributions, consisting of a multivariate solution that accounts for correlations between ore tonnage and metal grades and an empirical solution that utilizes simple random sampling with replacement to reproduce coupled grades and tonnages from the input data. We present simulations for contained ore and metal for three case studies representing tungsten skarn, komatiite-hosted nickel, and sediment-hosted carbonate amagmatic zinc-lead (Mississippi Valley-type) deposits. Employing the methods presented here yields quantitative mineral resource assessment results that more closely reflect the empirical distributions of grades and tonnages observed in nature and expands the applicability of these tools for ongoing critical mineral resource assessments.

Applied Computing and Geosciences↗
USGS2025

Extracting data from maps: Lessons learned from the artificial intelligence for critical mineral assessment competition

The U.S. Geological Survey (USGS), Defense Advanced Projects Research Agency (DARPA), NASA Jet Propulsion Laboratory (JPL), and MITRE ran a 12-week machine learning competition aimed at accelerating development of AI tools for critical mineral assessments. The Artificial Intelligence for Critical Mineral Assessment Competition solicited innovative solutions for two challenges: 1) automated georeferencing of historical maps, and 2) automated feature extraction from historical maps. Competitors used a new dataset of historical map images to train, validate, and evaluate their models. Automated georeferencing pipelines attained a median root-mean square error of 1.1 km. Prompt-based extraction (i.e., with user input) of polygons, polylines, and points from geologic maps yielded median F1-scores of 0.77, 0.56, 0.35, respectively. Geologic maps pose numerous challenges for AI workflows because they vary significantly. However, despite its short duration, the competition yielded promising results that have since spurred further innovation in this area and led to the development of new AI tools to semi-automate key, time-consuming parts of the assessment workflow.

Applied Computing and Geosciences↗
USGS2011

Treading lightly on shifting ground: The direction and motivation of future geological research

The future of the geosciences and geological research will involve complex scientific challenges, primarily concerning global and regional environmental issues, in the next 20-30 years. It is quite reasonable to suspect, based on current political and socioeconomic events, that young geoscientists will be faced with and involved in helping to resolve some well defined problems: water and energy security, the effects of anthropogenic climate change, coastal sea level rise and development, and the mitigation of geohazards. It is how we choose to approach these challenges that will define our future. Interdisciplinary applied research, improved modeling and prediction augmented with faster and more sophisticated computing, and a greater role in creating and guiding public policy, will help us achieve our goals of a cleaner and safer Earth environment in the next 30 years. In the far future, even grander possibilities for eliminating the risk of certain geohazards and finding sustainable solutions to our energy needs can be envisioned. Looking deeper into the future, the possibilities for geoscience research push the limits of the imagination.

Episodes↗
USGS1978

Range charts and no-space graphs

No-space graphs present one solution to the familiar problem: given data on the occurrence of fossil taxa in separate, well-sampled sections, determine a range chart; that is, a reasonable working hypothesis of the total range in the area in question of each taxon studied. The solution presented here treats only the relative sequence of biostratigraphic events (first and last occurrences of taxa) and does not attempt to determine an amount of spacing between events. Relative to a hypothesized sequence, observed events in any section may be in-place or out-of-place. Out-of-place events may indicate (1) the event in question reflects a taxon that did not fill its entire range (unfilled-range event), or (2) the event in question indicates a need for the revision of the hypothesized sequence. A graph of relative position only (no-space graph) can be used to facilitate the recognition of in-place and out-of-place events by presenting a visual comparison of the observations from each section with the hypothesized sequence. The geometry of the graph as constructed here is such that in-place events will lie along a line series and out-of-place events will lie above or below it. First-occurrence events below the line series and last-occurrence events above the line series indicate unfilled ranges. First-occurrence events above the line series and last-occurrence events below the line series indicate a need for the revision of the hypothesis. Knowing this, the stratigrapher considers alternative positionings of the line series as alternative range hypotheses and seeks the line series that best fits his geologic and paleontologic judgment. No-space graphs are used to revise an initial hypothesis until a final hypothesis is reached. In this final hypothesis every event is found in-place in at least one section, and all events in all sections may be interpreted to represent in-place events or unfilled-range events. No event may indicate a need for further range revision. The application of the no-space graph method requires the assumption of lack of reworking and the assumption that taxa that are present in a single horizon indicate taxa whose ranges overlap. When applied to hypothetical and actual data, the no-space graph technique produces geologically reasonable range charts that compare favorably with results produced by other methods.

Computers & Geosciences↗
USGS2026

3D semantic mapping of surface geological features

Semantic mapping in 3D is fundamental to a wide range of geoscientific studies and applications, including geomorphology, hazard assessment, and environmental monitoring. However, automatically segmenting geological features from large-scale photogrammetric datasets remains a significant challenge. We present a methodology to address this gap. Using overlapping images collected over environments of interest, Structure-from-Motion (SfM) produces georeferenced point clouds and estimates camera poses. Existing large vision models, such as Segment Anything Model, segment objects in the images, generating pixel-segmentation associations. To produce pixel-point associations, we project the points back onto the camera image planes. As objects are independently segmented across multiple images with different perspectives, we develop a segmentation mosaicking algorithm to build probabilistic point-segmentation associations that combines the pixel-segmentation associations and pixel-point associations. Our methodology is validated using both synthetic data generated by Kubric and real-world UAV-SfM data. The implementation is designed to be compatible with existing SfM software, including Agisoft and OpenDroneMap, for photogrammetry mapping in geoscience studies. As a case study, we apply our method to the semantic mapping of precariously balanced rocks (PBRs), which provide upper-bound constraints on historical ground motion shaking intensity. To support object-level identification of PBRs, we additionally integrated Grounding DINO, enabling text-prompted segmentation of features of interest within UAV imagery. This case study demonstrates the effectiveness of our method in generating a 3D semantic map of PBRs, enabling spatial distribution of PBR fragility for earthquake hazard analysis.

Computers & Geosciences↗
USGS2020

Acoustic Sediment Estimation Toolbox (ASET): A software package for calibrating and processing TRDI ADCP data to compute suspended-sediment transport in sandy rivers

Quantifying suspended-sediment transport is critical for a variety of disciplines related to the management of water resources. However, the number of gauging stations and monitoring networks in most rivers around the world is insufficient to improve understanding of river dynamics and support water resource management decisions. This is mainly due to the high operational costs and intensive labor involved in traditional sediment measurement techniques, especially in sand bed rivers where coarse material varies spatially in the river cross section. Recently, the acoustic surrogate method has received attention as a potentially accurate surrogate technology for estimating suspended-sediment concentrations. In addition, the acoustic surrogate method, through use of acoustic Doppler current profilers (ADCPs), has the advantage of being able to simultaneously measure the flow velocity field and cross-sectional area when moving-boat measurements are performed. In spite of the important advances made in the implementation of this technique, there are no widely-available, free tools for processing the ADCP acoustic signal cross section measurements which include options to extrapolate velocity and sediment in unmeasured ADCP zones and develop calibrations with physical samples. This paper presents a new software called Acoustic Sediment Estimation Toolbox (ASET), which enables the user to develop a calibration between the acoustic signal collected with a down-looking Teledyne RD Instruments ADCP and sediment concentrations determined using traditional sediment sampling techniques. Moreover, ASET software uses dynamic ADCP measurements to estimate the total suspended-sediment transport through a river cross section. The theoretical framework and data processing routines applied by each module in ASET are presented. Finally, a comparison is made between the results obtained by ASET and by traditional methodologies for computing suspended-sediment transport in a large river system (Paraná River, Argentina).

Computers & Geosciences↗
USGS2008

The GIS Weasel: An interface for the development of geographic information used in environmental simulation modeling

The GIS Weasel is a freely available, open-source software package built on top of ArcInfo Workstation?? [ESRI, Inc., 2001, ArcInfo Workstation (8.1 ed.), Redlands, CA] for creating maps and parameters of geographic features used in environmental simulation models. The software has been designed to minimize the need for GIS expertise and automate the preparation of the geographic information as much as possible. Although many kinds of data can be exploited with the GIS Weasel, the only information required is a raster dataset of elevation for the user's area of interest (AOI). The user-defined AOI serves as a starting point from which to create maps of many different types of geographic features, including sub-watersheds, streams, elevation bands, land cover patches, land parcels, or anything else that can be discerned from the available data. The GIS Weasel has a library of over 200 routines that can be applied to any raster map of geographic features to generate information about shape, area, or topological association with other features of the same or different maps. In addition, a wide variety of parameters can be derived using ancillary data layers such as soil and vegetation maps.

Computers & Geosciences↗
USGS1993

The pyramid system for multiscale raster analysis

Geographical research requires the management and analysis of spatial data at multiple scales. As part of the U.S. Geological Survey's global change research program a software system has been developed that reads raster data (such as an image or digital elevation model) and produces a pyramid of aggregated lattices as well as various measurements of spatial complexity. For a given raster dataset the system uses the pyramid to report: (1) mean, (2) variance, (3) a spatial autocorrelation parameter based on multiscale analysis of variance, and (4) a monofractal scaling parameter based on the analysis of isoline lengths. The system is applied to 1-km digital elevation model (DEM) data for a 256-km2 region of central California, as well as to 64 partitions of the region. PYRAMID, which offers robust descriptions of data complexity, also is used to describe the behavior of topographic aspect with scale. ?? 1993.

Computers & Geosciences↗
USGS2005

Automated mapping of hammond's landforms

We automated a method for mapping Hammond's landforms over large landscapes using digital elevation data. We compared our results against Hammond's published landform maps, derived using manual interpretation procedures. We found general agreement in landform patterns mapped by the manual and the automated approaches, and very close agreement in characterization of local topographic relief. The two approaches produced different interpretations of intermediate landforms, which relied upon quantification of the proportion of landscape having gently sloping terrain. This type of computation is more efficiently and consistently applied by computer than human. Today's ready access to digital data and computerized geospatial technology provides a good foundation for mapping terrain features, but the mapping criteria guiding manual techniques in the past may not be appropriate for automated approaches. We suggest that future efforts center on the advantages offered by digital advancements in refining an approach to better characterize complex landforms.

IEEE Geoscience and Remote Sensing Letters↗
USGS2009

Sediment-hosted zinc-lead deposits of the world— Database and grade and tonnage models

This report provides information on sediment-hosted zinc-lead mineral deposits based on the geologic settings that are observed on regional geologic maps. The foundation of mineral-deposit models is information about known deposits. The purpose of this publication is to make this kind of information available in digital form for sediment-hosted zinc-lead deposits. Mineral-deposit models are important in exploration planning and quantitative resource assessments: Grades and tonnages among deposit types are significantly different, and many types occur in different geologic settings that can be identified from geologic maps. Mineral-deposit models are the keystone in combining the diverse geoscience information on geology, mineral occurrences, geophysics, and geochemistry used in resource assessments and mineral exploration. Too few thoroughly explored mineral deposits are available in most local areas for reliable identification of the important geoscience variables, or for robust estimation of undiscovered deposits - thus, we need mineral-deposit models. Globally based deposit models allow recognition of important features because the global models demonstrate how common different features are. Well-designed and -constructed deposit models allow geologists to know from observed geologic environments the possible mineral-deposit types that might exist, and allow economists to determine the possible economic viability of these resources in the region. Thus, mineral-deposit models play the central role in transforming geoscience information to a form useful to policy makers. This publication contains a computer file of information on sediment-hosted zinc-lead deposits from around the world. It also presents new grade and tonnage models for nine types of these deposits and a file allowing locations of all deposits to be plotted in Google Earth. The data are presented in FileMaker Pro, Excel and text files to make the information available to as many as possible. The value of this information and any derived analyses depends critically on the consistent manner of data gathering. For this reason, we first discuss the rules applied in this compilation. Next, the fields of the data file are considered. Finally, we provide new grade and tonnage models that are, for the most part, based on a classification of deposits using observable geologic units from regional-scaled maps.

Open-File Report↗
USGS2016

Dynamic reusable workflows for ocean science

Digital catalogs of ocean data have been available for decades, but advances in standardized services and software for catalog search and data access make it now possible to create catalog-driven workflows that automate — end-to-end — data search, analysis and visualization of data from multiple distributed sources. Further, these workflows may be shared, reused and adapted with ease. Here we describe a workflow developed within the US Integrated Ocean Observing System (IOOS) which automates the skill-assessment of water temperature forecasts from multiple ocean forecast models, allowing improved forecast products to be delivered for an open water swim event. A series of Jupyter Notebooks are used to capture and document the end-to-end workflow using a collection of Python tools that facilitate working with standardized catalog and data services. The workflow first searches a catalog of metadata using the Open Geospatial Consortium (OGC) Catalog Service for the Web (CSW), then accesses data service endpoints found in the metadata records using the OGC Sensor Observation Service (SOS) for in situ sensor data and OPeNDAP services for remotely-sensed and model data. Skill metrics are computed and time series comparisons of forecast model and observed data are displayed interactively, leveraging the capabilities of modern web browsers. The resulting workflow not only solves a challenging specific problem, but highlights the benefits of dynamic, reusable workflows in general. These workflows adapt as new data enters the data system, facilitate reproducible science, provide templates from which new scientific workflows can be developed, and encourage data providers to use standardized services. As applied to the ocean swim event, the workflow exposed problems with two of the ocean forecast products which led to improved regional forecasts once errors were corrected. While the example is specific, the approach is general, and we hope to see increased use of dynamic notebooks across the geoscience domains.

Journal of Marine Science and Engineering↗
USGS2000

Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

Computers & Geosciences↗
USGS2026

The path to FAIR research models: Lessons learned

Numerical modeling of Earth surface processes emerged as an important scientific tool in the late 1960s to mid-1970s, driven by the development of finite element methods in computer science. These advancements, initially applied in civil engineering, enabled scientists to simulate complex geological phenomena. At that time, models were often only described in publications, access was limited to researchers with direct connections to the developers, and the code was rarely documented for reuse, limiting their application beyond the original research context. The FAIR principles (Findability, Accessibility, Interoperability, and Reusability) as applied to data began to take shape in the 21st century with the rise of open science, digital repositories, and standardized data sharing frameworks. In the late 2010s, grassroots movements began to apply some of the FAIRness goals to numerical models. Subsequently, more formalized FAIR model principles were developed that addressed the specific needs of the scientific modeling community, resulting in the formulation of the FAIR principles for research software (FAIR4RS). In this study, we examine the development and implementation of strategies by two geoscience research infrastructures – the CSDMS (Community Surface Dynamics Modeling System) Model Repository and the U.S. Geological Survey Model Catalog – to enhance the FAIRness of models guided by FAIR4RS. Some of the development and implementation efforts described predate the formalization of FAIR and FAIR4RS principles, making this an ongoing and adaptive process. We evaluate the temporal progression towards increased FAIR4RS alignment across three phases of research infrastructure development: prototype, refinement, and growth and iteration. Although certain principles were more straightforward to implement early in prototypes of the catalog infrastructures, others required broader community collaboration during refinement, and some continue to pose practical challenges in the growth and iteration phase. By tracing these dynamics, our aim is to provide insights that can guide other modeling initiatives in effectively adopting FAIR4RS principles within their communities.

Geoscientific Model Development↗
USGS1994

Digital hum filtering

Data may be overprinted by a steady-state cyclical noise (hum). Steady-state indicates that the noise is invariant with time; its attributes, frequency, amplitude, and phase, do not change with time. Hum recorded on seismic data usually is powerline noise and associated higher harmonics; leakage from full-waveform rectified cathodic protection devices that contain the odd higher harmonics of powerline frequencies; or vibrational noise from mechanical devices. The fundamental frequency of powerline hum may be removed during data acquisition with the use of notch filters. Unfortunately, notch filters do not discriminate signal and noise, attenuating both. They also distort adjacent frequencies by phase shifting. Finally, they attenuate only the fundamental mode of the powerline noise; higher harmonics and frequencies other than that of powerlines are not removed. Digital notch filters, applied during processing, have many of the same problems as analog filters applied in the field. The method described here removes hum of a particular frequency. Hum attributes are measured by discrete Fourier analysis, and the hum is canceled from the data by subtraction. Errors are slight and the result of the presence of (random) noise in the window or asynchrony of the hum and data sampling. Error is minimized by increasing window size or by resampling to a finer interval. Errors affect the degree of hum attenuation, not the signal. The residual is steady-state hum of the same frequency. ?? 1994.

Computers & Geosciences↗