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USGS2013

The Anemomilos prediction methodology for Dst

This paper describes new capabilities for operational geomagnetic D isturbance s torm t ime (Dst) index forecasts. We present a data‐driven, deterministic algorithm called Anemomilos for forecasting Dst out to a maximum of 6 days for large, medium, and small storms, depending upon transit time to the Earth. This capability is used for operational satellite management and debris avoidance in Low Earth Orbit (LEO). Anemomilos has a 15 min cadence, 1 h time granularity, 144 h prediction window (+6 days), and up to 1 h latency. A new finding is that nearly all flare events above a certain irradiance threshold, occurring within a defined solar longitude/latitude region and having sufficient estimated liftoff velocity of ejected material, will produce a geoeffective Dst perturbation. Three solar observables are used for operational Dst forecasting: flare magnitude, integrated flare irradiance through time, and event location. Magnitude is a proxy for ejecta quantity or mass and, combined with speed derived from the integrated flare irradiance, represents the kinetic energy. Speed is estimated as the line‐of‐sight velocity for events within 45° radial of solar disk center. Storms resulting from high‐speed streams emanating from coronal holes are not modeled or predicted. A new result is that solar disk, not limb, observable features are used for predictive techniques. Comparisons between Anemomilos predicted and measured Dst for every hour over 25 months in three continuous time frames between 2001 (high solar activity), 2005 (low solar activity), and 2012 (rising solar activity) are shown. The Anemomilos operational algorithm was developed for a specific customer use related to thermospheric mass density forecasting. It is an operational space weather technology breakthrough using solar disk observables to predict geomagnetically effective Dst up to several days at 1 h time granularity. Real‐time forecasts are presented at http://sol.spacenvironment.net/~sam_ops/index.html?

Space Weather↗

USGS1988

Radarclinometry: Bootstrapping the radar reflectance function from the image pixel-signal frequency distribution and an altimetry profile

A method is derived for determining the dependence of radar backscatter on incidence angle that is applicable to the region corresponding to a particular radar image. The method is based on enforcing mathematical consistency between the frequency distribution of the image's pixel signals (histogram of DN values with suitable normalizations) and a one-dimensional frequency distribution of slope component, as might be obtained from a radar or laser altimetry profile in or near the area imaged. In order to achieve a unique solution, the auxiliary assumption is made that the two-dimensional frequency distribution of slope is isotropic. The backscatter is not derived in absolute units. The method is developed in such a way as to separate the reflectance function from the pixel-signal transfer characteristic. However, these two sources of variation are distinguishable only on the basis of a weak dependence on the azimuthal component of slope; therefore such an approach can be expected to be ill-conditioned unless the revision of the transfer characteristic is limited to the determination of an additive instrumental background level. The altimetry profile does not have to be registered in the image, and the statistical nature of the approach minimizes pixel noise effects and the effects of a disparity between the resolutions of the image and the altimetry profile, except in the wings of the distribution where low-number statistics preclude accuracy anyway. The problem of dealing with unknown slope components perpendicular to the profiling traverse, which besets the one-to-one comparison between individual slope components and pixel-signal values, disappears in the present approach. In order to test the resulting algorithm, an artificial radar image was generated from the digitized topographic map of the Lake Champlain West quadrangle in the Adirondack Mountains, U.S.A., using an arbitrarily selected reflectance function. From the same map, a one-dimensional frequency distribution of slope component was extracted. The algorithm recaptured the original reflectance function to the degree that, for the central 90% of the data, the discrepancy translates to a RMS slope error of 0.1 ???. For the central 99% of the data, the maximum error translates to 1 ???; at the absolute extremes of the data the error grows to 6 ???. ?? 1988 Kluwer Academic Publishers.

Earth, Moon and Planets↗

USGS1996

Application of a feedforward neural network in the search for kuroko deposits in the Hokuroku District, Japan

A feedforward neural network with one hidden layer and five neurons was trained to recognize the distance to kuroko mineral deposits. Average amounts per hole of pyrite, sericite, and gypsum plus anhydrite as measured by X-rays in 69 drillholes were used to train the net. Drillholes near and between the Fukazawa, Furutobe, and Shakanai mines were used. The training data were selected carefully to represent well-explored areas where some confidence of the distance to ore was assured. A logarithmic transform was applied to remove the skewness of distance and each variable was scaled and centered by subtracting the median and dividing by the interquartile range. The learning algorithm of annealing plus conjugate gradients was used to minimize the mean squared error of the scaled distance to ore. The trained network then was applied to all of the 152 drillholes that had measured gypsum, sericite, and pyrite. A contour plot of the neural net predicted distance to ore shows fairly wide areas of 1 km or less to ore; each of the known deposit groups is within the 1 km contour. The high and low distances on the margins of the contoured distance plot are in part the result of boundary effects of the contouring algorithm. For example, the short distances to ore predicted west of the Shakanai (Hanaoka) deposits are in basement. However, the short distances to ore predicted northeast of Furotobe, just off the figure, coincide with the location of the Nurukawa kuroko deposit and the Omaki deposit, south of the Shakanai-Hanaoka deposits, seems to be on an extension of short distance to ore contour, but is beyond the 3 km limit from drillholes. Also of interest are some areas only a few kilometers from the Fukazawa and Shakanai groups of deposits that are estimated to be many kilometers from ore, apparently reflecting the network's recognition of the extreme local variability of the geology near some deposits.

Mathematical Geology↗

USGS2008

Rayleigh-wave dispersive energy imaging using a high-resolution linear radon transform

Multichannel Analysis of Surface Waves (MASW) analysis is an efficient tool to obtain the vertical shear-wave profile. One of the key steps in the MASW method is to generate an image of dispersive energy in the frequency-velocity domain, so dispersion curves can be determined by picking peaks of dispersion energy. In this paper, we propose to image Rayleigh-wave dispersive energy by high-resolution linear Radon transform (LRT). The shot gather is first transformed along the time direction to the frequency domain and then the Rayleigh-wave dispersive energy can be imaged by high-resolution LRT using a weighted preconditioned conjugate gradient algorithm. Synthetic data with a set of linear events are presented to show the process of generating dispersive energy. Results of synthetic and real-world examples demonstrate that, compared with the slant stacking algorithm, high-resolution LRT can improve the resolution of images of dispersion energy by more than 50%. ?? Birkhaueser 2008.

Pure and Applied Geophysics↗

USGS2012

Sea lamprey orient toward a source of a synthesized pheromone using odor-conditioned rheotaxis

Characterization of vertebrate chemo-orientation strategies over long distances is difficult because it is often not feasible to conduct highly controlled hypothesis-based experiments in natural environments. To overcome the challenge, we couple in-stream behavioral observations of female sea lampreys ( Petromyzon marinus ) orienting to plumes of a synthesized mating pheromone, 7a,12a,24-trihydroxy-5a-cholan-3-one-24-sulfate (3kPZS), and engineering algorithms to systematically test chemo-orientation hypotheses. In-stream field observations and simulated movements of female sea lampreys according to control algorithms support that odor-conditioned rheotaxis is a component of the mechanism used to track plumes of 3kPZS over hundreds of meters in flowing water. Simulated movements of female sea lampreys do not support that rheotaxis or klinotaxis alone is sufficient to enable the movement patterns displayed by females in locating 3kPZS sources in the experimental stream. Odor-conditioned rheotaxis may not only be effective at small spatial scales as previous described in crustaceans, but may also be effectively used by fishes over hundreds of meters. These results may prove useful for developing management strategies for the control of invasive species that exploit the odor-conditioned tracking behavior and for developing biologically inspired navigation strategies for robotic fish.

Behavioral Ecology and Sociobiology↗

USGS2019

Alignment of surface water ontologies: A comparison of manual and automated approaches

More data are being collected about the world around us than ever before, but effectively using this information requires different data stores to be integrated in such a way that they can be seamlessly queried and analyzed. Automated alignment algorithms exist to facilitate this data integration challenge. In this paper we examine the utility of two current leading automated alignment systems to integrate four ontologies from the surface water domain. We show that the performance of such systems in this domain lags behind their results on popular benchmarks, and therefore incorporate the alignment task described here into the set of benchmarks used by the alignment community. In addition, we show that, with minor modifications, existing alignment algorithms can be used effectively within a semi-automated alignment system for the surface water domain.

Journal of Geographical Systems↗

USGS2011

Empirical methods for detecting regional trends and other spatial expressions in antrim shale gas productivity, with implications for improving resource projections using local nonparametric estimation techniques

The primary objectives of this research were to (1) investigate empirical methods for establishing regional trends in unconventional gas resources as exhibited by historical production data and (2) determine whether or not incorporating additional knowledge of a regional trend in a suite of previously established local nonparametric resource prediction algorithms influences assessment results. Three different trend detection methods were applied to publicly available production data (well EUR aggregated to 80-acre cells) from the Devonian Antrim Shale gas play in the Michigan Basin. This effort led to the identification of a southeast–northwest trend in cell EUR values across the play that, in a very general sense, conforms to the primary fracture and structural orientations of the province. However, including this trend in the resource prediction algorithms did not lead to improved results. Further analysis indicated the existence of clustering among cell EUR values that likely dampens the contribution of the regional trend. The reason for the clustering, a somewhat unexpected result, is not completely understood, although the geological literature provides some possible explanations. With appropriate data, a better understanding of this clustering phenomenon may lead to important information about the factors and their interactions that control Antrim Shale gas production, which may, in turn, help establish a more general protocol for better estimating resources in this and other shale gas plays.

Michigan↗

USGS2013

A framework for quantitative assessment of impacts related to energy and mineral resource development

Natural resource planning at all scales demands methods for assessing the impacts of resource development and use, and in particular it requires standardized methods that yield robust and unbiased results. Building from existing probabilistic methods for assessing the volumes of energy and mineral resources, we provide an algorithm for consistent, reproducible, quantitative assessment of resource development impacts. The approach combines probabilistic input data with Monte Carlo statistical methods to determine probabilistic outputs that convey the uncertainties inherent in the data. For example, one can utilize our algorithm to combine data from a natural gas resource assessment with maps of sage grouse leks and piñon-juniper woodlands in the same area to estimate possible future habitat impacts due to possible future gas development. As another example: one could combine geochemical data and maps of lynx habitat with data from a mineral deposit assessment in the same area to determine possible future mining impacts on water resources and lynx habitat. The approach can be applied to a broad range of positive and negative resource development impacts, such as water quantity or quality, economic benefits, or air quality, limited only by the availability of necessary input data and quantified relationships among geologic resources, development alternatives, and impacts. The framework enables quantitative evaluation of the trade-offs inherent in resource management decision-making, including cumulative impacts, to address societal concerns and policy aspects of resource development.

Natural Resources Research↗

USGS2021

Machine learning can assign geologic basin to produced water samples using major ion geochemistry

Understanding the geochemistry of waters produced during petroleum extraction is essential to informing the best treatment and reuse options, which can potentially be optimized for a given geologic basin. Here, we used the US Geological Survey’s National Produced Waters Geochemical Database (PWGD) to determine if major ion chemistry could be used to classify accurately a produced water sample to a given geologic basin based on similarities to a given training dataset. Two datasets were derived from the PWGD: one with seven features but more samples (PWGD7), and another with nine features but fewer samples (PWGD9). The seven-feature dataset, prior to randomly generating a training and testing (i.e., validation) dataset, had 58,541 samples, 20 basins, and was classified based on total dissolved solids (TDS), bicarbonate (HCO 3 ), Ca, Na, Cl, Mg, and sulfate (SO 4 ). The nine-feature dataset, prior to randomly splitting into a training and testing (i.e., validation) dataset, contained 33,271 samples, 19 basins, and was classified based on TDS, HCO 3 , Ca, Na, Cl, Mg, SO 4 , pH, and specific gravity. Three supervised machine learning algorithms—Random Forest, k-Nearest Neighbors, and Naïve Bayes—were used to develop multi-class classification models to predict a basin of origin for produced waters using major ion chemistry. After training, the models were tested on three different datasets: Validation7, Validation9, and one based on data absent from the PWGD. Prediction accuracies across the models ranged from 23.5 to 73.5% when tested on the two PWGD-based datasets. A model using the Random Forest algorithm predicted most accurately compared to all other models tested. The models generally predicted basin of origin more accurately on the PWGD7-based dataset than on the PWGD9-based dataset. An additional dataset, which contained data not in the PWGD, was used to test the most accurate model; results suggest that some basins may lack geochemical diversity or may not be well described, while others may be geochemically diverse or are well described. A compelling result of this work is that a produced water basin of origin can be determined using major ions alone and, therefore, deep basinal fluid compositions may not be as variable within a given basin as previously thought. Applications include predicting the geochemistry of produced fluid prior to drilling at different intervals and assigning historical produced water data to a producing basin.

Natural Resources Research↗

USGS2017

A physics-based earthquake simulator and its application to seismic hazard assessment in Calabria (Southern Italy) region

The use of a newly developed earthquake simulator has allowed the production of catalogs lasting 100 kyr and containing more than 100,000 events of magnitudes ≥4.5. The model of the fault system upon which we applied the simulator code was obtained from the DISS 3.2.0 database, selecting all the faults that are recognized on the Calabria region, for a total of 22 fault segments. The application of our simulation algorithm provides typical features in time, space and magnitude behavior of the seismicity, which can be compared with those of the real observations. The results of the physics-based simulator algorithm were compared with those obtained by an alternative method using a slip-rate balanced technique. Finally, as an example of a possible use of synthetic catalogs, an attenuation law has been applied to all the events reported in the synthetic catalog for the production of maps showing the exceedance probability of given values of PGA on the territory under investigation.

Calabria region↗

USGS2017

Development of a multimetric index for integrated assessment of salt marsh ecosystem condition

Tools for assessing and communicating salt marsh condition are essential to guide decisions aimed at maintaining or restoring ecosystem integrity and services. Multimetric indices (MMIs) are increasingly used to provide integrated assessments of ecosystem condition. We employed a theory-based approach that considers the multivariate relationship of metrics with human disturbance to construct a salt marsh MMI for five National Parks in the northeastern USA. We quantified the degree of human disturbance for each marsh using the first principal component score from a principal components analysis of physical, chemical, and land use stressors. We then applied a metric selection algorithm to different combinations of about 45 vegetation and nekton metrics (e.g., species abundance, species richness, and ecological and functional classifications) derived from multi-year monitoring data. While MMIs derived from nekton or vegetation metrics alone were strongly correlated with human disturbance ( r values from −0.80 to −0.93), an MMI derived from both vegetation and nekton metrics yielded an exceptionally strong correlation with disturbance ( r = −0.96). Individual MMIs included from one to five metrics. The metric-assembly algorithm yielded parsimonious MMIs that exhibit the greatest possible correlations with disturbance in a way that is objective, efficient, and reproducible.

Estuaries and Coasts↗

USGS2023

Chapter 12 - Explainable AI for understanding ML-derived vegetation products

Current machine learning applications and algorithms have developed promise to produce autonomous systems that automatically perceive, learn, predict, and act on their own. However, the effectiveness of these systems is limited by the machine's current inability to explain their decisions, algorithmic paths, and actions to human users. The purpose of this chapter is to apply explainable artificial intelligence (XAI) to black-box models using an example of the U.S. Geological Survey's LANDFIRE Existing Vegetation Type (EVT). This chapter also demonstrates the tools developed to assist scientists/analysts in understanding and trusting prediction outcomes of vegetation type that streamline development of the LANDFIRE EVT product.

Book chapter↗

USGS2025

Karhunen–Loève deep learning method for surrogate modeling and approximate Bayesian parameter estimation

We evaluate the performance of the Karhunen–Loève Deep Neural Network (KL-DNN) framework for surrogate modeling and approximate Bayesian parameter estimation in partial differential equation models. In the surrogate model, the Karhunen–Loève (KL) expansions are used for the dimensionality reduction of the number of unknown parameters and variables, and a deep neural network is employed to relate the reduced space of parameters to that of the state variables. The KL-DNN surrogate model is used to formulate a maximum-a-posteriori-like least-squares problem, which is randomized to draw samples of the posterior distribution of the parameters. We test the proposed framework for a hypothetical unconfined aquifer via comparison with the forward MODFLOW and inverse PEST++ iterative ensemble smoother (IES) solutions as well as the state-of-the-art Fourier neural operator (FNO) and deep operator networks (DeepONets) operator learning surrogate models. Our results show that the KL-DNN surrogate model outperforms FNO and DeepONet for forward predictions. For solving inverse problems, the randomized algorithm provides the same or more accurate Bayesian predictions of the parameters than IES as evidenced by the higher log predictive probability of both the estimated parameter field and the forecast hydraulic head. The posterior mean obtained from the randomized algorithm is closer to the reference parameter field than that obtained with FNO as the maximum a posteriori estimate.

Advances in Water Resources↗

USGS2012

Relative azimuth inversion by way of damped maximum correlation estimates

Horizontal seismic data are utilized in a large number of Earth studies. Such work depends on the published orientations of the sensitive axes of seismic sensors relative to true North. These orientations can be estimated using a number of different techniques: SensOrLoc (Sensitivity, Orientation and Location), comparison to synthetics (Ekstrom and Busby, 2008), or by way of magnetic compass. Current methods for finding relative station azimuths are unable to do so with arbitrary precision quickly because of limitations in the algorithms (e.g. grid search methods). Furthermore, in order to determine instrument orientations during station visits, it is critical that any analysis software be easily run on a large number of different computer platforms and the results be obtained quickly while on site. We developed a new technique for estimating relative sensor azimuths by inverting for the orientation with the maximum correlation to a reference instrument, using a non-linear parameter estimation routine. By making use of overlapping windows, we are able to make multiple azimuth estimates, which helps to identify the confidence of our azimuth estimate, even when the signal-to-noise ratio (SNR) is low. Finally, our algorithm has been written as a stand-alone, platform independent, Java software package with a graphical user interface for reading and selecting data segments to be analyzed.

Computers & Geosciences↗

USGS2020

A two-stage step-wise framework for fast optimization of well placement in coalbed methane reservoirs

Coalbed methane (CBM) has emerged as a clean energy resource in the global energy mix, especially in countries such as Australia, China, India and the USA. The economical and successful development of CBM requires a thorough evaluation and optimization of well placement prior to field-scale exploitation. This paper presents a two-stage, step-wise optimization framework to obtain the optimal placement of wells for large-scale development of CBM reservoirs. In the first stage, an optimal uniform well pattern is obtained by optimizing well pattern description parameters with the particle swarm optimization (PSO) algorithm. Subsequently, the location and status (active/inactive) of each well are perturbed and optimized within the patterns through the integration of the generalized pattern search (GPS) algorithm and a quality map (QM) representing the production potential. This framework was tested in a synthetic anthracite CBM reservoir in the Qinshui basin (with high gas content and low permeability) and a real field high volatile bituminous reservoir in the Illinois basin (with low gas content and high permeability). The results show that: (i) significant variations in the net present value (NPV) exist with respect to different uniform well patterns (even for cases where the total number of wells are identical), the optima of which can be efficiently determined by the PSO within 100 numerical simulation runs; (ii) the optimization of well perturbations by the GPS results in a more noticeable improvement in NPVs for the synthetic (12.3%) than for the real field model (4.6%); (iii) for the low permeable synthetic model with narrow optimal well spacings (320 m × 200 m), the contribution of the optimization of well perturbation to the NPV increment is heavily dependent on the uniform well placement solution; (iv) for the high permeable real field model with large optimal well spacings (1300 m × 1300 m), the initial uniform well placement has a very minor effect on the subsequent well perturbation solutions in terms of NPV; (v) the proposed framework significantly outperforms the conventional well-by-well concatenation procedure in terms of computational efficiency, robustness and optimal criteria set for production potential.

International Journal of Coal Geology↗

USGS2007

An efficient mode-splitting method for a curvilinear nearshore circulation model

A mode-splitting method is applied to the quasi-3D nearshore circulation equations in generalized curvilinear coordinates. The gravity wave mode and the vorticity wave mode of the equations are derived using the two-step projection method. Using an implicit algorithm for the gravity mode and an explicit algorithm for the vorticity mode, we combine the two modes to derive a mixed difference–differential equation with respect to surface elevation. McKee et al.'s [McKee, S., Wall, D.P., and Wilson, S.K., 1996. An alternating direction implicit scheme for parabolic equations with mixed derivative and convective terms. J. Comput. Phys., 126, 64–76.] ADI scheme is then used to solve the parabolic-type equation in dealing with the mixed derivative and convective terms from the curvilinear coordinate transformation. Good convergence rates are found in two typical cases which represent respectively the motions dominated by the gravity mode and the vorticity mode. Time step limitations imposed by the vorticity convective Courant number in vorticity-mode-dominant cases are discussed. Model efficiency and accuracy are verified in model application to tidal current simulations in San Francisco Bight.

Coastal Engineering↗

USGS2026

HyFlood: A surrogate-model-based framework for compound coastal flooding

Compound coastal flooding is a major threat to low-lying coastal regions and is expected to intensify under future climate change projections. However, modeling the joint interaction of waves, storm surge, tides, and rainfall remains computationally demanding, limiting the development of fast and reliable forecast tools. Here we present HyFlood, a hybrid statistical-numerical downscaling framework capable of computing and mapping high-resolution compound flood hazards while substantially reducing the computational cost compared with fully process-based hydrodynamic modeling. HyFlood combines statistical sampling and selection algorithms with a cascade of reduced-complexity surrogate models that emulate nearshore wave transformation, surf-zone hydrodynamics, and coastal, fluvial, and pluvial flooding. The surrogate models employ machine-learning and regression algorithms applied to a low-dimensional representation of the flooding outputs, obtained through statistical dimensionality reduction. The framework is demonstrated in southern O'ahu, Hawai'i, a region exposed to elevated sea levels driven by tides, waves, and storm surge along with frequent precipitation-driven flash flooding. Validation of the surrogates against the physics-based model outputs demonstrates that HyFlood accurately reproduces daily maxima of spatially distributed flooding depths. This hybrid approach offers a scalable and efficient tool to better quantify how changes in flooding drivers translate into hazard and impact assessments, and to support compound-flood risk assessments and climate-change adaptation planning.

Hawaii↗

USGS2014

Development of a generic auto-calibration package for regional ecological modeling and application in the Central Plains of the United States

Process-oriented ecological models are frequently used for predicting potential impacts of global changes such as climate and land-cover changes, which can be useful for policy making. It is critical but challenging to automatically derive optimal parameter values at different scales, especially at regional scale, and validate the model performance. In this study, we developed an automatic calibration (auto-calibration) function for a well-established biogeochemical model—the General Ensemble Biogeochemical Modeling System (GEMS)-Erosion Deposition Carbon Model (EDCM)—using data assimilation technique: the Shuffled Complex Evolution algorithm and a model-inversion R package—Flexible Modeling Environment (FME). The new functionality can support multi-parameter and multi-objective auto-calibration of EDCM at the both pixel and regional levels. We also developed a post-processing procedure for GEMS to provide options to save the pixel-based or aggregated county-land cover specific parameter values for subsequent simulations. In our case study, we successfully applied the updated model (EDCM-Auto) for a single crop pixel with a corn–wheat rotation and a large ecological region (Level II)—Central USA Plains. The evaluation results indicate that EDCM-Auto is applicable at multiple scales and is capable to handle land cover changes (e.g., crop rotations). The model also performs well in capturing the spatial pattern of grain yield production for crops and net primary production (NPP) for other ecosystems across the region, which is a good example for implementing calibration and validation of ecological models with readily available survey data (grain yield) and remote sensing data (NPP) at regional and national levels. The developed platform for auto-calibration can be readily expanded to incorporate other model inversion algorithms and potential R packages, and also be applied to other ecological models.

Ecological Informatics↗