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  • 04.04. Geology  (7)
  • Crustal deformation (cf. Earthquake precursor: deformation or strain)  (7)
  • Sediment transport  (7)
  • E62
  • Elsevier B.V.  (14)
  • Am. Geophys. Un. & Geol. Soc. Am.  (4)
  • Am. Geophys. Union & Geol. Soc. Am.
  • Bonn: Institute for the Study of Labor (IZA)
Collection
  • 1
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    Elsevier B.V.
    Publication Date: 2021-02-01
    Description: In supervised classification, we search criteria allowing us to decide whether a sample belongs to a certain class of patterns. The identification of such decision functions is based on examples where we know a priori to which class they belong. The distinction of seismic signals, produced from earthquakes and nuclear explosions, is a classical problem of discrimination using classification with supervision. We move on from observed data—signals originating from known earthquakes and nuclear tests—and search for criteria on how to assign a class to a signal of unknown origin. We begin with Principal Component Analysis (PCA) and Fisher's Linear Discriminant Analysis (FLDA), identifying a linear element separating groups at best. PCA, FLDA, and likelihood-based approaches make use of statistical properties of the groups. Considering only the number of misclassified samples as a cost, we may prefer alternatives, such as the Multilayer Perceptrons (MLPs). The Support Vector Machines (SVMs) use a modified cost function, combining the criterion of the minimum number of misclassified samples with a request of separating the hulls of the groups with a margin as wide as possible. Both SVMs and MLPs overcome the limits of linear discrimination. A famous example for the advantages of the two techniques is the eXclusive OR (XOR) problem, where we wish to form classes of objects having the same parity—even, e.g., (0,0), (1,1) or odd, e.g., (0,1), (1,0). MLPs and SVMs offer effective methods for the identification of nonlinear decision functions, allowing us to resolve classification problems of any complexity provided the data set used during earning is sufficiently large. In Hidden Markov Models (HMMs), we consider observations where their meaning depends on their context. Observations form a causal chain generated by a hidden process. In Bayesian Networks (BNs) we represent conditional (in)dependencies between a set of random variables by a graphical model. In both HMMs and BNs, we aim at identifying models and parameters that explain observations with a highest possible degree of probability.
    Description: Published
    Description: 33-85
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Keywords: pattern recognition ; supervised learning ; Support Vector Machines ; Multilayer Perceptrons ; Hidden Markov Models ; Bayesian Networks ; 04.04. Geology ; 04.06. Seismology ; 04.07. Tectonophysics ; 04.08. Volcanology ; 05.04. Instrumentation and techniques of general interest
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: book chapter
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  • 2
    Publication Date: 2021-02-01
    Description: Patterns and objects are described by a variety of characteristics, namely features and feature vectors. Features can be numerical, ordinal, and categorical. Patterns can be made up of a number of objects, such as in speech processing. In geophysics, numerical features are the most common ones and we focus on those. The choice of appropriate features requires a priori reasoning about the physical relation between patterns and features. We present strategies for feature identification and procedures suitable for pattern recognition. In time series analysis and image processing, the direct use of raw data is not feasible. Procedures of feature extraction, based on locally encountered characteristics of the data, are applied. Here we present the problem of delineating segments of interest in time series and textures in image processing. In transformations, we “translate” our raw data to a form suitable for learning. In Principal Component Analysis, we rotate the original features to a system of uncorrelated variables, limiting redundancy. Independent Component Analysis follows a similar strategy, transforming our data into variables independent of each other. Fourier transform and wavelet transform are based on the representation of the original data as a series of basis functions—sines and cosines or finite-length wavelets. Redundancy reduction is achieved considering the contributions of the single basis functions. Even though a large number of features help to solve a classification problem, feature vectors with high dimensions pose severe problems. Besides the computational burden, we encounter problems known under the term “curse of dimensionality.” The curse of dimensionality entails the necessity of feature selection and reduction, which includes a priori considerations as well as redundancy reduction. The significance of features may be evaluated with tests, such as Student’s t or Hotelling's T2, and, in more complex problems, with cross-validation methods.
    Description: Published
    Description: 3-13
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Keywords: pattern recognition ; objects ; features ; 04.04. Geology ; 04.06. Seismology ; 04.07. Tectonophysics ; 04.08. Volcanology ; 05.04. Instrumentation and techniques of general interest
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: book chapter
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  • 3
    Publication Date: 2021-02-01
    Description: In this chapter, we deal with a posterior analysis of supervised and unsupervised learning techniques. Concerning supervised learning, we discuss methods of cross-validation and assessment of uncertainty of tests by means of the “Receiver Operation Curve” and the “Kappa-Statistics.” We show the importance of appropriate target information. Furthermore, features are critical; when they are not properly chosen, they fail to describe objects in a unique way. A critical attitude is mandatory to validate the success of an application. A high score of success does not automatically mean that a method is truly effective. At the same time, users should not despair when the desired success is not achieved. A posteriori analysis on the reasons for an apparent failure may provide useful insights into the problem. Targets may not be appropriately defined, features can be inadequate, etc. Problems can be often fixed by adjusting a few choices; sometimes a change of strategy may be necessary to improve results. In unsupervised learning, we ask whether the structures revealed in the data are meaningful. Cluster analysis offers rules giving formal answers to this question; however, such rules are not generally applicable. In some cases, a heuristic approach may be necessary.
    Description: Published
    Description: 237-259
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Keywords: pattern recognition ; a posteriori analysis ; supervised learning ; unsupervised learning ; cross validation ; assessment of uncertainty ; Receiver Operation Curve ; Kappa-Statistics ; 04.04. Geology ; 04.06. Seismology ; 04.07. Tectonophysics ; 04.08. Volcanology ; 05.04. Instrumentation and techniques of general interest
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: book chapter
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  • 4
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    Elsevier B.V.
    Publication Date: 2021-02-01
    Description: Unsupervised learning is based on the definition of an appropriate metrics defining the similarity of patterns. On the basis of the metrics, we form groups or clusters of patterns following various strategies. In partitioning cluster analysis, we form disjoint clusters. Being faced with data, where clusters still exhibit heterogeneities or subclusters, we may adopt the strategy of hierarchical clustering, which leads to the generation of the so-called dendrograms. In the partitioning strategy, we choose a priori the number of clusters we wish to form, whereas in the hierarchical strategy, the number of clusters depends on the resolution we want to have. Density-based clustering considers local structures of a data set. We consider a unit volume in our data space and derive the density of samples within this volume. Moving toward neighboring volumes, we verify whether the number of samples has dropped below a threshold. If this is the case, we identify a heterogeneity, otherwise we join the neighboring volumes to a common cluster. Self-Organizing Maps (SOMs) provide a way of representing multidimensional data in much lower dimensional spaces than the original data set. The process of reducing the dimensionality of vectors is essentially a data compression technique known as vector quantization. The SOM technique creates a network that stores information in a way that it maintains the topological relationships within the patterns of the data set. Each node of the network represents a number of patterns. Assigning a color code to the nodes, the representation of pattern characteristics with high-dimensional feature vectors becomes extremely effective.
    Description: Published
    Description: 87-124
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Keywords: pattern recognition ; unsupervised learning ; cluster analysis ; Density-based clustering ; Self-Organizing Maps ; 04.04. Geology ; 04.06. Seismology ; 04.07. Tectonophysics ; 04.08. Volcanology ; 05.04. Instrumentation and techniques of general interest
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: book chapter
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  • 5
    Publication Date: 2021-02-01
    Description: This chapter demonstrates how Unsupervised Learning can be applied in Geophysics. It starts with an example of clustering seismic spectra obtained on Stromboli volcano. K-means clustering as well as clustering using the Adaptive Criterion are applied. The latter criterion is preferred as it better matches the statistical characteristics of the data. Clusters show close relation to the state of volcanic activity. Density based clustering reveals groups whose hulls can be of irregular shape. This makes the method attractive, among others, for the identification of structural elements in geology, which often do not have a simple geometry. An example application is discussed considering the distribution of earthquake locations on Mt Etna, which clearly evidence structures already identified by other, independent evidences. Using SOM we aim at data reduction and effective graphical visualization. In an example for climate data we demonstrate the application of SOM for zoning purposes. Besides, the temporal evolution of spectral seismic data recorded on Mt Etna can be effectively monitored using SOM. We further illustrate the use of SOM for directional data, which can be handled best using a toroidal sheet geometry. We discuss this using a data set of seismic moment tensors of Mediterranean earthquakes.
    Description: Published
    Description: 189-234
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Keywords: pattern recognition ; unsupervised learning ; Density based clustering ; Stromboli ; earthquakes ; volcanic activity ; structural data ; seismic moment tensors ; 04.04. Geology ; 04.06. Seismology ; 04.07. Tectonophysics ; 04.08. Volcanology ; 05.04. Instrumentation and techniques of general interest
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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  • 6
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    Elsevier B.V.
    Publication Date: 2021-02-01
    Description: In this chapter, we present scripts and programs that accompany this book. Five MATLAB scripts regard simple examples related to supervised learning, that is, linear discrimination, the perceptron, support vector machines, and hidden Markov models. Seven scripts are devoted to unsupervised learning, such as K-means and fuzzy clustering, agglomerative clustering, density-based clustering, and clustering of patterns where features are correlated. These scripts provide a starting point for the reader, who can adjust and modify the codes with respect to proper needs. Besides, we provide sources and executables of programs that can be readily applied to larger and more complex datasets. These programs regard supervised learning using multilayerperceptron and support vector machines. KKAnalysis is a toolbox for unsupervised learning and offers various options of clustering and the use of self-organizing maps. The programs offer graphical user interfaces (GUI) to facilitate their use and create both graphical and alphanumeric output that can be used in further processing steps. The programs come along with real-world datasets that are also discussed in the example applications presented in various chapters of the book. Other propaedeutic material can be found in a folder called “miscellaneous.”
    Description: Published
    Description: 261-313
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Keywords: pattern recognition ; software manuals ; MATLAB scripts ; 04.04. Geology ; 04.06. Seismology ; 04.07. Tectonophysics ; 04.08. Volcanology ; 05.04. Instrumentation and techniques of general interest
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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  • 7
    Publication Date: 2021-02-15
    Description: This chapter presents applications of supervised learning in various geophysical disciplines, being them seismology, geodesy, magnetism, and others. For all examples, we provide a brief introduction to the geophysical background. Practical aspects, such as normalization issues and feature selection, are discussed. A posteriori considerations shed light on the geophysical problem, such as the importance of model parameters in regression, the possible nonuniqueness in inversion, and flaws in the definition of targets. We demonstrate multilayer perceptrons (MLPs) as classifiers of seismic waveforms. Besides, we show how the use of MLP is straightforward in the context of inversion of various kinds of data, for example, seismic, geodetic, and magnetic. Regression with MLP is applied to magnetotelluric and seismic data. Multiclass classification with support vector machine (SVM) is discussed for infrasound waveforms and volcanic rocks using geochemical characteristics. We introduce the use of SVM in the context of regression, which is formally less immediate than for MLP, but yields good results. An example deals with empirical ground motion estimation during earthquakes. In hidden Markov models and Bayesian networks one considers the interrelation between observations rather than single patterns. We show their benefits in various applications, from seismic waveform classification aimed at the forecast of volcanic unrest up to their use in tsunami early-warning systems.
    Description: Published
    Description: 127-187
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Keywords: pattern recognition ; supervised learning ; multilayer perceptrons ; seismic data ; magnetotelluric data ; infrasound waveforms ; volcanic rocks ; geochemical characteristics ; 04.04. Geology ; 04.06. Seismology ; 04.07. Tectonophysics ; 04.08. Volcanology ; 05.04. Instrumentation and techniques of general interest
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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  • 8
    Publication Date: 2022-05-25
    Description: This paper is not subject to U.S. copyright. The definitive version was published in Continental Shelf Research 42 (2012): 51–63, doi:10.1016/j.csr.2012.05.001.
    Description: Long Bay is a sediment-starved, arcuate embayment located along the US East Coast connecting both South and North Carolina. In this region the rates and pathways of sediment transport are important because they determine the availability of sediments for beach nourishment, seafloor habitat, and navigation. The impact of storms on sediment transport magnitude and direction were investigated during the period October 2003–April 2004 using bottom mounted flow meters, acoustic backscatter sensors and rotary sonars deployed at eight sites offshore of Myrtle Beach, SC, to measure currents, water levels, surface waves, salinity, temperature, suspended sediment concentrations, and bedform morphology. Measurements identify that sediment mobility is caused by waves and wind driven currents from three predominant types of storm patterns that pass through this region: (1) cold fronts, (2) warm fronts and (3) low-pressure storms. The passage of a cold front is accompanied by a rapid change in wind direction from primarily northeastward to southwestward. The passage of a warm front is accompanied by an opposite change in wind direction from mainly southwestward to northeastward. Low-pressure systems passing offshore are accompanied by a change in wind direction from southwestward to southeastward as the offshore storm moves from south to north. During the passage of cold fronts more sediment is transported when winds are northeastward and directed onshore than when the winds are directed offshore, creating a net sediment flux to the north–east. Likewise, even though the warm front has an opposite wind pattern, net sediment flux is typically to the north–east due to the larger fetch when the winds are northeastward and directed onshore. During the passage of low-pressure systems strong winds, waves, and currents to the south are sustained creating a net sediment flux southwestward. During the 3-month deployment a total of 8 cold fronts, 10 warm fronts, and 10 low-pressure systems drove a net sediment flux southwestward. Analysis of a 12-year data record from a local buoy shows an average of 41 cold fronts, 32 warm fronts, and 26 low-pressure systems per year. The culmination of these events would yield a cumulative net inner-continental shelf transport to the south–west, a trend that is further verified by sediment textural analysis and bedform morphology on the inner-continental shelf.
    Description: This research was funded by the South Carolina Coastal Erosion Project(http://pubs.usgs.gov/fs/2005/3041/), a cooperative study supported by the US Geological Survey and the South Carolina Sea Grant Consortium(Sea Grant Project no:R/CP-11).
    Keywords: Sediment transport ; Long Bay ; South Carolina ; Storm fronts
    Repository Name: Woods Hole Open Access Server
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  • 9
    Publication Date: 2022-05-25
    Description: This paper is not subject to U.S. copyright. The definitive version was published in Estuarine, Coastal and Shelf Science 93 (2011): 142-150, doi:10.1016/j.ecss.2011.04.004.
    Description: Simulations of estuarine bathymetric change over decadal timescales require methods for idealization and reduction of forcing data and boundary conditions. Continuous simulations are hampered by computational and data limitations and results are rarely evaluated with observed bathymetric change data. Bathymetric change data for Suisun Bay, California span the 1867–1990 period with five bathymetric surveys during that period. The four periods of bathymetric change were modeled using a coupled hydrodynamic-sediment transport model operated at the tidal-timescale. The efficacy of idealization techniques was investigated by discontinuously simulating the four periods. The 1867–1887 period, used for calibration of wave energy and sediment parameters, was modeled with an average error of 37% while the remaining periods were modeled with error ranging from 23% to 121%. Variation in post-calibration performance is attributed to temporally variable sediment parameters and lack of bathymetric and configuration data for portions of Suisun Bay and the Delta. Modifying seaward sediment delivery and bed composition resulted in large performance increases for post-calibration periods suggesting that continuous simulation with constant parameters is unrealistic. Idealization techniques which accelerate morphological change should therefore be used with caution in estuaries where parameters may change on sub-decadal timescales. This study highlights the utility and shortcomings of estuarine geomorphic models for estimating past changes in forcing mechanisms such as sediment supply and bed composition. The results further stress the inherent difficulty of simulating estuarine changes over decadal timescales due to changes in configuration, benthic composition, and anthropogenic forcing such as dredging and channelization.
    Description: This study was supported by the U.S Geological Survey’s Priority Ecosystems Science program, CALFED Bay/Delta Program, and the University of California Center forWater Resources.
    Keywords: Estuarine geomorphology ; Sediment transport ; Modeling ; Hindcasting
    Repository Name: Woods Hole Open Access Server
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  • 10
    Publication Date: 2022-05-25
    Description: This paper is not subject to U.S. copyright. The definitive version was published in Ocean Modelling 35 (2010): 230-244, doi:10.1016/j.ocemod.2010.07.010.
    Description: Understanding the processes responsible for coastal change is important for managing our coastal resources, both natural and economic. The current scientific understanding of coastal sediment transport and geology suggests that examining coastal processes at regional scales can lead to significant insight into how the coastal zone evolves. To better identify the significant processes affecting our coastlines and how those processes create coastal change we developed a Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) Modeling System, which is comprised of the Model Coupling Toolkit to exchange data fields between the ocean model ROMS, the atmosphere model WRF, the wave model SWAN, and the sediment capabilities of the Community Sediment Transport Model. This formulation builds upon previous developments by coupling the atmospheric model to the ocean and wave models, providing one-way grid refinement in the ocean model, one-way grid refinement in the wave model, and coupling on refined levels. Herein we describe the modeling components and the data fields exchanged. The modeling system is used to identify model sensitivity by exchanging prognostic variable fields between different model components during an application to simulate Hurricane Isabel during September 2003. Results identify that hurricane intensity is extremely sensitive to sea surface temperature. Intensity is reduced when coupled to the ocean model although the coupling provides a more realistic simulation of the sea surface temperature. Coupling of the ocean to the atmosphere also results in decreased boundary layer stress and coupling of the waves to the atmosphere results in increased bottom stress. Wave results are sensitive to both ocean and atmospheric coupling due to wave–current interactions with the ocean and wave growth from the atmosphere wind stress. Sediment resuspension at regional scale during the hurricane is controlled by shelf width and wave propagation during hurricane approach.
    Keywords: Coupled models ; ROMS ; SWAN ; WRF ; Sediment transport
    Repository Name: Woods Hole Open Access Server
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  • 11
    Publication Date: 2022-05-25
    Description: This paper is not subject to U.S. copyright. The definitive version was published in Continental Shelf Research 30 (2010): 761-780, doi:10.1016/j.csr.2010.01.011.
    Description: Sediment transport and the potential for erosion or deposition have been investigated on the Palos Verdes (PV) and San Pedro shelves in southern California to help assess the fate of an effluent-affected deposit contaminated with DDT and PCBs. Bottom boundary layer measurements at two 60-m sites in spring 2004 were used to set model parameters and evaluate a one-dimensional (vertical) model of local, steady-state resuspension, and suspended-sediment transport. The model demonstrated skill (Brier scores up to 0.75) reproducing the magnitudes of bottom shear stress, current speeds, and suspended-sediment concentrations measured during an April transport event, but the model tended to underpredict observed rotation in the bottom-boundary layer, possibly because the model did not account for the effects of temperature–salinity stratification. The model was run with wave input estimated from a nearby buoy and current input from four to six years of measurements at thirteen sites on the 35- and 65-m isobaths on the PV and San Pedro shelves. Sediment characteristics and erodibility were based on gentle wet-sieve analysis and erosion-chamber measurements. Modeled flow and sediment transport were mostly alongshelf toward the northwest on the PV shelf with a significant offshore component. The 95th percentile of bottom shear stresses ranged from 0.09 to 0.16 Pa at the 65-m sites, and the lowest values were in the middle of the PV shelf, near the Whites Point sewage outfalls where the effluent-affected layer is thickest. Long-term mean transport rates varied from 0.9 to 4.8 metric tons m−1 yr−1 along the 65-m isobaths on the PV shelf, and were much higher at the 35-m sites. Gradients in modeled alongshore transport rates suggest that, in the absence of a supply of sediment from the outfalls or PV coast, erosion at rates of not, vert, similar0.2 mm yr−1 might occur in the region southeast of the outfalls. These rates are small compared to some estimates of background natural sedimentation rates (not, vert, similar5 mm yr−1), but do not preclude higher localized rates near abrupt transitions in sediment characteristics. However, low particle settling velocities and strong currents result in transport length-scales that are long relative to the narrow width of the PV shelf, which combined with the significant offshore component in transport, means that transport of resuspended sediment towards deep water is as likely as transport along the axis of the effluent-affected deposit.
    Description: These studies were funded by the US Environmental Protection Agency Palos Verdes Super fund remediation project
    Keywords: Sediment transport ; Erodibility ; DDT ; PCBs ; Numerical model ; USA ; California ; Palos Verdes
    Repository Name: Woods Hole Open Access Server
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  • 12
    Publication Date: 2022-05-25
    Description: This paper is not subject to U.S. copyright. The definitive version was published in Ocean Modelling 33 (2010): 299-313, doi:10.1016/j.ocemod.2010.03.003.
    Description: A variety of algorithms are available for parameterizing the hydrodynamic bottom roughness associated with grain size, saltation, bedforms, and wave–current interaction in coastal ocean models. These parameterizations give rise to spatially and temporally variable bottom-drag coefficients that ostensibly provide better representations of physical processes than uniform and constant coefficients. However, few studies have been performed to determine whether improved representation of these variable bottom roughness components translates into measurable improvements in model skill. We test the hypothesis that improved representation of variable bottom roughness improves performance with respect to near-bed circulation, bottom stresses, or turbulence dissipation. The inner shelf south of Martha’s Vineyard, Massachusetts, is the site of sorted grain-size features which exhibit sharp alongshore variations in grain size and ripple geometry over gentle bathymetric relief; this area provides a suitable testing ground for roughness parameterizations. We first establish the skill of a nested regional model for currents, waves, stresses, and turbulent quantities using a uniform and constant roughness; we then gauge model skill with various parameterization of roughness, which account for the influence of the wave-boundary layer, grain size, saltation, and rippled bedforms. We find that commonly used representations of ripple-induced roughness, when combined with a wave–current interaction routine, do not significantly improve skill for circulation, and significantly decrease skill with respect to stresses and turbulence dissipation. Ripple orientation with respect to dominant currents and ripple shape may be responsible for complicating a straightforward estimate of the roughness contribution from ripples. In addition, sediment-induced stratification may be responsible for lower stresses than predicted by the wave–current interaction model.
    Description: Funding was provided through the Office of Naval Research Ripples DRI and U.S. Geological Survey Coastal and Marine Geology Program.
    Keywords: Sediment transport ; Roughness ; Bottom-boundary layer ; Model skill
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  • 13
    Publication Date: 2022-05-25
    Description: This paper is not subject to U.S. copyright. The definitive version was published in Continental Shelf Research 28 (2008): 257-282, doi:10.1016/j.csr.2007.08.008.
    Description: Massachusetts Bay is a semi-enclosed embayment in the western Gulf of Maine about 50 km wide and 100 km long. Bottom sediment resuspension is controlled predominately by storm-induced surface waves and transport by the tidal- and wind-driven circulation. Because the Bay is open to the northeast, winds from the northeast (‘Northeasters’) generate the largest surface waves and are thus the most effective in resuspending sediments. The three-dimensional oceanographic circulation model Regional Ocean Modeling System (ROMS) is used to explore the resuspension, transport, and deposition of sediment caused by Northeasters. The model transports multiple sediment classes and tracks the evolution of a multilevel sediment bed. The surficial sediment characteristics of the bed are coupled to one of several bottom-boundary layer modules that calculate enhanced bottom roughness due to wave–current interaction. The wave field is calculated from the model Simulating WAves Nearshore (SWAN). Two idealized simulations were carried out to explore the effects of Northeasters on the transport and fate of sediments. In one simulation, an initially spatially uniform bed of mixed sediments exposed to a series of Northeasters evolved to a pattern similar to the existing surficial sediment distribution. A second set of simulations explored sediment-transport pathways caused by storms with winds from the northeast quadrant by simulating release of sediment at selected locations. Storms with winds from the north cause transport southward along the western shore of Massachusetts Bay, while storms with winds from the east and southeast drive northerly nearshore flow. The simulations show that Northeasters can effectively transport sediments from Boston Harbor and the area offshore of the harbor to the southeast into Cape Cod Bay and offshore into Stellwagen Basin. This transport pattern is consistent with Boston Harbor as the source of silver found in the surficial sediments of Cape Cod Bay and Stellwagen Basin.
    Description: We gratefully acknowledge support from the USGS Mendenhall Post-Doctoral Research Program for John C. Warner.
    Keywords: Sediment transport ; Three-dimensional numerical model ; Storms ; Northeasters ; Multiple grain sizes ; USA ; Gulf of Maine ; Massachusetts Bay
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  • 14
    Publication Date: 2022-05-25
    Description: This paper is not subject to U.S. copyright. The definitive version was published in Continental Shelf Research 26 (2006): 2029-2049, doi:10.1016/j.csr.2006.07.022.
    Description: A field experiment was carried out in Massachusetts Bay in August 1998 to assess the role of large-amplitude internal waves (LIWs) in resuspending bottom sediments. The field experiment consisted of a four-element moored array extending from just west of Stellwagen Bank (90-m water depth) across Stellwagen Basin (85- and 50-m water depth) to the coast (24-m water depth). The LIWs were observed in packets of 5–10 waves, had periods of 5–10 min and wavelengths of 200–400 m, and caused downward excursions of the thermocline of as much as 30 m. At the 85-m site, the current measured 1 m above bottom (mab) typically increased from near 0 to 0.2 m/s offshore in a few minutes upon arrival of the LIWs. At the 50-m site, the near-bottom offshore flow measured 6 mab increased from about 0.1 to 0.4–0.6 m/s upon arrival of the LIWs and remained offshore in the bottom layer for 1–2 h. The near-bottom currents associated with the LIWs, in concert with the tidal currents, were directed offshore and sufficient to resuspend the bottom sediments at both the 50- and 85-m sites. When LIWs are present, they may resuspend sediments for as long as 5 hours each tidal cycle as they travel westward across Stellwagen Basin. At 85-m water depth, resuspension associated with LIWs is estimated to occur for about 0.4 days each summer, about the same amount of time as caused by surface waves.
    Description: MBIWE98 was supported by the USGS and the Office of Naval Research (ONR). The long-term observations at LT-A and LT-B were conducted under a Joint Funding Agreement between the USGS and the Massachusetts Water Resources Authority and an Inter-Service Agreement with the US Coast Guard. A. Scotti received support from the WHOI Postdoctoral Scholar program, the Johnson Foundation, the USGS, and ONR through grant N00014-01-1-0172; R. Beardsley through ONR grants N00014-98-1-0059, N00014-00-1-0210 and the WHOI Smith Chair in Coastal Physical Oceanography; and S. Anderson through ONR grant N000140-97-1-0158.
    Keywords: Internal waves ; Sediment transport ; Massachusetts Bay ; Stellwagen Bank
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  • 15
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    Am. Geophys. Union & Geol. Soc. Am.
    In:  Bull., Polar Proj. OP-O3A4, Dynamics of Passive Margins, Berlin, Am. Geophys. Union & Geol. Soc. Am., vol. 24, no. 4, pp. 159-165, (ISBN 0080419208)
    Publication Date: 1982
    Keywords: Geothermics ; Tectonics ; Crustal deformation (cf. Earthquake precursor: deformation or strain) ; Plate tectonics
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  • 16
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    Am. Geophys. Union & Geol. Soc. Am.
    In:  Bull., Open-File Rept., Dynamics of Passive Margins, Washington, D.C., Am. Geophys. Union & Geol. Soc. Am., vol. 24, no. 16, pp. 184-196, (ISBN 1-86239-165-3, vi + 330 pp.)
    Publication Date: 1982
    Keywords: Plate tectonics ; passive ; margins ; Crustal deformation (cf. Earthquake precursor: deformation or strain) ; Geol. aspects
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  • 17
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    Unknown
    Am. Geophys. Union & Geol. Soc. Am.
    In:  Washington, Am. Geophys. Union & Geol. Soc. Am., vol. 1, pp. 6322, (ISBN 0-521-79203-7)
    Publication Date: 1980
    Keywords: Plate tectonics ; Dynamic ; Stress ; Tectonics ; Handbook of geophysics ; Geol. aspects ; CRUST ; Crustal deformation (cf. Earthquake precursor: deformation or strain) ; Gravimetry, Gravitation
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  • 18
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    Unknown
    Am. Geophys. Un. & Geol. Soc. Am.
    In:  Professional Paper, Dynamics of Plate Interiors, Boulder, Colorado, Am. Geophys. Un. & Geol. Soc. Am., vol. 1, no. 16, pp. 155-162, (ISBN 0080419208)
    Publication Date: 1980
    Keywords: Crustal deformation (cf. Earthquake precursor: deformation or strain) ; Geodesy ; Instruments
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  • 19
    facet.materialart.
    Unknown
    Am. Geophys. Un. & Geol. Soc. Am.
    In:  Bull., Polar Proj. OP-O3A4, Dynamics of Plate Interiors, Boulder, Colorado, Am. Geophys. Un. & Geol. Soc. Am., vol. 1, no. 4, pp. 99-110, (ISBN 0080419208)
    Publication Date: 1980
    Keywords: Tectonics ; Modelling ; Crustal deformation (cf. Earthquake precursor: deformation or strain)
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  • 20
    facet.materialart.
    Unknown
    Am. Geophys. Un. & Geol. Soc. Am.
    In:  Dynamics of Plate Interiors, Boulder, Colorado, Am. Geophys. Un. & Geol. Soc. Am., vol. 1, no. 16, pp. 5-20, (ISBN 0080419208)
    Publication Date: 1980
    Keywords: Review article ; rifting ; Crustal deformation (cf. Earthquake precursor: deformation or strain)
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  • 21
    facet.materialart.
    Unknown
    Am. Geophys. Un. & Geol. Soc. Am.
    In:  Professional Paper, Dynamics of Plate Interiors, Boulder, Colorado, Am. Geophys. Un. & Geol. Soc. Am., vol. 1, no. 16, pp. 27-36, (ISBN 0080419208)
    Publication Date: 1980
    Keywords: Crustal deformation (cf. Earthquake precursor: deformation or strain) ; Plate tectonics ; Elasticity
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