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In this paper, we describe the scientific basis for the source and ground-motion models applied in the 2008 National Seismic Hazard Maps, the development of new products that are used for building design and risk analyses, relationships between the hazard maps and design maps used in building codes, and potential future improvements to the hazard maps.
The practice of geologic mapping is undergoing conceptual and methodological transformation. Profound changes in digital technology in the past 10 yr have potential to impact all aspects of geologic mapping. The future of geologic mapping as a relevant scientific enterprise depends on widespread adoption of new technology and ideas about the collection, meaning, and utility of geologic map data. It is critical that the geologic community redefine the primary elements of the traditional paper geologic map and improve the integration of the practice of making maps in the field and office with the new ways to record, manage, share, and visualize their underlying data. A modern digital geologic mapping model will enhance scientific discovery, meet elevated expectations of modern geologic map users, and accommodate inevitable future changes in technology.
This paper is a guide to the X-ray examination of clay minerals; it incorporates background information concerning the principal crystallographic features of clay minerals, and how this is used in the X-ray identification of these minerals, together with laboratory techniques and the application of X-ray diffractometry to the diagnosis of the clay minerals in natural sedimentary materials.
The inorganic geochemistry of three domed ombrogenous peat deposits in Riau and West Kalimantan provinces, Indonesia, was investigated as a possible modern analogue for certain types of low-ash, low-sulfur coal. Mineral matter entering the deposits is apparently limited to small amounts from the allogenic sources of dryfall, rainfall, and diffusion from substrate pore water. In the low-ash peat in the interior of the deposits, a large portion of the mineral matter is authigenic and has been mobilized and stabilized by hydrological, chemical, and biological processes and conditions. Ash yield and sulfur content are low through most of the peat deposits and average 1.1% and 0.14%, respectively, on a moisture-free basis. Ash and sulfur contents only exceed 5% and 0.3%, respectively, near the base of the deposits, with maximum concentrations of 19.9% ash and 0.56% sulfur. Peat water in all three deposits has a low pH, about 4 units, and low dissolved cation concentration, averaging 14 ppm. Near the base, in the geographic interior of each peat deposit, pH is about two units higher and dissolved cation concentration averages 110 ppm. Relative concentrations of the inorganic constituents vary, resulting in chemical facies in the peat. In general, Si, Al, and Fe are the abundant inorganic constituents, although Mg, Ca, and Na dominate in the middle horizon in the geographic interior of coastal peat deposits. The composition of the three deposits reported in this paper indicates that domed ombrogenous peat deposits will result in low ash and sulfur coal, probably less than 10% ash and 1% sulfur, even if marine rocks are laterally and vertically adjacent to the coal.
Recent sedimentation patterns in the central Sumatra basin, Republic of Indonesia, may help to explain the cyclic stratigraphy of the Pennsylvanian System of the eastern United States. Modern influx of fluvial siliciclastic sediment to the epeiric seas of the Sunda shelf, including the Strait of Malacca, appears to be highly restricted by rain forest cover within the ever-wet climate belt of equatorial Sumatra. As a result, much of the marine and estuarine environments appear to be erosional or nondepositional except for localized deposition of sediment in slack water areas, such as the down-stream end of islands. Contemporaneously, thick (>13 m), laterally extensive (>70,000 km 2 ), peat deposits are forming on poorly drained coastal lowlands. Modern peat formation in this study, therefore, is not coeval with aggrading fluvial siliciclastic systems, a situation that commonly is assumed in many depositional models of coal formation. The stratigraphy of Pleistocene and Holocene sediments on the Sunda shelf, as well as those of the Pennsylvanian System, appears to be better explained by the allocyclic controls of climate and sea-level change on sediment flux rather than by depositional models that are based on autocyclic processes. The objective of this paper is to evaluate allocyclic and autocyclic controls on sedimentation in an epeiric setting in a humid (ever-wet) tropical region. Of particular interest are the factors that control peat formation and siliciclastic sediment flux in rivers, estuaries, and open marine environments.
40 Ar/ 39 Ar plateau age spectra of seven sanidine samples from the Fire Clay tonstein (Middle Pennsylvanian), collected along a 300-km traverse in the Appalachian basin, range from 310.3 to 311.4 Ma. All plateau ages agree, within the limits of analytical precision, with their respective total gas ages. This agreement, together with the reproducibility between samples, suggests the analyzed samples did not contain any significant contaminant feldspar. The mean of these seven plateau ages, 310.9 ± 0.8 Ma, is interpreted to represent a precise numerical estimate of time of eruption and deposition of this tonstein and the coal bed in which it is found. The lack of any discernible difference between the age of two samples of the Fire Clay tonstein collected from east of the Pine Mountain thrust fault, along with the age of five samples from west of this fault, suggests that the Fire Clay tonstein has been reliably correlated with a tonstein on the Cumberland overthrust sheet. This correlation, together with the age data presented in this paper, indicates that the Pine Mountain thrust fault must be younger than the 310.9-Ma age obtained for the Fire Clay tonstein. The Fire Clay tonstein is biostratigraphically correlated with the Trace Creek Shale Member of the Atoka Formation in the Midcontinent of North America and with a position near the Westphalian B-C boundary in Western Europe. Our age of 310.9 ± 0.8 Ma for the Westphalian B-C boundary represents a well-constrained point, useful for the numerical refinement of the geologic time scale.
During the past thirty years the development of improved methods of analysis, especially optical spectrography, x-ray spectrography, and colorimetric methods, has resulted in a wealth of new data on the abundances of many elements. Nevertheless, present estimates differ greatly in reliability; some, such as the estimate for bismuth, are based on very few actual determinations, whereas others, such as that for gallium, are based on analyses of many samples. This paper reviews the estimates of abundance for the elements bismuth, germanium, gallium, and yttrium, taken as examples to illustrate the wide variation in reliability. For gallium and yttrium, a study has been made of spectrographic analyses by four laboratories of more than 600 chemically analyzed rocks from many areas of the world. Comparison shows that these furnish valuable data on abundance, although attention must be directed to regional variation. The comparison also shows the need for continued interlaboratory standardization. The estimate for bismuth is based on so few determinations that no appraisal of its accuracy can be made. The estimate for germanium is based on very few determinations but is almost certainly of the right order of magnitude, though perhaps slightly too high. The estimates for gallium and yttrium, based on many determinations, are almost certainly of the right order of magnitude, though that for gallium may be slightly low and that for yttrium may be substantially low.
This paper proposes a physics-guided machine learning approach that combines machine learning models and physics-based models to improve the prediction of water flow and temperature in river networks. We first build a recurrent graph network model to capture the interactions among multiple segments in the river network. Then we transfer knowledge from physics-based models to guide the learning of the machine learning model. We also propose a new loss function that balances the performance over different river segments. We demonstrate the effectiveness of the proposed method in predicting temperature and streamflow in a subset of the Delaware River Basin. In particular, the proposed method has brought a 33%/14% accuracy improvement over the state-of-the-art physics-based model and 24%/14% over traditional machine learning models (e.g., LSTM) in temperature/streamflow prediction using very sparse (0.1%) training data. The proposed method has also been shown to produce better performance when generalized to different seasons or river segments with different streamflow ranges.
Effective training of advanced ML models requires large amounts of labeled data, which is often scarce in scientific problems given the substantial human labor and material cost to collect labeled data. This poses a challenge on determining when and where we should deploy measuring instruments (e.g., in-situ sensors) to collect labeled data efficiently. This problem differs from traditional pool-based active learning settings in that the labeling decisions have to be made immediately after we observe the input data that come in a time series. In this paper, we develop a real-time active learning method that uses the spatial and temporal contextual information to select representative query samples in a reinforcement learning framework. To reduce the need for large training data, we further propose to transfer the policy learned from simulation data which is generated by existing physics-based models. We demonstrate the effectiveness of the proposed method by predicting streamflow and water temperature in the Delaware River Basin given a limited budget for collecting labeled data. We further study the spatial and temporal distribution of selected samples to verify the ability of this method in selecting informative samples over space and time.
Accurate predictions of water temperature are the foundation for many decisions and regulations, with direct impacts on water quality, fishery yields, and power production. Building accurate broad-scale models for lake temperature prediction remains challenging in practice due to the variability in the data distribution across different lake systems monitored by static and time-series data. In this paper, to tackle the above challenges, we propose a novel machine learning based approach for integrating static and time-series data in deep recurrent models, which we call Invertibility-Aware-Long Short-Term Memory(IA-LSTM), and demonstrate its effectiveness in predicting lake temperature. Our proposed method integrates components of the Invertible Network and LSTM to better predict temperature profiles (forward modeling) and infer the static features (i.e., inverse modeling) that can eventually enhance the prediction when static variables are missing. We evaluate our method on predicting the temperature profile of 450 lakes in the Midwestern U.S. and report relative improvement of 4% to capture data heterogeneity and simultaneously outperform baseline predictions by 12% when static features are unavailable.
Surge and wave forcing from Hurricane Matthew caused a breach south of Matanzas Inlet (FL, USA) on a complex barrier island, including sandy dunes, hard structures (residential buildings and a highway), wetlands, and the US Intracoastal Waterway. In this paper, the skill of the XBeach model to predict hurricane-induced barrier island overwash, dune erosion, and breaching is demonstrated. The location of the breach is predicted correctly if bottom roughness based on land cover is used to calculate bed shear stresses. While the dunes are initially lowered by wave attack and surge from the ocean side, the main driver for breach formation is the water level difference between the back-barrier and nearshore, causing an ocean-directed outflow of water after the peak of the storm.
The DUring Nearshore Event eXperiment (DUNEX) is an academic, federal agency, and non-government collaborative community experiment supported by and planned under the U.S. Coastal Research Program (USCRP) to study nearshore coastal processes during storms. The motivation for DUNEX is to improve understanding, numerical representation, and prediction of storm processes and impacts; to exchange research ideas, to improve strategies for short- and long-term coastal resilience, and to develop more effective communication methods for coastal communities impacted by storms. This paper describes the DUNEX pilot study (Fall 2019) and the full experiment (Fall 2020) focused on storm measurements to be conducted along a 160-km stretch of the northern Outer Banks, North Carolina. The US Army Corps of Engineer’s Field Research Facility (FRF) will serve as the logistical base for the operation. Researchers may participate by planning and conducting an experiment, attending or leading a training session, or volunteering to assist with field work.
Arctic barrier islands and spits are dynamic features influenced by a variety of oceanographic, geologic, and environmental factors. Many serve as habitat and protection for native species and shelter the coast from waves and storms that can flood and erode the adjacent mainland. This paper summarizes results of a study documenting changes to barrier morphology along the North Slope coast of Alaska between the United States-Canadian border and Cape Beaufort, from 1947 to 2020. Changes considered include number of barriers, area and perimeter, shoreline length, barrier sinuosity and width, presence and number of relict terminus features, presence and coverage of tundra vegetation, barrier orientation, termini migration rates, and elevation metrics. Wave conditions are also summarized and related to changes in barrier morphology. The results of this study help to better predict future barrier evolution and prevalence along Alaska’s coast by increasing our understanding of Arctic barrier development, migration, and degradation via the evaluation of historical morphometrics.
The U.S. Geological Survey (USGS) provides operational forecasts of total water levels (TWL) and coastal change. Uncertainties around forecast TWL are based on the temporal and spatial range of observed beach slopes near the forecast site. This paper investigates other sources of uncertainty that are not accounted for, focusing on four beaches where the USGS has deployed remote cameras, and on outer Cape Cod, which has diverse bar morphologies. We find that the range of runup indicated by ten formulae is nearly as large as the variations caused by the range of beach slopes. A formula that accounts for bar morphology substantially decreases calculated runup, and might improve forecasts. Errors in the timing of forecast storm landfall generate uncertainties in TWL where tides are large. Analyses suggest that the effect of off-normal incident waves is relatively small. These results suggest opportunities for improving the TWL forecasts.
Anthropogenic activities primarily impact coasts by reducing sediment inputs, altering sediment transport processes, and accelerating sediment losses to the offshore. These activities include: sand and gravel extraction, navigation and shore protection works; non-structural shoreline management strategies such as beach nourishment, sand by-passing and beach scraping, dams and flood control works; channel and inlet dredging; subsidence caused by fluid extraction and reduction of carbonate beach material. Although many of these activities have improved the quality of life, they also have had unintended effects on the coast. The issues that arise from human alterations of the coast are common to many coastal regions around the world; this paper draws from several areas of the United States to present an overview and provisional assessment of the economic consequences of anthropogenic activities along the Pacific coast.
Large bottom roughness is a characteristic of most coral reef environments and this has been shown to have a substantial impact on hydrodynamic processes in these environments. In this paper, we evaluate suspended sediment concentration (SSC) data as well detailed hydrodynamic data over a coral reef flat in Ningaloo Reef, Western Australia, to understand how this bottom roughness affects these processes. A well-developed logarithmic velocity layer consistently developed above a canopy layer during the experiment. Estimates of bottom stresses from these logarithmic profiles were comparable with estimates obtained directly from turbulent Reynolds stresses, and an order of magnitude greater than those typically reported for sandy beach environments having similar flow. Nevertheless, the sediment grain size distribution of the suspended load was very fine relative to what should be mobilized by these stresses, indicated the large roughness substantially suppressed sediment transport.
Development of a comprehensive and stakeholder-driven Regional Sediment Management plan can provide the basis for long-term sustainable resource use and protection. This paper highlights three operational components that can positively influence sediment management at a regional scale, including (1) integration of an operational sediment budget, (2) development of a monitoring and adaptive management plan, and (3) development of a regional sediment availability and allocation program. These components seek to incorporate science and adaptive management through implementation of an organized and well-documented decision making process. They represent a coordinated framework that could serve as a guide for unifying financial investments in regional sediment management plans. Collectively, they establish an integrated process for addressing uncertainties about future system change in light of shrinking federal and state budgets, competing demands for sediment resources within riverine and marine waters, and policy considerations related to sediment/water use (e.g., navigation and commerce versus environmental management).
In California, sea-level rise during the 21 st century threatens to accelerate coastal cliff recession rates. To forecast such changes for managers and policymakers, models must play a key role. In this paper, we extend a ~70-year long dataset of measured historic sea cliff retreat rates in Southern California into the 21 st century using a suite of simple analytical and empirical models. Ensemble results suggest that coastal cliff recession rates could increase on average by 0.09-0.22 m yr -1 for a 0.5-1.0 m rise in sea level by 2100, 27-67% faster than historical rates. The basic models used herein will serve as a baseline against which more complex, process-based and statistical (Bayesian) forecasts will be compared. The application of different models, with varying levels of detail, to the same geomorphic problem will provide a comprehensive forecast and address the question of how to reduce model complexity while minimizing uncertainty.