scholarly journals ENVIRONMENTAL CONTROLS ON LITTORAL SAND TRANSPORT

1988 ◽  
Vol 1 (21) ◽  
pp. 92 ◽  
Author(s):  
Paul D. Komar
Keyword(s):  
Environmental Factors ◽  
Field Data ◽  
Measurement Techniques ◽  
Breaking Waves ◽  
Total Power ◽  
Sand Transport ◽  
Longshore Current ◽  
Data Set ◽  
Environmental Controls ◽  
Gravel Beaches

Quantities of sand transported along beaches are generally related to the "longshore component of wave power", F^, through the proportionality is = KF£ where l8 is the immersed-weight sand transport rate and K is a dimensionless proportionality factor. A more-generally applicable relationship is that of Bagnold, ls = K'(ECn)bvL/um where (ECn)b is the energy flux or total power of the breaking waves, y^ is the longshore current, um is the mean orbital velocity under the waves, and K' is another dimensionless coefficient. It is apparent that sediment transport rates on beaches should depend on environmental factors such as the grain diameter or settling velocity, and possibly on factors such as the beach slope or wave steepness. However, examinations of such dependencies for K and K' within the field data are hampered by problems with large random scatter within any one data set, and by systematic differences between separate studies which have employed diverse measurement techniques. Examinations of the field data for K and K' variations indicate that meaningful dependencies on sediment grain diameters and other factors cannot be established with confidence in the sand-size range. Limited data available from gravel beaches support the expected decreases in K and K' with increasing grain sizes. These data are too few in numbers to establish firm trends, but do suggest that future investigations to establish dependencies on environmental factors would be most profitably undertaken on gravel beaches. The measurements collected in recent years from sand beaches suggest revisions in average K and K' coefficients to be used in transport evaluations, but such revisions must be coordinated such that K/K' = 2.7 so as to maintain agreement with the longshore current data.

scholarly journals TOWARD AN IMPROVED EMPIRICAL FORMULA FOR LONGSHORE SAND TRANSPORT

1988 ◽  
Vol 1 (21) ◽  
pp. 88 ◽  
Author(s):  
Nicholas C. Kraus ◽  
Kathryn J. Gingerich ◽  
Julie Dean Rosati
Keyword(s):  
Oscillatory Flow ◽  
Field Experiments ◽  
Surf Zone ◽  
Transport Rate ◽  
Sand Transport ◽  
Longshore Current ◽  
Wave Energy Dissipation ◽  
Total Transport ◽  
Local Wave ◽  
Correction Terms

This paper presents results of two field experiments performed using portable traps to obtain point measurements of the longshore sand transport rate in the surf zone. The magnitude of the transport rate per unit width of surf zone is found to depend on the product of the local wave height and mean longshore current speed, but correlation is much improved by including two correction terms, one accounting for local wave energy dissipation and the other for the fluctuation in the longshore current. The field transport rates are also found to be compatible with laboratory rates obtained under combined unidirectional and oscillatory flow. Total transport rates previously reported for this experiment program are revised with recently determined sand trapping efficiencies.

Adaptive sampling of potential-field data: A direct approach to compressive inversion

Geophysics ◽  
2014 ◽  
Vol 79 (1) ◽  
pp. IM1-IM9 ◽  
Author(s):  
Nathan Leon Foks ◽  
Richard Krahenbuhl ◽  
Yaoguo Li
Keyword(s):  
Potential Field ◽  
Adaptive Sampling ◽  
Field Data ◽  
Computational Cost ◽  
Large Field ◽  
Magnetic Data ◽  
Data Sampling ◽  
Data Types ◽  
Data Set ◽  
Potential Field Data

Compressive inversion uses computational algorithms that decrease the time and storage needs of a traditional inverse problem. Most compression approaches focus on the model domain, and very few, other than traditional downsampling focus on the data domain for potential-field applications. To further the compression in the data domain, a direct and practical approach to the adaptive downsampling of potential-field data for large inversion problems has been developed. The approach is formulated to significantly reduce the quantity of data in relatively smooth or quiet regions of the data set, while preserving the signal anomalies that contain the relevant target information. Two major benefits arise from this form of compressive inversion. First, because the approach compresses the problem in the data domain, it can be applied immediately without the addition of, or modification to, existing inversion software. Second, as most industry software use some form of model or sensitivity compression, the addition of this adaptive data sampling creates a complete compressive inversion methodology whereby the reduction of computational cost is achieved simultaneously in the model and data domains. We applied the method to a synthetic magnetic data set and two large field magnetic data sets; however, the method is also applicable to other data types. Our results showed that the relevant model information is maintained after inversion despite using 1%–5% of the data.

Building training data set for deep learning-based P- and S-wave separation: Field data case

2021 ◽  
Author(s):  
Yanwen Wei ◽  
Yunyue Elita Li ◽  
Haohuan Fu
Keyword(s):  
Deep Learning ◽  
Field Data ◽  
Training Data ◽  
S Wave ◽  
Data Set ◽  
Wave Separation ◽  
Separation Field

Distribution of Wave Impact Forces From Breaking and Non-Breaking Waves

2009 ◽  
Author(s):  
Anne M. Fullerton ◽  
Thomas C. Fu ◽  
Edward S. Ammeen
Keyword(s):  
Free Surface ◽  
Wave Height ◽  
Breaking Waves ◽  
Qualitative Assessment ◽  
Total Force ◽  
Impact Loads ◽  
Wave Impact ◽  
Data Set ◽  
Wave Characteristics ◽  
Wide Range

Impact loads from waves on vessels and coastal structures are highly complex and may involve wave breaking, making these changes difficult to estimate numerically or empirically. Results from previous experiments have shown a wide range of forces and pressures measured from breaking and non-breaking waves, with no clear trend between wave characteristics and the localized forces and pressures that they generate. In 2008, a canonical breaking wave impact data set was obtained at the Naval Surface Warfare Center, Carderock Division, by measuring the distribution of impact pressures of incident non-breaking and breaking waves on one face of a cube. The effects of wave height, wavelength, face orientation, face angle, and submergence depth were investigated. A limited number of runs were made at low forward speeds, ranging from about 0.5 to 2 knots (0.26 to 1.03 m/s). The measurement cube was outfitted with a removable instrumented plate measuring 1 ft2 (0.09 m2), and the wave heights tested ranged from 8–14 inches (20.3 to 35.6 cm). The instrumented plate had 9 slam panels of varying sizes made from polyvinyl chloride (PVC) and 11 pressure gages; this data was collected at 5 kHz to capture the dynamic response of the gages and panels and fully resolve the shapes of the impacts. A Kistler gage was used to measure the total force averaged over the cube face. A bottom mounted acoustic Doppler current profiler (ADCP) was used to obtain measurements of velocity through the water column to provide incoming velocity boundary conditions. A Light Detecting and Ranging (LiDAR) system was also used above the basin to obtain a surface mapping of the free surface over a distance of approximately 15 feet (4.6 m). Additional point measurements of the free surface were made using acoustic distance sensors. Standard and high-speed video cameras were used to capture a qualitative assessment of the impacts. Impact loads on the plate tend to increase with wave height, as well as with plate inclination toward incoming waves. Further trends of the pressures and forces with wave characteristics, cube orientation, draft and face angle are investigated and presented in this paper, and are also compared with previous test results.

scholarly journals Coupled hydrogeophysical parameter estimation using a sequential Bayesian approach

2010 ◽  
Vol 14 (3) ◽  
pp. 545-556 ◽  
Author(s):  
J. Rings ◽  
J. A. Huisman ◽  
H. Vereecken
Keyword(s):  
Water Content ◽  
Particle Filtering ◽  
Field Data ◽  
Synthetic Data ◽  
Time Domain Reflectometry ◽  
Second Step ◽  
Parameter Estimates ◽  
Posterior Distributions ◽  
Data Set ◽  
Resistivity Tomography

Abstract. Coupled hydrogeophysical methods infer hydrological and petrophysical parameters directly from geophysical measurements. Widespread methods do not explicitly recognize uncertainty in parameter estimates. Therefore, we apply a sequential Bayesian framework that provides updates of state, parameters and their uncertainty whenever measurements become available. We have coupled a hydrological and an electrical resistivity tomography (ERT) forward code in a particle filtering framework. First, we analyze a synthetic data set of lysimeter infiltration monitored with ERT. In a second step, we apply the approach to field data measured during an infiltration event on a full-scale dike model. For the synthetic data, the water content distribution and the hydraulic conductivity are accurately estimated after a few time steps. For the field data, hydraulic parameters are successfully estimated from water content measurements made with spatial time domain reflectometry and ERT, and the development of their posterior distributions is shown.

scholarly journals Normalized nonzero-lag crosscorrelation elastic full-waveform inversion

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. R1-R10 ◽  
Author(s):  
Zhendong Zhang ◽  
Tariq Alkhalifah ◽  
Zedong Wu ◽  
Yike Liu ◽  
Bin He ◽  
...  
Keyword(s):  
Field Data ◽  
Extreme Values ◽  
Time Lag ◽  
Waveform Inversion ◽  
Optimal Time ◽  
Full Waveform Inversion ◽  
Data Set ◽  
Velocity Models ◽  
Full Waveform ◽  
The Earth

Full-waveform inversion (FWI) is an attractive technique due to its ability to build high-resolution velocity models. Conventional amplitude-matching FWI approaches remain challenging because the simplified computational physics used does not fully represent all wave phenomena in the earth. Because the earth is attenuating, a sample-by-sample fitting of the amplitude may not be feasible in practice. We have developed a normalized nonzero-lag crosscorrelataion-based elastic FWI algorithm to maximize the similarity of the calculated and observed data. We use the first-order elastic-wave equation to simulate the propagation of seismic waves in the earth. Our proposed objective function emphasizes the matching of the phases of the events in the calculated and observed data, and thus, it is more immune to inaccuracies in the initial model and the difference between the true and modeled physics. The normalization term can compensate the energy loss in the far offsets because of geometric spreading and avoid a bias in estimation toward extreme values in the observed data. We develop a polynomial-type weighting function and evaluate an approach to determine the optimal time lag. We use a synthetic elastic Marmousi model and the BigSky field data set to verify the effectiveness of the proposed method. To suppress the short-wavelength artifacts in the estimated S-wave velocity and noise in the field data, we apply a Laplacian regularization and a total variation constraint on the synthetic and field data examples, respectively.

scholarly journals A review of biomass burning emissions, part II: Intensive physical properties of biomass burning particles

2004 ◽  
Vol 4 (5) ◽  
pp. 5135-5200 ◽  
Author(s):  
J. S. Reid ◽  
R. Koppmann ◽  
T. F. Eck ◽  
D. P. Eleuterio
Keyword(s):  
Particle Size ◽  
Biomass Burning ◽  
Measurement Techniques ◽  
Emission Factors ◽  
Future Research ◽  
Data Set ◽  
Particle Properties ◽  
In Situ Experiments ◽  
Literature Base ◽  
Biomass Burning Particles

Abstract. The last decade has seen tremendous advances in atmospheric aerosol particle research that is often performed in the context of climate and global change science. Biomass burning, one of the largest sources of accumulation mode particles globally, has been closely studied for its radiative, geochemical, and dynamic impacts. These studies have taken many forms including laboratory burns, in situ experiments, remote sensing, and modeling. While the differing perspectives of these studies have ultimately improved our qualitative understanding of biomass burning issues, the varied nature of the work make inter-comparisons and resolutions of some specific issues difficult. In short, the literature base has become a milieu of small pieces of the biomass-burning puzzle. This manuscript, the second part of four, examines the properties of biomass-burning particle emissions. Here we review and discuss the literature concerning the measurement of smoke particle size, chemistry, thermodynamic properties, and emission factors. Where appropriate, critiques of measurement techniques are presented. We show that very large differences in measured particle properties have appeared in the literature, in particular with regards to particle carbon budgets. We investigate emissions uncertainties using scale analyses, which shows that while emission factors for grass and brush are relatively well known, very large uncertainties still exist in emission factors of boreal, temperate and some tropical forests. Based on an uncertainty analysis of the community data set of biomass burning measurements, we present simplified models for particle size and emission factors. We close this review paper with a discussion of the community experimental data, point to lapses in the data set, and prioritize future research topics.

scholarly journals Evaluation of the snow regime in dynamic vegetation land surface models using field measurements

2013 ◽  
Vol 7 (3) ◽  
pp. 2333-2372
Author(s):  
E. Kantzas ◽  
M. Lomas ◽  
S. Quegan ◽  
E. Zakharova
Keyword(s):  
Land Surface ◽  
Field Data ◽  
Climate Models ◽  
Optimization Techniques ◽  
The Arctic ◽  
Surface Model ◽  
Land Surface Models ◽  
Snow Density ◽  
Data Set ◽  
Surface Models

Abstract. An increasing number of studies have demonstrated the significant climatic and ecological changes occurring in the northern latitudes over the past decades. As coupled, earth-system models attempt to describe and simulate the dynamics and complex feedbacks of the Arctic environment, it is important to reduce their uncertainties in short-term predictions by improving the description of both the systems processes and its initial state. This study focuses on snow-related variables and extensively utilizes a historical data set (1966–1996) of field snow measurements acquired across the extend of the Former Soviet Union (FSU) to evaluate a range of simulated snow metrics produced by a variety of land surface models, most of them embedded in IPCC-standard climate models. We reveal model-specific issues in simulating snow dynamics such as magnitude and timings of SWE as well as evolution of snow density. We further employ the field snow measurements alongside novel and model-independent methodologies to extract for the first time (i) a fresh snow density value (57–117 kg m–3) for the region and (ii) mean monthly snowpack sublimation estimates across a grassland-dominated western (November–February) [9.2, 6.1, 9.15, 15.25] mm and forested eastern sub-sector (November–March) [1.53, 1.52, 3.05, 3.80, 12.20] mm; we subsequently use the retrieved values to assess relevant model outputs. The discussion session consists of two parts. The first describes a sensitivity study where field data of snow depth and snow density are forced directly into the surface heat exchange formulation of a land surface model to evaluate how inaccuracies in simulating snow metrics affect important modeled variables and carbon fluxes such as soil temperature, thaw depth and soil carbon decomposition. The second part showcases how the field data can be assimilated with ready-available optimization techniques to pinpoint model issues and improve their performance.

scholarly journals A rapid deployment instrument network for temporarily monitoring volcanic SO<sub>2</sub> emissions – a study case from Telica volcano

2014 ◽  
Vol 4 (1) ◽  
pp. 191-211
Author(s):  
V. Conde ◽  
D. Nilsson ◽  
B. Galle ◽  
R. Cartagena ◽  
A. Muñoz
Keyword(s):  
Measurement Techniques ◽  
Active Volcano ◽  
Optical Absorption Spectroscopy ◽  
So2 Emissions ◽  
Volcanic Gas ◽  
Data Set ◽  
So2 Flux ◽  
Geophysical Processes ◽  
Rapid Deployment ◽  
Short Time

Abstract. Volcanic gas emissions play a crucial role in describing geophysical processes; hence measurements of magmatic gases such as SO2 can be used as tracers prior and during volcanic crises. Different measurement techniques based on optical spectroscopy have provided valuable information when assessing volcanic crises. This paper describes the design and implementation of a network of spectroscopic instruments based on Differential Optical Absorption Spectroscopy (DOAS) for remote sensing of volcanic SO2 emissions, which is robust, portable and can be deployed in relative short time. The setup allows the processing of raw data in situ even in remote areas with limited accessibility, and delivers pre-processed data to end-users in near real time even during periods of volcanic crisis, via a satellite link. In addition, the hardware can be used to conduct short term studies of volcanic plumes in remotes areas. The network was tested at Telica, an active volcano located in western Nicaragua, producing what is so far the largest data set of continuous SO2 flux measurements at this volcano.

scholarly journals Dynamics of the cyanobacterial water bloom with focus to Microcystis and its relationship with environmental factors in Brno reservoir

2013 ◽  
Vol 61 (5) ◽  
pp. 1383-1390
Author(s):  
Lucie Straková ◽  
Radovan Kopp ◽  
Eliška Maršálková ◽  
Blahoslav Maršálek
Keyword(s):  
Environmental Factors ◽  
Point Sources ◽  
Water Bloom ◽  
Data Set ◽  
New Information ◽  
Phytoplankton Communities ◽  
Management Effort ◽  
Phosphate Loading ◽  
Treatment Facilities

Our paper brings new information about long-term changes of the phytoplankton communities in the Brno reservoir with the focus on the Microcystis abundance using the semi-monthly monitoring data covering the period 2006–2012. The main aim is to extract from this long-term data set differences in number of Microcystis cells depending on environmental factors. The development of cyanobacteria in Brno reservoir is caused by excessive phosphate loading from wastewater treatment facilities upstream and from non–point sources along the Svratka river. It focuses management effort on upstream controls of reservoir condition. High abundance in millions of cyanobacteria cells in 1ml observed in Brno reservoir before was reduced to values in the order of thousands cells in 1ml in last two years through a combination of measures (liming, precipitation of phosphorus on inflow, aeration and destratification). Phytoplankton composition was also changed and at the expense of cyanobacteria promoted the development of green algae and diatoms.

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