Sparse Field Data Research Articles

Acoustic field visualization and source localization via physics-informed learning of sparse data with adaptive sampling

Traditional methods for acoustic field visualization require considerable effort for capturing large amounts of acoustic data to achieve a high resolution field map, highly limiting their widespread use. In this study, we propose an approach for acoustic field visualization based on physics-informed neural networks (PINNs) by using a small amount of data, subsequently realizing accurate acoustic source localization. First, we present a PINN model integrated with an acoustic Helmholtz equation and adaptive sampling, the performance of which is testified via numerical simulations. The “no mesh” character of PINN enables achieving high resolution acoustic field visualization without requiring the capture of numerous data in advance. Furthermore, we experimentally validate the performance of the proposed method, which demonstrates that the acoustic sources can be precisely localized with sparse field data acquisition within a small area. This work would find potential applications in various acoustics, such as acoustic communication, biomedical imaging, and virtual reality.

City Bus Reliability Measurement Based on Sparse Field Data Supported by Selected State Space Models

City Bus Reliability Measurement Based on Sparse Field Data Supported by Selected State Space Models

Multivariate copula-based framework for stochastic analysis of landslide runout distance

The increasing frequency of landslide disasters worldwide highlights the importance of accurate prediction of post-failure runout distances for effective risk management. However, the prediction of runout distances of gravitational mass flow remains challenging due to the inherent complex heterogeneities of geomaterials and their inter-correlated spatially varying mechanical properties. To address this challenge, this study proposes a novel multivariate stochastic method based on the combination of the Generalised Interpolation Material Point (GIMP) analysis and the multivariate copula-based approach. The method considers geotechnical uncertainties by simulating copula-based cross-correlated random fields from sparse field data and incorporating them into the GIMP analysis through Monte Carlo simulations (MCS). Two slope cases with similar geometries but different sources of probability information are presented to illustrate the effectiveness of the proposed method. The results show that considering the influence of multivariate random fields can significantly affect the post-failure analysis of landslides. Both slope cases show that over 40 % of all MCS samples exceed the deterministic case, which indicates that the current deterministic analysis notably underestimates the risk associated with large runout distances of landslides. This study highlights the necessity of considering the interdependency of spatial heterogeneity in post-failure modelling of landslides.

Investigating embedded data distribution strategy on reconstruction accuracy of flow field around the crosswind-affected train based on physics-informed neural networks

PurposePhysics-informed neural networks (PINNs) have become a new tendency in flow simulation, because of their self-advantage of integrating both physical and monitored information of fields in solving the Navier–Stokes equation and its variants. In view of the strengths of PINN, this study aims to investigate the impact of spatially embedded data distribution on the flow field results around the train in the crosswind environment reconstructed by PINN.Design/methodology/approachPINN can integrate data residuals with physical residuals into the loss function to train its parameters, allowing it to approximate the solution of the governing equations. In addition, with the aid of labelled training data, PINN can also incorporate the real site information of the flow field in model training. In light of this, the PINN model is adopted to reconstruct a two-dimensional time-averaged flow field around a train under crosswinds in the spatial domain with the aid of sparse flow field data, and the prediction results are compared with the reference results obtained from numerical modelling.FindingsThe prediction results from PINN results demonstrated a low discrepancy with those obtained from numerical simulations. The results of this study indicate that a threshold of the spatial embedded data density exists, in both the near wall and far wall areas on the train’s leeward side, as well as the near train surface area. In other words, a negative effect on the PINN reconstruction accuracy will emerge if the spatial embedded data density exceeds or slips below the threshold. Also, the optimum arrangement of the spatial embedded data in reconstructing the flow field of the train in crosswinds is obtained in this work.Originality/valueIn this work, a strategy of reconstructing the time-averaged flow field of the train under crosswind conditions is proposed based on the physics-informed data-driven method, which enhances the scope of neural network applications. In addition, for the flow field reconstruction, the effect of spatial embedded data arrangement in PINN is compared to improve its accuracy.

Turbulent Particle Flow in Spent Nuclear Fuel Storage Systems

Deposition models are being developed with the commercial computational fluid dynamics software STAR-CCM+ to evaluate particulate deposition on spent nuclear fuel (SNF) canisters. The primary particulate of concern is chloride salts, which are dispersed in the atmosphere and then deposited onto the canisters. During dry storage, the primary degradation process is likely to be chloride-induced stress corrosion cracking (CISCC) at the heat-affected zones of the canister welds. It is known that stainless steel canisters are susceptible to CISCC; however, the rate of chloride deposition onto the canisters is poorly known, based on sparse field data from a small number of sites. This paper describes work looking at various approaches to modeling turbulence, such as Reynolds-averaged Navier Stokes (RANS) and large eddy simulation (LES), and its impact on particle flow and deposition within a ventilated SNF storage system. The deposition rate is determined for a vertical canister system and a horizontal canister system. LES has the potential to simulate turbulent flows more accurately versus RANS, but is much more computationally expensive. A k-omega version of the RANS turbulence model was used for this study. The computational efficient RANS steady-state model predicted a similar canister deposition result as the LES simulation for a vertical canister storage system. For a horizontal storage system, the RANS steady-state model predicted more particles depositing on the canister than the LES simulation, providing a conservative estimation for particle deposition. A wall correction factor was added to the RANS model to dampen the turbulence fluctuation normal to a surface that left undamped, leads to the RANS model overpredicting deposition along a surface for smaller particles. This work was done to further development of deposition models that could be used to plan and inform SNF canister aging management programs with predictive models for the timing and occurrence of canister CISCC. This work is part of a larger effort tasked with understanding the likelihood of canister degradation due to CISCC. These models are still under development, and testing is needed for validation. However, these models are being presented now to demonstrate to canister vendors, utilities, regulators, and stakeholders the value of this type of modeling.

Degradation of ice-wedge polygons leads to increased fluxes of water and DOC

Ice-wedge polygon landscapes make up a substantial part of high-latitude permafrost landscapes. The hydrological conditions shape how these landscapes store and release organic carbon. However, their coupled water‑carbon dynamics are poorly understood as field measurements are sparse in smaller catchments and coupled hydrology-dissolved organic carbon (DOC) models are not tailored for these landscapes. Here we present a model that simulates the hydrology and associated DOC export of high-centered and low-centered ice-wedge polygons and apply the model to a small catchment with abundant polygon coverage along the Yukon Coast, Canada. The modeled seasonal pattern of water and carbon fluxes aligns with sparse field data. These modeled seasonal patterns indicate that early-season runoff is mostly surficial and generated by low-centered polygons and snow trapped in troughs of high-centered polygons. High-centered polygons show potential for deeper subsurface flow under future climate conditions. This suggests that high-centered polygons will be responsible for an increasing proportion of annual DOC export compared to low-centered polygons. Warming likely shifts low-centered polygons to high-centered polygons, and our model shows that this shift will cause a deepening of the active layer and a lengthening of the thawing season. This, in turn, intensifies seasonal runoff and DOC flux, mainly through its duration. Our model provides a physical hypothesis that can be used to further quantify and refine our understanding of hydrology and DOC export of arctic ice-wedge polygon terrain.

A Dynamic Occupancy Model for Interacting Species with Two Spatial Scales

AbstractOccupancy models have been extended to account for either multiple spatial scales or species interactions in a dynamic setting. However, as interacting species (e.g., predators and prey) often operate at different spatial scales, including nested spatial structure might be especially relevant to models of interacting species. Here we bridge these two model frameworks by developing a multi-scale, two-species occupancy model. The model is dynamic, i.e. it estimates initial occupancy, colonization and extinction probabilities—including probabilities conditional to the other species’ presence. With a simulation study, we demonstrate that the model is able to estimate most parameters without marked bias under low, medium and high average occupancy probabilities, as well as low, medium and high detection probabilities, with only a small bias for some parameters in low-detection scenarios. We further evaluate the model’s ability to deal with sparse field data by applying it to a multi-scale camera trapping dataset on a mustelid-rodent predator–prey system. Most parameters are estimated with low uncertainty (i.e. narrow posterior distributions). More broadly, our model framework creates opportunities to explicitly account for the spatial structure found in many spatially nested study designs, and to study interacting species that have contrasting movement ranges with camera traps.Supplementary materials accompanying this paper appear online.

A dense light field reconstruction algorithm for four-dimensional optical flow constraint equation

Dense light field sampling is an important basis for refocusing, depth estimation and 3-D imaging. It is difficult to obtain high resolution dense light field with a large-scale camera array and expensive equipment. At the same time, the current storage devices and transmission bandwidth also limit this technology's post-processing and application. In order to effectively reconstruct the angle domain of the light field based on the sparse light field data, this paper analyzes the correlation and constraint relationship between the optical flow field and the motion field of multi-view images in the same scene, extends the traditional optical flow constraint equation of two-dimensional imaging to the optical flow constraint equation of four-dimensional light field, and establishes an effective mathematical model. The coordinate position of the original pixel in the new angle image is determined by coordinate search, and its intensity is reconstructed. The experimental results of multi-scene dense reconstruction show that the proposed method can reconstruct the texture, shadow and color information in the light field of a long-baseline scene with high quality. The quantitative evaluation results show that the algorithm can be applied to dense light field reconstruction of complex scenes. The algorithm in this paper is only suitable for the case of optical flow constraint in a linear light field, and the follow-up research will focus on the case of nonlinear optical flow constraint.

Model Predictions of Wave Overwash Extent Into the Marginal Ice Zone

AbstractIn the marginal ice zone (MIZ), where ocean waves and sea ice interact, waves can produce flows of water across ice floe surfaces in a process known as wave overwash. Overwash potentially influences wave propagation characteristics, floe thermodynamics, and floe surface biological and chemical processes. However, the extent of the MIZ affected by overwash and its dependence on prevailing wave and ice conditions is unknown. In this paper, we propose a model of overwash extent caused by irregular incoming waves into a MIZ consisting of a random floe field. We validate the overwash extent model against laboratory experiments. We use the model to study mild to extreme incoming waves to floe field characteristics of the spring–summer ice retreat and autumn–winter ice advance and with compact ice edges. Overwash is typically predicted to extend a few kilometers and is generally greater for the autumn–winter advance than the spring–summer retreat. The model predictions provide a basis for improved understanding of the impacts of ocean waves on the ice cover. We also apply the model to incoming waves and a floe field with a diffuse ice edge representative of conditions during a field experiment, predicting overwash extents up to 16 km. During the field experiment, the wave and ice floe properties were intermittently monitored by a camera system, demonstrating how the sparse field data available on overwash can be advanced.

Evaluating Groundwater Storage Change and Recharge Using GRACE Data: A Case Study of Aquifers in Niger, West Africa

Accurately assessing groundwater storage changes in Niger is critical for long-term water resource management but is difficult due to sparse field data. We present a study of groundwater storage changes and recharge in Southern Niger, computed using data from NASA Gravity Recovery and Climate Experiment (GRACE) mission. We compute a groundwater storage anomaly estimate by subtracting the surface water anomaly provided by the Global Land Data Assimilation System (GLDAS) model from the GRACE total water storage anomaly. We use a statistical model to fill gaps in the GRACE data. We analyze the time period from 2002 to 2021, which corresponds to the life span of the GRACE mission, and show that there is little change in groundwater storage from 2002–2010, but a steep rise in storage from 2010–2021, which can partially be explained by a period of increased precipitation. We use the Water Table Fluctuation method to estimate recharge rates over this period and compare these values with previous estimates. We show that for the time range analyzed, groundwater resources in Niger are not being overutilized and could be further developed for beneficial use. Our estimated recharge rates compare favorably to previous estimates and provide managers with the data required to understand how much additional water could be extracted in a sustainable manner.

Using rapid repeat SAR interferometry to improve hydrodynamic models of flood propagation in coastal wetlands

The propagation of tides and riverine floodwater in coastal wetlands is controlled by subtle topographic differences and a thick vegetation canopy. High-resolution numerical models have been used in recent years to simulate fluxes across wetlands. However, these models are based on sparse field data that can lead to unreliable results. Here, we utilize high spatial-resolution, rapid repeat interferometric data from the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) to provide a synoptic measurement of sub-canopy water-level change resulting from tide propagation into wetlands. These data are used to constrain crucial model parameters and improve the performance and realism of simulations of the Wax Lake wetlands in coastal Louisiana (USA). A sensitivity analysis shows that the boundary condition of river discharge should be calibrated first, followed by iterative correction of terrain elevation specified originally by a Digital Terrain Model derived from LiDAR measurements. The calibration of bed friction becomes important only with the boundary and topography calibrated. With the model parameters calibrated, the overall Nash-Sutcliffe model efficiency for water-level change increases from 0.15 to 0.53 with the RMSE reduced by 26%. In areas with dense wetland grasses, the LiDAR signal is unable to reach the soil surface, but the L-band UAVSAR instrument detects changes in water levels that can be used to infer the true ground elevation. The high spatial resolution and repeat-acquisition frequency (minutes to hours) observations provided by UAVSAR represent a groundbreaking opportunity for a deeper understanding of the complex hydrodynamics of coastal wetlands.

Reconstruction of multipolar point sources with multi-frequency sparse far field data

The inverse acoustic scattering of point sources using multi-frequency sparse far field patterns is studied. The object may be a sum of monopoles and dipoles. We show that the locations and scattering strengths of the point sources can be uniquely identified by the multi-frequency far fields taken at sparse observation directions. Based on the uniqueness analyses, two schemes are proposed to distinguish the dipoles and monopoles, locate the positions and recover the scattering strengths. Numerical examples in two dimensions are presented to validity the effectiveness and robustness of the proposed methods. In particular, the numerical reconstructions indicate that the proposed methods are fast, stable and independent of the types, number or positions of the unknown point sources.

Largest marsh in New England near a precipice

The Great Marsh in northern Massachusetts and southern New Hampshire is the largest salt marsh in New England and includes the marsh surrounding Plum Island Sound, one of the most studied coastal wetlands in the United States. A primary goal in marsh research today is predicting their resilience in a regime of sea-level rise, which has become increasingly based on modeling studies with sparse or site-specific field data input. Here, we present the results of a vertical accretion study using 210Pb and 137Cs geochronology developed from 50-cm long auger cores taken at 15 locations throughout the PIS marsh. Two significant outliers were omitted from the final analyses due to their unusual depositional settings. The 137Cs spike produced the shortest-term record and lowest average accretion rate (2.5 mm/yr). Of the various 210Pb accretion models (Constant Flux-Constant Sedimentation: CF-CS, Constant Rate of Supply: CRS, Constant Initial Concentration: CIC), we chose the CF-CS model accretion rate for comparison to other environmental parameters and future marsh projections due to the robust R2 linear regression values and its wide use. The 13 stations exhibited an average accretion rate of 3.13 mm/yr, which is 0.6 mm/yr greater than the average rate reported elsewhere in New England. Individual accretion rates show no correlation with geography, elevation, bulk density, loss on ignition, distance to tidal channels, or peat thickness, indicating the complexity of the interacting factors that control marsh growth. Using moderate sea-level rise predictions, we show that large portions of the high marsh are likely to be converted to low marsh by 2050, and that the entire marsh high platform will become low marsh prior to 2070. This is the first time that wide spread field data are used to project a major transformation of the Plum Island Sound platform marsh that undoubtedly will lead to dramatic changes to marsh drainage, backbarrier hypsometry, tidal current dominance, and redistribution of sediment reservoirs, that will include the fronting barrier islands.

Generation of Spatial Profiles and Mapping of Volcanic Ash Distribution

Defining spatial distribution of airborne volcanic ash in the neighbourhood of an erupting volcano is a synoptic scale problem, severely impacting lives and livelihoods. Robust algorithms are needed to model such complex phenomenon from sparse field data. This study investigated optimal modelling of the spatial dispersion of ash using Empirical Bayesian Kriging (EBK): a geostatistical, probabilistic algorithm. Both distance and ash temperature values of samples from the 2010 Icelandic eruption were spatially correlated using semivariograms to generate prediction and error surfaces. Results showed that block averages were 90% accurate as validated against NCEP NWP model data. The work supports the utility of EBK in datasets where spatial autocorrelation is not significant. Furthermore, the results could help generate risk maps to delineate safety zones for aircrafts.

Multi-fidelity approach to Bayesian parameter estimation in subsurface heat and fluid transport models

The increased use of the urban subsurface for competing purposes, such as anthropogenic infrastructures and geothermal energy applications, leads to an urgent need for large-scale sophisticated modelling approaches for coupled mass and heat transfer. However, such models are subject to large uncertainties in model parameters, the physical model itself and in available measured data, which is often rare. Thus, the robustness and reliability of the computer model and its outcomes largely depend on successful parameter estimation and model calibration, which are hampered by the computational burden of large-scale coupled models.To tackle this problem, we develop a novel Bayesian approach for parameter estimation, which allows us to account for different sources of uncertainty, is capable of dealing with sparse field data and makes optimal use of the output data from expensive numerical model runs. This is achieved by combining output data from different models that represent the same physical problem, but at different levels of fidelity, e.g. reflected by different spatial resolution. By applying this new approach to a 1D analytical heat transfer model and a large-scale semi-3D numerical model while using synthetic data, we show that the accuracy and precision of parameter estimation by this multi-fidelity framework by far exceeds the standard single-fidelity results. The consideration of different error terms in the Bayesian framework also allows assessment of the model bias and the discrepancy between the different fidelity levels. These are emulated by Gaussian Process models, which facilitate re-iteration of the parameter estimation without additional model runs.

Dense light field reconstruction algorithm based on dictionary learning

The camera array is an important tool to obtain the light field of target in space. The method of obtaining high angular resolution light field by a large-scaled dense camera array increases the difficulty of sampling and the equipment cost. At the same time, the demand for synchronization and transmission of a large number of data also limits the sampling rate of light field. In order to complete the dense reconstruction of sparse sampling of light field, we analyze the correlation and redundancy of multi-view images in the same scene based on sparse light field data, then establish an effective mathematical model of light field dictionary learning and sparse coding. The trained light field atoms can sparsely express the local spatial-angular consistency of light field, and the four-dimensional (4D) light field patches can be reconstructed from a two-dimensional (2D) local image patch centered around each pixel in the sensor. The global and local constraints of the four-dimensional light field are mapped into the low-dimensional space by the dictionary. These constraints are shown as the sparsity of each vector in the sparse representation domain, the constraints between the positions of non-zero elements and their values. According to the constraints among sparse encoding elements, we establish the sparse encoding recovering model of virtual angular image, and propose the sparse encoding recovering method in the transform domain. The atoms of light field in dictionary are screened and the patches of light field are represented linearly by the sparse representation matrix of the virtual angular image. In the end, the virtual angular images are constructed by image fusion after sparse inverse transform. According to multi-scene dense reconstruction experiments, the effectiveness of the proposed method is verified. The experimental results show that the proposed method can recover the occlusion, shadow and complex illumination in satisfying quality. That is to say, it can be used for dense reconstruction of sparse light field in complex scene. In our study, the dense reconstruction of linear sparse light field is achieved. In the future, the dense reconstruction of nonlinear sparse light field will be studied to promote the practical application of light field imaging.

Simulated Soil Organic Carbon Response to Tillage, Yield, and Climate Change in the Southeastern Coastal Plains.

Intensive tillage, low-residue crops, and a warm, humid climate have contributed to soil organic carbon (SOC) loss in the southeastern Coastal Plains region. Conservation (CnT) tillage and winter cover cropping are current management practices to rebuild SOC; however, there is sparse long-term field data showing how these management practices perform under variable climate conditions. The objectives of this study were to use CQESTR, a process-based C model, to simulate SOC in the top 15 cm of a loamy sand soil (fine-loamy, kaolinitic, thermic Typic Kandiudult) under conventional (CvT) or CnT tillage to elucidate the impact of projected climate change and crop yields on SOC relative to management and recommend the best agriculture management to increase SOC. Conservation tillage was predicted to increase SOC by 0.10 to 0.64 Mg C ha for six of eight crop rotations compared with CvT by 2033. The addition of a winter crop [rye ( L.) or winter wheat ( L.)] to a corn ( L.)-cotton ( L.) or corn-soybean [ (L.) Merr.] rotation increased SOC by 1.47 to 2.55 Mg C ha. A continued increase in crop yields following historical trends could increase SOC by 0.28 Mg C ha, whereas climate change is unlikely to have a significant impact on SOC except in the corn-cotton or corn-soybean rotations where SOC decreased up to 0.15 Mg C ha by 2033. The adoption of CnT and cover crop management with high-residue-producing corn will likely increase SOC accretion in loamy sand soils. Simulation results indicate that soil C saturation may be reached in high-residue rotations, and increasing SOC deeper in the soil profile will be required for long-term SOC accretion beyond 2030.

Using digital soil maps to infer edaphic affinities of plant species in Amazonia: Problems and prospects.

Amazonia combines semi‐continental size with difficult access, so both current ranges of species and their ability to cope with environmental change have to be inferred from sparse field data. Although efficient techniques for modeling species distributions on the basis of a small number of species occurrences exist, their success depends on the availability of relevant environmental data layers. Soil data are important in this context, because soil properties have been found to determine plant occurrence patterns in Amazonian lowlands at all spatial scales. Here we evaluate the potential for this purpose of three digital soil maps that are freely available online: SOTERLAC, HWSD, and SoilGrids. We first tested how well they reflect local soil cation concentration as documented with 1,500 widely distributed soil samples. We found that measured soil cation concentration differed by up to two orders of magnitude between sites mapped into the same soil class. The best map‐based predictor of local soil cation concentration was obtained with a regression model combining soil classes from HWSD with cation exchange capacity (CEC) from SoilGrids. Next, we evaluated to what degree the known edaphic affinities of thirteen plant species (as documented with field data from 1,200 of the soil sample sites) can be inferred from the soil maps. The species segregated clearly along the soil cation concentration gradient in the field, but only partially along the model‐estimated cation concentration gradient, and hardly at all along the mapped CEC gradient. The main problems reducing the predictive ability of the soil maps were insufficient spatial resolution and/or georeferencing errors combined with thematic inaccuracy and absence of the most relevant edaphic variables. Addressing these problems would provide better models of the edaphic environment for ecological studies in Amazonia.

Numerical Modeling of Soil Evaporation Process and Its Stages Dividing during a Drying Cycle

The soil water evaporation is a critical component of both the surface energy balance and water balance, affecting the mass and energy exchange between the land and the atmosphere. Evaporation process is involved in the highly complex interactions between media properties, transport processes, and boundary conditions. So, it is difficult to accurately determine these near-surface highly dynamic processes based only on the sparse field data and on the measurement-based methods. The objective of this paper was to obtain a detailed description of the soil water evaporation process and to better understand the evolutions of variables involved in the evaporation process during different stages of evaporation. To do this, a numerical simulation experiment in a bare silt soil was conducted to reproduce the soil drying process during a 20-d period after a 2-cm rainfall event. According to simulation results, the whole 20-d simulation period was divided into two main stages as well as a transient period from stage 1 to stage 2. Diurnal patterns of energy and water balance components, soil moisture, soil temperature, water fluxes, evaporation rate, dry surface layer (DSL), and evaporation zone during this drying process were fully described, which, in turn, could be as the possible indicators for judging the shift of stages of evaporation.

Identification of high nature value grassland with remote sensing and minimal field data

In the last 50 years intensification of agricultural land use systems drastically reduced extensively used grassland areas. These areas are of high ecological value due to high species richness and occurring rare species. Therefore, recent European Union (EU) laws stipulate the conservation and monitoring of this farmland, also called “high nature value” (HNV) farmland. As a consequence of these new laws, a so called HNV indicator system was implemented that requires all EU member states to establish a nationwide monitoring system for HNV areas. These monitoring systems are challenged among other by the difficult differentiation between grassland types which today at fine scale is only possible with time and cost intensive field work. Due to this high work-load and financial limitations, nationwide field campaigns have to be sample-based and hence will not deliver a spatially consistent result.In this study, we examine whether low and high nature value grasslands can be differentiated with remotely-sensed reflectance data, which could support existing field survey-based monitoring approaches. We used multi-seasonal, multispectral remote sensing data (RapidEye) in combination with sparse field data (collected in southern Germany) and three one-class classifiers to classify A) HNV grassland against other areas and to differentiate between B) three quality classes of HNV grassland according to the current German HNV monitoring approach.The results for A) indicated high performances of the tested approaches to identify HNV grassland areas. Biased support vector machine delivered best overall results (high detection rate and low false positive rates). However, the results also showed a consistent underestimation of HNV grasslands. Results for B) showed that a separation into several HNV quality classes is not possible with any of the tested approaches.We conclude that with the presented approach HNV grasslands can be identified from the landscape matrix based on its spectral signal. Combining the presented approach with an object oriented classifier or with land registry data could further improve the results.