Field Estimation Research Articles

Estimating temperature fields in flows from sensors

Active control of scalar transport (e.g. temperature or chemical species) in engineering flows by feedback of intermediate scalar fields to the controller to determine the control action can substantially improve the performance of such processes. Key for practical application of such state-feedback control is the fast and accurate estimation of scalar fields from discrete sensor data (e.g. thermocouples in case of heat transfer), since the full field can usually not be measured. This study develops a state observer that enables such sensor-based state estimation for a representative problem: the temperature evolution during heat transfer in the 2D Rotated Arc Mixer (RAM). State observers with this capability are to the best of our knowledge non-existent in the literature. The performance of the observer is investigated by its ability to reconstruct known temperature fields from the corresponding values at the sensor locations using fields obtained from (i) numerical simulations and (ii) infrared thermography in a RAM laboratory set-up. This shows a good performance (for typical engineering applications) that under realistic conditions most likely depends mainly on the modelling accuracy for the physical system.

Application of commercial microwave links (CMLs) attenuation for quantitative estimation of precipitation

AbstractPrecipitation estimation models are typically sourced by rain gauges, weather radars and satellite observations. A relatively new technique of precipitation estimation relies on the network of Commercial Microwave Links (CMLs) employed for cellular communication networks: the rain‐inducted attenuation in the links enables the precipitation estimation. In the paper, it is analysed to what extent the precipitation derived from CML attenuation data is useful in estimation of the precipitation field with the high temporal and spatial resolution required in nowcasting models. Two methods of determination of precipitation along CMLs from attenuation of signal with several frequencies were proposed. Then, in order to generate precipitation field, three approaches for assigning appropriate precipitation values to a specific point or set of pixels along the link are developed and tested. The CML‐based estimates are compared with point observations from manual rain gauges and multi‐source precipitation fields using daily and half‐hourly accumulations. It was found that the CML‐based precipitation fields are much worse than radar‐derived estimates. At the same time, they had slightly poorer reliability than spatially interpolated telemetric rain gauge data and significantly higher reliability than satellite estimates. Furthermore, the impact of link characteristics, such as length and frequency, on the reliability of CML‐based precipitation estimates is analysed.

Estimation of Oil Reservoir Transmissivity and Storativity Fields Using a Radial Basis Function Network Based on Inverse Problem Solving

Estimation of Oil Reservoir Transmissivity and Storativity Fields Using a Radial Basis Function Network Based on Inverse Problem Solving

Cfr (v2024.1.26): a Python package for climate field reconstruction

Abstract. Climate field reconstruction (CFR) refers to the estimation of spatiotemporal climate fields (such as surface temperature) from a collection of pointwise paleoclimate proxy datasets. Such reconstructions can provide rich information on climate dynamics and provide an out-of-sample validation of climate models. However, most CFR workflows are complex and time-consuming, as they involve (i) preprocessing of the proxy records, climate model simulations, and instrumental observations; (ii) application of one or more statistical methods; and (iii) analysis and visualization of the reconstruction results. Historically, this process has lacked transparency and accessibility, limiting reproducibility and experimentation by non-specialists. This article presents an open-source and object-oriented Python package called cfr that aims to make CFR workflows easy to understand and conduct, saving climatologists from technical details and facilitating efficient and reproducible research. cfr provides user-friendly utilities for common CFR tasks such as proxy and climate data analysis and visualization, proxy system modeling, and modularized workflows for multiple reconstruction methods, enabling methodological intercomparisons within the same framework. The package is supported with extensive documentation of the application programming interface (API) and a growing number of tutorial notebooks illustrating its usage. As an example, we present two cfr-driven reconstruction experiments using the PAGES 2k temperature database applying the last millennium reanalysis (LMR) paleoclimate data assimilation (PDA) framework and the graphical expectation–maximization (GraphEM) algorithm, respectively.

An Efficient Tidal Dissipation Mechanism via Stellar Magnetic Fields

Recent work suggests that inwardly propagating internal gravity waves (IGWs) within a star can be fully converted to outward magnetic waves if they encounter a sufficiently strong magnetic field. The resulting magnetic waves dissipate as they propagate outward to regions with lower Alfvén velocity. While tidal forcing is known to excite IGWs, this conversion and subsequent damping of magnetic waves have not been explored as a tidal dissipation mechanism. In particular, stars with sufficiently strong magnetic fields could fully dissipate tidally excited waves, yielding the same tidal evolution as the previously studied “traveling wave regime.” Here, we evaluate the viability of this mechanism using stellar models of stars with convective cores (F-type stars in the mass range of 1.2–1.6 M ⊙), which were previously thought to be weakly tidally dissipative (due to the absence of nonlinear gravity-wave breaking). The criterion for wave conversion to operate is evaluated for each stellar mass using the properties of each star’s interior along with estimates of the magnetic field produced by a convective core dynamo under the assumption of equipartition between kinetic (convective) and magnetic energies. Our main result is that this previously unexplored source of efficient tidal dissipation can operate in stars within this mass range for significant fractions of their lifetimes. This tidal dissipation mechanism appears to be consistent with the observed inspiral of WASP-12b and more generally could play an important role in the orbital evolution of hot Jupiters—and to lower-mass ultra-short-period planets—orbiting F-type stars.

A serendipity fully discrete div-div complex on polygonal meshes

In this work we address the reduction of face degrees of freedom (DOFs) for discrete elasticity complexes. Specifically, using serendipity techniques, we develop a reduced version of a recently introduced two-dimensional complex arising from traces of the three-dimensional elasticity complex. The keystone of the reduction process is a new estimate of symmetric tensor-valued polynomial fields in terms of boundary values, completed with suitable projections of internal values for higher degrees. We prove an extensive set of new results for the original complex and show that the reduced complex has the same homological and analytical properties as the original one. This paper also contains an appendix with proofs of general Poincaré–Korn-type inequalities for hybrid fields.

Time Series Field Estimation of Rice Canopy Height Using an Unmanned Aerial Vehicle-Based RGB/Multispectral Platform

Crop plant height is a critical parameter for assessing crop physiological properties, such as above-ground biomass and grain yield and crop health. Current dominant plant height estimation methods are based on digital surface model (DSM) and vegetation indexes (VIs). However, DSM-based methods usually estimate plant height by growth stages, which would result in some discontinuity between growth stages due to different fitting curves. Additionally, there has been limited research on the application of VI-based plant height estimation for multiple crop species. Thus, this study investigated the validity and challenges associated with these methods for estimating canopy heights of multi-variety rice throughout the entire growing season. A total of 474 rice varieties were cultivated in a single season, and RGB images including red, green, and blue bands, DSMs, multispectral images including near infrared and red edge bands, and manually measured plant heights were collected in 2022. DSMs and 26 commonly used VIs were employed to estimate rice canopy heights during the growing season. The plant height estimation using DSMs was performed using different quantiles (50th, 75th, and 95th), while two-stage linear regression (TLR) models based on each VI were developed. The DSM-based method at the 95th quantile showed high accuracy, with an R2 value of 0.94 and an RMSE value of 0.06 m. However, the plant height estimation at the early growth stage showed lower effectiveness, with an R2 < 0. For the VIs, height estimation with MTCI yielded the best results, with an R2 of 0.704. The first stage of the TLR model (maximum R2 = 0.664) was significantly better than the second stage (maximum R2 = 0.133), which indicated that the VIs were more suitable for estimating canopy height at the early growth stage. By grouping the 474 varieties into 15 clusters, the R2 values of the VI-based TLR models exhibited inconsistencies across clusters (maximum R2 = 0.984; minimum R2 = 0.042), which meant that the VIs were suitable for estimating canopy height in the cultivation of similar or specific rice varieties. However, the DSM-based method showed little difference in performance among the varieties, which meant that the DSM-based method was suitable for multi-variety rice breeding. But for specific clusters, the VI-based methods were better than the DSM-based methods for plant height estimation. In conclusion, the DSM-based method at the 95th quantile was suitable for plant height estimation in the multi-variety rice breeding process, and we recommend using DSMs for plant height estimation after 26 DAT. Furthermore, the MTCI-based TLR model was suitable for plant height estimation in monoculture planting or as a correction for DSM-based plant height estimation in the pre-growth period of rice.

Interpolation problems for random fields on Sierpinski’s carpet

The prediction of stochastic processes and the estimation of random fields of different natures is becoming an increasingly common field of research among scientists of various specialties. However, an analysis of papers across different estimating problems shows that a dynamic approach over an iterative and recursive interpolation of random fields on fractal is still an open area of investigation. There are many papers related to the interpolation problems of stationary sequences, estimation of random fields, even on the perforated planes, but all of this still provides a place for an investigation of a more complicated structure like a fractal, which might be more beneficial in appliances of certain industry fields. For example, there has been a development of mobile phone and WiFi fractal antennas based on a first few iterations of the Sierpinski carpet. In this paper, we introduce an estimation for random fields on the Sierpinski carpet, based on the usage of the known spectral density, and calculation of the spectral characteristic that allows an estimation of the optimal linear functional of the omitted points in the field. We give coverage of an idea of stationary sequence estimating that is necessary to provide a basic understanding of the approach of the interpolation of one or a set of omitted values. After that, the expansion to random fields allows us to deduce a dynamic approach on the iteration steps of the Sierpinski carpet. We describe the numerical results of the initial iteration steps and demonstrate a recurring pattern in both the matrix of Fourier series coefficients of the spectral density and the result of the optimal linear functional estimation. So that it provides a dependency between formulas of the different initial sizes of the field as well as a possible generalizing of the solution for N-steps in the Sierpinski carpet. We expect that further evaluation of the mean squared error of this estimation can be used to identify the possible iteration step when further estimation becomes irrelevant, hence allowing us to reduce the cost of calculations and make the process viable.

Crop coefficients of natural wetlands and riparian vegetation to compute ecosystem evapotranspiration and the water balance

AbstractWetlands, namely the riparian ones, play a major role in landscape and water resources functionalities and provide enormous opportunities for ecosystems services. However, their area at globe scale is continuously decreasing due to appropriation by the riverain communities or by allocation of water resources to other uses, namely irrigation, in prejudice of natural wetlands. Due to the high competition for water, namely for agricultural irrigation, the calculation of the vegetation evapotranspiration (ETc), i.e. the consumptive water use of the wetland ecosystems, is mandatory for determining water supply–demand balance at various scales. Providing for the basin and local levels the reason for this review study on ETc to be presented in an irrigation focused Journal. The review also aims to make available adequate Kc values relative to these ecosystems in an ongoing update of FAO guidelines on evapotranspiration. The review on ETc of natural wetlands focused on its computation adopting the classical FAO method, thus the product of the FAO-PM grass reference ETo by the vegetation specific Kc, i.e., ETc = Kc ETo. This approach is not only the most common in agriculture but is also well used in natural wetlands studies, with Kc values fully related with vegetation ecosystems characteristics. A distinction was made between riparian and non-riparian wetland ecosystems due to differences between main types of water sources and main vegetation types. The Kc values are tabulated through grouping wetlands according to the climate since the variability of Kc with vegetation, soil, and water availability would require data not commonly available from the selected studies. Tabulated values appear to be coherent and appropriate to support field estimation of Kc and ETc for use in wetlands water balance when not measured but weather data may be available to compute the grass reference ETo. ETc and the water balance could then be estimated since they are definitely required to further characterization and monitoring of wetlands, defining measures for their protection, and assessing ecosystems’ services.

Virtual sensing for real-time strain field estimation and its verification on a laboratory-scale jacket structure under water waves

This study aims to achieve real-time estimation of the full-field strain distribution in a structure by signals measured from several strain gauges attached to the structure. Our virtual sensing procedure is developed based on finite element formulation and employs the mode superposition approach. To verify the feasibility of the proposed procedure, numerical and experimental tests are conducted on a laboratory-scale offshore jacket structure subjected to water waves. Key aspects addressed in this study include the selection of displacement modes and the division of strain signals. The experiments are performed in an ocean basin, and comprehensive explanations are provided for the jacket prototype design, implementations, experimental setup, and wave loading conditions. The performance of the proposed virtual sensing procedure is thoroughly assessed through various evaluation measures, enhancing the understanding of its capabilities and limitations in practical applications.

Field estimation of fallen deadwood volume under different management approaches in two European protected forested areas

Abstract Fallen deadwood is essential for biodiversity and nutrient cycling in forest ecosystems. In modern forest management, there is growing interest in developing accurate and efficient methods for field estimation of deadwood volume due to its many benefits (e.g. carbon storage, habitat creation, erosion control). The most common methods for deadwood inventories are fixed-area sampling (FAS) and line-intersect sampling (LIS) methods. While the estimations of deadwood volume by LIS generally show results comparable to FAS estimations, active management (e.g. production forestry clearcutting, logging, and thinning activities) can impair LIS accuracy by changing local deadwood patterns. Yet, the comparison of LIS and FAS methods has typically focused on production forests where deadwood is limited and deadwood volumes are comparably low. In this study, we assessed fallen deadwood volume in two large national parks—one being a more actively managed landscape (including, e.g., selective thinning for maintaining cultural–historical values and enhancing recreational opportunities) with overall lower levels of fallen deadwood, and the other having a strict non-intervention approach with higher levels of deadwood. No significant differences between average FAS and LIS estimations of deadwood volumes were detected. Additional experimentations using simulated data under varied stand conditions confirmed these results. Although line-intersect sampling showed a slight overestimation and some variability at the individual plot level, it remains an efficient, time-saving field sampling method providing comparable results to the more laborious fixed-area sampling. Line-intersect sampling may be especially suitable for rapid field inventories where relative changes in deadwood volume rather than absolute deadwood volumes are of large interest. Due to its practicality, flexibility, and relative accuracy, line-intersect sampling may gain wider use in natural resource management to inform national park managers, foresters, and ecologists.

Pro+: Automated protrusion and critical shear stress estimates from 3D point clouds of gravel beds

AbstractThe dimensionless critical shear stress (τ*c) needed for the onset of sediment motion is important for a range of studies from river restoration projects to landscape evolution calculations. Many studies simply assume a τ*c value within the large range of scatter observed in gravel‐bedded rivers because direct field estimates are difficult to obtain. Informed choices of reach‐scale τ*c values could instead be obtained from force balance calculations that include particle‐scale bed structure and flow conditions. Particle‐scale bed structure is also difficult to measure, precluding wide adoption of such force‐balance τ*c values. Recent studies have demonstrated that bed grain size distributions (GSD) can be determined from detailed point clouds (e.g. using G3Point open‐source software). We build on these point cloud methods to introduce Pro+, software that estimates particle‐scale protrusion distributions and τ*c for each grain size and for the entire bed using a force‐balance model. We validated G3Point and Pro+ using two laboratory flume experiments with different grain size distributions and bed topographies. Commonly used definitions of protrusion may not produce representative τ*c distributions, and Pro+ includes new protrusion definitions to better include flow and bed structure influences on particle mobility. The combined G3Point/Pro+ provided accurate grain size, protrusion and τ*c distributions with simple GSD calibration. The largest source of error in protrusion and τ*c distributions were from incorrect grain boundaries and grain locations in G3Point, and calibration of grain software beyond comparing GSD is likely needed. Pro+ can be coupled with grain identifying software and relatively easily obtainable data to provide informed estimates of τ*c. These could replace arbitrary choices of τ*c and potentially improve channel stability and sediment transport estimates.

Validation of echo planar imaging based diffusion-weighted magnetic resonance imaging on a 0.35 T MR-Linac

Background and PurposeThe feasibility of acquiring diffusion-weighted imaging (DWI) images on an MR-Linac for quantitative response assessment during radiotherapy was explored. DWI data obtained with a Spin Echo Echo Planar Imaging sequence adapted for a 0.35 T MR-Linac were examined and compared with DWI data from a conventional 3 T scanner. Materials and MethodsApparent diffusion coefficient (ADC) measurements and a distortion correction technique were investigated using DWI-calibrated phantoms and in the brains of seven volunteers. All DWI utilized two phase-encoding directions for distortion correction and off-resonance field estimation. ADC maps in the brain were analyzed for automatically segmented normal tissues. ResultsPhantom ADC measurements on the MR-Linac were within a 3 % margin of those recorded by the 3 T scanner. The maximum distortion observed in the phantom was 2.0 mm prior to correction and 1.1 mm post-correction on the MR-Linac, compared to 6.0 mm before correction and 3.6 mm after correction at 3 T. In vivo, the average ADC values for gray and white matter exhibited variations of 14 % and 4 %, respectively, for different selections of b-values on the MR-Linac. Distortions in brain images before correction, estimated through the off-resonance field, reached 2.7 mm on the MR-Linac and 12 mm at 3 T. ConclusionAccurate ADC measurements are achievable on a 0.35 T MR-Linac, both in phantom and in vivo. The selection of b-values significantly influences ADC values in vivo. DWI on the MR-Linac demonstrated lower distortion levels, with a maximum distortion reduced to 1.1 mm after correction.

Nonlinear Gradient Field Estimation in Diffusion MRI Tensor Simulation.

Gradient nonlinearities not only induce spatial distortion in magnetic resonance imaging (MRI), but also introduce discrepancies between intended and acquired diffusion sensitization in diffusion weighted (DW) MRI. Advances in scanner performance have increased the importance of correcting gradient nonlinearities. The most common approaches for gradient nonlinear field estimations rely on phantom calibration field maps which are not always feasible, especially on retrospective data. Here, we derive a quadratic minimization problem for the complete gradient nonlinear field (L(r)). This approach starts with corrupt diffusion signal and estimates the L(r) in two scenarios: (1) the true diffusion tensor known and (2) the true diffusion tensor unknown (i.e., diffusion tensor is estimated). We show the validity of this mathematical approach, both theoretically and through tensor simulation. The estimated field is assessed through diffusion tensor metrics: mean diffusivity (MD), fractional anisotropy (FA), and principal eigenvector (V1). In simulation with 300 diffusion tensors, the study shows the mathematical model is not ill-posed and remains stable. We find when the true diffusion tensor is known (1) the change in determinant of the estimated L(r) field and the true field is near zero and (2) the median difference in estimated L(r) corrected diffusion metrics to true values is near zero. We find the results of L(r) estimation are dependent on the level of L(r) corruption. This work provides an approach to estimate gradient field without the need for additional calibration scans. To the best of our knowledge, the mathematical derivation presented here is novel.

Accounting for residual errors in atmosphere–ocean background models applied in satellite gravimetry

The Atmosphere and Ocean non-tidal De-aliasing Level-1B (AOD1B) product is widely used in precise orbit determination and satellite gravimetry to correct for transient effects of atmosphere–ocean mass variability that would otherwise alias into monthly mean global gravity fields. The most recent release is based on the global ERA5 reanalysis and ECMWF operational data together with simulations from the general ocean circulation model MPIOM consistently forced with fields from the corresponding atmospheric dataset. As background models are inevitably imperfect, residual errors will consequently propagate into the resulting geodetic products. Accounting for uncertainties of the background model data in a statistical sense, however, has been shown before to be a useful approach to mitigate the impact of residual errors leading to temporal aliasing artefacts. In light of the changes made in the new release RL07 of AOD1B, previous uncertainty assessments are deemed too pessimistic and thus need to be revisited. We here present an analysis of the residual errors in AOD1B RL07 based on ensemble statistics derived from different atmospheric reanalyses, including ERA5, MERRA2 and JRA55. For the oceans, we investigate the impact of both the forced and intrinsic variability through differences in MPIOM simulation experiments. The atmospheric and oceanic information is then combined to produce a new time-series of true errors, called AOe07, which is applicable in combination with AOD1B RL07. AOe07 is further complemented by a new spatial error variance–covariance matrix. Results from gravity field recovery simulation experiments for the planned Mass-Change and Geosciences International Constellation (MAGIC) based on GFZ’s EPOS software demonstrate improvements that can be expected from rigorously implementing the newly available stochastic information from AOD1B RL07 into the gravity field estimation process.

Two-Point Localization Algorithm of a Magnetic Target Based on Tensor Geometric Invariant.

Currently, magnetic gradient tensor-based localization methods face challenges such as significant errors in geomagnetic field estimation, susceptibility to local optima in optimization algorithms, and inefficient performance. In addressing these issues, this article propose a two-point localization method under the constraint of overlaying geometric invariants. This method initially establishes the relationship between the target position and the magnetic gradient tensor by substituting an intermediate variable for the magnetic moment. Exploiting the property of the eigenvector corresponding to the minimum absolute eigenvalue being perpendicular to the target position vector, this constraint is superimposed to formulate a nonlinear system of equations of the target's position. In the process of determining the target position, the Nara method is employed for obtaining the initial values, followed by the utilization of the Levenberg-Marquardt algorithm to derive a precise solution. Experimental validation through both simulations and experiments confirms the effectiveness of the proposed method. The results demonstrate its capability to overcome the challenges faced by a single-point localization method in the presence of some errors in geomagnetic field estimation. In comparison to traditional two-point localization methods, the proposed method exhibits the highest precision. The localization outcomes under different noise conditions underscore the robust noise resistance and resilience of the proposed method.

Impacts of temporal resolution of atmospheric de-aliasing products on gravity field estimation

SUMMARY Despite the increasing accuracies of GRACE (Gravity Recovery and Climate Experiment)/GRACE-FO (GRACE Follow-On) gravity field models through worldwide endeavours, the temporal aliasing effect caused by the imperfect background models used in gravity field modelling is still a crucial factor that degrades the quality of gravity field solutions. Since the important role of temporal resolution of atmospheric de-aliasing models, this paper specifically investigates the influence of temporal resolution on gravity field modelling from the perspectives of frequency, spectral and spatial domains. To this end, we introduced the gravitational acceleration and geoid height derived from the static gravity field GOCO06s in the inner integral. The introduction of the static gravity field has a comparable impact on LRI (Laser Ranging Interferometers) range-rate residuals as the accuracy of the LRI range-rate data, despite its magnitude of being less than 0.1 mm in the spatial domain. This finding also highlights the significance of error level in existing de-aliasing products as a crucial factor that restricts the current accuracy of gravity field solutions. Further analyses show that increasing the temporal resolution from 3 to 1 hr has an insignificant impact on the gravity solutions in both the frequency and spectral domains, which is also smaller than that caused by using different atmospheric data sets. However, in the spatial domain, LRI range-rate residuals can be effectively mitigated in certain regions of the Southern Hemisphere at mid- and high-latitudes by increasing the temporal resolution. Particularly, the discrepancies of mass change estimates brought about by enhancing temporal resolution have distinct characteristics, especially in the Congo River and the Amazon River Basins. The mass changes in terms of equivalent water height derived by using P4M6 filtering show that the maximum root mean square value of spatial differences caused by improving the temporal resolution of the atmospheric de-aliasing models can reach ∼13.4 mm in the subregion of the Congo River Basin. However, using different atmospheric data sets can lead to a maximum difference of ∼16.5 mm. For the Amazon River Basin, the corresponding maximum discrepancy is ∼18.1 mm, and that caused by improving temporal resolution is ∼9.4 mm. We further divide the Congo River Basin into several subregions using a lat-lon regular grid with a spatial resolution of 3°. The subsequent time-series results of mass changes reveal that the maximum contribution of temporal resolution and changes in the atmospheric data sets can reach 11.09 and 21.24 per cent, respectively. This suggests that it is necessary to consider the temporal resolution of de-aliasing products when studying mass changes at a regional scale.

Geoelectric Field Estimations During Geomagnetic Storm in North China From SinoProbe Magnetotelluric Impedances

AbstractEvaluating the impact of geomagnetic disturbances on power grid infrastructure is critical to mitigate the risk posed by geomagnetically induced currents (GICs). In this paper, the geoelectric field and induced voltage distribution in North China were estimated from the SinoProbe magnetotelluric (MT) impedance data together with the geomagnetic observatory data of six INTERMAGNET stations recorded during the significant geomagnetic storm of 17th March 2015. The measured impedances from 119 SinoProbe MT sites were convolved with geomagnetic observatory data to account for the Earth's complex three‐dimensional electrical resistivity structure. The resultant geoelectric field was then used to model the induced voltage distribution across the regional power transmission network in North China. Due to the large inter‐site distances of the SinoProbe MT program, the derived geoelectric field is mostly homogeneous, except in the Ordos Basin that displays a polarization of the geoelectric field, and with higher magnitudes in the orogenic belts. The estimated geoelectric fields in Taihang‐Lvliang, Yanshan, and Luxi orogenic belts of North China were large (>1 V/km) during the storm, due to high‐resistivity lithosphere resulting in large voltage gradients in the Earth. However, in relation to locations of major power transmission lines, only the central part of North China experienced induced voltages exceeding 100 V.

Analysis of protected species observer data: Strengths, weaknesses, and application in the assessment of marine mammal responses to seismic surveys in the northern Gulf of Mexico 2002-2015.

Visual observation data collected by protected species observers (PSOs) is required per regulations stipulated in Notices to Lessees (NTLs) and geophysical survey Permits (Form BOEM-0328) issued to seismic operators in the Gulf of Mexico (GOM). Here, data collected by certified and trained PSOs during seismic surveys conducted between 2002-2015 were compiled and analyzed to assess utility in assessing marine mammal responses to seismic noise and effectiveness of required mitigation measures. A total of 3,886 agency-required bi-weekly PSO Effort and Sightings reports were analyzed comprising 598,319 hours of PSO visual effort and 15,117 visual sighting records of marine mammals. The observed closest point of approach (CPA) distance was statistically compared across five species groupings for four airgun activity levels (full, minimum source, ramp up, silent). Whale and dolphin detections were significantly farther from airgun array locations during full power operations versus silence, indicating some avoidance response to full-power operations. Dolphin CPA distances were also significantly farther from airguns operating at minimum source than silence. Blackfish were observed significantly farther from the airgun array during ramp up versus both full and minimum source activities. Blackfish were observed significantly closer to the airgun array during silent activities versus at full, minimum source, and ramp up activities. Beaked whales had the largest mean CPA for detection distance compared to all other species groups. Detection distances for beaked whales were not significantly differences between full and silent operations; however, the sample size was very low. Overall results are consistent with other studies indicating that marine mammals may avoid exposure to airgun sounds based on observed distance from the seismic source during specified source activities. There was geographic variability in sighting rates associated with specific areas of interest within the GOM. This study demonstrates that agency required PSO reports provide a robust and useful data set applicable to impact assessments; management, policy and regulatory decision making; and qualitative input for regional scientific, stock assessment and abundance studies. However, several improvements in content and consistency would facilitate finer-scale analysis of some topics (e.g., effort associated with specific activities, observer biases, sound field estimation) and support statistical comparisons that could provide further insight into marine mammal responses and mitigation efficacy.

Data-driven model order reduction for sensor positioning and indirect reconstruction with noisy data: Application to a Circulating Fuel Reactor

Sensor positioning and real-time estimation of non-observable fields is an open question in the nuclear sector, especially for advanced nuclear reactors. In Circulating Fuel Reactors (CFR), liquid fuel and coolant are homogeneously mixed, and thus these reactors will not have internal structures, making sensor positioning in the primary circuit, including the core, an unresolved problem, making most of the core blind to sensors. Thus, the possibility of estimating the system state in the whole domain using a few local measurements has important implications for safety, monitoring, and control both in nominal and accidental conditions. In this context, the integrated Model Order Reduction and Data Assimilation framework offers intriguing opportunities to reliably combine experimental data and background knowledge from a reduced mathematical model. This work discusses and applies innovative methods within this framework, based on the Generalized Empirical Interpolation and the Indirect Reconstruction algorithms, to a proposed concept of CFR. This work aims to identify the optimal sensor positioning within the core and assess the feasibility of reconstructing the quantities of interest starting only from transient sparse data on fuel temperature, possibly noisy, and testing the predictive capabilities of the discussed methods.