Depth Uncertainty Research Articles

A Likelihood-Based Triangulation Method for Uncertainties in Through-Water Depth Mapping

Coastal environments, which are crucial for economic and strategic reasons, heavily rely on accurate bathymetry for safe navigation and resource monitoring. Recent advancements in through-water photogrammetry have shown promise in mapping shallow waters efficiently. However, robust uncertainty modeling methods for these techniques, especially in challenging coastal environments, are lacking. This study introduces a novel likelihood-based approach for through-water photogrammetry, focusing on uncertainties associated with camera pose—a key factor affecting depth mapping accuracy. Our methodology incorporates probabilistic modeling and stereo-photogrammetric triangulation to provide realistic estimates of uncertainty in Water Column Depth (WCD) and Water–Air Interface (WAI) height. Using simulated scenarios for both drone and airborne surveys, we demonstrate that viewing geometry and camera pose quality significantly influence resulting uncertainties, often overshadowing the impact of depth itself. Our results reveal the superior performance of the likelihood ratio statistic in scenarios involving high attitude noise, high flight altitude, and complex viewing geometries. Notably, drone-based applications show particular promise, achieving decimeter-level WCD precision and WAI height estimations comparable to high-quality GNSS measurements when using large samples. These findings highlight the potential of drone-based surveys in producing more accurate bathymetric charts for shallow coastal waters. This research contributes to the refinement of uncertainty quantification in bathymetric charting and sets a foundation for future advancements in through-water surveying methodologies.

Remote sensing of wave-orbital velocities in the surfzone

Wave-orbital velocities are estimated with particle image velocimetry (PIV) applied to rapid sequences of images of the surfzone surface obtained with a low-cost camera mounted on an amphibious tripod. Time series and spectra of the remotely sensed cross-shore wave-orbital velocities are converted to the depth of colocated acoustic Doppler velocimeters (ADVs), using linear finite depth theory. These converted velocities are similar to the velocities measured in situ (mean nRMSE for time series=16% and for spectra=10%). Small discrepancies between depth-attenuated surface and in situ currents may be owing to errors in the surface velocity measurements, uncertainties in the water depth, the vertical elevation of the ADVs, and the neglect of nonlinear effects when using linear finite depth theory. These results show the potential to obtain spatially dense estimates of wave velocities using optical near-field remote methods during field campaigns and continuous monitoring operations.

Determination of optimal solar-viewing geometry for in-flight polarization calibration using sun glint over ocean

The sun glint has been proven to be a valuable natural polarization calibration target because it is strongly polarized, and its polarization characteristics can be accurately simulated with models. It is convenient to calibrate the satellite’s in-flight polarimetry by comparing the polarization simulations with actual measurements. Meanwhile, the accuracy of polarization simulation at the top of the atmosphere (TOA) over sun glint is affected by several atmospheric and oceanic surface factors and depends on the specific solar-viewing geometry. In this paper, the sensitivity of the degree of linear polarization (DOLP) at the TOA to the uncertainties of the aerosol optical depth, aerosol model, absorption gas content (CWV, O3), sea surface instantaneous wind speed (WS), and chlorophyll concentration (Chl) under different solar-viewing geometries is analyzed via radiative transfer simulation. The error budgets indicate that aerosols and WS are the main error factors for polarization calibration, while the uncertainties of Chl and absorbing gases can be disregarded. The total DOLP error increases with the solar zenith angle and viewing zenith angle (i.e., the increase of atmospheric optical path) and the sun glint angle (SGA, the angle between the viewing and the specular directions of the sun) (i.e., the decrease of sun glint brightness). The dependence of the total DOLP error on SGA decreases with the WS (i.e., the increase of sun glint spot area and the decrease of the sun glint intensity) and increases with the wavelength (i.e., the decrease of atmospheric scattering contribution). Based on the error budgets, an optimized solar-viewing geometry screening strategy is proposed to ensure that the simulated DOLP error is limited to 0.02. The in-flight DOLP calibration result of POLDER/PARASOL shows that the proposed screening strategy obtained more calibration samples and covered a wider range of DOLP, especially for the samples with DOLP of less than 0.2, compared with the screening strategies of Toubbe et al. [IEEE Trans. Geosci. Remote Sens. 37, 513 (1999)IGRSD20196-289210.1109/36.739104]and Hagolle et al. [IEEE Trans. Geosci. Remote Sens. 42, 1472 (2004)IGRSD20196-289210.1109/TGRS.2004.826805] in previous work. The smaller standard error (SE) of the samples indicates more stable calibration results obtained for the optimized strategy. This research presents an optimized strategy for screening the solar-viewing geometry of the samples to calibrate satellite in-flight polarization measurements using the sun glint.

Uncertainty in model estimates of global groundwater depth

Abstract Knowing the depth at which groundwater can be found below the land surface is critical for understanding its potential accessibility by ecosystems and society. Uncertainty in global scale water table depth (WTD) limits our ability to assess groundwater’s role in a water cycle altered by changing climate, land cover, and human water use. Global groundwater models offer a top–down pathway to gain this knowledge, but their uncertainty is currently poorly quantified. Here, we investigate four global groundwater models and reveal steady-state WTD disagreements of more than 100 m for one-third of the global land area. We find that model estimates of land areas with shallow groundwater at <10 m depth vary from 10% to 71% (mean of 23%). This uncertainty directly translates into subsequent assessments, as land areas with potential groundwater accessibility for forests, population, and areas equipped for irrigation, differ substantially depending on the chosen model. We explore reasons for these differences and find that contrary to observations, 3 out of 4 models show deeper water tables in humid than in arid climates and greatly overestimate how strongly topographic slope controls WTD. These results highlight substantial uncertainty associated with any global-scale groundwater analysis, which should be considered and ultimately reduced.

Dynamic Scene Representation for Docking in Urban Waters Using a Stereo Camera

Abstract To navigate and dock safely in urban waters, an autonomous ferry must assess its safe navigation paths by identifying navigable free space and potential obstacles. A deep understanding of its surroundings is crucial for situational awareness. Knowing which objects are static and dynamic is an indispensable ingredient for path planning algorithms and collision avoidance systems, and is especially useful for docking operations, as moving obstacles can interfere with the optimal path to the dock. To handle all types of obstacles, we present a novel dynamic scene representation for docking in urban waters using a short baseline stereo camera. Obstacles protruding from the water surface are represented with vertically oriented rectangles, known as stixels. The stixels are tracked over consecutive frames with dense optical flow. Together with stereo correspondences, their 3D motion is estimated. Stixels representing the same object are grouped together into line segments which are classified as either dynamic or static based on the motion of its associated stixels. Our dynamic representation presents objects in the 3D camera coordinate system as a bird’s eye view map. Additionally, stereo depth uncertainty can significantly impact depth estimates and is considered using a Kalman filter, providing smooth position and velocity estimates. We test our dynamic scene representation on a real docking scenario recorded with an autonomous ferry using a stereo camera. We evaluate the accuracy of the reconstructed 3D scene points and the velocity estimates of the dynamic objects using LiDAR and GNSS.

Shear-Wave Velocity Data Assists Modeling for Offshore Windfarm Development

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 35354, “Pseudo-3D Underwater Multichannel Analysis of Surface Waves for Offshore Windfarm Development,” by Stephan Unterseh and Luis McArthur, TotalEnergies, and Edouard Mouton, SismOcean. The paper has not been peer reviewed. Copyright 2024 Offshore Technology Conference. _ In the course of a research and development (R&D) project dedicated to quantitative ground modeling, underwater multichannel analysis surface-wave (UMASW) data were acquired within an offshore windfarm area currently in development in the North Sea. The goal of the project described in the complete paper was to test the added value of the shear wave velocity (Vs) interpreted from the UMASW, particularly as a propagation tool of geotechnical information. While UMASW, together with seismic refraction, is regularly used to characterize the ground for burial or trenching purposes, it appears also to bring valuable design parameters for offshore wind turbine foundations. Introduction To investigate the value of Vs in the performance of quantitative ground modeling studies, an internal R&D initiative was launched aimed at acquiring Vs data and the associated uncertainties of foundation depth of an offshore windfarm currently in development. These data will be tested at a later stage for the propagation of geotechnical parameters used in foundation design. The acquisition consisted of obtaining Vs data through UMASW methodologies around two distinct geotechnical boreholes and along a corridor tying the two areas. The UMASW commonly is used in combination with seismic refraction to perform burial assessment analysis of cable or pipeline or to characterize ground conditions before performance of horizontal deviated drilling. Thus, the data acquisition did not require extensive hardware development. A unique pseudo-3D processing technique nevertheless was implemented to allow for 3D data visualization and ground characterization. Site Conditions The selected site lies in the North Sea in water depths ranging from 20 to 30 m. The area was first investigated with standard geophysical techniques. Preliminary geotechnical investigation included 6-m-deep vibrocores and 60-m-deep drilled boreholes, with alternating cone penetration tests and push sampling and coring. P-S logging also was performed at key geotechnical locations to gather 1D primary wave velocities (Vp) and Vs. The seabed generally consists of a thin cover of Holocene dense sand, occasionally with boulders, overlying quaternary subglacial formation deposits, including moraine, gravelly sand, and clays of variable thicknesses. The bedrock is generally Jurassic and includes dense sand; hard clay; weak claystone; and, locally, strong limestone bands. The test locations were selected within the coverage of the geophysical data set in areas where variable lithologies occur and where the seabed was found free of boulders and outcrops because these are detrimental to UMASW acquisition. The lithology of the two boreholes performed within the test area is indicated in Table 1 of the complete paper.

Solving East Nile Delta's imaging challenges through new ocean-bottom-node acquisition and advanced imaging

The East Nile Delta, located offshore Egypt, is a challenging area for seismic imaging due to the presence of substantial velocity heterogeneity created by the complex Messinian unconformity and overlaying fault structures. Existing towed-streamer (TS) seismic data have been pushed to their technical limit but remain insufficient for adequate representation of the subsurface. Previous studies have shown that a step change in imaging is possible if advanced acquisition, processing, and velocity model building techniques are employed. Atoll Field is a candidate to benefit from such improvements in seismic data. Results show that ocean-bottom-node acquisition provides improved spatial sampling and higher resolution. Acquiring seismic data with offsets of more than 30 km facilitated the generation of a robust high-frequency velocity model that better captures subsurface complexity. This paper outlines the acquisition and processing, with a focus on the velocity model building process. The generated subsurface model is effectively used for imaging with conventional migration algorithms and full-waveform-inversion-derived reflectivity. Results show a significant step change from previous TS images at the Oligocene target, with channel features clearly imaged. This provides an improved understanding of the depositional systems and reduced depth uncertainty.

Study Of Mixing Enhancement Of The 3-D Acoustofluidic Mixer Using Digital In-Line Holographic Micro Particle Tracking Velocimetry

In this study, the 3-D flow patterns and mixing performances are investigated for an acoustofluidic Y-junction micromixer using Digitial In-line Holographic Micro-Particle Tracking Velocimetry (DIHμPTV) and epi-fluorescence flow visualization. Two parallel longitudinal spines were placed in the channel to induce sustaining solenoidal 3-D acoustic streaming vortices, which work as the secondary flow in the mixing channel to reduce the diffusion mixing length and in turn enhance the mixing performances. The two spines have a triangular cross-section, with a base width of 0.2mm and a height of 0.3mm, placed 1.2mm apart from each other in a 2.8mm width and 1mm height channel. The Y-junction has two configurations, one has the spines directly connected to the side wall at the Y-junction, and the other microchannel has a gap between the two spines and a flat junction shape. The driving mechanism is provided by two piezoelectric disks, operating at 12 kHz oscillation frequency and 20V peak-to-peak amplitude. The 3-D flow patterns were recorded by the DIHμPTV in two measurement volumes to fully cover the test section’s height. The holographic reconstruction scheme is based on the 1st Rayleigh-Sommerfeld solution with an extra deconvolution step to improve the accuracy of the reconstruction of the depth position. The deconvolution step utilizes a simulated hologram of the Point-Spread Function (PSF) and reduces the depth uncertainty during the deconvolution of the raw data. The mixing performances of the acoustofluidic Y-junction micro-mixers were evaluated by the epi-fluorescence experiments, and the mixing indices were calculated and compared to verify the improvements compared to the Yjunction micromixers without the acoustic streaming vortices. The results confirmed the improvement of the mixing and the sustaining 3-D flow patterns even with a high flow rate, which could not be achieved with 2-D sharp-edge obstructions tested previously. The novel configuration may have the potential to inspire designs of high-throughput and low-pressure loss active micro-mixers.

Novel approaches to uncertainty estimation in seismic subsurface characterization

Uncertainty estimation in subsurface characterization workflows is an important input to decision-making in earth science-related problems. We present three methods to characterize seismic-related uncertainty, each of which includes a real data case study. Two of these methods are designed to characterize depth uncertainty in positioning of migrated reflectors. Such estimates may be used in derisking well depth prognoses, analysis of well misties, and for estimating ranges on resource volume. The first method derives rapid and robust estimates of depth uncertainty around an existing velocity model using traditional velocity analysis to constrain the solution space while limiting a-priori constraints, consistent with a frequentist approach to uncertainty characterization. The second method characterizes depth uncertainty more rigorously in respect to the physics and prior information available by performing full-waveform inversion in a Bayesian framework. This method produces similar uncertainty estimates to the first in the case of simple velocity models but is more accurate where the overburden is complex; however, it requires significantly greater computational expense, thus limiting current practical applications. The third method is an amplitude variation with angle (AVA) inversion for reservoir properties designed to output uncertainty products for interpreters, utilizing the Bayesian framework and Zoeppritz equations to define the forward physics. Application to an offshore Egypt field demonstrates that it can generate reliable estimates of reservoir properties (e.g., lithofluid type or shale volume) including uncertainty, useful in various parts of subsurface characterization. These results also show that the method could provide improved point estimates of reservoir properties compared to conventional deterministic AVA inversion approaches. There is usually a trade-off between increasing the accuracy of subsurface characterization versus creating faster, less expensive, and more readily understood workflows for practitioners. We discuss how the relative importance of these competing factors should be considered within the context of how the outputs will be used.

Predicting maximum scour depth uncertainty downstream of grade control structures: a multi-model approach with Bayesian model averaging

This research proposes a novel multi-model combination approach to overcome the limitations of traditional methods in predicting the scour depth downstream of grade control structures. The study evaluates 26 scenarios using a combination of five individual models and assesses the efficiency of four combination methods. Results indicate that GEP and SVM outperform traditional combination methods. Additionally, the study employs Bayesian Model Average (BMA) to assess the uncertainty originating from individual models. The research concludes by providing predictive equations for GEP and SVM, enabling users to apply these methods for accurate maximum scour depth prediction. In the ideal case, the SVM model had an R 2 = 0.91, with an RMSE of 0.84, whereas the GEP model achieved an R 2 = 0.94, with an RMSE of 0.67. The proposed multi-model approach is more reliable and accurate for predicting maximum scour depth downstream of GCS.

Utilizing Data-Driven Approaches to Forecast Fluctuations in Groundwater Table

Accurate forecasting of fluctuations in groundwater table is crucial for the effective management of regional water resources. This study explores the potential of utilizing remotely sensed satellite data to predict and forecast water table variations. Specifically, two Artificial Neural Network (ANN) models were developed to simulate water table fluctuations at two distinct well sites, namely BA Ea 18 and FR Df 35 in Maryland. One model leveraged the relationship between variations in brightness temperature and water table depth, while the other model was founded on the association between changes in soil moisture and water table depth. These models were trained and validated using recorded water table depths from the aforementioned wells, brightness temperature data acquired from the Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E), and soil moisture information generated using the Land Data Assimilation System (LDAS). All models exhibited strong performance in predicting and forecasting water table fluctuations, with root mean square errors ranging from 0.043 m to 0.047 m for a 12-month forecasting horizon. Sensitivity tests revealed that the models displayed greater sensitivity to uncertainties in water table depth compared to uncertainties in both brightness temperature and soil moisture content. This underscores the feasibility of constructing an ANN-based water table prediction model, even in cases where high-resolution remotely sensed data is unavailable. In such situations, the model’s efficacy is contingent on the compatibility of the time series trends in data, such as brightness temperature or soil moisture, with those observed at the study site.

Estimating uncertainty of mean water table depth in the contiguous United States using highly parameterized linear inverse method

Because of spatiotemporal observation gaps and prohibitive computational cost, estimating uncertainty of mean water table depth in the continental scale is a challenging problem. Based on the groundwater flow of mean water table, we first analyzed uncertainty of the mean water table depth in the contiguous US using a highly parameterized linear inverse method with unknown boundary condition and source/sink terms. To estimate uncertainty of the mean heads, the total relative errors of temporal heads and mean heads for all observed wells with more than one head instead of the relative error of temporal heads and mean head for each observed well was proposed to obtain t Location-scale and Gaussian distributions of the total relative errors in the synthetic cases. The results indicate that uncertainties of the mean heads by t Location-scale distribution are more accurate than those by Gaussian distribution. Then, we applied the inverse method and t Location-scale distribution of the total relative errors in the contiguous US to estimate uncertainty of the mean water table depth during 1900–2016. The results demonstrate that uncertainty in the K values did not result in large uncertainty of the mean water table depth. Uncertainty of the mean water table depth in the contiguous US was first estimated based on variation of hydraulic conductivity and the observed mean water table, where 100 realizations of the mean water table depth were obtained by t Location-scale distribution. The results demonstrate both the feasibility and high precision of the inverse method.

Monte Carlo evaluation of uncertainties in photon and electron TG-51 absorbed dose calibration.

The accuracy of dose delivery to all patients treated with medical linacs depends on the accuracy of beam calibration. Dose delivery cannot be any more accurate than this. Given the importance of this, it seems worthwhile taking another look at the expected uncertainty in TG-51 photon dose calibration and a first look at electron calibration. This work builds on the 2014 addendum to TG-51 for photons and adds to it by also considering electrons. In that publication, estimates were made of the uncertainty in the dose calibration. In this paper, we take a deeper look at this important issue. The methodology used here is more rigorous than previous determinations as it is based on Monte Carlo simulation of uncertainties. It is assumed that mechanical QA has been performed following TG-142 prior to beam calibration and that there are no uncertainties that exceed the tolerances specified by TG-142. Despite the different methodology and assumptions, the estimated uncertainty in photon beam calibration is close to that in the addendum. The careful user should be able to easily reach a 95% confidence interval (CI) of±2.3% for photon beam calibration with standard instrumentation. For electron beams calibrated with a Farmer chamber, the estimated uncertainties are slightly larger, and the 95% CI is ±2.6% for 6 MeV and slightly smaller than this for 18 MeV. There is no clear energy dependence in these results. It is unlikely that the user will be able to improve on these uncertainties as the dominant factor in the uncertainty resides in the ion chamber dose calibration factor . For both photons and electrons, reduction in the ion chamber depth uncertainty below about 0.5mm and SSD uncertainty below 1mm have almost no effect on the total dose uncertainty, as uncertainties beyond the user's control totally dominate under these circumstances.

An Ensemble Learning Model Reveals Accelerated Reductions in Snow Depth Over Arctic Sea Ice Under High‐Emission Scenarios

AbstractThere are significant differences in snow depth predictions among different earth system models, and many models underestimate snow depth, restricting their application. Here, major factors influencing snow depth changes in the Coupled Model Intercomparison Project Phase 6 (CMIP6) were identified and evaluated. Based on satellite‐derived snow depth and CMIP6 data, an ensemble learning model based on multiple deep learning methods (hereafter referred to as the Multi‐DL model) was developed to predict future snow depth. According to satellite observations and two Operation IceBridge products, the Multi‐DL model yielded root mean square errors of 7.48, 6.20, and 6.17 cm. A continuous decrease in snow depth was observed from 2002 to 2100, and the rate of decrease accelerated with increasing emissions. Under the highest emission scenario, the first snow‐free year occurred in 2047, within the same decade as the first ice‐free year (2056). The predicted warm season snow depth was sensitive to sea ice velocity, sea ice concentration (siconc), precipitation, sea surface temperature (tos) and albedo, while the predicted cold season snow depth was sensitive to tos, air temperature, and siconc. The above parameters introduce some snow depth uncertainty. This method provides new ideas for predicting snow depth, and the generated snow depth records can provide data support for formulating Arctic‐related policies.

Sensitivity of global direct aerosol shortwave radiative forcing to uncertainties in aerosol optical properties

Abstract. New satellite missions promise global reductions in the uncertainties in aerosol optical properties, but it is unclear how those reductions will propagate to uncertainties in the shortwave (SW) direct aerosol radiative effect (DARE) and direct aerosol radiative forcing (DARF), which are currently large, on the order of at least 20 %. In this work, we build a Monte Carlo framework to calculate the impact of uncertainties in aerosol optical depth (AOD), single scattering albedo (SSA), and the asymmetry parameter on the uncertainty in shortwave DARE and DARF. This framework uses the results of over 2.3 million radiative transfer simulations to calculate global clear-sky DARE and DARF based on a range of uncertainties in present-day and pre-industrial aerosol optical properties, representative of existing and future global observing systems. We find the 1σ uncertainty varies between ±0.23 and ±1.91 W m−2 (5 % and 42 %) for the top-of-atmosphere (TOA) clear-sky DARE and between ±0.08 and ±0.47 W m−2 (9 % and 52 %) for the TOA DARF. At the TOA, AOD uncertainty is the main contributor to overall uncertainty except over bright surfaces where SSA uncertainty contributes most. We apply regionally varying uncertainties to represent current measurement uncertainties and find that aerosol optical property uncertainties represent 24 % of TOA DARE and DARF. Reducing regionally varying optical property uncertainties by a factor of 2 would reduce their contributions to TOA DARE and DARF uncertainty proportionally. Applying a simple scaling to all-sky conditions, aerosol optical property uncertainty contributes to about 25 % total uncertainty in TOA, all-sky SW DARE, and DARF. Compared to previous studies which considered uncertainties in non-aerosol variables, our results suggest that the aerosol optical property uncertainty accounts for one-third to half of the total direct SW uncertainty. Recent and future progress in constraining aerosol optical properties using ground-based or satellite retrievals could be translated into DARE and DARF uncertainty using our freely available framework.

FusionFormer: A Concise Unified Feature Fusion Transformer for 3D Pose Estimation

Depth uncertainty is a core challenge in 3D human pose estimation, especially when the camera parameters are unknown. Previous methods try to reduce the impact of depth uncertainty by multi-view and/or multi-frame feature fusion to utilize more spatial and temporal information. However, they generally lead to marginal improvements and their performance still cannot match the camera-parameter-required methods. The reason is that their handcrafted fusion schemes cannot fuse the features flexibly, e.g., the multi-view and/or multi-frame features are fused separately. Moreover, the diverse and complicated fusion schemes make the principle for developing effective fusion schemes unclear and also raises an open problem that whether there exist more simple and elegant fusion schemes. To address these issues, this paper proposes an extremely concise unified feature fusion transformer (FusionFormer) with minimized handcrafted design for 3D pose estimation. FusionFormer fuses both the multi-view and multi-frame features in a unified fusion scheme, in which all the features are accessible to each other and thus can be fused flexibly. Experimental results on several mainstream datasets demonstrate that FusionFormer achieves state-of-the-art performance. To our best knowledge, this is the first camera-parameter-free method to outperform the existing camera-parameter-required methods, revealing the tremendous potential of camera-parameter-free models. These impressive experimental results together with our concise feature fusion scheme resolve the above open problem. Another appealing feature of FusionFormer we observe is that benefiting from its effective fusion scheme, we can achieve impressive performance with smaller model size and less FLOPs.

Deep Semantic Graph Transformer for Multi-View 3D Human Pose Estimation

Most Graph Convolutional Networks based 3D human pose estimation (HPE) methods were involved in single-view 3D HPE and utilized certain spatial graphs, existing key problems such as depth ambiguity, insufficient feature representation, or limited receptive fields. To address these issues, we propose a multi-view 3D HPE framework based on deep semantic graph transformer, which adaptively learns and fuses multi-view significant semantic features of human nodes to improve 3D HPE performance. First, we propose a deep semantic graph transformer encoder to enrich spatial feature information. It deeply mines the position, spatial structure, and skeletal edge knowledge of joints and dynamically learns their correlations. Then, we build a progressive multi-view spatial-temporal feature fusion framework to mitigate joint depth uncertainty. To enhance the pose spatial representation, deep spatial semantic feature are interacted and fused across different viewpoints during monocular feature extraction. Furthermore, long-time relevant temporal dependencies are modeled and spatial-temporal information from all viewpoints is fused to intermediately supervise the depth. Extensive experiments on three 3D HPE benchmarks show that our method achieves state-of-the-art results. It can effectively enhance pose features, mitigate depth ambiguity in single-view 3D HPE, and improve 3D HPE performance without providing camera parameters. Codes and models are available at https://github.com/z0911k/SGraFormer.

Improving depth uncertainty in plenoptic camera-based velocimetry

This work describes the development of a particle tracking velocimetry (PTV) algorithm designed to improve three-dimensional (3D), three-component velocity field measurements using a single plenoptic camera. Particular focus is on mitigating the longstanding depth uncertainty issues that have traditionally plagued plenoptic particle image velocimetry (PIV) experiments by leveraging the camera’s ability to generate multiple perspective views of a scene in order to assist both particle triangulation and tracking. 3D positions are first estimated via light field ray bundling (LFRB) whereby particle rays are projected into the measurement volume using image-to-object space mapping. Tracking is subsequently performed independently within each perspective view, providing a statistical amalgamation of each particle’s predicted motion through time in order to help guide 3D trajectory estimation while simultaneously protecting the tracking algorithm from physically unreasonable fluctuations in particle depth positions. A synthetic performance assessment revealed a reduction in the average depth errors obtained by LFRB as compared to the conventional multiplicative algebraic reconstruction technique when estimating particle locations. Further analysis using a synthetic vortex ring at a magnification of − 0.6 demonstrated plenoptic-PIV capable of maintaining the equivalent of 0.1–0.15 voxel accuracy in the depth domain at a spacing to displacement ratio of 5.3–10.5, an improvement of 84–89% compared to plenoptic-PIV. Experiments were conducted at a spacing to displacement ratio of approximately 5.8 to capture the 3D flow field around a rotor within the rotating reference frame. The resulting plenoptic-PIV/PTV vector fields were evaluated with reference to a fixed frame stereoscopic-PIV (stereo-PIV) validation experiment. A systematic depth-wise (radial) component of velocity directed toward the wingtip, consistent with observations from prior literature and stereo-PIV experiments, was captured by plenoptic-PTV at magnitudes similar to the validation data. In contrast, the plenoptic-PIV did not discern any coherent indication of radial motion. Our algorithm constitutes a significant advancement in enhancing the functionality and versatility of single-plenoptic camera flow diagnostics by directly addressing the primary limitation associated with plenoptic imaging.Graphical abstract

Performance of an approximate Bayes factor based detector for environmentally informed active sonar

Developed here are a space of approximate Bayes Factor (BF) inference processors for high frequency broadband active sonar with short vertical arrays operating in shallow water ocean waveguides that exploit relatively depth invariant modes of propagation. The relevant information regarding the refractive media, rough surface and volume reverberation are incorporated to build the marginal likelihoods under each of the composite hypotheses of null and alternative. Acoustic scattering from a mobile object of interest under depth uncertainty characterizes the compound alternative hypothesis. Approximations are presented and inferences regarding the presence of the mobile body of interest are determined against a composite null hypothesis of reverberation and ambient acoustic noise. The approximate BF processor is shown to be a time-varying quadratic form of array observations over the beam-delay space. We demonstrate the sub-space processing of depth invariant modes at range and illustrate the BF inferential approach on a few representative waveguides. Performance in classic terms of probability of detection as a function of false alarm rate are presented. [This work supported by the Office of Naval Research.]

Quality Control of DAS VSP Data in Desert Environment Using Simulations and Matching Filters.

The unconsolidated near surface and large, daily temperature variations in the desert environment degrade the vertical seismic profiling (VSP) data, posing the need for rigorous quality control. Distributed acoustic sensing (DAS) VSP data are often benchmarked using geophone surveys as a gold standard. This study showcases a new simulation-based way to assess the quality of DAS VSP acquired in the desert without geophone data. The depth uncertainty of the DAS channels in the wellbore is assessed by calibrating against formation depth based on the concept of conservation of the energy flux. Using the 1D velocity model derived from checkshot data, we simulate both DAS and geophone VSP data via an elastic pseudo-spectral finite difference method, and estimate the source and receiver signatures using matching filters. These field geophone data show high amplitude variations between channels that cannot be replicated in the simulation. In contrast, the DAS simulation shows a high visual similarity with the field DAS first arrival waveforms. The simulated source and receiver signatures are visually indistinguishable from the field DAS data in this study. Since under perfect conditions, the receiver signatures should be invariant with depth, we propose a new DAS data quality control metric based on local variations of the receiver signatures which does not require geophone measurements.