Field Acquisition Research Articles

Real-time 3D temperature field reconstruction for aluminum alloy forging die using Swin Transformer integrated deep learning framework

Temperature field distribution in forging dies is crucial for quality control and defect prevention, particularly for aluminum alloys. Current methods are limited to discrete points or surface measurements, making real-time three-dimensional temperature field acquisition challenging. In this paper, a novel Swin Transformer-integrated deep learning framework is proposed for real-time 3D temperature field reconstruction of forging dies, pioneering the application of transformer architecture in physical field prediction. In this framework, numerical simulations are first conducted to provide ground truth and fundamental insights into the temperature evolution, and then limited sparse thermal sensors are utilized to offer corrected real-time input parameters. The model for 3D temperature field reconstruction is developed through the combination of Swin Transformers with the U-shaped encoder-decoder structure, which is trained and tested with various sensor configurations, initialization methods, and datasets, including actual experiments. The results demonstrate that the proposed Swin-UNETR model achieves 3D temperature field prediction with time cost of 0.98 s per frame, mean absolute error of 0.8658 °C, showing a 17.23 % improvement over the next best CNN-based model (ResUNet3D at 1.0461 °C), and a 4.63 % improvement over the next best machine learning model (LightGBM at 0.9078 °C), which can be attributed to the Swin Transformer’s ability to capture both local and global contextual information and shifted window mechanism. The proposed method holds significant implications for ensuring the forming quality of forgings and propelling the development of digital twin technology in forging processes.

Spectral whitening based seismic data preprocessing technique to improve the quality of surface wave's velocity spectra

A high-quality surface wave velocity spectrum, also known as a dispersion image, is paramount for any MASW survey to accurately predict subsurface earth properties. The presence of diversified noise during field acquisition and dissimilar attenuation due to mechanical and radial damping makes it challenging for any wavefield transformation technique to produce a detailed and precise velocity spectrum. Standard surface wave data preprocessing techniques, such as trace normalization and bandpass filtering, along with postprocessing techniques like frequency-wise amplitude normalization, fail to address all these issues appropriately. In this paper, we present a spectral whitening-based data preprocessing technique that can adequately eradicate most of the shortcomings associated with different wavefield transformation techniques. Instead of normalizing each trace, it normalizes the amplitude of every frequency present in the seismogram. The spectral whitening can regain the relative amplitude losses due to both radial and mechanical damping, thus improving the signal-to-noise ratio. Along with diversified field data including Love and Rayleigh wave surveys, a synthetic dataset is used to demonstrate the efficacy of the proposed technique. Furthermore, field noise is added to random traces to test the ability of the proposed technique to filter asymmetric noise. Overall, the spectral whitening procedure significantly improves the quality of the velocity spectrum and produces a sharper dispersion image with well-separated modes. The work presented here enhances our ability to interpret surface wave velocity spectra precisely and helps explore accurate properties of the subsurface earth. It can help avoid the need for repeated field tests in cases of extremely noisy data, thereby significantly reducing costs and saving time.

Anaplastic thyroid carcinoma: vimentin segregates at the invasive front of tumors in a murine xenograft model.

Anaplastic thyroid carcinoma (ATC) ranks among the most lethal human cancers. Increased migratory and invasive capabilities arecritical in malignancy and areoften secondary to epithelial-mesenchymal transition (EMT). However, it is not clear whether the invasive behavior of ATC is associated with the presence of EMT. In this study, we used a murine xenograft model (4-week-old male BALB/c NU/NU mice) with the human anaplastic cell line, FRO. We adopted an automated, eye-independent method to reconstruct the total/subtotal area of the tumors. To probe EMT, we evaluated the immunostaining of mesenchymal/epithelial markers at the front and center of the tumors. The transplanted cells invariably gave rise to tumor masses that histologically closely replicated patient tumors. The staining with hematoxylin-eosin and immunostaining with cytokeratin 18, an epithelial marker, were similar. However, the immunostaining of cytokeratin 18 versus vimentin, a mesenchymal marker, were strikingly dissimilar, since vimentin showed a staining concentrated at the front, rapidly declining towards the center of the tumor. The overlay, after color conversion, of cytokeratin and vimentin staining showed maximal coincidence at the front, which was rapidly lost towards the center. The results show EMT signs at the front of the ATC, which are probably at the basis of its tremendous invasiveness. Moreover, methodologically, an automated "eye-independent" acquisition of the total/subtotal area of the tumors drove the selection of second, high-magnification, automated field acquisition. Future studies may extend these results along the perspective of a personalized diagnostic procedure.

Dynamic Evolution Law of Production Stress Field in Fractured Tight Sandstone Horizontal Wells Considering Stress Sensitivity of Multiple Media

Inter-well frac-hit has become an important challenge in the development of unconventional oil and gas resources such as fractured tight sandstone. Due to the presence of hydraulic fracturing fractures, secondary induced fractures, natural fractures, and other seepage media in real formations, the acquisition of stress fields requires the coupling effect of seepage and stress. In this process, there is also stress sensitivity, which leads to unclear dynamic evolution laws of stress fields and increases the difficulty of the staged multi-cluster fracturing of horizontal wells. The use of a multi-stage stress-sensitive horizontal well production stress field prediction model is an effective means of analyzing the influence of natural fracture parameters, main fracture parameters, and multi-stage stress sensitivity coefficients on the stress field. This article considers multi-stage stress sensitivity and, based on fractured sandstone reservoir parameters, establishes a numerical model for the dynamic evolution of the production stress field in horizontal wells with matrix self-supporting fracture-supported fracture–seepage–stress coupling. The influence of various factors on the production stress field is analyzed. The results show that under constant pressure production, for low-permeability reservoirs, multi-stage stress sensitivity has a relatively low impact on reservoir stress, and the amplitude of principal stress change in the entire fracture length direction is only within the range of 0.27%, with no significant change in stress distribution; The parameters of the main fracture have a significant impact on the stress field, with a variation amplitude of within 2.85%. The ability of stress to diffuse from the fracture tip to the surrounding areas is stronger, and the stress concentration area spreads from an elliptical distribution to a semi-circular distribution. The random natural fracture parameters have a significant impact on pore pressure. As the density and angle of the fractures increase, the pore pressure changes within the range of 3.32%, and the diffusion area of pore pressure significantly increases, making it easy to communicate with the reservoir on both sides of the fractures.

Efficient light field acquisition for integral imaging with adaptive viewport optimization.

Light field displays reconstruct 3D scenes through integral imaging. However, inefficient light ray acquisition degrades the visual experience, while the fixed position of the exit pupil limits viewer mobility. In this paper, we propose a novel light field acquisition method employing parallax mapping techniques, coupled with adaptive viewport optimization based on eye tracking data. The parallax mapping relationship between camera pose variation and pixel offset facilitates partitioned rendering in integral image generation, and layer stacking is conducted to incorporate multiple depth cues. An eye tracking module is integrated to detect binocular landmarks and adaptively optimize screen segmentation, thus shifting the viewport to accommodate eye positions. Experimental results demonstrate correct refocusing cues and occlusion relationships, showing robustness in displaying complex scenes. The viewing zone has been expanded by at least twice, and the dynamic display performance meets real-time visual requirements.

Improvements of 177Lu SPECT images from sparsely acquired projections by reconstruction with deep-learning-generated synthetic projections

BackgroundFor dosimetry, the demand for whole-body SPECT/CT imaging, which require long acquisition durations with dual-head Anger cameras, is increasing. Here we evaluated sparsely acquired projections and assessed whether the addition of deep-learning-generated synthetic intermediate projections (SIPs) could improve the image quality while preserving dosimetric accuracy.MethodsThis study included 16 patients treated with 177Lu-DOTATATE with SPECT/CT imaging (120 projections, 120P) at four time points. Deep neural networks (CUSIPs) were designed and trained to compile 90 SIPs from 30 acquired projections (30P). The 120P, 30P, and three different CUSIP sets (30P + 90 SIPs) were reconstructed using Monte Carlo-based OSEM reconstruction (yielding 120P_rec, 30P_rec, and CUSIP_recs). The noise levels were visually compared. Quantitative measures of normalised root mean square error, normalised mean absolute error, peak signal-to-noise ratio, and structural similarity were evaluated, and kidney and bone marrow absorbed doses were estimated for each reconstruction set.ResultsThe use of SIPs visually improved noise levels. All quantitative measures demonstrated high similarity between CUSIP sets and 120P. Linear regression showed nearly perfect concordance of the kidney and bone marrow absorbed doses for all reconstruction sets, compared to the doses of 120P_rec (R2 ≥ 0.97). Compared to 120P_rec, the mean relative difference in kidney absorbed dose, for all reconstruction sets, was within 3%. For bone marrow absorbed doses, there was a higher dissipation in relative differences, and CUSIP_recs outperformed 30P_rec in mean relative difference (within 4% compared to 9%). Kidney and bone marrow absorbed doses for 30P_rec were statistically significantly different from those of 120_rec, as opposed to the absorbed doses of the best performing CUSIP_rec, where no statistically significant difference was found.ConclusionWhen performing SPECT/CT reconstruction, the use of SIPs can substantially reduce acquisition durations in SPECT/CT imaging, enabling acquisition of multiple fields of view of high image quality with satisfactory dosimetric accuracy.

Quantitative, real-time scintillation imaging for experimental comparison of different dose and dose rate estimations in UHDR proton pencil beams.

Ultra-high dose rate radiotherapy (UHDR-RT) has demonstrated normal tissue sparing capabilities, termed the FLASH effect; however, available dosimetry tools make it challenging to characterize the UHDR beams with sufficiently high concurrent spatial and temporal resolution. Novel dosimeters are needed for safe clinical implementation and improved understanding of the effect ofUHDR-RT. Ultra-fast scintillation imaging has been shown to provide a unique tool for spatio-temporal dosimetry of conventional cyclotron pencil beam scanning (PBS) deliveries, indicating the potential use for characterization of UHDR PBS proton beams. The goal of this work is to introduce this novel concept and demonstrate its capabilities in recording high-resolution dose rate maps at FLASH-capable proton beam currents, as compared to log-based dose rate calculation, internally developed UHDR beam simulation, and a fast point detector (EDGE diode). The light response of a scintillator sheet located at isocenter and irradiated by PBS proton fields (40-210 nA, 250 MeV) was imaged by an ultra-fast iCMOS camera at 4.5-12 kHz sampling frequency. Camera sensor and image intensifier gain were optimized to maximize the dynamic range; the camera acquisition rate was also varied to evaluate the optimal sampling frequency. Large field delivery enabled flat field acquisition for evaluation of system response homogeneity. Image intensity was calibrated to dose with film and the recorded spatio-temporal data was compared to a PPC05 ion chamber, log-based reconstruction, and EDGE diode. Dose and dose rate linearity studies were performed to evaluate agreement under various beam conditions. Calculation of full-field mean and PBS dose rate maps were calculated to highlight the importance of high resolution, full-field information in UHDRstudies. Camera response was linear with dose (R2 = 0.997) and current (R22 = 0.98) in the range from 2-22 Gy and 40-210 nA, respectively, when compared to ion chamber readings. The deviation of total irradiation time calculated with the imaging system from the log file recordings decreased from 0.07% to 0.03% when imaging at 12 kfps versus 4.5 kfps. Planned and delivered spot positions agreed within 0.2 0.1 mm and total irradiation time agreed within 0.2 0.2 ms when compared with the log files, indicating the high concurrent spatial and temporal resolution. For all deliveries, the PBS dose rate measured at the diode location agreed between the imaging and the diode within 3% 2% and with the simulation within 5% 3% CONCLUSIONS: Full-field mapping of dose and dose rate is imperative for complete understanding of UHDR PBS proton dose delivery. The high linearity and various spatiotemporal metric reporting capabilities confirm the continued use of this camera system for UHDR beam characterization, especially for spatially resolved dose rateinformation.

Meningeal contrast enhancement in multiple sclerosis: Assessment of field strength, acquisition delay, and clinical relevance.

Leptomeningeal enhancement (LME) on post-contrast FLAIR is described as a potential biomarker of meningeal inflammation in multiple sclerosis (MS). Here we report an assessment of the impact of MRI field strength and acquisition timing on meningeal contrast enhancement (MCE). This was a cross-sectional, observational study of 95 participants with MS and 17 healthy controls (HC) subjects. Each participant underwent an MRI of the brain on both a 7 Tesla (7T) and 3 Tesla (3T) MRI scanner. 7T protocols included a FLAIR image before, soon after (Gd+ Early 7T FLAIR), and 23 minutes after gadolinium (Gd+ Delayed 7T FLAIR). 3T protocol included FLAIR before and 21 minutes after gadolinium (Gd+ Delayed 3T FLAIR). LME was seen in 23.3% of participants with MS on Gd+ Delayed 3T FLAIR, 47.4% on Gd+ Early 7T FLAIR (p = 0.002) and 57.9% on Gd+ Delayed 7T FLAIR (p < 0.001 and p = 0.008, respectively). The count and volume of LME, leptomeningeal and paravascular enhancement (LMPE), and paravascular and dural enhancement (PDE) were all highest for Gd+ Delayed 7T FLAIR and lowest for Gd+ Delayed 3T FLAIR. Non-significant trends were seen for higher proportion, counts, and volumes for LME and PDE in MS compared to HCs. The rate of LMPE was different between MS and HCs on Gd+ Delayed 7T FLAIR (98.9% vs 82.4%, p = 0.003). MS participants with LME on Gd+ Delayed 7T FLAIR were older (47.6 (10.6) years) than those without (42.0 (9.7), p = 0.008). 7T MRI and a delay after contrast injection increased sensitivity for all forms of MCE. However, the lack of difference between groups for LME and its association with age calls into question its relevance as a biomarker of meningeal inflammation in MS.

Assessment of Calcaneal Spongy Bone Magnetic Resonance Characteristics in Women: A Comparison between Measures Obtained at 0.3 T, 1.5 T, and 3.0 T.

There is a growing interest in bone tissue MRI and an even greater interest in using low-cost MR scanners. However, the characteristics of bone MRI remain to be fully defined, especially at low field strength. This study aimed to characterize the signal-to-noise ratio (SNR), T2, and T2* in spongy bone at 0.3 T, 1.5 T, and 3.0 T. Furthermore, relaxation times were characterized as a function of bone-marrow lipid/water ratio content and trabecular bone density. Thirty-two women in total underwent an MR-imaging investigation of the calcaneus at 0.3 T, 1.5 T, and 3.0 T. MR-spectroscopy was performed at 3.0 T to assess the fat/water ratio. SNR, T2, and T2* were quantified in distinct calcaneal regions (ST, TC, and CC). ANOVA and Pearson correlation statistics were used. SNR increase depends on the magnetic field strength, acquisition sequence, and calcaneal location. T2* was different at 3.0 T and 1.5 T in ST, TC, and CC. Relaxation times decrease as much as the magnetic field strength increases. The significant linear correlation between relaxation times and fat/water found in healthy young is lost in osteoporotic subjects. The results have implications for the possible use of relaxation vs. lipid/water marrow content for bone quality assessment and the development of quantitative MRI diagnostics at low field strength.

Lightweight all-focused light field rendering

This paper proposes a novel real-time method for high-quality view interpolation from light field. The proposal is a lightweight method, which can be used with consumer GPU, reaching same or better quality than existing methods, in a shorter time, with significantly smaller memory requirements. Light field belongs to image-based rendering methods that can produce realistic images without computationally demanding algorithms. The novel view is synthesized from multiple input images of the same scene, captured at different camera positions. Standard rendering techniques, such as rasterization or ray-tracing, are limited in terms of quality, memory footprint, and speed. Light field rendering methods often produce unwanted artifacts resembling ghosting or blur in certain parts of the scene due to unknown geometry of the scene. The proposed method estimates the geometry for each pixel as an optimal focusing distance to mitigate the artifacts. The focusing distance determines which pixels from the input images are mixed to produce the final view. State-of-the-art methods use a constant-step pixel matching scan that iterates over a range of focusing distances. The scan searches for a distance with the smallest color dispersion of the contributing pixels, assuming that they belong to the same spot in the scene. The paper proposes an optimal scanning strategy of the focusing range, an improved color dispersion metric, and other minor improvements, such as sampling block size adjustment, out-of-bounds sampling, and filtering. Experimental results show that the proposal uses less resources, achieves better visual quality, and is significantly faster than existing light field rendering methods. The proposal is 8× faster than the methods in the same category. The proposal uses only four closest views from the light field data and reduces the necessary data transfer. Existing methods often require the full light field grid, which is typically 8 × 8 images large. Additionally, a new 4K light field dataset, containing scenes of various types, was created and published. An optimal novel method for light field acquisition is also proposed and used to create the dataset.

Seismic data reconstruction method using generative adversarial network based on moment reconstruction error constraint.

The seismic data acquired are usually spatially undersampled due to the constraints of the field acquisition environment. However, the removal of multiple waves, offsets, and inversions requires high regularity and integrity of seismic data. Therefore, reasonable data reconstruction methods are usually applied to the missing data in the indoor processing stage to recover regular seismic data. The traditional reconstruction methods for seismic data reconstruction are generally based on some assumptions (e.g., assuming that the data satisfies linearity or sparsity, etc.) and have some limitations of use. To overcome the applicability problem of traditional seismic data reconstruction methods, this article proposes a generative adversarial network (GAN) seismic data reconstruction method based on moment reconstruction error constraints. The method can extract the deep features of the data nonlinearly without any assumptions. First, the error function in the GAN is improved, and the commonly used joint error function of adversarial loss plus L1/L2 amplitude reconstruction loss is improved to a new error function consisting of adversarial loss and moment reconstruction loss weighting. Then, an adversarial network data reconstruction generation method based on the moment reconstruction error constraint is given. Next, an experimental analysis of different types of data missing was carried out using theoretical model data, and the study method was analyzed by interpolation errors. Finally, actual seismic data is used to further validate the effect of the research method. The experimental results show that the improved algorithm performs superiorly in dealing with the data reconstruction problem. Compared with the error function of conventional GAN optimization, the reconstruction results of GAN based on the moment reconstruction error constraint have better amplitude preservation.

Exploring limitations in the induced polarization versus surface conductivity relationship in the case of wetland soils

Recent induced polarization studies suggest that the real part of surface conductivity ([Formula: see text]) scales linearly with the imaginary conductivity ([Formula: see text] = [Formula: see text]) or normalized chargeability (Mn) for a range of soil types. The coefficients of this relationship l and l_Mn ( l = [Formula: see text]/[Formula: see text] or l_Mn = Mn/[Formula: see text]) allow the separation of the surface and electrolytic conductivities from the bulk conductivity. However, the dependence of these constants on varying soil physicochemical properties, including under unsaturated conditions, is yet to be assessed. Using estimates of [Formula: see text] from 18 undisturbed soil samples from a restored wetland and [Formula: see text] measured over a frequency range of 0.01 Hz to 10 kHz, the [Formula: see text] and [Formula: see text] were compared with the laboratory measurements of soil properties. Also, l and l_Mn were calculated for each soil sample and regressed them against the soil properties. We find an apparent dependence of l on soil texture, bulk density, organic matter, and moisture contents, with coefficients of determination ([Formula: see text]) ranging from 0.5 to 0.65 at low frequencies (e.g., 1 Hz) but not at high frequencies (e.g., 936 Hz). This dependence of l on soil texture results from the insensitivity of [Formula: see text] at low frequency to [Formula: see text] and, by implication, to the soil properties controlling [Formula: see text]. In contrast, l_Mn indicates no correlation with the soil properties because Mn is linearly correlated with [Formula: see text] and correlated with the soil properties controlling [Formula: see text]. Our results call for caution on the application of [Formula: see text] at a single frequency as a proxy of [Formula: see text] because [Formula: see text] is not necessarily correlated with [Formula: see text] across all soil types. Although using l_Mn derived from multifrequency measurements overcomes this limitation, field acquisition of spectral information (e.g., up to 1000 Hz) remains a challenge.

Feature-enhanced fiber bundle imaging based on light field acquisition

Feature-enhanced fiber bundle imaging based on light field acquisition

High-density analysis of surface wave profile imaging based on a multiple coverage common-midpoint signal-couple array

Surface wave exploration technology has been extensively used in the inspection of construction engineering quality and shallow surface surveys. To enhance the efficiency of surface wave exploration field acquisition and achieve high-precision and high-density surface wave profile imaging, a wireless distributed seismic surface wave signal acquisition system has been developed based on the principles of active source transient surface wave signal acquisition and dispersion curve calculation methods. For the purpose of achieving rapid multiple coverage signal acquisition and enhancing fieldwork efficiency, a method for rapidly configuring common-midpoint signal couples (CMCs) for multiple coverage common-shot signal acquisition has been devised, and a high-precision visualization method for dispersion curve calculation based on the CMC array has been formulated. When compared with the multichannel analysis of the surface wave method under identical conditions, the CMC array can effectively enhance surface wave dispersion curve survey station density and lateral resolution, thereby enabling high-density analysis of surface wave profile imaging. Through model analysis and field examples related to construction quality detection, including foundation compactness and earth and rock mixture compactness, it has been demonstrated that this method offers significant advantages in terms of high accuracy, high density, and a wide application range. These advantages greatly enhance the efficiency of surface wave exploration and the accuracy of profile imaging for the construction of engineering projects.

Light Field Acquisition Using Shack-Hartmann Wavefront Sensor and Its Application to Holographic Stereogram

Light Field Acquisition Using Shack-Hartmann Wavefront Sensor and Its Application to Holographic Stereogram

Cross-View Recurrence-Based Self-Supervised Super-Resolution of Light Field

Compared with external-supervised learning-based (ESLB) methods, self-supervised learning-based (SSLB) methods can overcome the domain gap problem caused by different light field (LF) acquisition conditions, which results in the performance degradation of light field super-resolution on unseen test datasets. Current SSLB methods exploit the cross-scale recurrence feature in the single view image for super-resolution, ignoring the correlation information among views. Different from previous works, we propose a cross-view recurrence-based self-supervised mapping framework to correlate complementary information among views in the down-scaled input LF. Specifically, the cross-view recurrence information consists of geometry structure features and similar structure features. The former is to provide sub-pixel information according to disparity correlations among adjacent views, and the latter is to acquire similar color and contour information among arbitrary views, which can compensate for error disparity guidance of geometry structure features in sharp variance areas. Moreover, instead of the widely used “All-to-All” strategy, we propose a “Part-to-Part” mapping strategy, which is better competent for SSLB approaches with limited training examples solely extracted from input LF. Finally, considering that self-supervised methods need to retrain from the beginning toward each test image, based on the proposed “part-to-part” strategy, an efficient end-to-end network is designed to extract these cross-view features for superior SASR performance with less training time. Experiment results demonstrate that our method outperforms other state-of-the-art ESLB methods on both large and small domain gap cases. Compared with the only SSLB method (LFZSSR [1]), our approach achieves better performance with 524 times less training time.

Retrieving BRDFs from UAV-based radiometers for fiducial reference measurements: caveats and recommendations

Surface Bidirectional reflectance distribution function (BRDF) is a key intrinsic geophysical variable depending only on the characteristics of the observed medium. It is therefore the most suitable measurand to support the definition of fiducial reference measurements (FRM). Field acquisition of surface reflectance data relies on substantial assumptions and simplifications, often without accounting for their impact. For example, the BRDF is a theoretical concept and can never be measured in the field. In contrast, the hemispherical conical reflectance factor (HCRF), which is the measurand obtained during field campaigns, is impacted by all scene elements and is not intrinsic to the surface. This study analyses the impact of four parameters (atmospheric scattering, measurement device field of view cropping, acquisition duration, non-Lambertian reference panels) on HCRF estimation. Simulations are performed on a 3D vegetation scene, using the new radiative transfer model Eradiate. It is found that among the aforementioned parameters, atmospheric scattering alone leads to a relative root-mean-square error (RRMSE) of more than 10% between HCRF and reference Bidirectional reflectance factor (BRF).

Integrated methodology to link geochemical and geophysical-lab data in a geophysical investigation of a slag heap for resource quantification

The increasing need to find alternative stocks of critical raw materials drives to revisit the residues generated during the former production of mineral and metallic raw materials. Geophysical methods contribute to the sustainable characterization of metallurgical residues inferring on their composition, zonation and volume(s) estimation. Nevertheless, more quantitative approaches are needed to link geochemical or mineralogical analyses with the geophysical data. In this contribution, we describe a methodology that integrates geochemical and geophysical laboratory measurements to interpret geophysical field data solving a classification problem. The final aim is to estimate volume(s) of different types of materials to assess the potential resource recovery. We illustrate this methodology with a slag heap composed of residues from a former iron and steel factory. First, we carried out a 3D field acquisition using electrical resistivity tomography (ERT) and induced polarization (IP), based on which, a sampling survey was designed. We conducted laboratory measurements of ERT, IP, spectral induced polarization (SIP), and X-ray fluorescence analysis, based on which, 4 groups of different chemical composition were identified. Then we carried out a 3D probabilistic classification of the field data, based on 2D kernel density estimators (for each group) fitted to the inverted data collocated with the samples. The estimated volumes based on the classification model were: 4.17 × 103 m3 ± 12 %, 1.888 × 105 m3 ± 12 %, 59.4 × 103 m3 ± 19 %, and 2.30 × 104 m3 ± 21% for the groups ordered with an increasing metallic content. The uncertainty ranges were derived from comparing the volumes with and without considering the probabilities associated to the classification. We found that a representative sampling and the definition of the KDE bandwidths are defining elements in the classification and ultimately the estimation of volumes. This methodology is suitable to quantitatively interpret geophysical data in terms of the geochemical composition of the materials, integrating uncertainties both in the classification and the estimation of volumes. Furthermore, several crucial elements in the investigation of metallurgical residues could be applied in a real case study, e.g., geophysical field acquisition, sampling and lab measurements.

Mapping Tree Water Deficit with UAV Thermal Imaging and Meteorological Data

The mapping of forest stands and individual trees affected by drought stress is a crucial step in targeted forest management, aimed at fostering resilient and diverse forests. Unoccupied aerial vehicle (UAV)-based thermal sensing is a promising method for obtaining high-resolution thermal data. However, the reliability of typical low-cost sensors adapted for UAVs is compromised due to various factors, such as internal sensor dynamics and environmental variables, including solar radiation intensity, relative humidity, object emissivity and wind. Additionally, accurately assessing drought stress in trees is a complex task that usually requires laborious and cost-intensive methods, particularly in field settings. In this study, we investigated the feasibility of using the thermal band of the Micasense Altum multispectral sensor, while also assessing the potential for modelling tree water deficit (TWD) through point dendrometers and UAV-derived canopy temperature. Our indoor tests indicated that using a limited number of pixels (< 3) could result in temperature errors exceeding 1 K. However, enlarging the spot-size substantially reduced the mean difference to 0.02 K, validated against leaf temperature sensors. Interestingly, drought-treated (unwatered) leaves exhibited a higher root mean squared error (RMSE) (RMSE = 0.66 K and 0.73 K) than watered leaves (RMSE = 0.55 K and 0.53 K), likely due to lower emissivity of the dry leaves. Comparing field acquisition methods, the mean standard deviation (SD) for tree crown temperature obtained from typical gridded flights was 0.25 K with a maximum SD of 0.59 K (n = 12). In contrast, a close-range hovering method produced a mean SD of 0.09 K and a maximum SD of 0.1 K (n = 8). Modelling the TWD from meteorological and point dendrometer data for the 2021 growth season (n = 2928) yielded an R2 = 0.667 using a generalised additive model (GAM) with vapor pressure deficit (VPD), wind speed, and solar radiation as input features. A point dendrometer lag of one hour was also implemented. When predicting individual tree TWD with UAV-derived tree canopy temperature, relative humidity, and air temperature, an RMSE of 4.92 (μm) and R2 of 0.87 were achieved using a GAM. Implementing leaf-to-air pressure deficit (LVPD) as an input feature resulted in an RMSE of 6.87 (μm) and an R2 of 0.71. This novel single-shot approach demonstrates a promising method to acquire thermal data for the purpose of mapping TWD of beech trees on an individual basis. Further testing and development are imperative, and additional data from drought periods, point dendrometers, and high-resolution meteorological sources are required.

Experimental reconstruction of sound fields over large spatial domains

This talk discusses sensing methods and reconstruction techniques for characterizing sound fields over large spatial domains. We focus specifically on reconstructing reverberant soundfields in rooms and enclosures. Two complementary approaches are presented in the talk: on the one hand, we present a method that leverages on modeling the spatio-temporal and statistical properties of enclosed sound fields via wave expansions, enabling to estimate the sound field over a large volume of space. On the other hand, we present a Physics Informed Neural Network, which learns the governing wave equation using a small set of measured data and enables to meaningfully interpolate and extrapolate the sound field to any position where no observations are available. Experiments in real rooms are presented, including an opera hall and an auditoria for oral communication. The experiments successfully demonstrate the volumetric acquisition and three-dimensional characterization of the sound fields. The presented techniques can be of relevance in applications pertaining to immersive and navigable audio, architectural acoustics and heritage preservation.