Spectral Quality Research Articles

AB-PLS-DA: Pansharpening tailored for Scanning Electron Microscopy and Energy-Dispersive X-ray Spectrometry multimodal fusion

Pansharpening constitutes a category of data fusion techniques designed to enhance the spatial resolution of multispectral (MS) images by integrating spatial details from a high-resolution panchromatic (PAN) image. This process combines the high-spectral data of MS images with the rich spatial information of the PAN image, resulting in a pansharpened output ideal for more effective image analysis, such as object detection and environmental monitoring. Traditionally developed for satellite data, our paper introduces a novel pansharpening approach customized for the fusion of Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectrometry (EDS) data. The proposed method, grounded in Partial Least Squares regression with Discriminant Analysis (PLS-DA), significantly boosts the spatial resolution of EDS data while preserving spectral details. A key feature of this approach involves partitioning the PAN image into intensity bins and dynamically adapting this division in cases of overlapping compounds with similar average atomic numbers. We evaluate the method’s effectiveness using in-house EDS images obtained from both even and uneven sample surfaces. Comparative analysis against existing benchmarks and state-of-the-art pansharpening techniques demonstrates superior performance in both spectral and spatial quality indicators for our method.

Fast high-resolution prospective motion correction for single-voxel spectroscopy.

To develop a fast high-resolution image-based motion correction method using spiral navigators with multislice-to-volume registration. A semi-LASER sequence was modified to include a multislice spiral navigator for prospective motion correction (∼305 ms including acquisition, processing, and feedback) as well as shim and frequency navigators for prospective shim and frequency correction (∼100 ms for each). MR spectra were obtained in the prefrontal cortex in five healthy subjects at 3 T with and without prospective motion and shim correction. The effect of key navigator parameters (number of slices, image resolution, and excitation flip angle) on registration accuracy was assessed using simulations. Without prospective motion and shim correction, spectral quality degraded significantly in the presence of voluntary motion. In contrast, with prospective motion and shim correction, spectral quality was improved (metabolite linewidth = 6.7 ± 0.6 Hz, SNR= 67 ± 9) and in good agreement with baseline data without motion (metabolite linewidth = 6.9 ± 0.9 Hz, SNR = 73 ± 9). In addition, there was no significant difference in metabolites concentrations measured without motion and with prospective motion and shim correction in the presence of motion. Simulations showed that the registration precision was comparable when using three navigator slices with 3 mm resolution and when using the entire volume (all slices) with 8 mm resolution. The proposed motion correction scheme allows fast and precise prospective motion and shim correction for single-voxel spectroscopy at 3 T. With 3 mm resolution, only a few navigator slices are necessary to achieve excellent motion correction performance.

Impact of photon flux density and light spectral quality on biomass production and arbutin compound accumulation in Origanum majorana L. plantlets

Impact of photon flux density and light spectral quality on biomass production and arbutin compound accumulation in Origanum majorana L. plantlets

Spectral effects and enhancement quantification in healthy human saliva with surface-enhanced Raman spectroscopy using silver nanopillar substrates.

Raman spectroscopy as a diagnostic tool for biofluid applications is limited by low inelastic scattering contributions compared to the fluorescence background from biomolecules. Surface-enhanced Raman spectroscopy (SERS) can increase Raman scattering signals, thereby offering the potential to reduce imaging times. We aimed to evaluate the enhancement related to the plasmonic effect and quantify the improvements in terms of spectral quality associated with SERS measurements in human saliva. Dried human saliva was characterized using spontaneous Raman spectroscopy and SERS. A fabrication protocol was implemented leading to the production of silver (Ag) nanopillar substrates by glancing angle deposition. Two different imaging systems were used to interrogate saliva from 161 healthy donors: a custom single-point macroscopic system and a Raman micro-spectroscopy instrument. Quantitative metrics were established to compare spontaneous RS and SERS measurements: the Raman spectroscopy quality factor (QF), the photonic count rate (PR), the signal-to-background ratio (SBR). SERS measurements acquired with an excitation energy four times smaller than with spontaneous RS resulted in improved QF, PR values an order of magnitude larger and a SBR twice as large. The SERS enhancement reached 100×, depending on which Raman bands were considered. Single-point measurement of dried saliva with silver nanopillars substrates led to reproducible SERS measurements, paving the way to real-time tools of diagnosis in human biofluids.

Identification of Counterfeit Tobacco using Attenuated Total Reflectance Fourier Transform Infrared, Raman, and Near-Infrared Spectroscopy

Counterfeit and illicit tobacco may contain potentially toxic organic impurities that result in adverse health effects for the consumer. Carcinogenic substances have been found in elevated concentrations in counterfeit tobacco. The current study explored swift and cost-effective methods, such as Fourier-transform infrared, Raman, and near-infrared spectroscopy to identify counterfeit tobacco products. Moreover, it also assessed the spectral quality based on range, number of peaks, maximum peak intensity, and signal-to-noise ratio. In spectral analysis, infrared exhibited superior quality over Raman and near-infrared with a higher signal-to-noise ratio. Near-infrared, although with a limited wavelength range, showed good quality. Raman, however, displayed poor spectral quality due to noise interference. Identification success varied with chemo-metric methods, with infrared correctly identifying four out of five counterfeit tobacco products and near-infrared identifying three out of five. Raman failed in identification, while infrared showed success with some mismatches using principal component analysis. Near-infrared and Raman successfully identified counterfeit tobacco, with near-infrared providing the most distinct separation. Quantification results varied across spectroscopy techniques and chemo-metric methods.

MULTISPECTRAL IMAGE RESTORATION USING A VECTOR-VALUED REACTION-DIFFUSION BASED MIXED NOISE REMOVAL TECHNIQUE

Abstract. A novel multispectral image filtering technique is proposed in this article. Since the multispectral images are often corrupted by mixed Poisson-Gaussian noise during the sensing and acquisition process, a nonlinear anisotropic diffusion-based restoration approach that deals efficiently with this type of noise mixture is considered here. A second-order vector-valued reaction-diffusion model that leads to a system of well-posed single-valued anisotropic diffusion equations coupled by correlation terms is introduced for this purpose. A finite difference method-based fast-converging approximation algorithm that solves numerically this nonlinear diffusion-based system is then proposed. This iterative numerical approximation scheme is successfully used for removing both the additive Gaussian and quantum noises while preserving the essential features of the multi-valued image. The effectiveness of the described mixed denoising technique is illustrated by the results of the restoration experiments and method comparisons that are also presented here. The proposed restoration approach enhances considerably the spectral image quality, making it well-prepared for the further MSI analysis and computer vision processes, such as the geospatial and remote sensing image analysis.

Far-red light and temperature interactively regulate plant growth and morphology of lettuce and basil

Both the spectral quality of light, particularly the ratio of red (R; 600–700 nm) to far-red light (FR; 700–800 nm), and temperature can regulate phytochrome activities, subsequently influencing plant growth and morphology. However, our understanding of how light quality and temperature interact to control phytochrome-mediated morphological responses is largely limited to hypocotyl elongation in Arabidopsis seedlings. In this study, we quantified the interactive effects between FR light and temperature on morphological traits of agronomic and horticultural significance in two crops with contrasting morphology and growth habits, lettuce (Lactuca sativa L.) ‘Rex’ and basil (Ocimum basilicum) ‘Genovese’. Plants were grown under blue (B), green (G), R, and FR LED light with three spectral treatments [B25G25R200 (0%FR), B25G25R175FR25 (10%FR), B25G25R150FR50 (20%FR); the subscript represents photon flux density in μmol m−2 s−1] and five temperature regimes [Day (℃)/Night (℃); 20/20, 24/24, 28/28, 20/28 (–DIF), 28/20 (+DIF); DIF represents differential day/night temperature]. Total photon flux density in each spectral treatment was 250 μmol m−2 s−1 (400–800 nm; 12-h photoperiod). As FR light percentage and temperature increased, leaf length and plant height increased in both species. However, the effects of FR light and temperature were interdependent. Specifically, the stimulative effects of FR light on extension growth generally diminished under warmer temperatures and in the –DIF treatment (20/28 ℃). Total leaf area increased with increasing FR percentage under 20/20 and 24/24 ℃ treatments in both lettuce and basil. However, under 28/28 ℃, increasing the FR percentage from 0% to 20% caused a decrease in lettuce leaf area while having no effect on basil leaf area. Similar to responses in total leaf area, dry biomass significantly increased with increasing FR percentage under cooler temperatures. Under warm temperature of 28/28 ℃, dry biomass decreased with increasing FR percentage in lettuce, whereas no significant difference was observed in basil. Due to the interaction between FR and temperature, phytochrome photoequilibrium (PPE) estimated from photoconversions alone was less predictive of plant morphology under varying temperature conditions. Those results indicate that light spectrum and temperature interactively regulate the activity of phytochromes, plant morphology, and subsequent plant growth in both lettuce and basil, and thus should be co-optimized in controlled environment crop production systems.

TOWARDS HIGH RESOLUTION FEATURE MAPPNG WITH SENTINEL-2 IMAGES

Abstract. High resolution feature mapping from medium resolution imageries gained special attention among remote sensing user community with the launch of Copernicus’ Sentinel-2 mission due to its capability to provide global coverage with relatively high revisit time at no cost. In this paper, we have examined and evaluated the potential of high resolution (2.5m) feature mapping from Sentinel-2 imageries with the aid of artificial intelligence. Generative adversarial network (GAN) is used as single image super resolution (SISR) technology in this study. And SPOT satellite imageries are used as corresponding high-resolution images. From qualitative and quantitative analysis of the experimental results found that spectral quality of the generated images is adequate for remote sensing applications. In conclusion, high resolution feature mapping from Sentinel-2 images found to be feasible to a greater extent for remote sensing applications.

Automated High-Order Shimming for Neuroimaging Studies.

B0 inhomogeneity presents a significant challenge in MRI and MR spectroscopy, particularly at high-field strengths, leading to image distortion, signal loss, and spectral broadening. Existing high-order shimming methods can alleviate these issues but often require time-consuming and subjective manual selection of regions of interest (ROIs). To address this, we proposed an automated high-order shimming (autoHOS) method, incorporating deep-learning-based brain extraction and image-based high-order shimming. This approach performs automated real-time brain extraction to define the ROI of the field map to be used in the shimming algorithm. The shimming performance of autoHOS was assessed through in vivo echo-planar imaging (EPI) and spectroscopic studies at both 3T and 7T field strengths. AutoHOS outperforms linear shimming and manual high-order shimming, enhancing both the image and spectral quality by reducing the EPI image distortion and narrowing the MRS spectral lineshapes. Therefore, autoHOS demonstrated a significant improvement in correcting B0 inhomogeneity while eliminating the need for additional user interaction.

Research on the Inversion Model of Cultivated Land Quality Using High-Resolution Remote Sensing Data

Cultivated land quality is an essential measure of cultivated land production capability. Establishing a cultivated land quality inversion model based on high-resolution remote sensing data provides a scientific basis for regional cultivated land resource management and sustainable utilization. Utilizing field survey data, cultivated land quality evaluation data, and high-resolution remote sensing data, a spectral index-cultivated land quality model was constructed and optimized with the machine learning method, and cultivated land quality inversion and verification in Chuzhou City in 2021 were carried out. The results showed that the distribution of cultivated land quality in the study area depicted with the remote sensing inversion model based on random forest was consistent with the actual cultivated land quality. Although the accuracy of the SVT-CLQ inversion model established using four spectral indices is slightly lower than that of the MSVT-CLQ group established using 15 indices, it can still accurately reflect the distribution of cultivated land quality in the study area. Compared with the two models of the MSVT-CLQ and SVT-CLQ groups, the field survey data of sampling points is reduced, the time and energy of field sampling and analysis are correspondingly saved, the efficiency of cultivated land quality evaluation is improved, and the dynamic monitoring and rapid evaluation of cultivated land quality are realized.

A variational driven optimization framework for pansharpening of multispectral images

ABSTRACT Pansharpening is an established remote sensing image fusion technique that yields a high-resolution multispectral (HRMS) image. Despite the fact that the advanced technologies like sparse coding and deep learning have achieved a remarkable improvement in solving the pansharpening problem, a unified model is required to further enhance the fusion quality. The variational optimisation (VO) mechanism has gained interest of most of the researchers in recent years. In this article, pansharpening is designated as a constrained optimisation problem with a data generative term and two regularisers to ameliorate spatial details and spectral information. The gradient information is exploited to impart the spatial details from the panchromatic image to the fused image. The correlation among multispectral image bands inspired to promote the spectral quality of the HRMS image as well as to reduce the distortion. Consequently, the optimisation problem is efficiently solved for the required HRMS image using the operator splitting approach. The extensive experiments performed in accordance with the well-known protocols rationalise that the proposed model outperforms most of the state-of-the-art methods in terms of objective metrics and visual outcomes.

Unraveling Molecular Composition in Biological Samples-Systematic Evaluation of Statistical Methods for the Analysis of Hyperspectral Raman Data.

Recently, confocal Raman microscopy has gained popularity in biomedical research for studying tissues in healthy and diseased state due to its ability to acquire chemically selective data in a noninvasive approach. However, biological samples, such as brain tissue, are inherently difficult to analyze due to the superposition of molecules in the Raman spectra and low variation of spectral features within the sample. The analysis is further impeded by pathological hallmarks, for example beta-amyloid (Aβ) plaques in Alzheimer's disease, which are often solely characterized by subtle shifts in the respective Raman peaks. To unravel the underlying molecular information, convoluted statistical procedures are inevitable. Unfortunately, such statistical methods are often inadequately described, and most natural scientists lack knowledge of their appropriate use, causing unreproducible results and stagnation in the application of hyperspectral Raman imaging. Therefore, we have set out to provide a comprehensive guide to address these challenges with the example of a complex hyperspectral data set of brain tissue samples with Aβ plaques. Our study encompasses established as well as novel statistical methods, including univariate analysis, principal component analysis, cluster analysis, spectral unmixing, and 2D correlation spectroscopy, and critically compares the outcomes of each analysis. Moreover, we transparently demonstrate the effect of preprocessing decisions like denoising and scaling techniques, providing valuable insights into implications of spectral quality for data evaluation. Thereby, this study provides a comprehensive evaluation of analysis approaches for complex hyperspectral Raman data, laying out a blueprint for elucidating meaningful information from biological samples in chemical imaging.

A Multi-sensor Analysis of Selected Reflectance-Based Crop Coefficient Models for Daily Maize Evapotranspiration Estimation

This study evaluated three reflectance-based crop coefficient models (RBCC) for daily maize actual evapotranspiration (ETa) estimates, using multispectral data from spaceborne, airborne, and proximal platforms. The goal was to identify the optimal multispectral sensor that gives more accurate daily ETa estimates. The remote sensing (RS) multispectral platforms included Landsat-8, Sentinel-2, Planet CubeSat, handheld multispectral radiometer (MSR), and unmanned aerial system or UAS, spatial resolution from 30 m to 0.03 m. Three RBCC models that use different vegetation indices as input variables were evaluated in the study. One RBCC uses the normalized difference vegetation index (NDVI). The second model uses the soil-adjusted vegetation index (SAVI), and the third model uses canopy cover (fc). The data for this study were from two maize research sites in Greeley and Fort Collins, Colorado, USA, collected in 2020 and 2021. The Greeley site had a subsurface drip system, while the Fort Collins site had surface irrigation (furrow). Daily maize ETa predictions were compared with observed daily maize ETa data from an Eddy Covariance system installed at each research site. Results indicated that, depending on the RS of ETa algorithm and platform, the optimal input RS data was different. The MSR sensor (1 m) provided the best remote sensing data (input) for the SAVI-based RBCC ETa model, with a maize ETa error (MBE±RMSE) of -0.13 (-3%)±0.67 (16%) mm/d. Sentinel-2 was the best sensor for the remaining two RBCC daily maize ETa algorithms, since the errors for the NDVI-based and fc-based RBCC models for maize ETa were 0.21 (5%)±0.78 (18%) mm/d and 0.59 (14%)±1.07 (25%) mm/d, respectively. These results indicate the need for methods to improve the spectral quality of the remote sensing data to improve spatial ETa estimates and advance sustainable irrigation water management.

Learning Time-multiplexed phase-coded apertures for snapshot spectral-depth imaging.

Depth and spectral imaging are essential technologies for a myriad of applications but have been conventionally studied as individual problems. Recent efforts have been made to optically encode spectral-depth (SD) information jointly in a single image sensor measurement, subsequently decoded by a computational algorithm. The performance of single snapshot SD imaging systems mainly depends on the optical modulation function, referred to as codification, and the computational methods used to recover the SD information from the coded measurement. The optical modulation has been conventionally realized using coded apertures (CAs), phase masks, prisms or gratings, active illumination, and many others. In this work, we propose an optical modulation (codification) strategy that employs a color-coded aperture (CCA) in conjunction with a time-varying phase-coded aperture and a spatially-varying pixel shutter, thus yielding an effective time-multiplexed coded aperture (TMCA). We show that the proposed TMCA entails a spatially-variant point spread function (PSF) for a constant depth in a scene, which, in turn, facilitates the distinguishability, and therefore, better recovery of the depth information. Further, the selective filtering of specific spectral bands by the CCA encodes relevant spectral information that is disentangled using a reconstruction algorithm. We leverage the advances of deep learning techniques to jointly learn the optical modulation and the computational decoding algorithm in an end-to-end (E2E) framework. We demonstrate via simulations and with a real testbed prototype that the proposed TMCA strategy outperforms state-of-the-art snapshot SD imaging alternatives in both spectral and depth reconstruction quality.

Application timing and duration of LED and HPS supplements differentially influence yield, nutrient bioaccumulation, and light use efficiency of greenhouse basil across seasons.

Three primary factors that impact plant growth and development are light quantity, quality, and duration. Commercial growers can manipulate these parameters using light-emitting diodes (LEDs) to optimize biomass yield and plant quality. There is significant potential to synergize supplemental lighting (SL) parameters with seasonal variation of ambient sunlight to optimize crop light use efficiency (LUE), which could increase biomass while reducing SL electricity costs. To determine the best lighting characteristics and durations for different crops, particularly for enhancing the yield and nutritional quality of high-value specialty crops produced in greenhouses during the winter, a thorough efficacy comparison of progressive incremental daily light integrals (DLIs) using LED and high-pressure sodium (HPS) sources is required. The purpose of this study was to compare the effects of differential application timing and DLIs of supplemental blue (B)/red (R) narrowband wavelengths from LED lighting systems and HPS lamps on greenhouse hydroponic basil (Ocimum basilicum var. 'Genovese') production. We assessed edible biomass, nutrient bioaccumulation, and LUE. Nine light treatments included: one non-supplemented natural light (NL) control, two end-of-day (EOD) HPS treatments applied for 6h and 12h, five EOD 20B/80R LED treatments applied for 3h, 6h, 9h, 12h, 18h, and one continuous LED treatment (24h). Each SL treatment provided 100 µmol·m-2·s-1. The DLI of the NL control averaged 9.9mol·m-2·d-1 during the growth period (ranging from 4 to 20mol·m-2·d-1). SL treatments and growing seasons significantly impacted biomass and nutrient bioaccumulation; some SL treatments had lower yields than the non-supplemented NL control. January growing season produced the lowest fresh mass (FM) and dry mass (DM) values compared to November, which had the highest. Mineral analyses revealed that both growing seasons and lighting types impacted macro and micronutrient accumulation. Additionally, the efficiency of each treatment in converting electrical energy into biomass varied greatly. EOD supplements using LED and HPS lighting systems both have merits for efficiently optimizing yield and nutrient accumulation in basil; however, biomass and nutrient tissue concentrations highly depend on seasonal variation in ambient sunlight in conjunction with a supplement's spectral quality, DLI, and application schedule.

An improved method for measuring epi-wafer thickness based on the infrared interference principle: Addressing interference quality and multiple interferences in double-layer structures

The precise measurement of the thickness of substrate and epitaxial layers in epi-wafers is essential in the semiconductor chip fabrication process. This study presents a new method for measuring the thickness of epi-wafers using Fourier Transform Infrared (FTIR) technology, which allows for precise, simultaneous, and fast measurement of both substrate and epitaxial layers. To address the low accuracy of traditional FTIR techniques, a novel VMD-LSP algorithm is introduced, capable of precisely extracting frequency components from the original spectral signal to calculate the precise thickness of the substrate and epitaxial layers. This study also introduces an adaptive spectral excision algorithm and a high-precision thickness extraction algorithm to handle variations in the infrared light absorption of different epi-wafer materials, leading to weakened interference signals, and the challenges that stem from the unique double-layer structure of the wafer. These algorithms efficiently reduce the impact of poor spectral quality and multiple interferences on measurement results, resulting in improved measurement accuracy and stability. During the experimental phase, we compared our results obtained by ellipsometry as truth to evaluate the accuracy and stability of our proposed method. The results show an 85% reduction in measurement error and a 65% improvement in stability compared to traditional methods. As a result, our method meets the need for fast and efficient epi-wafer thickness measurement in semiconductor chip manufacturing, providing a feasible and effective solution to the challenge of epi-wafer thickness measurement.

Impact of repeated short light exposures on sustained pupil responses in an fMRI environment.

Light triggers numerous non-image-forming, or non-visual, biological effects. The brain correlates of these non-image-forming effects have been investigated, notably using magnetic resonance imaging and short light exposures varying in irradiance and spectral quality. However, it is not clear whether non-image-forming responses estimation may be biased by having light in sequential blocks, for example, through a potential carryover effect of one light onto the next. We reasoned that pupil light reflex was an easy readout of one of the non-image-forming effects of light that could be used to address this issue. We characterised the sustained pupil light reflex in 13-16 healthy young individuals under short light exposures during three distinct cognitive processes (executive, emotional and attentional). Light conditions pseudo-randomly alternated between monochromatic orange light (0.16 melanopic equivalent daylight illuminance lux) and polychromatic blue-enriched white light of three different levels (37, 92, 190 melanopic equivalent daylight illuminance lux). As expected, higher melanopic irradiance was associated with larger sustained pupil light reflex in each cognitive domain. This result was stable over the light sequence under higher melanopic irradiance levels compared with lower ones. Exploratory frequency-domain analyses further revealed that sustained pupil light reflex was more variable under lower melanopic irradiance levels. Importantly, sustained pupil light reflex varied across tasks independently of the light condition, pointing to a potential impact of light history and/or cognitive context on sustained pupil light reflex. Together, our results emphasise that the distinct contribution and adaptation of the different retinal photoreceptors influence the non-image-forming effects of light and therefore potentially their brain correlates.

Wavelength dependences of the optical/UV and X-ray luminosity correlations of quasars

ABSTRACT The interband correlations between optical/ultraviolet (UV) and X-ray luminosities of active galactic nuclei (AGNs) are important for understanding the disc–coronal connection, as well as using AGN as standard candles for cosmology. It is conventional to measure the X-ray luminosity at rest-frame 2 keV and compare to the UV luminosity at the rest-frame 2500 Å, but the wavelength dependence was never well explored. In this work, we adopt a well-defined sample of 1169 unobscured quasars in the redshift range 0.13–4.51, and apply the direct-correlation method to explore how the correlation with the 2 keV luminosity changes at different optical/UV wavelengths, from 1280 to 5550 Å where the spectral quality is high. We find that the luminosity at all UV continuum wavelengths correlates with the X-ray luminosity similarly to that at 2500 Å, and that these correlations are better than at the optical wavelengths. Strong self-correlation is also found in the broad-band optical/UV continuum, supporting the scenario that it is dominated by the disc emission. Correlations of various emission lines are also investigated (e.g. C iv, C iii], Mg ii, Hβ, and [O iii]λλ4959/5007), including the Baldwin effect and correlations involving linewidths. We find the forms of these line correlations are different, and they are also different from their underlying continua, suggesting various complexities in the line-generation process. We discuss these results in the disc-wind scenario. Our study confirms that the rest-frame 2500 Å is a good wavelength to represent the optical/UV continual properties of quasars, and shows the advantages of the direct-correlation method.

Regional spectral characteristics, quality factor and site responses in western-central Sichuan, China (I): Application of parametric generalized inversion technique

To study the source, site amplification and seismic wave attenuation characteristics in the Sichuan area, 1242 ground motion recordings corresponding to 62 earthquake events with Ms 4.3–6.4 recorded by 55 observation stations in the study area were selected. The source spectra, site effects and frequency-related quality factors in the western Sichuan area were inverted by the generalized inversion technique. Then, the source parameters, such as corner frequency, seismic moment and stress drop, were estimated by a grid-searching tool. In the generalized inversion method, a bedrock station was used to remove the trade-off between the site and source components. The inverted quality factor of 0.25–20 Hz frequency band is characterized as Q(f) = 219.4f0.7383. The results indicate that the inverted source spectrum is well matched with the ω2 source model, in which the stress parameter values range between 0.2 MPa and 3.5 MPa, with an average value of 1.2 MPa and source radii between 0.3 and 4 km. In addition, a comparison between the inversion results obtained from the horizontal-to-vertical spectral ratio and the generalized inversion methods indicates that these two approaches actually describe the same level of site effect, while the averaged site effect obtained by the generalized inversion method is always greater than the result obtained by the horizontal-to-vertical spectral ratio method. Finally, a residual defined as a function of hypocentral distance and moment magnitude was used to investigate whether there was any bias in the inversion results. The results indicate that the geometric spreading and source model used in this study resulted in an underestimation of the Fourier amplitude level in the far field and for large earthquakes. The results of this study can be applied in ground motion simulation and prediction to help the study of seismic disasters and risk assessment in the study area.

Coded aperture snapshot hyperspectral light field tomography.

Multidimensional imaging has emerged as a powerful technology capable of simultaneously acquiring spatial, spectral, and depth information about a scene. However, existing approaches often rely on mechanical scanning or multi-modal sensing configurations, leading to prolonged acquisition times and increased system complexity. Coded aperture snapshot spectral imaging (CASSI) has introduced compressed sensing to recover three-dimensional (3D) spatial-spectral datacubes from single snapshot two-dimensional (2D) measurements. Despite its advantages, the reconstruction problem remains severely underdetermined due to the high compression ratio, resulting in limited spatial and spectral reconstruction quality. To overcome this challenge, we developed a novel two-stage cascaded compressed sensing scheme called coded aperture snapshot hyperspectral light field tomography (CASH-LIFT). By appropriately distributing the computation load to each stage, this method utilizes the compressibility of natural scenes in multiple domains, reducing the ill-posed nature of datacube recovery and achieving enhanced spatial resolution, suppressed aliasing artifacts, and improved spectral fidelity. Additionally, leveraging the snapshot 3D imaging capability of LIFT, our approach efficiently records a five-dimensional (5D) plenoptic function in a single snapshot.