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Riethmüller, T. L., and S. K. Solanki. "The potential of many-line inversions of photospheric spectropolarimetric data in the visible and near UV." Astronomy & Astrophysics 622 (January 24, 2019): A36. http://dx.doi.org/10.1051/0004-6361/201833379.
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Our knowledge of the lower solar atmosphere is mainly obtained from spectropolarimetric observations, which are often carried out in the red or infrared spectral range and almost always cover only a single or a few spectral lines. Here we compare the quality of Stokes inversions of only a few spectral lines with many-line inversions. In connection with this, we have also investigated the feasibility of spectropolarimetry in the short-wavelength range, 3000 Å−4300 Å, where the line density but also the photon noise are considerably higher than in the red, so that many-line inversions could be particularly attractive in that wavelength range. This is also timely because this wavelength range will be the focus of a new spectropolarimeter in the third science flight of the balloon-borne solar observatory SUNRISE. For an ensemble of state-of-the-art magneto-hydrodynamical atmospheres we synthesize exemplarily spectral regions around 3140 Å (containing 371 identified spectral lines), around 4080 Å (328 lines), and around 6302 Å (110 lines). The spectral coverage is chosen such that at a spectral resolving power of 150 000 the spectra can be recorded by a 2K × 2K detector. The synthetic Stokes profiles are degraded with a typical photon noise and afterward inverted. The atmospheric parameters of the inversion of noisy profiles are compared with the inversion of noise-free spectra. We find that significantly more information can be obtained from many-line inversions than from a traditionally used inversion of only a few spectral lines. We further find that information on the upper photosphere can be significantly more reliably obtained at short wavelengths. In the mid and lower photosphere, the many-line approach at 4080 Å provides equally good results as the many-line approach at 6302 Å for the magnetic field strength and the line-of-sight (LOS) velocity, while the temperature determination is even more precise by a factor of three. We conclude from our results that many-line spectropolarimetry should be the preferred option in the future, and in particular at short wavelengths it offers a high potential in solar physics.
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Hall, R. L. "Geometric spectral inversion." Journal of Physics A: Mathematical and General 28, no. 6 (March 23, 1995): 1771–86. http://dx.doi.org/10.1088/0305-4470/28/6/028.
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Rubino, J. Germán, and Danilo Velis. "Thin-bed prestack spectral inversion." GEOPHYSICS 74, no. 4 (July 2009): R49—R57. http://dx.doi.org/10.1190/1.3148002.
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Prestack seismic data has been used in a new method to fully determine thin-bed properties, including the estimation of its thickness, P- and S-wave velocities, and density. The approach requires neither phase information nor normal-moveout (NMO) corrections, and assumes that the prestack seismic response of the thin layer can be isolated using an offset-dependent time window. We obtained the amplitude-versus-angle (AVA) response of the thin bed considering converted P-waves, S-waves, and all the associated multiples. We carried out the estimation of the thin-bed parameters in the frequency (amplitude spectrum) domain using simulated annealing. In contrast to using zero-offset data, the use of AVA data contributes to increase the robustness of this inverse problem under noisy conditions, as well as to significantly reduce its inherent nonuniqueness. To further reduce the nonuniqueness, and as a means to incorporate a priori geologic or geophysical information (e.g., well-log data), we imposed appropriate bounding constraints to the parameters of the media lying above and below the thin bed, which need not be known accurately. We tested the method by inverting noisy synthetic gathers corresponding to simple wedge models. In addition, we stochastically estimated the uncertainty of the solutions by inverting different data sets that share the same model parameters but are contaminated with different noise realizations. The results suggest that thin beds can be characterized fully with a moderate to high degree of confidence below tuning, even when using an approximate wavelet spectrum.
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Hofmann, Ryan A., Kevin P. Reardon, Ivan Milic, Momchil E. Molnar, Yi Chai, and Han Uitenbroek. "Evaluating Non-LTE Spectral Inversions with ALMA and IBIS." Astrophysical Journal 933, no. 2 (July 1, 2022): 244. http://dx.doi.org/10.3847/1538-4357/ac6f00.
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Abstract We present observations of a solar magnetic network region in the millimeter continuum with the Atacama Large Millimeter/submillimeter Array (ALMA) and in the Ca 8542 and Na 5896 Å spectral lines with the Interferometric Bidimensional Spectrometer (IBIS). Our goal is to compare the measurement of local gas temperatures provided by ALMA with the temperature diagnostics provided by non-LTE inversions using the STockholm inversion Code (STiC). In performing these inversions, we find that using column mass as the reference height scale, rather than optical depth, provides more reliable atmospheric profiles above the temperature minimum and that the treatment of non-LTE hydrogen ionization brings the inferred chromospheric temperatures into better agreement with the ALMA measurements. The Band 3 brightness temperatures are higher but well correlated spatially with the inversion-derived temperatures at the height of formation of the Ca 8542 line core. The Band 6 temperatures instead do not show good correlations with the temperatures at any specific layer in the inverted atmospheres. We then performed inversions that included the millimeter-continuum intensities as an additional constraint. Incorporating Band 3 generally resulted in atmospheres showing a strong temperature rise in the upper atmosphere, while including Band 6 led to significant regions of anomalously low temperatures at chromospheric heights. This is consistent with the idea that the Band 6 emission can come from a combination of heights ranging from the temperature minimum to upper chromosphere. The poor constraints on the chromospheric electron density with existing inversion codes introduces difficulties in determining the height(s) of formation of the millimeter continuum as well as uncertainties in the temperatures derived from the spectral lines.
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Muyzert, Everhard. "Seabed property estimation from ambient-noise recordings: Part 2 — Scholte-wave spectral-ratio inversion." GEOPHYSICS 72, no. 4 (July 2007): U47—U53. http://dx.doi.org/10.1190/1.2719062.
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Having knowledge of the near-surface shear-velocity model is useful for various seismic processing methods such as shear-wave static estimation, wavefield separation, and geohazard prediction. I present a new method to derive a 2D near-surface shear-velocity model from ambient-noise recordings made at the seafloor. The method relies on inverting horizontal- and vertical-amplitude spectra of Scholte waves propagating in the seafloor. I compare the commonly used horizontal-over-vertical spectral ratio with three alternative spectral-ratio definitions through modeling. The modeling shows that the vertical-over-total spectral ratio has some favorable properties for inversion. I describe a nonlinear inversion method for the vertical-to-total spectral ratio of the Scholte waves and apply it to an ambient-noise data set recorded by an ocean-bottom-cable (OBC) system. A 1D near-surface shear-velocity model is derived through a joint inversion of the spectral-ratio and phase-velocity data. A 2D shear-velocity model is obtained through a local inversion of the spectral ratios averaged over small groups of receivers and shows evidence for lateral heterogeneity. The newly developed method for deriving near-surface shear-velocity distribution by inverting the Scholte-wave spectral ratio measured from seabed noise provides great opportunities for estimating the shallow-seabed shear velocity in deep water. Another benefit of the method is that, with the OBC system, no additional hardware is needed; only additional recording time is required. In this case, half an hour is sufficient.
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Qi, Haixia, Bingyu Zhu, Lingxi Kong, Weiguang Yang, Jun Zou, Yubin Lan, and Lei Zhang. "Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves." Applied Sciences 10, no. 7 (March 26, 2020): 2259. http://dx.doi.org/10.3390/app10072259.
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The purpose of this study is to determine a method for quickly and accurately estimating the chlorophyll content of peanut plants at different plant densities. This was explored using leaf spectral reflectance to monitor peanut chlorophyll content to detect sensitive spectral bands and the optimum spectral indicators to establish a quantitative model. Peanut plants under different plant density conditions were monitored during three consecutive growth periods; single-photon avalanche diode (SPAD) and hyperspectral data derived from the leaves under the different plant density conditions were recorded. By combining arbitrary bands, indices were constructed across the full spectral range (350–2500 nm) based on blade spectra: the normalized difference spectral index (NDSI), ratio spectral index (RSI), difference spectral index (DSI) and soil-adjusted spectral index (SASI). This enabled the best vegetation index reflecting peanut-leaf SPAD values to be screened out by quantifying correlations with chlorophyll content, and the peanut leaf SPAD estimation models established by regression analysis to be compared and analyzed. The results showed that the chlorophyll content of peanut leaves decreased when plant density was either too high or too low, and that it reached its maximum at the appropriate plant density. In addition, differences in the spectral reflectance of peanut leaves under different chlorophyll content levels were highly obvious. Without considering the influence of cell structure as chlorophyll content increased, leaf spectral reflectance in the visible (350–700 nm): near-infrared (700–1300 nm) ranges also increased. The spectral bands sensitive to chlorophyll content were mainly observed in the visible and near-infrared ranges. The study results showed that the best spectral indicators for determining peanut chlorophyll content were NDSI (R520, R528), RSI (R748, R561), DSI (R758, R602) and SASI (R753, R624). Testing of these regression models showed that coefficient of determination values based on the NDSI, RSI, DSI and SASI estimation models were all greater than 0.65, while root mean square error values were all lower than 2.04. Therefore, the regression model established according to the above spectral indicators was a valid predictor of the chlorophyll content of peanut leaves.
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Xue, Yun, Bin Zou, Yimin Wen, Yulong Tu, and Liwei Xiong. "Hyperspectral Inversion of Chromium Content in Soil Using Support Vector Machine Combined with Lab and Field Spectra." Sustainability 12, no. 11 (May 29, 2020): 4441. http://dx.doi.org/10.3390/su12114441.
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Chromium is not only an essential trace element for the growth and development of living organisms; it is also a heavy metal pollutant. Excessive chromium in farmland soil will not only cause harm to crops, but could also constitute a serious threat to human health through the cumulative effect of the food chain. The determination of heavy metals in tailings of farmland soil is an essential means of soil environmental protection and sustainable development. Hyperspectral remote sensing technology has good characteristics, e.g., high speed, macro, and high resolution, etc., and has gradually become a focus of research to determine heavy metal content in soil. However, due to the spectral variation caused by different environmental conditions, the direct application of the indoor spectrum to conduct field surveys is not effective. Soil components are complex, and the effect of linear regression of heavy metal content is not satisfactory. This study builds indoor and outdoor spectral conversion models to eliminate soil spectral differences caused by environmental conditions. Considering the complex effects of soil composition, we introduce a support vector machine model to retrieve chromium content that has advantages in solving problems such as small samples, non-linearity, and a large number of dimensions. Taking a mining area in Hunan, China as a test area, this study retrieved the chromium content in the soil using 12 combination models of three types of spectra (field spectrum, lab spectrum, and direct standardization (DS) spectrum), two regression methods (stepwise regression and support vector machine regression), and two factors (strong correlation factor and principal component factor). The results show that: (1) As far as the spectral types are concerned, the inversion accuracy of each combination of the field spectrum is generally lower than the accuracy of the corresponding combination of other spectral types, indicating that field environmental interference affects the modeling accuracy. Each combination of DS spectra has higher inversion accuracy than the corresponding combination of field spectra, indicating that DS spectra have a certain effect in eliminating soil spectral differences caused by environmental conditions. (2) The inversion accuracy of each spectrum type of SVR_SC (Support Vector Regression_Strong Correlation) is the highest for the combination of regression method and inversion factor. This indicates the feasibility and superiority of inversion of heavy metals in soil by a support vector machine. However, the inversion accuracy of each spectrum type of SVR_PC (Support Vector Regression_Principal Component) is generally lower than that of other combinations, which indicates that, to obtain superior inversion performance of SVR, the selection of characteristic factors is very important. (3) Through principal component regression analysis, it is found that the pre-processed spectrum is more stable for the inversion of Cr concentration. The regression coefficients of the three types of differential spectra are roughly the same. The five statistically significant characteristic bands are mostly around 384–458 nm, 959–993 nm, 1373–1448 nm, 1970–2014 nm, and 2325–2400 nm. The research results provide a useful reference for the large-scale normalization monitoring of chromium-contaminated soil. They also provide theoretical and technical support for soil environmental protection and sustainable development.
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Wang, Weiyan, Yungui Zhang, Zhihong Li, Qingli Liu, Wenqiang Feng, Yulan Chen, Hong Jiang, Hui Liang, and Naijie Chang. "Fourier-Transform Infrared Spectral Inversion of Soil Available Potassium Content Based on Different Dimensionality Reduction Algorithms." Agronomy 13, no. 3 (February 21, 2023): 617. http://dx.doi.org/10.3390/agronomy13030617.
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Estimating the available potassium (AK) in soil can help improve field management and crop production. Fourier-transform infrared (FTIR) spectroscopy is one of the most promising techniques for the fast and real-time analysis of soil AK content. However, the successful estimation of soil AK content by FTIR depends on the proper selection of appropriate spectral dimensionality reduction techniques. To magnify the subtle spectral signals concerning AK content and improve the understanding of the characteristic FTIR wavelengths of AK content, a total of 145 soil samples were collected in an agricultural site located in the southwest part of Sichuan, China, and three typical spectral dimensionality reduction methods—the successive projections algorithm (SPA), simulated annealing algorithm (SA) and competitive adaptive reweighted sampling (CARS)—were adopted to select the appropriate spectral variable. Then, partial least squares regression (PLSR) was utilized to establish AK inversion models by incorporating the optimal set of spectral variables extracted by different dimensionality reduction algorithms. The accuracy of each inversion model was tested based on the coefficient of determination (R2), root mean square error (RMSE) and mean absolute value error (MAE), and the contribution of the inversion model variables was explored. The results show that: (1) The application of spectral dimensionality reduction is a useful technique for isolating specific components of multicomponent spectra, and as such is a powerful tool to improve and expand the predicted potential of the spectroscopy of soil AK content. Compared with the SA and CARS algorithms, the SPA was more suitable for soil AK content inversion. (2) The inversion model results showed that the characteristic wavelengths were mainly around 777 nm, 1315 nm, 1375 nm, 1635 nm, 1730 nm and 3568–3990 nm. (3) Comparing the performances of different inversion models, the SPA–PLSR model (R2= 0.49, RMSE = 22.80, MAE = 16.82) was superior to the SA–PLSR and CARS–PLSR models, which has certain guiding significance for the rapid detection of soil AK content.
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Neukirch, Maik, Antonio García-Jerez, Antonio Villaseñor, Francisco Luzón, Jacques Brives, and Laurent Stehly. "On the Utility of Horizontal-to-Vertical Spectral Ratios of Ambient Noise in Joint Inversion with Rayleigh Wave Dispersion Curves for the Large-N Maupasacq Experiment." Sensors 21, no. 17 (September 4, 2021): 5946. http://dx.doi.org/10.3390/s21175946.
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Horizontal-to-Vertical Spectral Ratios (HVSR) and Rayleigh group velocity dispersion curves (DC) can be used to estimate the shallow S-wave velocity (VS) structure. Knowing the VS structure is important for geophysical data interpretation either in order to better constrain data inversions for P-wave velocity (VP) structures such as travel time tomography or full waveform inversions or to directly study the VS structure for geo-engineering purposes (e.g., ground motion prediction). The joint inversion of HVSR and dispersion data for 1D VS structure allows characterising the uppermost crust and near surface, where the HVSR data (0.03 to 10s) are most sensitive while the dispersion data (1 to 30s) constrain the deeper model which would, otherwise, add complexity to the HVSR data inversion and adversely affect its convergence. During a large-scale experiment, 197 three-component short-period stations, 41 broad band instruments and 190 geophones were continuously operated for 6 months (April to October 2017) covering an area of approximately 1500km2 with a site spacing of approximately 1 to 3km. Joint inversion of HVSR and DC allowed estimating VS and, to some extent density, down to depths of around 1000m. Broadband and short period instruments performed statistically better than geophone nodes due to the latter’s gap in sensitivity between HVSR and DC. It may be possible to use HVSR data in a joint inversion with DC, increasing resolution for the shallower layers and/or alleviating the absence of short period DC data, which may be harder to obtain. By including HVSR to DC inversions, confidence improvements of two to three times for layers above 300m were achieved. Furthermore, HVSR/DC joint inversion may be useful to generate initial models for 3D tomographic inversions in large scale deployments. Lastly, the joint inversion of HVSR and DC data can be sensitive to density but this sensitivity is situational and depends strongly on the other inversion parameters, namely VS and VP. Density estimates from a HVSR/DC joint inversion should be treated with care, while some subsurface structures may be sensitive, others are clearly not. Inclusion of gravity inversion to HVSR/DC joint inversion may be possible and prove useful.
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Cięszczyk, Sławomir. "A Multi-Band Integrated Virtual Calibration-Inversion Method for Open Path FTIR Spectrometry." Metrology and Measurement Systems 20, no. 2 (June 1, 2013): 287–98. http://dx.doi.org/10.2478/mms-2013-0025.
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Abstract This paper addresses problems arising from in situ measurement of gas content and temperature. Such measurements can be considered indirect. Transmittance or natural radiation of a gas is measured directly. The latter method (spectral radiation measurement) is often called spectral remote sensing. Its primary uses are in astronomy and in the measurement of atmospheric composition. In industrial processes, in situ spectroscopic measurements in the plant are often made with an open path Fourier Transform Infrared (FTIR) spectrometer. The main difficulty in this approach is related to the calibration process, which often cannot be carried out in the manner used in the laboratory. Spectral information can be obtained from open path spectroscopic measurements using mathematical modeling, and by solving the inverse problem. Determination of gas content based on spectral measurements requires comparison of the measured and modeled spectra. This paper proposes a method for the simultaneous use of multiple lines to determine the gas content. The integrated absorptions of many spectral lines permits calculation of the average band absorption. An inverse model based on neural networks is used to determine gas content based on mid-infrared spectra at variable temperatures.
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Gafeira, R., D. Orozco Suárez, I. Milić, C. Quintero Noda, B. Ruiz Cobo, and H. Uitenbroek. "Machine learning initialization to accelerate Stokes profile inversions." Astronomy & Astrophysics 651 (July 2021): A31. http://dx.doi.org/10.1051/0004-6361/201936910.
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Context. At present, an exponential growth in scientific data from current and upcoming solar observatories is expected. Most of the data consist of high spatial and temporal resolution cubes of Stokes profiles taken in both local thermodynamic equilibrium (LTE) and non-LTE spectral lines. The analysis of such solar observations requires complex inversion codes. Hence, it is necessary to develop new tools to boost the speed and efficiency of inversions and reduce computation times and costs. Aims. In this work we discuss the application of convolutional neural networks (CNNs) as a tool to advantageously initialize Stokes profile inversions. Methods. To demonstrate the usefulness of CNNs, we concentrate in this paper on the inversion of LTE Stokes profiles. We use observations taken with the spectropolarimeter on board the Hinode spacecraft as a test bench mark. First, we carefully analyse the data with the SIR inversion code using a given initial atmospheric model. The code provides a set of atmospheric models that reproduce the observations well. These models are then used to train a CNN. Afterwards, the same data are again inverted with SIR but using the trained CNN to provide the initial guess atmospheric models for SIR. Results. The CNNs allow us to significantly reduce the number of inversion cycles when used to compute initial guess model atmospheres (‘assisted inversions’), therefore decreasing the computational time for LTE inversions by a factor of two to four. CNNs alone are much faster than assisted inversions, but the latter are more robust and accurate. CNNs also help to automatically cluster pixels with similar physical properties, allowing the association with different solar features on the solar surface, which is useful when inverting huge datasets where completely different regimes are present. The advantages and limitations of machine learning techniques for estimating optimum initial atmospheric models for spectral line inversions are discussed. Finally, we describe a python wrapper for the SIR and DeSIRe codes that allows for the easy setup of parallel inversions. The tool implements the assisted inversion method described in this paper. The parallel wrapper can also be used to synthesize Stokes profiles with the RH code. Conclusions. The assisted inversions can speed up the inversion process, but the efficiency and accuracy of the inversion results depend strongly on the solar scene and the data used for the CNN training. This method (assisted inversions) will not obviate the need for analysing individual events with the utmost care but will provide solar scientists with a much better opportunity to sample large amounts of inverted data, which will undoubtedly broaden the physical discovery space.
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Yuan, Sanyi, and Shangxu Wang. "Spectral sparse Bayesian learning reflectivity inversion." Geophysical Prospecting 61, no. 4 (February 27, 2013): 735–46. http://dx.doi.org/10.1111/1365-2478.12000.
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Hall, Richard L., and Wolfgang Lucha. "Geometric spectral inversion for singular potentials." Journal of Mathematical Physics 52, no. 11 (November 2011): 112102. http://dx.doi.org/10.1063/1.3657346.
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Koza, J., A. Kučera, J. Rybák, and H. Wöhl. "Photospheric modeling through spectral line inversion." Astronomy & Astrophysics 458, no. 3 (September 12, 2006): 941–51. http://dx.doi.org/10.1051/0004-6361:20065679.
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Jannaud, L. R., P. M. Adler, and C. G. Jacquin. "Spectral analysis and inversion of codas." Journal of Geophysical Research 96, B11 (1991): 18215. http://dx.doi.org/10.1029/91jb01427.
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Agrawal, Piyush, Mark P. Rast, and Basilio Ruiz Cobo. "An Iterative OLA Method for Inversion of Solar Spectropolarimetric Data. I. Single- and Multiple-variable Inversions of Thermodynamic Quantities." Astrophysical Journal 944, no. 1 (February 1, 2023): 111. http://dx.doi.org/10.3847/1538-4357/aca536.
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Abstract This paper describes an adaptation of the Optimally Localized Averaging (OLA) inversion technique, originally developed for geo- and helioseismological applications, to the interpretation of solar spectroscopic data. It focuses on inverting the thermodynamical properties of the solar atmosphere, assuming that the atmosphere and radiation field are in local thermodynamic equilibrium (LTE). We leave inversions of magnetic field and non-LTE inversions for future work. The advantage with the OLA method is that it computes solutions that are optimally depth resolved with minimal crosstalk error between variables. Additionally, the method allows for direct assessment of the vertical resolution of the inverted solutions. The primary challenges faced when adapting the method to spectroscopic inversions originate with the possible large-amplitude differences between the atmospheric model used to initiate the inversion and the underlying atmosphere it aims to recover, necessitating the development of an iterative scheme. Here, we describe the iterative OLA method we have developed for both single and multivariable inversions and demonstrate its performance on simulated data and synthesized spectra. We note that, when carrying out multivariable inversions, employing response function amplification factors can address the inherent spectral sensitivity bias that makes it hard to invert for less spectrally sensitive variables. The OLA method can, in most cases, reliably invert as well as or better than the frequently employed Stokes Inversion based on Response functions (SIR) scheme, but some difficulties remain. In particular, the method struggles to recover large-scale offsets in the atmospheric stratification. We propose future strategies to improve this aspect.
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Yin, Changming, Binbin He, Xingwen Quan, Marta Yebra, and Gengke Lai. "Remote Sensing of Burn Severity Using Coupled Radiative Transfer Model: A Case Study on Chinese Qinyuan Pine Fires." Remote Sensing 12, no. 21 (November 2, 2020): 3590. http://dx.doi.org/10.3390/rs12213590.
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Burn severity mapping is critical to quantifying fire impact on key ecological processes and post-fire forest management. Satellite remote sensing has the advantages of high spatial-temporal resolution and large-scale monitoring and provides a more efficient way to evaluate forest fire burn severity than traditional field or aerial surveys. However, the proportion of tree canopy cover (TCC) affects the spectral signal received by remote sensing sensors from the background charcoal and ash. Consequently, not considering this factor normally leads a spectral confusion in burn severity retrieval. In this study, the burn severity of two Qinyuan forest fires was estimated using a coupled Radiative Transfer Model (RTM) and Sentinel-2A Multi-Spectral Instrument (MSI) reflectance data. A two-layer Canopy Reflectance Model (ACRM) RTM was coupled with the GeoSail RTM by replacing the spectra of the background input of GeoSail RTM to simulate the spectra of the three-layered forests for burn severity retrieval measured as the Composite Burn Index (CBI). The TCC data was then served to RTM parameterization and constrain the backward inversion procedure of the coupled RTM to alleviate spectral confusion. Finally, the inversion retrievals were evaluated using 163 field measured CBI. The coupled RTM can simulate the radiative transfer characteristics of three-layer vegetation and has greater potential to accurately estimate burn severity worldwide. To evaluate the merit of our proposed method, the CBI was estimated through coupled RTM inversion with TCC constraint (CP_RTM+TCC), coupled RTM inversion with global optimal search (CP-RTM+GOS), Forest Reflectance and Transmittance (FRT) RTM inversion with TCC constraint (FRT+TCC), and random forest (RF) algorithm. The results showed that the method proposed in this study (CP_RTM+TCC) yielded the highest estimation accuracy (R2 = 0.92, RMSE = 0.2) among the four methods used as benchmark, indicating its reasonable ability to assist forest managers to better understand post-fire vegetation regeneration and forest management.
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Li, H., T. del Pino Alemán, J. Trujillo Bueno, and R. Casini. "TIC: A Stokes Inversion Code for Scattering Polarization with Partial Frequency Redistribution and Arbitrary Magnetic Fields." Astrophysical Journal 933, no. 2 (July 1, 2022): 145. http://dx.doi.org/10.3847/1538-4357/ac745c.
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Abstract We present the Tenerife Inversion Code (TIC), which has been developed to infer the magnetic and plasma properties of the solar chromosphere and transition region via full Stokes inversion of polarized spectral lines. The code is based on the HanleRT forward engine, which takes into account many of the physical mechanisms that are critical for a proper modeling of the Stokes profiles of spectral lines originating in the tenuous and highly dynamic plasmas of the chromosphere and transition region: the scattering polarization produced by quantum level imbalance and interference (atomic polarization), the effects of frequency coherence in polarized resonance scattering (partial redistribution), and the impact of arbitrary magnetic fields on the atomic polarization and the radiation field. We present first results of atmospheric and magnetic inversions, and discuss future developments for the project.
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Milić, I., and M. van Noort. "Spectropolarimetric NLTE inversion code SNAPI." Astronomy & Astrophysics 617 (September 2018): A24. http://dx.doi.org/10.1051/0004-6361/201833382.
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Context. Inversion codes are computer programs that fit a model atmosphere to the observed Stokes spectra, thus retrieving the relevant atmospheric parameters. The rising interest in the solar chromosphere, where spectral lines are formed by scattering, requires developing, testing, and comparing new non-local thermal equilibrium (NLTE) inversion codes. Aims. We present a new NLTE inversion code that is based on the analytical computation of the response functions. We named the code SNAPI, which is short for spectropolarimetic NLTE analytically powered inversion. Methods. SNAPI inverts full Stokes spectrum in order to obtain a depth-dependent stratification of the temperature, velocity, and the magnetic field vector. It is based on the so-called node approach, where atmospheric parameters are free to vary in several fixed points in the atmosphere, and are assumed to behave as splines in between. We describe the inversion approach in general and the specific choices we have made in the implementation. Results. We test the performance on one academic problem and on two interesting NLTE examples, the Ca II 8542 and Na I D spectral lines. The code is found to have excellent convergence properties and outperforms a finite-difference based code in this specific implementation by at least a factor of three. We invert synthetic observations of Na lines from a small part of a simulated solar atmosphere and conclude that the Na lines reliably retrieve the magnetic field and velocity in the range −3 < logτ < −0.5.
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Yuan, Cheng, and Mingjun Su. "Seismic spectral sparse reflectivity inversion based on SBL-EM: experimental analysis and application." Journal of Geophysics and Engineering 16, no. 6 (October 18, 2019): 1124–38. http://dx.doi.org/10.1093/jge/gxz082.
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Abstract In this paper, we propose a new method of seismic spectral sparse reflectivity inversion that, for the first time, introduces Expectation-Maximization-based sparse Bayesian learning (SBL-EM) to enhance the accuracy of stratal reflectivity estimation based on the frequency spectrum of seismic reflection data. Compared with the widely applied sequential algorithm-based sparse Bayesian learning (SBL-SA), SBL-EM is more robust to data noise and, generally, can not only find a sparse solution with higher precision, but also yield a better lateral continuity along the final profile. To investigate the potential of SBL-EM in a seismic spectral sparse reflectivity inversion, we evaluate the inversion results by comparing them with those of a SBL-SA-based approach in multiple aspects, including the sensitivity to different frequency bands, the robustness to data noise, the lateral continuity of the final profiles and so on. Furthermore, we apply the mean square error (MSE), residual variance (RV) of seismograms and residual energy (RE) between the frequency spectra of the true and inverted reflectivity model to highlight the advantages of the proposed method over the SBL-SA-based approach in terms of spectral sparse reflectivity inversion within a sparse Bayesian learning framework. Multiple examples, including both numerical and field experiments, are carried out to validate the proposed method.
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Castro, Raúl R., M. Mucciarelli, F. Pacor, P. Federici, and A. Zaninetti. "Determination of the characteristic frequency of two dams located in the region of Calabria, Italy." Bulletin of the Seismological Society of America 88, no. 2 (April 1, 1998): 503–11. http://dx.doi.org/10.1785/bssa0880020503.
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Abstract We estimated the characteristic frequency of vibration of two dams located in southern Italy using spectral amplitudes of 13 local earthquakes recorded by three-component digital stations installed on top of the structures and on the free field. One of the dams (ARV), located in Arvo lake, is an earth dam, and the other (TRP) is an arch dam located in the lake Ampollino. We separated the source and path effects from the records on the dams by using spectral ratios between the horizontal and the vertical components of motion (H/V ratios). We also calculated simultaneous inversions for source, site, and path effects using different site constraints. In general, the characteristic frequencies (fn) retrived from both approaches are consistent with the expected natural frequency of resonance of the structures. For ARV, we estimated from the spectral records an fn = 2.5 Hz and a value of 2.0 Hz when we calculated the one-dimensional response using the SHAKE code (Schnabel et al., 1972). For TRP, the results of the inversion and the spectral ratio between the free-field site give fn = 8.0 Hz. We concluded that the inversion technique gives reliable estimates of fn for the sites analyzed.
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de la Cruz Rodríguez, J., J. Leenaarts, S. Danilovic, and H. Uitenbroek. "STiC: A multiatom non-LTE PRD inversion code for full-Stokes solar observations." Astronomy & Astrophysics 623 (March 2019): A74. http://dx.doi.org/10.1051/0004-6361/201834464.
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The inference of the underlying state of the plasma in the solar chromosphere remains extremely challenging because of the nonlocal character of the observed radiation and plasma conditions in this layer. Inversion methods allow us to derive a model atmosphere that can reproduce the observed spectra by undertaking several physical assumptions. The most advanced approaches involve a depth-stratified model atmosphere described by temperature, line-of-sight velocity, turbulent velocity, the three components of the magntic field vector, and gas and electron pressure. The parameters of the radiative transfer equation are computed from a solid ground of physical principles. In order to apply these techniques to spectral lines that sample the chromosphere, nonlocal thermodynamical equilibrium effects must be included in the calculations. We developed a new inversion code STiC (STockholm inversion Code) to study spectral lines that sample the upper chromosphere. The code is based on the RH forward synthesis code, which we modified to make the inversions faster and more stable. For the first time, STiC facilitates the processing of lines from multiple atoms in non-LTE, also including partial redistribution effects (PRD) in angle and frequency of scattered photons. Furthermore, we include a regularization strategy that allows for model atmospheres with a complex depth stratification, without introducing artifacts in the reconstructed physical parameters, which are usually manifested in the form of oscillatory behavior. This approach takes steps toward a node-less inversion, in which the value of the physical parameters at each grid point can be considered a free parameter. In this paper we discuss the implementation of the aforementioned techniques, the description of the model atmosphere, and the optimizations that we applied to the code. We carry out some numerical experiments to show the performance of the code and the regularization techniques that we implemented. We made STiC publicly available to the community.
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Savage, Sabrina L., Amy R. Winebarger, Ken Kobayashi, P. S. Athiray, Dyana Beabout, Leon Golub, Robert W. Walsh, et al. "The First Flight of the Marshall Grazing Incidence X-Ray Spectrometer (MaGIXS)." Astrophysical Journal 945, no. 2 (March 1, 2023): 105. http://dx.doi.org/10.3847/1538-4357/acbb58.
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Abstract The Marshall Grazing Incidence X-ray Spectrometer (MaGIXS) sounding rocket experiment launched on 2021 July 30 from the White Sands Missile Range in New Mexico. MaGIXS is a unique solar observing telescope developed to capture X-ray spectral images of coronal active regions in the 6–24 Å wavelength range. Its novel design takes advantage of recent technological advances related to fabricating and optimizing X-ray optical systems, as well as breakthroughs in inversion methodologies necessary to create spectrally pure maps from overlapping spectral images. MaGIXS is the first instrument of its kind to provide spatially resolved soft X-ray spectra across a wide field of view. The plasma diagnostics available in this spectral regime make this instrument a powerful tool for probing solar coronal heating. This paper presents details from the first MaGIXS flight, the captured observations, the data processing and inversion techniques, and the first science results.
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Dong, Peng. "Compensating power depletion due to stimulated Raman scattering in high-power delivery fiber via spectral inversion revisited in a view of experimental implementation." JOURNAL OF ADVANCES IN PHYSICS 14, no. 1 (March 31, 2018): 5268–74. http://dx.doi.org/10.24297/jap.v14i1.7157.
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Broadband spectral inversion was proved theoretically to be an effective method to compensate power depletion due to stimulated Raman scattering in high-power delivery fiber. A critical difficulty in implementing the method in experiment is to realize broadband spectral inversion of incoherent light as Raman Stokes waves are incoherent due to their origin from spontaneous emission noise. Broadband spectral inversion of incoherent light is investigated experimentally in this article. A beam from an amplified spontaneous emission (ASE) light source is used as an approximated Raman Stokes waves in the experiment. ASE Spectrum of width of 10.5nm is inverted via four-wave mixing in a highly nonlinear dispersion-shifted fiber in efficiency of -10dB without significant spectrum deformation. A theoretical model for four-wave mixing of ASE incoherent light is established, and based on which the limitation in more broadband spectral inversion of ASE incoherent light is analyzed.
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Abd El-Hamid, Hazem T., and Guan Hong. "Hyperspectral remote sensing for extraction of soil salinization in the northern region of Ningxia." Modeling Earth Systems and Environment 6, no. 4 (June 12, 2020): 2487–93. http://dx.doi.org/10.1007/s40808-020-00829-3.
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Abstract Soil salinization affects negatively on agricultural productivity in the semiarid region of Ningxia. In this study, the performance of inversion model to determine soil salinization was assessed using some analysis and reflectance of wavelength. About 42 vegetation samples and 42 soil samples were collected for model extraction. Hyper-spectral data processing method was used to analyze spectral characteristics of different levels of salinization area vegetation. Spectral data were transformed in 16 different approaches, including root mean squares, logarithm, inversion logarithm, and first-order differentiation. After the transformation, the obtained soil and vegetation characteristics spectra correlate well with soil salt content, built soil index, and many vegetation indices. Nonlinear regression was employed to establish soil salinization remote sensing monitoring model. By comparing various spectral transformations, the first-order differential of soil spectral was the most sensitive to soil salinization degrees. The model of the current research was based on salinity index (SI) and improved soil-adjusted vegetation index (MSAVI). The correlation between simulated values and measured values was 0.758. Therefore, remote sensing monitoring derived from MSAVI–SI can greatly improve the dynamic and periodical monitoring of soil salinity in the study area.
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Boatwright, John. "Regional propagation characteristics and source parameters of earthquakes in northeastern North America." Bulletin of the Seismological Society of America 84, no. 1 (February 1, 1994): 1–15. http://dx.doi.org/10.1785/bssa0840010001.
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Abstract The vertical components of the S wave trains recorded on the Eastern Canadian Telemetered Network (ECTN) from 1980 through 1990 have been spectrally analyzed for source, site, and propagation characteristics. The data set comprises some 1033 recordings of 97 earthquakes whose magnitudes range from M ≈ 3 to 6. The epicentral distances range from 15 to 1000 km, with most of the data set recorded at distances from 200 to 800 km. The recorded S wave trains contain the phases S, SmS, Sn, and Lg and are sampled using windows that increase with distance; the acceleration spectra were analyzed from 1.0 to 10 Hz. To separate the source, site, and propagation characteristics, an inversion for the earthquake corner frequencies, low-frequency levels, and average attenuation parameters is alternated with a regression of residuals onto the set of stations and a grid of 14 distances ranging from 25 to 1000 km. The iteration between these two parts of the inversion converges in about 60 steps. The average attenuation parameters obtained from the inversion were Q = 1997 ± 10 and γ = 0.998 ± 0.003. The most pronounced variation from this average attenuation is a marked deamplification of more than a factor of 2 at 63 km and 2 Hz, which shallows with increasing frequency and increasing distance out to 200 km. The site-response spectra obtained for the ECTN stations are generally flat. The source spectral shape assumed in this inversion provides an adequate spectral model for the smaller events (Mo < 3 × 1021 dyne-cm) in the data set, whose Brune stress drops range from 5 to 150 bars. For the five events in the data set with Mo ≧ 1023 dyne-cm, however, the source spectra obtained by regressing the residuals suggest that an ω2 spectrum is an inadequate model for the spectral shape. In particular, the corner frequencies for most of these large events appear to be split, so that the spectra exhibit an intermediate behavior (where |ü(ω)| is roughly proportional to ω).
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Shen, Lanzhi, Maofang Gao, Jingwen Yan, Qizhi Wang, and Hua Shen. "Winter Wheat SPAD Value Inversion Based on Multiple Pretreatment Methods." Remote Sensing 14, no. 18 (September 18, 2022): 4660. http://dx.doi.org/10.3390/rs14184660.
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SPAD value was measured by a portable chlorophyll instrument, which can reflect the relative chlorophyll content of vegetation well. Chlorophyll is an important organic chemical substance in plants that acquires and transmits energy during photosynthesis. The continuous spectral curve of winter wheat can be obtained rapidly in a specific band range by using hyperspectral remote sensing technology to estimate the SPAD value of winter wheat, which is of great significance to the growth monitoring and yield estimation research of winter wheat. In this study, with winter wheat as the research object, the spectral data and corresponding SPAD value in different growth stages were used as the data source, 20 kinds of data preprocessing spectra and sensitive spectral indices set the data as model input values, the partial least square regression (PLSR) model was established to estimate the SPAD value, and the model estimation results of different model input values at different growth stages were compared in detail. The results showed that the set of sensitive spectral indices selected in this study as input values can effectively improve the accuracy and stability of the PLSR model. In addition, the effects of 20 spectral data pretreatment methods on the estimation results of the SPAD value were compared and analyzed in different growth stages. It was found that the spectral data pretreated by the combination of wavelet packet denoising, first-order derivative transformation and principal component analysis can improve the accuracy and stability of PLSR model, and it is suitable for all growth stages. The results also showed that the estimation model is highly sensitive to the standard deviation of the SPAD value (STDchl) in sample sets. When the standard deviation is greater than 5.5 SPAD, the larger the STDchl is, the higher the model estimation accuracy is, and the more stable the model is. At this time, the model estimation accuracy is higher (R2V is greater than 0.5, ratio of performance to deviation is greater than 1.4), which can meet the estimation requirements of the SPAD value.
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Gao, S. "A Bayesian nonlinear inversion of seismic body-wave attenuation factors." Bulletin of the Seismological Society of America 87, no. 4 (August 1, 1997): 961–70. http://dx.doi.org/10.1785/bssa0870040961.
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Abstract It is a well-known fact that the uncertainties in measuring relative attenuation factors within a local or regional seismic network are usually high, due to noise of different kinds and unrealistic assumptions. Numerical experiments using nine synthetic seismograms, created using t* values ranging from 0.1 to 0.9 sec, reveal that the commonly used spectral ratio method is strongly affected by the selection of data processing parameters such as width of the spectral smoothing window, reference station, and so on. The numerical experiments demonstrate that a Bayesian nonlinear inversion approach that directly matches the spectra is better at finding the correct parameters used to generate the synthetic seismograms. The Bayesian inversion approach uses a priori information to simultaneously search for the t* values, the common spectrum for all the records from an event, and the near-receiver amplification factors by using all the recordings from an event. When z, the ratio of Gaussian noise to signal, ≦ 0.1, the spectral ratio and Bayesian methods yield similar results with mean t* measurement errors <0.05 sec. For 0.1 < z ≦ 0.8, the mean errors of the spectral ratio method are larger than 0.1 sec and in some cases as large as 0.6 sec, while those of the Bayesian method are less than 0.08 sec. Frequency-independent t* and near-receiver amplification factors are assumed. A multi-step procedure is proposed to reject records with a large misfit.
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Fu, Chengbiao, Heigang Xiong, and Anhong Tian. "Fractional Modeling for Quantitative Inversion of Soil-Available Phosphorus Content." Mathematics 6, no. 12 (December 14, 2018): 330. http://dx.doi.org/10.3390/math6120330.
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The study of field spectra based on fractional-order differentials has rarely been reported, and traditional integer-order differentials only perform the derivative calculation for 1st-order or 2nd-order spectrum signals, ignoring the spectral transformation details between 0th-order to 1st-order and 1st-order to 2nd-order, resulting in the problem of low-prediction accuracy. In this paper, a spectral quantitative analysis model of soil-available phosphorus content based on a fractional-order differential is proposed. Firstly, a fractional-order differential was used to perform a derivative calculation of original spectral data from 0th-order to 2nd-order using 0.2-order intervals, to obtain 11 fractional-order spectrum data. Afterwards, seven bands with absolute correlation coefficient greater than 0.5 were selected as sensitive bands. Finally, a stepwise multiple linear regression algorithm was used to establish a spectral estimation model of soil-available phosphorus content under different orders, then the prediction effect of the model under different orders was compared and analyzed. Simulation results show that the best order for a soil-available phosphorus content regression model is a 0.6 fractional-order, the coefficient of determination (), root mean square error (RMSE), and ratio of performance to deviation (RPD) of the best model are 0.7888, 3.348878, and 2.001142, respectively. Since the RPD value is greater than 2, the optimal fractional model established in this study has good quantitative predictive ability for soil-available phosphorus content.
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Lestrade, J. F., J. C. Augereau, M. Booth, R. Adam, P. Ade, P. André, A. Andrianasolo, et al. "Debris disks around stars in the NIKA2 era." EPJ Web of Conferences 228 (2020): 00015. http://dx.doi.org/10.1051/epjconf/202022800015.
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The new NIKA2 camera at the IRAM 30m radiotelescope was used to observe three known debris disks in order to constrain the SED of their dust emission in the millimeter wavelength domain. We have found that the spectral index between the two NIKA2 bands (1mm and 2mm) is consistent with the Rayleigh-Jeans regime (λ-2), unlike the steeper spectra (λ-3) measured in the submillimeter-wavelength domain for two of the three disks - around the stars Vega and HD107146. We provide a succesful proof of concept to model this spectral inversion in using two populations of dust grains, those smaller and those larger than a grain radius a0 of 0.5mm. This is obtained in breaking the slope of the size distribution and the functional form of the absorption coefficient of the standard model. The third disk - around the star HR8799 - does not exhibit this spectral inversion but is also the youngest.
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Wagner, Paul-Remo, Stefano Marelli, and Bruno Sudret. "Bayesian model inversion using stochastic spectral embedding." Journal of Computational Physics 436 (July 2021): 110141. http://dx.doi.org/10.1016/j.jcp.2021.110141.
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AMAIKE, Fumio, and Kikuji KOBAYASHI. "Spectral Inversion Analysis Considering Apparent Incident Angle." Journal of JAEE 16, no. 9 (2016): 9_33–9_45. http://dx.doi.org/10.5610/jaee.16.9_33.
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Lewis, R. J. G., and J. R. Bishop. "Inversion of Time Domain Spectral IP Data." Exploration Geophysics 19, no. 1-2 (March 1988): 303–5. http://dx.doi.org/10.1071/eg988303.
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Plessix, R. É., and Y. Li. "Waveform acoustic impedance inversion with spectral shaping." Geophysical Journal International 195, no. 1 (July 6, 2013): 301–14. http://dx.doi.org/10.1093/gji/ggt233.
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Hall, Richard L. "Geometric spectral inversion by the WKB approximation." Physical Review A 51, no. 3 (March 1, 1995): 1787–91. http://dx.doi.org/10.1103/physreva.51.1787.
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Kim, Inwoong, Olga Vassilieva, Youichi Akasaka, Paparao Palacharla, and Tadashi Ikeuchi. "Enhanced Spectral Inversion for Fiber Nonlinearity Mitigation." IEEE Photonics Technology Letters 30, no. 23 (December 1, 2018): 2040–43. http://dx.doi.org/10.1109/lpt.2018.2875595.
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Jannaud, L. R., P. M. Adler, and C. G. Jacquin. "Spectral analysis and inversion of experimental codas." GEOPHYSICS 58, no. 3 (March 1993): 408–18. http://dx.doi.org/10.1190/1.1443424.
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A method developed for the determination of the characteristic lengths of an heterogeneous medium from the spectral analysis of codas is based on an extension of Aki’s theory to anisotropic elastic media. An equivalent Gaussian model is obtained and seems to be in good agreement with the two experimental data sets that illustrate the method. The first set was obtained in a laboratory experiment with an isotropic marble sample. This sample is characterized by a submillimetric length scale that can be directly observed on a thin section. The spectral analysis of codas and their inversion yields an equivalent correlation length that is in good agreement with the observed one. The second data set is obtained in a crosshole experiment at the usual scale of a seismic survey. The codas are recorded, analysed, and inverted. The analysis yields a vertical characteristic length for the studied subsurface that compares well with the characteristic length measured by seismic and stratigraphic logs.
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Kavetsky, A., and B. J. O'Mara. "Spectral line inversion as a diagnostic tool." Journal of Quantitative Spectroscopy and Radiative Transfer 33, no. 2 (February 1985): 93–100. http://dx.doi.org/10.1016/0022-4073(85)90096-2.
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Ghosh Roy, D. N., and L. D. Waters. "Steepest descent inversion of plasma spectral intensities." Journal of Quantitative Spectroscopy and Radiative Transfer 37, no. 1 (January 1987): 55–63. http://dx.doi.org/10.1016/0022-4073(87)90118-x.
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Bousserez, N., D. K. Henze, B. Rooney, A. Perkins, K. J. Wecht, A. J. Turner, V. Natraj, and J. R. Worden. "Constraints on methane emissions in North America from future geostationary remote sensing measurements." Atmospheric Chemistry and Physics Discussions 15, no. 13 (July 10, 2015): 19017–44. http://dx.doi.org/10.5194/acpd-15-19017-2015.
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Abstract. The success of future geostationary (GEO) satellite observation missions depends on our ability to design instruments that address their key scientific objectives. In this study, an Observation System Simulation Experiment (OSSE) is performed to quantify the constraints on methane (CH4) emissions in North America obtained from Short Wave Infrared (SWIR), Thermal Infrared (TIR) and multi-spectral measurements in geostationary orbit compared to existing SWIR low earth (LEO) measurements. A stochastic algorithm is used to compute the information content of a variational inversion at high spatial resolution (0.5° × 0.7°) using the GEOS-Chem chemical transport model and its adjoint. Both the SWIR LEO and TIR GEO configurations generally provide poor constraints on CH4 emissions (error reduction <30 %), with the exception of a few hotspots (e.g., Los Angeles, Toronto urban areas and Appalachian Mountains) where the error reduction is greater than 50 %. On weekly time scales and for a GEO orbit, the degree of freedom for signal (DOFs) of the inversion from multi-spectral observations (500) is a factor of two higher than that obtained from a SWIR instrument (255) due to the increase in measurement sensitivity to boundary layer concentrations in the multi-spectral case. On a monthly time scale and for a GEO orbit, a SWIR instrument would reduce error in emission estimates by more than 70 % for hotspots of CH4 sources (emissions > 4 × 105 kg day−1 grid−1) at model grid scale, while a TIR instrument would provide a relative error reduction of 25–60 % over those areas. While performing similarly for monthly inversions, a multi-spectral instrument would allow for more than 70 % error reduction for these emissions for 7 or 3 day inversions. Sensitivity of the inversions to error in boundary conditions are found to be negligible. Moreover, estimates of the model resolution matrix over significant emitting regions (CH4 emissions > 2 × 105 kg day−1 grid−1) show that for all instrument configurations in GEO orbit the inversion is able to independently constrain CH4 sources at spatial scales smaller than 200 km. These results highlight the importance of using observations sensitive to boundary layer concentrations (i.e., SWIR) to achieve significant improvements in constraining CH4 sources compared to current LEO capabilities.
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KATO, JOJI, and YOSHIHIKO KAWAMURA. "Gadolinium-Enhanced Three-dimensional MR Angiography Using Spectral Selective Inversion Pulse." Japanese Journal of Radiological Technology 54, no. 5 (1998): 624–29. http://dx.doi.org/10.6009/jjrt.kj00003109999.
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Paz Pellat, Fernando. "Correcciones atmosféricas relativas de imágenes de satélite: patrones invariantes y modelos atmosféricos." REVISTA TERRA LATINOAMERICANA 36, no. 1 (January 22, 2018): 1. http://dx.doi.org/10.28940/terra.v36i1.228.
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To use information obtained with satellite technology reliably, it is necessary to eliminate or reduce the disruptive effects associated with the spectral information captured by sensors on space platforms. In this paper we analyze the inversion of radiative models of the atmosphere, which consists in determining the additive and multiplicative constants in each spectral band to make the necessary atmospheric corrections. The methodology proposes the use of invariant patterns of soil lines and dense vegetation for the inversion of radiative models. The results showed that, without knowledge of the atmospheric model or the type of aerosol, soil line data were relatively insufficient (low correlation) to obtain the additive and multiplicative constants of the atmospheric inversions, with problems of multiple solutions in the inversion process. Under similar conditions, the same was found for additive constants with the dense vegetation line, but for the multiplicative constants the results were favorable (R2 > 0.9). In contrast, with the knowledge of the atmospheric model and the aerosol model, estimates of additive and multiplicative constants were highly satisfactory (R2 > 0.99) in both cases. For soil line inversions, only one constraint of the two available was used. In conclusion, the use of invariant soil-line patterns allows us to establish two basic relationships to invert the radiative simulations of the atmosphere, prior to functional compaction, and field measurements can be made so that the proposed atmospheric correction process in this work can be considered in absolute and not relative terms.
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Liu, L., and K. Shang. "MINERAL INFORMATION EXTRACTION BASED ON GAOFEN-5’S THERMAL INFRARED DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1157–60. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1157-2018.
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Gaofen-5 carries six instruments aimed at various land and atmosphere applications, and it’s an important unit of China High-resolution Earth Observation System. As Gaofen-5’s thermal infrared payload is similar to that of ASTER, which is widely used in mineral exploration, application of Gaofen-5’s thermal infrared data is discussed regarding its capability in mineral classification and silica content estimation. First, spectra of silicate, carbonate, sulfate minerals from a spectral library are used to conduct spectral feature analysis on Gaofen-5’s thermal infrared emissivities. Spectral indices of band emissivities are proposed, and by setting thresholds of these spectral indices, it can classify three types of minerals mentioned above. This classification method is tested on a simulated Gaofen-5 emissivity image. With samples acquired from the study area, this method is proven to be feasible. Second, with band emissivities of silicate and their silica content from the same spectral library, correlation models have been tried to be built for silica content inversion. However, the highest correlation coefficient is merely 0.592, which is much lower than that of correlation model built on ASTER thermal infrared emissivity. It can be concluded that GF-5’s thermal infrared data can be utilized in mineral classification but not in silica content inversion.
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Bonilla, Luis Fabián, Jamison H. Steidl, Grant T. Lindley, Alexei G. Tumarkin, and Ralph J. Archuleta. "Site amplification in the San Fernando Valley, California: Variability of site-effect estimation using the S-wave, coda, and H/V methods." Bulletin of the Seismological Society of America 87, no. 3 (June 1, 1997): 710–30. http://dx.doi.org/10.1785/bssa0870030710.
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Abstract During the months that followed the 17 January 1994 M 6.7 Northridge, California, earthquake, portable digital seismic stations were deployed in the San Fernando basin to record aftershock data and estimate site-amplification factors. This study analyzes data, recorded on 31 three-component stations, from 38 aftershocks ranging from M 3.0 to M 5.1, and depths from 0.2 to 19 km. Site responses from the 31 stations are estimated from coda waves, S waves, and ratios of horizontal to vertical (H/V) recordings. For the coda and the S waves, site response is estimated using both direct spectral ratios and a generalized inversion scheme. Results from the inversions indicate that the effect of Qs can be significant, especially at high frequencies. Site amplifications estimated from the coda of the vertical and horizontal components can be significantly different from each other, depending on the choice of the reference site. The difference is reduced when an average of six rock sites is used as a reference site. In addition, when using this multi-reference site, the coda amplification from rock sites is usually within a factor of 2 of the amplification determined from the direct spectral ratios and the inversion of the S waves. However, for nonrock sites, the coda amplification can be larger by a factor of 2 or more when compared with the amplification estimated from the direct spectral ratios and the inversion of the S waves. The H/V method for estimating site response is found to extract the same predominant peaks as the direct spectral ratio and the inversion methods. The amplifications determined from the H/V method are, however, different from the amplifications determined from the other methods. Finally, the stations were grouped into classes based on two different classifications, general geology and a more detailed classification using a quaternary geology map for the Los Angeles and San Fernando areas. Average site-response estimates using the site characterization based on the detailed geology show better correlation between amplification and surface geology than the general geology classification.
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Jagt, Lisanne, and Arwen Deuss. "Comparing one-step full-spectrum inversion with two-step splitting function inversion in normal mode tomography." Geophysical Journal International 227, no. 1 (June 23, 2021): 559–75. http://dx.doi.org/10.1093/gji/ggab240.
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SUMMARY Earth’s normal modes, or whole Earth oscillations, provide important constraints on Earth’s large-scale 3-D structure. In addition to constraining shear and compressional wave velocities, they are the only seismic data sensitive to density perturbations. Density is particularly difficult to determine, and previous studies have found contradicting results, hence the method chosen to invert normal mode data for 3-D structure becomes important. In the problem of inverting the measured frequency spectra for an earth model, we can take two approaches: (i) a one-step full-spectrum inversion, where normal mode spectra are directly inverted for a mantle model and (ii) a two-step splitting function inversion, where first the spectra are inverted for splitting functions, which are then inverted for a mantle model. Here we compare the methodology and results of both approaches, continuing the work done by Li et al. and Durek & Romanowicz, and extending it to higher spherical harmonic degrees. Using exactly the same normal mode data set, we use both inversion approaches to make 3-D shear wave velocity mantle models. Both approaches give models consistent with previous tomographic studies, although spectral misfits are consistently lower for the one-step full-spectrum inversion. We also show that we cannot draw any conclusions on odd-degree structure in the lower mantle with the currently available normal mode data sets.
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Jiang, Zongchen, Yi Ma, and Junfang Yang. "Inversion of the Thickness of Crude Oil Film Based on an OG-CNN Model." Journal of Marine Science and Engineering 8, no. 9 (August 25, 2020): 653. http://dx.doi.org/10.3390/jmse8090653.
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In recent years, marine oil spill accidents have occurred frequently, seriously endangering marine ecological security. It is highly important to protect the marine ecological environment by carrying out research on the estimation of sea oil spills based on remote sensing technology. In this paper, we combine deep learning with remote sensing technology and propose an oil thickness inversion generative adversarial and convolutional neural network (OG-CNN) model for oil spill emergency monitoring. The model consists of a self-expanding module for the oil film spectral feature data and an oil film thickness inversion module. The feature data self-expanding module can automatically select spectral feature intervals with good spectral separability based on the measured spectral data and then expand the number of samples using a generative adversarial network (GAN) to enhance the generalization of the model. The oil film thickness inversion module is based on a one-dimensional convolutional neural network (1D-CNN). It extracts the characteristics of the spectral feature data of oil film with different thicknesses, and then accurately inverts the oil film’s absolute thickness. In this study, emulsification was not a factor considered, the results show that the absolute oil thickness inversion accuracy of the OG-CNN model proposed in this paper can reach 98.12%, the coefficient of determination can reach 0.987, and the mean deviation remains within ±0.06% under controlled experimental conditions. In the model stability test, the model maintains relatively stable inversion results under the interference of random Gaussian noise. The accuracy of the oil film thickness inversion result remains above 96%, the coefficient of determination can reach 0.973, and the mean deviation is controlled within ±0.6%, which indicates excellent robustness.
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Puryear, Charles I., and John P. Castagna. "Layer-thickness determination and stratigraphic interpretation using spectral inversion: Theory and application." GEOPHYSICS 73, no. 2 (March 2008): R37—R48. http://dx.doi.org/10.1190/1.2838274.
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Spectral inversion is a seismic method that uses a priori information and spectral decomposition to improve images of thin layers whose thicknesses are below the tuning thickness. We formulate a method to invert frequency spectra for layer thickness and apply it to synthetic and real data using complex spectral analysis. Absolute layer thicknesses significantly below the seismic tuning thickness can be determined robustly in this manner without amplitude calibration. We extend our method to encompass a generalized reflectivity series represented by a summation of impulse pairs. Application of our spectral inversion to seismic data sets from the Gulf of Mexico results in reliable well ties to seismic data, accurate prediction of layer thickness to less than half the tuning thickness, and improved imaging of subtle stratigraphic features. Comparisons between well ties for spectrally inverted data and ties for conventional seismic data illustrate the superior resolution of the former. Several stratigraphic examples illustrate the various destructive effects of the wavelet, including creating illusory geologic information, such as false stratigraphic truncations that are related to lateral changes in rock properties, and masking geologic information, such as updip limits of thin layers. We conclude that data that are inverted spectrally on a trace-by-trace basis show greater bedding continuity than do the original seismic data, suggesting that wavelet side-lobe interference produces false bedding discontinuities.
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Rickett, James. "Integrated estimation of interval-attenuation profiles." GEOPHYSICS 71, no. 4 (July 2006): A19—A23. http://dx.doi.org/10.1190/1.2209722.
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Quantitative estimates of seismic attenuation are useful for a variety of applications, ranging from seismic-acquisition design, to seismic processing, amplitude analysis, and reservoir characterization. I frame the estimation of interval attenuation from a set of seismic wavelets as a linear inversion of their log-amplitude spectra. The initial spectrum at the first depth location and a set of depth-varying amplitude scalers are estimated simultaneously with an effective-attenuation [Formula: see text] profile. The algorithm can be regarded as a tomographic extension of the spectral-ratio method that uses all the information available in the amplitude spectra, appropriately weighted so that estimates are not biased by noise. Constraints can be applied to ensure the [Formula: see text] values vary smoothly, and solving for log [Formula: see text] rather than [Formula: see text] ensures only positive attenuation values. Tests on synthetic and field data illustrate the trade-off between vertical resolution and sensitivity to noise. A covariance study indicates that improvements in interval-attenuation estimates over the traditional spectral-ratio method come from systematic-noise handling and the explicit constraints on [Formula: see text], rather than the fact that the inversion ties the log-spectral data together with a single estimate of the spectrum at the first depth location.
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Lamb, W. "A spectral approach to an integral equation." Glasgow Mathematical Journal 26, no. 1 (January 1985): 83–89. http://dx.doi.org/10.1017/s0017089500005802.
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In a recent paper [7], Rooney used a technique involving the Mellin transform to obtain solutions in certain spaces ℒμ, ρ of an integral equation which had been studied previously by Šub-Sizonenko [9]. The integral equation in question can be written aswhere I denotes the identity operator and G0.1/2 is given bywith the inversion formula obtained by Rooney taking the formRooney verified that (1.1) and (1.2) formed an inversion pair in ℒμ, ρ for 1 ≤ p < ∞ and μ > 0.
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Matchev, Konstantin T., Katia Matcheva, and Alexander Roman. "Transverse Vector Decomposition Method for Analytical Inversion of Exoplanet Transit Spectra." Astrophysical Journal 939, no. 2 (November 1, 2022): 95. http://dx.doi.org/10.3847/1538-4357/ac82f3.
Full textAbstract:
Abstract We develop a new method for analytical inversion of binned exoplanet transit spectra and for retrieval of planet parameters. The method has a geometrical interpretation and treats each observed spectrum as a single vector r → in the multidimensional spectral space of observed bin values. We decompose the observed r → into two orthogonal components: a wavelength-independent component r → ∥ corresponding to the spectral mean across all observed bins, and a transverse component r → ⊥ that is wavelength dependent and contains the relevant information about the atmospheric chemistry. The method allows us to extract, without any prior assumptions or additional information, the relative mass (or volume) mixing ratios of the absorbers in the atmosphere, the scale height to stellar radius ratio, H/R S , and the atmospheric temperature. The method is illustrated and validated with several examples of increasing complexity.