Spectral Method Model Research Articles

Opportunities and Challenges in the Study of Exoplanetary Atmospheres during the JWST Era

Currently, more than 5 000 exoplanets have been detected, and exoplanetary science is evolving from a focus on exoplanet detection to a focus on comprehensive exoplanetary characterization. Over the past 20 years, through the atmospheric characterization of about 100 exoplanets, a basic framework has been established for atmospheric detection methods, a series of atmospheric spectral forward modeling and retrieval methods, and atmospheric theory for transiting and directly imaged planets. The James Webb Space Telescope (JWST) has unprecedented detection capabilities in the near to mid-infrared spectra, and high-quality data will drive the development of atmospheric theory and models. The early released scientific results of Cycle-1 have shown the ability of JWST to characterize the atmospheres of transiting and directly imaged exoplanets, as well as the initial constraints on the atmospheres of potentially habitable planets around nearby low-mass star. The pursuit of finely detailed exoplanet atmospheric characterization in the era of JWST has already begun, and in conjunction with future missions with atmospheric survey capabilities, such as ARIEL (Atmospheric Remote-sensing Infrared Exoplanet Large-survey) and large-aperture adaptive-optics ground-based telescopes in the next 5 years, will reveal the diversity of exoplanet atmospheres at a much deeper level.

Crustal Structures Inferred from Combined Terrestrial and Earth Gravity Data beneath the Babouri-Figuil and Mayo Oulo-Lere Basins, North Cameroon and South Chad

In this work, the study of the crustal structure of the Babouri-Figuil and Mayo Oulo-Lere sedimentary basins was carried out through the interpretation of gravity data. These data were obtained by combining the terrestrial gravity data obtained from the Earth Gravitational Model 2008. The analysis of the terrestrial Bouguer anomaly maps reveals both negative and positive anomalies. Negative anomalies, i.e., low-density signatures, are interpreted as specific rock types on the basis of the geological knowledge of the region while the positive anomalies are attributed to basaltic rocks underlying a generally granitic environment. The empirical method was used to distinguish anomalies due to deep structures from those due to near-surface structures. This method testifies that the residual map of degree 4 is appropriate. Six profiles are drawn on this residual Bouguer anomaly map and are interpreted using spectral analysis and 2D modeling methods. The results indicate that the mean depths of mass sources at the near-surface of the Babouri-Figuil and Mayo Oulo-Lere sedimentary basins are located at 1.50 km and 1.55 km, respectively. Moreover, the Babouri-Figuil Basin is constituted of two formations while the Mayo Oulo-Lere Basin exhibits three distinct formations. These models also help clarify the geological structure of the study area as well as the thicknesses of the sedimentary basins.

New data-driven estimation of metal element in rocks using a hyperspectral data and geochemical data

The inversion of Cu contents in rocks based on hyperspectral remote sensing is indicative of geochemical exploration as well as mineral resource exploration. Due to the strong redundancy of band information in hyperspectral data, characteristic band selection is the key to improving the accuracy of metal element inversion models in hyperspectral data. In this study, we proposed a framework that innovatively focuses on secondary screening method (SSM) as the core, allowing users to add different data preprocessing methods and machine learning algorithms to the framework based on expert experience, to obtain high-precision metal element inversion models suitable for sampling data. we first preprocessed the measured spectral curves using the first order derivative, second order derivative, continuum removal, multiple scattering correction and variance analysis to highlight band absorption characteristics. In addition, we performed SSM to obtain Cu-related characteristic bands, which were further used as input data to train and compare the performance of Partial Least Square Regression (PLSR), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Random Forest (RF) and Gradient Boosting Regression Tree (GBRT). The results show that both the spectral pretreatment and modeling method could potentially impact the inversion accuracy. The accuracy of the ensemble method applied in fresh faces data (i.e., a CR-SSM-RF with R2 values of 0.77 and CR-SSM-GBRT with R2 values of 0.81) performed better than other models with an accuracy improvement of up to 80%. Compared to the full band model, the accuracy of characteristic bands selection model increased 50%. This also illustrates that the CR-SSM-RF or CR-SSM-GBRT model significantly improves the accuracy of the inversion of Cu contents in rocks compared to the original spectral reflectance. Our study provides a new idea for selecting different spectral preprocessing methods as the input values for the future model and will also offer an executable framework for the inversion of Cu contents in rocks of different lithology on a large scale in the future.

Inversion of soil heavy metals in metal tailings area based on different spectral transformation and modeling methods

The exploitation of mineral resources has seriously polluted the environment around mines, notably in terms of heavy metal contamination of tailings pond soil. Hyperspectral remote sensing, as opposed to conventional on-site sampling and laboratory analysis, offers a potent tool for effective monitoring the content of soil heavy metals. Therefore, we investigated the inversion models of heavy metal content in metal tailings area based on measured hyperspectral and multispectral data. Hyperspectral and its transformation, as well as the simulated Landsat8-OLI multispectral were used for model inversion respectively. Stepwise Multiple Linear Regression (SMLR), Partial Least Squares Regression (PLSR) and Back Propagation Neuron Network (BPNN) were established to study the spectral inversion of eight heavy metals (Cu, Cd, Cr, Ni, Pb, Zn, As, and Hg). The direct inversion models were established on the basis of correlation analysis and the adjust coefficient of determination (Adjust_R2) and Root Mean Square Error (RMSE) were used for model evaluation. Then the best combination of spectral transformation and inversion model were explored. The model inversion results suggested that: (1) Hyperspectral transformation can generally improve the model accuracy, especially the second derivative spectral, based on which the training Adjust_R2 of Hg SMLR and PLSR models are as high as 0.795 and 0.802. (2) The BP neural network inversion based on the denoised hyperspectrum demonstrate that both the training and testing Adjust_R2 of Cd, Ni and Hg models are all greater than 0.5, indicating good applicability in practical extrapolation. (3) Both the training and testing Adjust_R2 of Cu and Hg PLSR models based on simulated R_Landsat8-OLI multispectral are greater than 0.5, and Hg has lower RMSE and lager Adjust_R2 with training and testing Adjust_R2 values of 0.833 and 0.553 respectively. (4) Multispectral remote sensing detection and mapping of Hg contamination were realized by the optimal simulation model of Hg. Hence, it is feasible to simulate the multispectral with hyperspectral data for investigating heavy metal contamination.

Semi-Supervised Deep Learning-Based Multi-component Spectral Calibration Modeling for UV-vis and Near-Infrared Spectroscopy without Information Loss.

Spectral analysis is an important method for characterizing and identifying chemical species. However, quantitative spectral analysis of multiple chemical properties in the real world has always been a challenging problem due to the strong correlation, massive noise, and serious information overlapping of the spectral features. Here, we present a new semi-supervised spectral calibration method based on information lossless decoupling of spectral features named NICEM. To realize the separation and extraction of key latent features, the method uses the flow-based model non-linear independent component estimation (NICE) to learn the sample distribution. The spectral data information is transformed into independent latent variables obeying Gaussian distribution by the reversible structure of deep network without information loss, so as to find the essential properties and realize the feature nonlinear decomposition. Moreover, the association between the input latent feature variables and attributes is evaluated by the maximum mutual information coefficient to eliminate the adverse effects of irrelevant information in the latent variable space and mine key information. Since the latent variables are independent in each dimension, the NICEM method is easier to establish an accurate semi-supervised multi-component calibration model even for high overlapping and complex spectral data. The applicability of the proposed spectral modeling method is demonstrated by using three ultraviolet-visible and near-infrared spectral data sets with 15 physical and chemical properties including diesel fuels, corn, and multi-metal ions solution. Results show that the proposed NICEM method has the highest determination coefficient (R2) and significantly improves extrapolation compared with the seven state-of-the-art methods. The proposed method is intuitive because it obviates complex feature engineering and prior knowledge and is a promising spectral calibration tool for quantitative analysis in other spectroscopy applications.

Accurate Modeling and Wave Propagation Analysis of Cracked Slender Structural Members by the Spectral Element Method

The analysis of elastic wave propagation in cracked structures is very useful in the crack detection by the ultrasonic guided wave method. This study presents an accurate spectral element modeling method for cracked slender structural members by using refined waveguide models and a more realistic crack model. Firstly, a spatial spectral beam element model is established for uncracked slender structural member based on the Love rod theory, the modified Timoshenko beam theory, and the Saint-Venant’s torsion theory. Then, the complete local additional flexibility matrix for crack in the structural member with rectangular cross section is derived from the theory of elastic fracture mechanics, and a two-node condensed spectral element model considering the stiffness coupling effect caused by the crack is established for cracked slender structural member. The wave response in cracked structures is solved by the numerical inverse Laplace transformation method. A thorough comparison of the wave responses in cracked structural member evaluated by the presented spectral element model and the 3D solid finite element model is given in the numerical example, which verifies the accuracy and high efficiency of the presented method.

A machine learning based line-by-line absorption coefficient model for the application of atmospheric carbon dioxide remote sensing

Space-based remote sensing, which is used to infer CO2 concentration from the satellite-measured atmospheric spectral absorption signals, is an effective way to obtain CO2 concentration data for greenhouse gas monitoring. The next-generation greenhouse gases monitoring satellites mainly address the challenge of improving the spatial and temporal resolutions of observations, which will dramatically increase the computational power required for the CO2 retrievals. One of the bottlenecks is the high computational cost for the radiative heat transfer calculations, which involve inefficient high-resolution (usually requires a line-by-line spectral resolution) spectral modeling. Therefore, developing a fast and accurate spectral modeling method becomes necessary to tackle this problem. In the present study, we presented a machine learning based line-by-line absorption coefficient calculation (prediction) method for CO2 in the applications of atmospheric remote sensing. By training an artificial neural network with data randomly generated from a line-by-line CO2 absorption coefficient look-up table, a compact, accurate and efficient absorption coefficient prediction model can be developed, which only takes the CO2 thermodynamic states as input. The proposed method has been tested by developing an absorption coefficient prediction model for the CO2 1.6 μm spectral band, which was later used to simulate the measured spectra for clear-sky conditions from the Greenhouse gases Observing SATellite (GOSAT) for several different locations around the world. Results have shown that the model is both accurate and efficient. In addition, the same approach has been applied to fit the absorption coefficient tables provided by the Orbiting Carbon Observatory (OCO)-2 mission and an accurate prediction model was also presented.

Estimation of Soil Organic Carbon Using Vis-NIR Spectral Data and Spectral Feature Bands Selection in Southern Xinjiang, China.

Soil organic carbon (SOC) plays an important role in the global carbon cycle and soil fertility supply. Rapid and accurate estimation of SOC content could provide critical information for crop production, soil management and soil carbon pool regulation. Many researchers have confirmed the feasibility and great potential of visible and near-infrared (Vis-NIR) spectroscopy in evaluating SOC content rapidly and accurately. Here, to evaluate the feasibility of different spectral bands variable selection methods for SOC prediction, we collected a total of 330 surface soil samples from the cotton field in the Alar Reclamation area in the southern part of Xinjiang, which is located in the arid region of northwest China. Then, we estimated the SOC content using laboratory Vis-NIR spectral. The Particle Swarm optimization (PSO), Competitive adaptive reweighted sampling (CARS) and Ant colony optimization (ACO) were adopted to select SOC feature bands. The partial least squares regression (PLSR), random forest (RF) and convolutional neural network (CNN) inversion models were constructed by using full-bands (400–2400 nm) spectra (R) and feature bands, respectively. And we also analyzed the effects of spectral feature band selection methods and modeling methods on the prediction accuracy of SOC. The results indicated that: (1) There are significant differences in the feature bands selected using different methods. The feature bands selected methods substantially reduced the spectral variable dimensionality and model complexity. The models built by the feature bands selected by CARS, PSO and ACO methods showed the different potential of improvement in model accuracy compared with the full-band models. (2) The CNN model had the best performance for predicting SOC. The R2 of the optimal CNN model is 0.90 in the validation, which was improved by 0.05 and 0.04 in comparison with the PLSR and RF model, respectively. (3) The highest prediction accuracy was archived by the CNN model using the feature bands selected by CARS (validation set R2 = 0.90, RMSE = 0.97 g kg−1, RPD = 3.18, RPIQ = 3.11). This study indicated that using the CARS method to select spectral feature bands, combined with the CNN modeling method can well predict SOC content with higher accuracy.

Improved generalized S-transform deconvolution for non-stationary seismic data

Improving the vertical resolution is one of the significant tasks for seismic data processing. Most traditional resolution-enhancement techniques assume that the seismic wavelet is time-invariant. However, the seismic wavelet varies with seismic wave propagation in the subsurface. To solve this issue, a new spectral-modeling method is proposed to extract the time-varying wavelet using improved generalized S-transform (IGST) and higher-order Fourier series. The IGST based on modified time-window function can effectively improve the resolution of the time-frequency (t-f) spectrum. The high-order Fourier series is used to fit on the logarithm t-f spectrum and achieve the high-precision time-varying wavelet. The proposed method is composed of four steps in the implementation. Firstly, the seismic data is decomposed by the IGST and converted to the logarithm t-f domain. Secondly, the time-varying wavelet spectrum is modeled at each time sample using a higher-order Fourier series. Thirdly, the boxcar smoothing method is used to smooth the time-varying wavelet spectrum and extract the time-varying wavelet with Hilbert transform. Finally, using the time-varying wavelet spectrum to spectrally balance seismic data to flatten the seismic response. Synthetic and field data examples demonstrate the feasibility of the proposed method in improving the signal-to-noise ratio and enhancing the vertical resolution.

Spectral element modeling and experimental investigations on vibration behaviors of imperfect plate considering irregular hole and curved crack

• A general spectral element modeling method is firstly developed. • The method is applied for imperfect plate considering irregular hole and curved crack. • Modal tests considering irregular hole and curved crack are firstly implemented. • Spectral element model has excellent predictive capability. A weak-form spectral element modeling and the corresponding experimental studies in this work are concerned to clarify free vibration mechanism for the plate of arbitrary shape having built-in complex hole and crack. To effectively address the discontinuity problem at irregular hole as well as crack of varying curvature, some quadrilateral plate elements with variable curvature edges are rigidly coupled together to form the investigated structural system. A flexible coordinate mapping strategy is here employed for satisfying the adaptability requirements of the introduced displacement functions to the sophisticated solution domain. Accordingly, the complete vibration equation of the physical model is solved based on the well-known first-order scheme of plate in conjunction with 2D spectral element functions, where the boundary constraints and compatibility relations in the form of energy quantification are imposed through the penalty function method. After that, modal experiments considering six unconstrained imperfect plates with pre-configured crack and hole, are incorporated with a series of acceptable solutions, provided by the available literature and some associated models of FEM, for disclosing the excellent predictive capability of the spectral element methodology in vibration performance. The reported eigenfrequencies and eigenmodes respecting arbitrarily shaped plate introducing intricate hole and curved crack are the first presented vibration solutions, and meaningful benchmarks for evaluating correctness of other numerical or analytical approaches.

A feasibility study on improving the non-invasive detection accuracy of bottled Shuanghuanglian oral liquid using near infrared spectroscopy

The ultimate goal of the study is to present a strategy to improve the accuracy of near-infrared spectroscopy detection of Shuanghuanglian oral liquid in glass bottles without damaging the primary packaging. we adopted the multi-position spectral modeling (MPSM) method to correct the spectral variation caused by the difference of bottle and measuring position, so as to improve the measurement accuracy and find the best site combination for measuring Shuanghuanglian oral liquid. Baicalin, total flavonoids and soluble solid contents were considered as the quality indicators of the oral liquid, and partial least squares (PLS) models were employed for the single-position and multi-position spectra, respectively. The root mean square error of the validation set (RMSEP) of the optimum multi-position models are 0.7412 mg/mL for baicalin, 1.1259 mg/mL for total flavonoids and 0.9491% for soluble solids contents. Compared with the traditional single-position spectral modeling method (SPSM method), MPSM method improved the prediction accuracy of baicalin, total flavonoids and soluble solid contents by 26.84%, 31.97% and 58.14% respectively. The results showed that the MPSM method can improve the measurement accuracy of bottled oral liquid and is an effective method to eliminate the uncertainty of measurement conditions.

Developing a Conditional Variational Autoencoder to Guide Spectral Data Augmentation for Calibration Modeling

To deal with the typically insufficiently labeled samples involved in practical spectroscopy measurements, a conditional variational autoencoder (CVAE) is proposed to guide the spectral data augmentation calibration modeling method for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurement. First, the CVAE is designed to generate the virtual spectra such that the augmentation training set is employed to develop the calibration model. To use the generated unlabeled samples for modeling with online measurement purposes, a semi-supervised ladder network (S2-LN)-based regression learning model is developed. The proposed method incorporates all generated virtual unlabeled samples with real labeled samples. An important advantage of this approach is that it ensures that the generated virtual spectra and the real labeled spectra are the same distribution, which in turn ensures the effectiveness of semi-supervised learning. A numerical simulation example and an experimental example of the glucose fermentation process illustrate the effectiveness of the approach.

Alternation in Effective Connectivity With Cognitive Aging: A Longitudinal Study of Elderly Populations.

In this research, we investigated the alterations in the directionality and strength of regional interactions within functionally changed brain networks and their relationship to cognitive decline during the aging process in normal elderly individuals. Thirty-seven cognitively normal elderly people received resting-state fMRI scans and cognitive assessments at baseline (age = 78.65 ± 3.56 years) and at 4-year follow-up. Functional connectivity analyses were used to identify networks containing brain regions whose functions changed with age as regions of interest. The spectral dynamic causal modeling (spDCM) method was used to estimate the causal interactions within networks in subjects at different time points and in subjects with different cognitive levels to explore the alterations with cognitive aging. The results showed that, at both time points, all the networks, except the frontal-parietal network (FPN) at baseline, had mutual interactions between each pair of nodes. Furthermore, when the subjects were divided with global cognition level, lost connections were only found in the subgroup with better performance. These indicated that elderly people appeared to need more interaction pathways between brain areas with cognitive decline. We also observed that the strength of the flow of information from the left angular gyrus to the precuneus, which is associated with activation of memory retrieval and the functional hub involved in various cognitive domains, was predictive of declines in executive function with the aging process, making it a potential predictor of such situation.

Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm.

Effective soil spectral band selection and modeling methods can improve modeling accuracy. To establish a hyperspectral prediction model of soil organic matter (SOM) content, this study investigated a forested Eucalyptus plantation in Huangmian Forest Farm, Guangxi, China. The Ranger and Lasso algorithms were used to screen spectral bands. Subsequently, models were established using four algorithms: partial least squares regression, random forest (RF), a support vector machine, and an artificial neural network (ANN). The optimal model was then selected. The results showed that the modeling accuracy was higher when band selection was based on the Ranger algorithm than when it was based on the Lasso algorithm. ANN modeling had the best goodness of fit, and the model established by RF had the most stable modeling results. Based on the above results, a new method is proposed in this study for band selection in the early phase of soil hyperspectral modeling. The Ranger algorithm can be applied to screen the spectral bands, and ANN or RF can then be selected to construct the prediction model based on different datasets, which is applicable to establish the prediction model of SOM content in red soil plantations. This study provides a reference for the remote sensing of soil fertility in forests of different soil types and a theoretical basis for developing portable equipment for the hyperspectral measurement of SOM content in forest habitats.

Spectral Modeling of Deformation-Relaxation Processes in the Functional Study of Polymer Textile Materials

Spectral modeling of deformation-relaxation processes in polymer textile materials is considered as a tool for studying their functionality. The described spectral modeling methods help to determine the performance properties of polymer materials and informs the design of new polymer textile materials having special functional properties.

Planetary gearbox spectral modeling based on the hybrid method of dynamics and LSTM

It is the prerequisite for mechanical diagnosis to clarify the mapping relations between vibrations and damage conditions based on the dynamic model analyses and the systematic experiments. However, there is a certain deviation from the actual measurements in the existing planetary dynamic modeling methods which utilizes linear models or just considering simple non-linear factors. To solve the problem, the hybrid spectral modeling method based on the fault dynamics analysis and the long short-term memory (LSTM) network is proposed in this paper. Firstly, the response of several parts in planetary sets are simulated by the classical dynamic method. Then, as an exploratory attempt, an LSTM network is used to rectify the predicted spectral deviation caused by the over-simplification and parameter errors of the dynamic models. From the inputs of simulated spectral sequences of the several components, the LSTM successfully predicts the spectral sequence of the observation. Finally, by the overlapping weighted average, the power spectral density of the planetary system is estimated through the Welch method. The effectiveness and superiority of the proposed method are verified via the experimental data of a planetary gearbox test rig.

Анализ динамики антропогенных изменений экосистем водосбора р. Кемь (бассейн Белого моря) с использованием данных Global Forest Change

The analysis of changes in the catchment vegetation cover as a result of industrial development of its territory, primarily deforestation, was carried out. For processing Landsat images, the spectral space modeling method was used, which provides higher reliability of the classification results compared to traditional methods. The most significant changes in the catchment occurred from the 1930s (the beginning of large-scale logging), until the end of the 1990s. Significantly, by more than 60 %, the area of old-growth forests decreased, their large fragments remained only in nature protected areas, as well as around large lakes and along large rivers, due to the presence of water protection zones. In the period from 2000 to 2018 due to deforestation, the area of productive forests decreased even more. Nevertheless, by 2019 the occurrence of secondary reforestation on significant areas (up to 70 %) is noted, which indicates a sufficiently high regeneration potential of the catchment ecosystems. To restore the spatial structure of the forest cover in the period before the start of the Landsat 5 operation, the method of tracking the trajectories of reforestation successions in the spectral space was used. A comparison with the results of the Global Forest Change 2000—2018 data (loss/gain) showed almost complete coincidence in cuttings (loss), but the method of reforestation trajectories showed many times higher results in forest gain, since it reveals forest regeneration at a much earlier stage. The developed methodology will be applied on the catchments of other tributaries of the White Sea, and the results will be used as input to an environmental-socio-economic cognitive model that predicts the state of ecosystems under climate changes and economic priorities.

Improving the detection accuracy of complex solution components based on multi-dimensional spectroscopy fusion method

In order to improve the accuracy of the detection of complex solution components in wedge-shaped sample pool, this paper obtained multi-dimensional spectroscopy (MDS) of multi-path non-linear features including samples by multi-position and multi-wavelength detection methods. And this research constructed a visualized light intensity distribution image by multi-dimensional spectroscopy fusion (MDSF) method. The detection of 46 samples of India Ink and Intralipd-20% mixed phantoms was carried out, and the visualized light intensity distribution image was analyzed and trained by partial least squares (PLS) method, and compared to training method that the raw data without the MDSF (Non-MDSF). In this study, the multi-dimensional spectral fusion modeling method is used to increase the correlation coefficient of the model by 1.29%, and the mean square error is reduced by 3.07%. The results show that the use of MDSF method to analyze the composition of complex solutions, the composition of the solution with scattering characteristics is fast and accurate, which is of great significance for the field of complex solution detection and analysis.

A two-position spectral modeling method to increase the robustness of NIR analysis model

In NIR quantitative analysis, the robustness of the model determines the value in its application. When the application and modeling conditions show substantial variation, the model becomes less robust, and the prediction ability of the model is significantly reduced. Based on the “multi-dimensional, multi-mode and multi-position” strategy of “M + N” theory, the two-position spectral modeling method for the NIR quantitative analysis of non-scattering or weakly scattering solutions is proposed. The external influence of measurement conditions on the robustness of the model is illustrated by significantly changing the position of the light source. The two-position spectral modeling method is used to suppress these effects, thereby maintaining a high prediction accuracy of the model. In this paper, a verification experiment was designed. Three different intensities were set for the light source. Spectra transmitted from 35 sample solutions containing the mixture of intralipid and ink, were measured at two positions. Models built with the NIR transmission spectra measured at two positions were compared with the models built with those measured at a single position. The experimental results showed that the RMSEP of two-position spectral modeling method was declined by 76% compared to that of the traditional method, which proved that the models established with a two-position spectra were more robust towards the intensity changes of incident light.

Multi-Ricker Spectral Modeling in the S-transform Domain for Enhancing Vertical Resolution of Seismic Reflection Data

DOI: 10.17014/ijog.6.3.223-233We present a relatively straightforward methodology for extending seismic bandwidth and hence enhancing the seismic resolution by performing time-variant deconvolution. We use the generalized S-transform (GST) approach in order to properly compute the time-frequency components of the seismic reflection trace. In estimating the time-variant wavelet, we propose a spectral modeling method named multi-Ricker spectral approximation (MRA). After obtaining the estimated wavelet spectrum at each time sample, a deconvolution filter can then be built and applied in the S-transform domain. This proposed time-variant seismic enhancement method needs neither information on subsurface attenuation model nor assumption that the subsurface reflectivity is random. It is a data-driven methodology which is based on the seismic data only. We validate this proposed method on a synthetic and apply to a field data. Results show that, after enhancement, overall seismic bandwidth can be extended resulting in higher vertical resolution. Correlation with VSP corridor stack at well location ensures that the generated reflection details after enhancement is geologically plausible.