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Phosphorus plays a vital role in the growth and development of rice in the cold northern regions, affecting the yield and quality of rice. The phosphorus content of leaves can indicate the nutritional status of rice. Rapid and accurate acquisition of the phosphorus content in leaves is the basis for ensuring healthy rice growth and maintaining stable and high rice yield. Hyperspectral technology can reflect the shape of rice leaves and then evaluate the phosphorus content in the leaves, so hyperspectral technology has the potential to estimate the phosphorus content in plant leaves quickly and accurately. The hyperspectral data of the rice leaves were pretreated using the SG smoothing method. The spectral characteristics of pretreated spectral data were extracted using principal component analysis (PCA) and linear discriminant analysis (LDA). Extreme learning machine (ELM) and Bat algorithm optimized extreme learning machine (BA-ELM) were constructed to retrieve the phosphorus content in rice leaves. The results show that there are seven feature vectors produced by the two methods, and the feature vectors selected by the two methods are used as inputs, respectively. The verification sets R2 and RMSE of the two models constructed using the feature reflectivity chosen by the LDA algorithm as input were between 0.603 and 0.604, and 0.025 and 0.032, respectively. Under the condition of the same inversion model, the model constructed by using the reflectivity of the features selected by the PCA algorithm as input has a better prediction effect, and the verification set R2 of the two models was between 0.685-0.765, and RMSE was between 0.022-0.038. In addition, when using the features selected by these two algorithms to model, comparing the prediction results of the two models, it was found that the accuracy of the BA-ELM was higher than that of ELM. Its determination coefficient R2 and RMSE of the verification set were 0.765 and 0.022, respectively. Because of this, the ELM optimized by principal component analysis and BA has certain advantages in the hyperspectral inversion of phosphorus content in rice leaves in cold regions, and can provide some reference for rapid and accurate detection of phosphorus content in rice leaves. [ABSTRACT FROM AUTHOR]

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The geological complexity of the karst regions presents significant challenges, with the permeability coefficient being a critical parameter for accurately analyzing seepage behavior in hydraulic engineering projects. To overcome the limitations of traditional inversion methods, which often exhibit low computational efficiency, poor accuracy, and instability, this study utilizes a finite-element forward model and orthogonal experimental design to establish a sample set for permeability-coefficient inversion. A surrogate model for seepage calculation based on the Random Forest (RF) algorithm is subsequently developed. Furthermore, the Secretary Bird Optimization Algorithm (SBOA) is incorporated to propose an intelligent RF–SBOA inversion method for permeability-coefficient estimation, which is validated through a case study of the C-pumped storage power station. The results demonstrate that the RF model's predictions for water levels at four boreholes closely align with the measured data, outperforming models such as CART, BP, and SVR. The SBOA effectively identifies the optimal geological permeability coefficient, with the borehole water-level inversion achieving a maximum relative error of only 0.128%, which meets the accuracy requirements for engineering applications. Additionally, the computed distribution of the natural seepage field is consistent with the typical distribution patterns observed in mountain seepage systems. During the normal water-storage phase, both the calculated seepage flow and gradient comply with engineering standards, while the seepage-field distribution aligns with empirical observations. This inversion model provides a rapid and accurate method for estimating the permeability coefficient of strata in the project area, with potential applicability to permeability inversion in other engineering geology contexts, thus demonstrating considerable practical value for engineering applications. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is a key variable in the study of the global climate system and one of the important parameters in the process of air–sea interaction. Therefore, the demand for SST is developing towards high quality and high precision. In this paper, the ability of the multi-layer perceptron (MLP) optimized by the Aquila optimizer (AO) to solve nonlinear problems and the advantages of the deep belief network (DBN) to effectively process complex data are used to construct the IMLP-DBN inversion algorithm. The algorithm takes into account the influences of atmospheric conditions and satellite zenith angles. The data set is the infrared remote sensing data of the moderate resolution imaging spectroradiometer (MODIS) and the actual measurement data of buoys on sunny days and few clouds. Analysis of the inversion results shows that the root mean square error (RMSE) of the inversion value and the measured value is 0.14, and the sum of square errors (SSE) is 0.78. Compared with the MOD28 product data, the RMSE and SSE of the inversion are reduced by 66% and 24%, respectively. [ABSTRACT FROM AUTHOR]

The physical properties of lunar regolith are crucial for exploration planning, hazard assessment, and characterizing scientific targets at global and polar scales. The dielectric constant, a key property, offers insights into lunar material distribution within the regolith and serves as a proxy for identifying volatile-rich regoliths. Miniature radio frequency (Mini-RF) on the Lunar Reconnaissance Orbiter (LRO) provides a potential tool for mapping the lunar regolith's physical nature and assessing the lunar volatile repository. This study presents global and polar S-band Mini-RF dielectric signatures of the Moon, obtained through a novel deep learning inversion model applied to Mini-RF mosaics. We achieved good agreement between training and testing of the model, yielding a coefficient of determination (R2 value) of 0.97 and a mean squared error of 0.27 for the dielectric constant. Significant variability in the dielectric constant is observed globally, with high-Ti mare basalts exhibiting lower values than low-Ti highland materials. However, discernibility between the South Pole–Aitken (SPA) basin and highlands is not evident. Despite similar dielectric constants on average, notable spatial variations exist within the south and north polar regions, influenced by crater ejecta, permanently shadowed regions, and crater floors. These dielectric differences are attributed to extensive mantling of lunar materials, impact cratering processes, and ilmenite content. Using the east- and west-looking polar mosaics, we estimated an uncertainty (standard deviation) of 1.01 in the real part and 0.03 in the imaginary part of the dielectric constant due to look direction. Additionally, modeling highlights radar backscatter sensitivity to incidence angle and dielectric constant at the Mini-RF wavelength. The dielectric constant maps provide a new and unique perspective of lunar terrains that could play an important role in characterizing lunar resources in future targeted human and robotic exploration of the Moon. [ABSTRACT FROM AUTHOR]

Purpose: Timely and accurate monitoring of soil salinity content (SSC) is essential for precise irrigation management of large-scale farmland. Uncrewed aerial vehicle (UAV) low-altitude remote sensing with high spatial and temporal resolution provides a scientific and effective technical means for SSC monitoring. Many existing soil salinity inversion models have only been tested by a single variable selection method or machine learning algorithm, and the influence of variable selection method combined with machine learning algorithm on the accuracy of soil salinity inversion remain further studied. Methods: Firstly, based on UAV multispectral remote sensing data, by extracting the spectral reflectance of each sampling point to construct 30 spectral indexes, and using the pearson correlation coefficient (PCC), gray relational analysis (GRA), variable projection importance (VIP), and support vector machine-recursive feature elimination (SVM-RFE) to screen spectral index and realize the selection of sensitive variables. Subsequently, screened and unscreened variables as model input independent variables, constructed 20 soil salinity inversion models based on the support vector machine regression (SVM), back propagation neural network (BPNN), extreme learning machine (ELM), and random forest (RF) machine learning algorithms, the aim is to explore the feasibility of different variable selection methods combined with machine learning algorithms in SSC inversion of crop-covered farmland. To evaluate the performance of the soil salinity inversion model, the determination coefficient (R2), root mean square error (RMSE) and performance deviation ratio (RPD) were used to evaluate the model performance, and determined the best variable selection method and soil salinity inversion model by taking alfalfa covered farmland in arid oasis irrigation areas of China as the research object. Results: The variable selection combined with machine learning algorithm can significantly improve the accuracy of remote sensing inversion of soil salinity. The performance of the models has been improved markedly using the four variable selection methods, and the applicability varied among the four methods, the GRA variable selection method is suitable for SVM, BPNN, and ELM modeling, while the PCC method is suitable for RF modeling. The GRA-SVM is the best soil salinity inversion model in alfalfa cover farmland, with Rv2 of 0.8888, RMSEv of 0.1780, and RPD of 1.8115 based on the model verification dataset, and the spatial distribution map of soil salinity can truly reflect the degree of soil salinization in the study area. Conclusion: Based on our findings, the variable selection combined with machine learning algorithm is an effective method to improve the accuracy of soil salinity remote sensing inversion, which provides a new approach for timely and accurate acquisition of crops covered farmland soil salinity information. [ABSTRACT FROM AUTHOR]

The elastic modulus is a basic parameter reflecting the deformation resistance and energy concentration characteristics of rock. The traditional testing method of the rock elastic modulus needs to core the surrounding rock and testing it in the laboratory, which has difficulty reflecting the rock mechanical properties under the engineering site environment, and uncontrolled broken surrounding rock is difficult to core for testing. Digital drilling technology provides a new idea for in situ testing of the rock’s elastic modulus. The key is to establish a quantitative relationship between the elastic modulus and drilling data. In this paper, the relationship between rock cutting energy density and drilling data is established, and a series of rock digital drilling tests are carried out. The response law of the drilling data and cutting energy density to the rock elastic modulus is clarified. The inversion model of the rock elastic modulus while drilling (DP-E model) is established. The verification test results indicate that the average difference rate of the test results based on the DP-E model is 7.06% compared with the compression test method, which verifies the effectiveness of the model. This study provides a theoretical basis for in situ testing of the rock elastic modulus.

Abstract This study aims to quantify the topography of the Moho boundary, the lower crust and uppermost mantle contact of Egypt, in order to estimate the crustal thickness variation and its link to the distribution of thermal anomalies under Egypt. This is accomplished by modeling satellite gravity, supported by the passive seismic constraints throughout Egypt. However, when estimating the thickness of the crust in Egypt using just seismic data, substantial uncertainty and deviation are produced due to the sparsely dispersed stations. Integrating seismic and gravity data minimizes uncertainty and improves estimate accuracy. The investigation is broken down into four stages, the first involving utilizing the Sentinel-3B satellite to create land surface temperature maps. The subsequent steps consist of gravity and seismic data adjustments, inversion and forward modeling. We used seismically restricted nonlinear inversion to look at Goco06s satellite gravity data to model the Moho’s topographic surface. The data gathered from deep seismic refraction and receiver functions adjusted the analyzed data. The inversion process relies on the adapted Bott's approach and Tikhonov regularization, using the assumption of the sphericity of the Earth planet. Reference values for depth of Moho and density contrast were set at 35 km and 500 kg/m3, respectively. The average statistical difference for Moho depth between gravity-based model and seismic data is − 0.10 km. Through forward gravity modeling, five gravity profiles were chosen and interpreted in 2.5D models. The results indicated that the Moho depth in the south varies from 35 to 39 km and decreases in the north and the Mediterranean. In upper Egypt, the highest Moho depth is 39 km. The depth varies beneath the Sinai Peninsula as it is about 35 km in its south, reaches 30 km in the northern portion, and ranges along the Red Sea’s Rift Margin from 29 to 32 km. Moreover, the final model shows the relation between Moho coincides with the surface temperature anomalies approved by satellite images and hot springs. The model reveals a correlation between Moho discontinuity and surface temperature anomalies, revealing the highest geothermal potential in a rectangular area in central Egypt, between latitudes 25°N and 30°N, based on satellite imagery and hot springs distribution.

This study aims to quantify the topography of the Moho boundary, the lower crust and uppermost mantle contact of Egypt, in order to estimate the crustal thickness variation and its link to the distribution of thermal anomalies under Egypt. This is accomplished by modeling satellite gravity, supported by the passive seismic constraints throughout Egypt. However, when estimating the thickness of the crust in Egypt using just seismic data, substantial uncertainty and deviation are produced due to the sparsely dispersed stations. Integrating seismic and gravity data minimizes uncertainty and improves estimate accuracy. The investigation is broken down into four stages, the first involving utilizing the Sentinel-3B satellite to create land surface temperature maps. The subsequent steps consist of gravity and seismic data adjustments, inversion and forward modeling. We used seismically restricted nonlinear inversion to look at Goco06s satellite gravity data to model the Moho's topographic surface. The data gathered from deep seismic refraction and receiver functions adjusted the analyzed data. The inversion process relies on the adapted Bott's approach and Tikhonov regularization, using the assumption of the sphericity of the Earth planet. Reference values for depth of Moho and density contrast were set at 35 km and 500 kg/m3, respectively. The average statistical difference for Moho depth between gravity-based model and seismic data is − 0.10 km. Through forward gravity modeling, five gravity profiles were chosen and interpreted in 2.5D models. The results indicated that the Moho depth in the south varies from 35 to 39 km and decreases in the north and the Mediterranean. In upper Egypt, the highest Moho depth is 39 km. The depth varies beneath the Sinai Peninsula as it is about 35 km in its south, reaches 30 km in the northern portion, and ranges along the Red Sea's Rift Margin from 29 to 32 km. Moreover, the final model shows the relation between Moho coincides with the surface temperature anomalies approved by satellite images and hot springs. The model reveals a correlation between Moho discontinuity and surface temperature anomalies, revealing the highest geothermal potential in a rectangular area in central Egypt, between latitudes 25°N and 30°N, based on satellite imagery and hot springs distribution. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose Timely and accurate monitoring of soil salinity content (SSC) is essential for precise irrigation management of large-scale farmland. Uncrewed aerial vehicle (UAV) low-altitude remote sensing with high spatial and temporal resolution provides a scientific and effective technical means for SSC monitoring. Many existing soil salinity inversion models have only been tested by a single variable selection method or machine learning algorithm, and the influence of variable selection method combined with machine learning algorithm on the accuracy of soil salinity inversion remain further studied. Methods Firstly, based on UAV multispectral remote sensing data, by extracting the spectral reflectance of each sampling point to construct 30 spectral indexes, and using the pearson correlation coefficient (PCC), gray relational analysis (GRA), variable projection importance (VIP), and support vector machine-recursive feature elimination (SVM-RFE) to screen spectral index and realize the selection of sensitive variables. Subsequently, screened and unscreened variables as model input independent variables, constructed 20 soil salinity inversion models based on the support vector machine regression (SVM), back propagation neural network (BPNN), extreme learning machine (ELM), and random forest (RF) machine learning algorithms, the aim is to explore the feasibility of different variable selection methods combined with machine learning algorithms in SSC inversion of crop-covered farmland. To evaluate the performance of the soil salinity inversion model, the determination coefficient (R2), root mean square error (RMSE) and performance deviation ratio (RPD) were used to evaluate the model performance, and determined the best variable selection method and soil salinity inversion model by taking alfalfa covered farmland in arid oasis irrigation areas of China as the research object. Results The variable selection combined with machine learning algorithm can significantly improve the accuracy of remote sensing inversion of soil salinity. The performance of the models has been improved markedly using the four variable selection methods, and the applicability varied among the four methods, the GRA variable selection method is suitable for SVM, BPNN, and ELM modeling, while the PCC method is suitable for RF modeling. The GRA-SVM is the best soil salinity inversion model in alfalfa cover farmland, with Rv2 of 0.8888, RMSEv of 0.1780, and RPD of 1.8115 based on the model verification dataset, and the spatial distribution map of soil salinity can truly reflect the degree of soil salinization in the study area. Conclusion Based on our findings, the variable selection combined with machine learning algorithm is an effective method to improve the accuracy of soil salinity remote sensing inversion, which provides a new approach for timely and accurate acquisition of crops covered farmland soil salinity information.

Efficient prediction and precise depiction of heavy metal concentrations in urban soil are essential for mitigating non-point source pollution and safeguarding public health. Therefore, this research investigated the estimation of soil heavy metal concentrations derived from Gaofen-5 (GF-5) hyperspectral images calibrated by the direct standardization (DS) algorithm. The inversion strategy for soil heavy metal concentrations in response to the two-dimensional soil spectral index (2D-SSI) was proposed by coupling Pearson correlation coefficient (r) and competitive adaptive reweighting algorithm (CARS) for feature selection. The results indicated that the optimal models based on 2D-SSI outperform the models based on calibrated, filtered original spectral bands. For Pb, Cu, Cd, and Hg, the optimal model determination coefficients for the validation data set (RV2) were 0.871 (SVM), 0.883 (BPNN), 0.834 (PLSR), and 0.907 (PLSR), respectively. The spectral features were highlighted in the two-dimensional feature space, and the predicted distribution of heavy metal concentrations was aligned with the observed ground measurements. This study revealed that the prediction strategy based on DS-corrected GF-5 AHSI images with constructed 2D-SSI features can serve as a reliable technical approach for soil heavy metal prediction and pollution prevention.

Soil nitrogen and phosphorus are directly related to soil quality and vegetation growth and are, therefore, a common research topic in studies on global climate change, material cycling, and information exchange in terrestrial ecosystems. However, collecting soil hyperspectral data under in situ conditions and predicting soil properties, which can effectively save time, manpower, material resources, and financial costs, have been generally undervalued. Recent optimization techniques have, however, addressed several of the limitations previously restricting this technique. In this study, hyperspectral data were taken from surface soils under different vegetation types in the wetlands of the Shaanxi Yellow River Wetland Provincial Nature Reserve. Through in situ original and first-order differential transformation spectral data, three prediction models for soil carbon, nitrogen, and phosphorus contents were established: partial least squares (PLSR), random forest (RF), and Gaussian process regression (GPR). The R² and RMSR of the constructed models were then compared to select the optimal model for evaluating soil content. The soil organic carbon, total nitrogen, and total phosphorus content models established based on the first-order differential had a higher accuracy when modeling and during model validation than those of other models. Moreover, the PLSR model based on the original spectrum and the Gaussian process regression model had a superior inversion performance. These results provide solid theoretical and technical support for developing the optimal model for the quantitative inversion of wetland surface soil carbon, nitrogen, and phosphorus based on in situ hyperspectral technology. [ABSTRACT FROM AUTHOR]

The safeguarding of scarce water resources is critically dependent on continuous water quality monitoring. Traditional methods like satellite imagery and automated underwater observation have limitations in cost-efficiency and frequency. Addressing these challenges, a ground-based remote sensing system for the high-frequency, real-time monitoring of water parameters has been developed. This system is encased in a durable stainless-steel shell, suited for outdoor environments, and features a compact hyperspectral instrument with a 4 nm spectral resolution covering a 350–950 nm wavelength range. In addition, it also integrates solar power, Wi-Fi, and microcomputers, enabling the autonomous long-term monitoring of water quality. Positioned on a rotating platform near the shore, this setup allows the spectrometer to quickly capture the reflective spectrum of water within 3 s. To assess its effectiveness, an empirical method correlated the reflective spectrum with the actual chlorophyll a(Chla) concentration. Machine learning algorithms were also used to analyze the spectrum’s relationship with key water quality indicators like total phosphorus (TP), total nitrogen (TN), and chemical oxygen demand (COD). Results indicate that the band ratio algorithm accurately determines Chla concentration (R-squared = 0.95; RMSD = 0.06 mg/L). For TP, TN, and COD, support vector machine (SVM) and linear models were highly effective, yielding R-squared values of 0.93, 0.92, and 0.88, respectively. This innovative hyperspectral water quality monitoring system is both practical and reliable, offering a new solution for effective water quality assessment. [ABSTRACT FROM AUTHOR]

Objectives: In order to improve the inversion accuracy of cylindrical oil tank height from satellite imagery, this paper introduces a height inversion model of oil tank using satellite imagery with the same name arc distance (SNAD). Methods: First, we define the concept of SNAD and clarify several main geometric elements and the physical meanings of oil tank on satellite image. Then, considering the isotropic characteristics of the cylindrical oil tank, we propose a height inversion model based on imaging simulation under multiple strategic conditions. Finally, we clarify each parameter in the inversion model and deduce the calculation method of key parameters. In order to verify the accuracy and applicability of the proposed model, we invert the height of two types of oil tanks in four high-resolution satellite images and compare with the conventional methods. Results: The proposed model can effectively realize the detection of SNAD and height inversion of cylindrical oil tank in multi-satellite images. The root mean square error of the proposed model ranges from 0.50 m to 1.00 m, with an average value of 0.78 m, while that of the conventional model ranges from 9.28 m to 9.59 m, with an average value of 9.44 m. The standard deviation of the proposed model ranges from 0.35 m to 0.63 m, with an average value of 0.46 m, while that of the conventional model ranges from 6.50 m to 6.69 m, with an average value of 6.60 m. Conclusions: The proposed model has the advantages of high inversion accuracy, excellent stability, strong universality, convenience and efficiency. It can provide a new solution to carry out oil tank height inversion based on high-resolution satellite image. [ABSTRACT FROM AUTHOR]

This paper introduces a new application of the Electrical Resistivity Imaging (ERI) method within the realm of structural assessment, deviating from its conventional use in geology. The study presents an innovative inversion model that incorporates the Levenberg-Marquardt algorithm, representing a notable leap in seamlessly integrating ERI into structural analysis. Rigorous validation of the inversion methodology is conducted through extensive benchmarking against simulated reference data, focusing on 1D and 2D resistivity distributions within timber specimens. By utilizing known resistivity fields, the paper quantitatively validates the accuracy of reconstructed models obtained through numerical simulations. Notably, both longitudinal and transverse surveys exhibit exceptional outcomes, showcasing a high correlation with the actual resistivity profiles, achieved within a concise 10-13 iterations. This meticulous validation process conclusively underscores the effectiveness and precision of the proposed inversion approach. Beyond its scientific contribution, this research expands the conventional boundaries of ERI application and establishes it as an invaluable tool for structural monitoring. [ABSTRACT FROM AUTHOR]

Grassland biomass is an important parameter of grassland ecosystems. The complexity of the grassland canopy vegetation spectrum makes the long-term assessment of grassland growth a challenge. Few studies have explored the original spectral information of typical grasslands in Inner Mongolia and examined the influence of spectral information on aboveground biomass (AGB) estimation. In order to improve the accuracy of vegetation index inversion of grassland AGB, this study combined ground and Unmanned Aerial Vehicle (UAV) remote sensing technology and screened sensitive bands through ground hyperspectral data transformation and correlation analysis. The narrow band vegetation indices were calculated, and ground and airborne hyperspectral inversion models were established. Finally, the accuracy of the model was verified. The results showed that: (1) The vegetation indices constructed based on the ASD FieldSpec 4 and the UAV were significantly correlated with the dry and fresh weight of AGB. (2) The comparison between measured R² with the prediction R² indicated that the accuracy of the model was the best when using the Soil-Adjusted Vegetation Index (SAVI) as the independent variable in the analysis of AGB (fresh weight/dry weight) and four narrow-band vegetation indices. The SAVI vegetation index showed better applicability for biomass monitoring in typical grassland areas of Inner Mongolia. (3) The obtained ground and airborne hyperspectral data with the optimal vegetation index suggested that the dry weight of AGB has the best fitting effect with airborne hyperspectral data, where y = 17.962e4.672x, the fitting R² was 0.542, the prediction R² was 0.424, and RMSE and REE were 57.03 and 0.65, respectively. Therefore, established vegetation indices by screening sensitive bands through hyperspectral feature analysis can significantly improve the inversion accuracy of typical grassland biomass in Inner Mongolia. Compared with ground monitoring, airborne hyperspectral monitoring better reflects the inversion of actual surface biomass. It provides a reliable modeling framework for grassland AGB monitoring and scientific and technological support for grazing management. [ABSTRACT FROM AUTHOR]

Abstract The gas pressure in front area of heading face is essential to dynamically evaluate coal and gas outburst during coal mining. In this work, a novel inversion model of gas pressure in front area of the heading face was established on premise of the hypothesis that a time-dependent zone of steady flow exists within newly exposed face. The key parameters in the inversion model were obtained based on the gas emission models and field data of gas emission rate in different times, which were used to calculate the volumes of gas emission from different sources. The results show that the percentage of gas emission from the heading face, coal wall and collapsed coal ranges from 7% to 47%, 47% to 82% and 2% to 11%, respectively. Based on the calculated volumes of gas emission and gas pressure inversion model, the gas pressure was obtained and transformed to the gas content. The absolute errors between the gas content tested and transformed in every hour is 0.4%–33%, which proved the rationality of gas pressure inversion model. Furthermore, the daily drifting footage, the radius of gas pressure boundary and the gas permeability coefficient of coal seam were confirmed to have a great effect on the result of gas pressure inversion. The inversion results verify that the speedy excavation can increase the risk of coal and gas outburst. This work produces a useful method for gas disaster prevention and control that converts the gas emission rate to an index of gas pressure within coal seam.

Multi-spectral remote sensing technology can retrieve water quality parameters based on remote sensing band information, reduce monitoring costs, improve monitoring speed and quality, and provide a new way for large-scale water environment monitoring. Through analyzing the correlation of Sentinel-2 multi-spectral images and measured water quality data of the Xiaolangdi Reservoir on the Yellow River, an inversion model of water quality parameters in the best spectral band was established, and the remote sensing inversion of chemical oxygen demand（COD), total phosphorus（TP), total nitrogen（TN） and ammonia nitrogen（NH3-N） of the Xiaolangdi Reservoir was carried out. The accuracy and stability of inversion models were verified and the spatial distribution of each water quality parameter was inverted. The results show that among the four water quality parameter inversion models, the precision and stability of COD model is the highest, followed by TP, TN and the lowest is NH3-N, the COD concentration at the outlet and some edges of the reservoir is relatively higher and the concentrations of TN, TP and NH3-N in the center of the reservoir are higher than those at the edges. [ABSTRACT FROM AUTHOR]

In order to obtain accurate production profile and fracture parameters of horizontal wells in shale gas reservoir from distributed temperature sensing (DTS) data, an efficient inversion method based on improved dichotomy (ID) algorithm has been developed. It has realized quantitative interpretation of production profile and fracture parameters. The synthetic case indicates the inversion calculation of the ID inversion model is 9 times faster than that of conventional inversion algorithm. Finally, the ID inversion model has been applied to a field case to inverse the DTS data. 69 effective artificial fractures have been identified. The production profile and fracture half-length of the field well are obtained. The interpreted fracture half-length indicates that the extension of the hydraulic fractures is extremely uneven. There are obvious dominant fractures in several fracturing stages of which the fracture half-length is more than 120 m, however, the average fracture half-length of the field case is only 61.21 m. The inversion simulated temperature profile matches the measured DTS data well. The interpreted production profile is in good agreement with the PLT testing results that verifies the reliability of the developed ID inversion model. [ABSTRACT FROM AUTHOR]

Remote sensing inversion technology can be used for water quality parameter inversion to realize water quality monitoring in large scale space. The current research on water quality parameter inversion is only for a single satellite. In order to make full use of satellite image resources, the remote sensing images of GF-1B \ C \ D satellite group are taken as the research object. The Mulan River is taken as the research area. The linear regression method is used to construct the regression equations of total phosphorus and ammonia nitrogen, and the inversion model of total phosphorus and ammonia nitrogen is determined according the evaluation parameters. The MSE of the total phosphorus inversion model is 0.049, and the correlation between the inversion value and the measured value is 0.701. The MSE of the ammonia nitrogen inversion model is 0.063, and the correlation between the inversion value and the measured value is 0.813. These data show that the inversion effect is good. The inversion models are applied to the GF-1D satellite remote sensing image on March 15, 2021 to obtain the large-scale spatial distribution maps of total phosphorus concentration and ammonia nitrogen concentration. The water quality classification maps of the the Mulan River in Putian urban area are obtained too, which are convenient for further analysis and evaluation of the water quality. [ABSTRACT FROM AUTHOR]

Satellite-derived bathymetry (SDB) with high spatial resolution effectively maps detailed information about shallow sea depths. Properly selecting atmospheric correction (AC) methods is crucial to obtaining precise bathymetric information from satellite data. In this work, three different AC methods (FLAASH, 6S, and DOS) were applied to GaoFen-2 imagery in Lingyang Reef, South China Sea. Water depth retrieval models, such as single-band, multiband, and band ratio models, were established by 470 points of in-situ water depth data and used for evaluating the model performances. Additionally, the optimal model was applied to depth inversion. The results show that the multiband model based on four bands performs well in this study area with R2=0.736. The choice of AC methods significantly affects SDB, although acceptable results can be derived without these methods. Specifically, the DOS method has the highest inversion accuracy, with a mean relative error and root mean square error of 14.05% and 3.31 m, respectively. Furthermore, the inversion accuracy in various depth ranges may be primarily influenced by suspended sediment concentration and bottom-type uniformity. This study provides a valuable reference for selecting AC approaches and inversion models in high-spatial-resolution SDB in shallow seas. [ABSTRACT FROM AUTHOR]

In order to rapidly and accurately monitor cadmium contamination in lettuce and understand the growth conditions of lettuce under cadmium pollution, lettuce is used as the test material. Under different concentrations of cadmium stress and at different growth stages, relative chlorophyll content of lettuce leaves, the cadmium content in the leaves, and the visible-near infrared reflectance spectra are detected and analyzed. An inversion model of the cadmium content and relative chlorophyll content in the lettuce leaves is established. The results indicate that cadmium concentrations of 1 mg/kg and 5 mg/kg promote relative chlorophyll content, while concentrations of 10 mg/kg and 20 mg/kg inhibit relative chlorophyll content. The cadmium content in the leaves increases with increasing cadmium concentrations. Cadmium stress caused a "blue shift" in the red edge position only during the mature period, while the red valley position underwent a "blue shift" during the seedling and growth periods and a "red shift" during the mature period. The green peak position exhibited a "blue shift". After model validation, it was found that the model constructed using the ratio of red edge area to yellow edge area and the normalized values of red edge area and yellow edge area effectively estimated the cadmium content in lettuce leaves. The model established using the normalized vegetation index of the red edge and the ratio of the peak green value to red shoulder amplitude can effectively estimate the relative chlorophyll content in lettuce leaves. This study demonstrates that the visible-near infrared spectroscopy technique holds great potential for monitoring cadmium contamination and estimating chlorophyll content in lettuce. [ABSTRACT FROM AUTHOR]

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

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The gas pressure in front area of heading face is essential to dynamically evaluate coal and gas outburst during coal mining. In this work, a novel inversion model of gas pressure in front area of the heading face was established on premise of the hypothesis that a time-dependent zone of steady flow exists within newly exposed face. The key parameters in the inversion model were obtained based on the gas emission models and field data of gas emission rate in different times, which were used to calculate the volumes of gas emission from different sources. The results show that the percentage of gas emission from the heading face, coal wall and collapsed coal ranges from 7% to 47%, 47% to 82% and 2% to 11%, respectively. Based on the calculated volumes of gas emission and gas pressure inversion model, the gas pressure was obtained and transformed to the gas content. The absolute errors between the gas content tested and transformed in every hour is 0.4%–33%, which proved the rationality of gas pressure inversion model. Furthermore, the daily drifting footage, the radius of gas pressure boundary and the gas permeability coefficient of coal seam were confirmed to have a great effect on the result of gas pressure inversion. The inversion results verify that the speedy excavation can increase the risk of coal and gas outburst. This work produces a useful method for gas disaster prevention and control that converts the gas emission rate to an index of gas pressure within coal seam. [ABSTRACT FROM AUTHOR]

【Objective】 Soil nitrogen and water are crucial factors influencing crop growth. Understanding their spatiotemporal variation at large scales is essential for improving agricultural management but challenging. This paper aims to investigate the application of airborne technologies for inversely estimating the spatiotemporal change in nitrogen and water in saline soils. 【Method】 The research area is located in southern Xinjiang. Remote sensing images were used to analyze the spectral characteristics of saline soils with different water, nitrogen, and salt contents. Inversion models for estimating water, nitrogen and salt contents were developed, using partial least squares regression (PLSR), support vector regression (SVR), and BP neural network (BPNN), respectively. The accuracy of each model was evaluated against ground-truth data. 【Result】 The characteristic bands of soil water are around 1 900 nm, the characteristic bands of soil nitrogen are between 1 490~1 506, 1 540~2 006, 2 011~2 500 nm, and the characteristic bands of soil salt are between 1 880~1 883 and 1 890~1 942 nm. The PLSR model has the best inversion effect on water, nitrogen and salt, followed by BPNN model and SVR model. 【Conclusion】 The characteristic spectral bands around 1 900 nm were sensitive to changes in soil water, nitrogen, and salt content. The optimal inversion model for estimating soil water, nitrogen, and salt involved using the Savitzky-Golay method for smoothing, principal component analysis for dimensionality reduction, and partial least squares regression for developing the inverse model.

Soil reflectance spectra and hyperspectral images have great potential to monitor and evaluate soil texture in large-scale scenarios. In hilly areas, sand, clay, and silt have similar spectral characteristics in visible, near-infrared, and short-wave infrared (VNIR-SWIR) reflection spectra. Soil texture spectra belong to mixed spectra despite some differences in particle size, mineral composition, and water content, making their distinction difficult. The accurate identification of the content within different particle sizes is difficult as it involves capturing spectral reflection features. Therefore, this study aimed to predict soil texture content through machine learning and unmixing the soil texture's spectra while also comparing their respective modelling performances. Taking typical cultivated land in the Jianghuai hills as an example, the GaoFen-5 Advanced Hyperspectral Imaging (GF-5 AHSI) laboratory spectra of soil samples were used to predict sand, silt, and clay particle contents using partial least squares regression (PLSR) and convolutional neural networks (CNNs). The entire spectra of VNIR-SWIR regions were smoothed, and the dimensions were reduced via principal component analysis (PCA). The prediction models of sand, silt, and clay particle content were constructed, and inversion maps were generated using AHSI. The results showed that the PCA-CNN model achieved a higher prediction precision than the PCA-PLSR in both ASD and GF-5 data. Clay content exhibited the highest predictive performance with a coefficient of determination (R2) of 0.948 and 0.908 and a root mean square error (RMSE) of 26.51 g/kg and 31.24 g/kg, respectively, which represented a 39.0% and 79.8% increase in R2 and a 57% and 57.1% decrease in RMSE compared to that of the PCA-PLSR. This method indicates that the PCA-CNN model can effectively achieve nonlinear interactions between multiple spectral components and better model and fit spectral mixing processes; moreover, it provides an alternative method for investigating the spatial distribution of soil texture. [ABSTRACT FROM AUTHOR]

Accurate identification of deep soil moisture content in farmland is a key basic technology to optimize the application of remote sensing interpretation results of soil surface moisture and promote moisture measurement irrigation. Based on the automatic monitoring data of soil moisture in typical Yellow River irrigated areas in Henan and Shandong, the change law of farmland soil moisture was analyzed, and the relationship between farmland soil surface and deep soil moisture was established. The studies have shown that there is a significant correlation between the soil moisture content at different observation depths, and the surface soil moisture content that can be identified by remote sensing can better reflect the deep soil moisture status. [ABSTRACT FROM AUTHOR]

Measuring the optical properties of highly diffuse materials is a challenge as it could be related to the white colour or an oversaturation of pixels in the acquisition system. We used a spatially resolved method and adapted a nonlinear trust-region algorithm to the fit Farrell diffusion theory model. We established an inversion method to estimate two optical properties of a material through a single reflectance measurement: the absorption and the reduced scattering coefficient. We demonstrate the validity of our method by comparing results obtained on milk samples, with a good fitting and a retrieval of linear correlations with the fat content, given by R 2 scores over 0.94 with low p-values. The values of absorption coefficients retrieved vary between 1 × 10 − 3 and 8 × 10 − 3 mm − 1 , whilst the values of the scattering coefficients obtained from our method are between 3 and 8 mm − 1 depending on the percentage of fat in the milk sample, and under the assumption of the anisotropy factor g > 0.8 . We also measured and analyzed the results on white paint and paper, although the paper results were difficult to relate to indicators. Thus, the method designed works for highly diffuse isotropic materials. [ABSTRACT FROM AUTHOR]

Rapid and accurate inversion of soil salinity is a key scientific problem that needs to be solved urgently. Due to the accuracy of UAV multispectral remote sensing inversion of salinity based on back propagation neural network (BPNN) is low, in this study, used the UAV multispectral image and field measurements of 60 soil surface salinity as data sources, 16 salinity indexs were constructed using the extracted spectral reflectance, and performed a gray relation analysis to screen salinity index features after applying a film removal to construct the BPNN salinity inversion model. Particle swarm optimization (PSO), thinking evolutionary algorithm (MEA), and genetic algorithm (GA) were applied to optimize the BPNN inverse model, respectively, and the optimization capabilities of the four algorithms were compared and evaluated to optimize the best optimization algorithm. The results showed that the GRA variable screening can effectively remove the redundant information of spectral parameters and reduce the complexity of the salinity inversion model; the PSO, MEA, and GA can effectively improve the robusticity of BPNN inversion model, and GA algorithm has the best optimization effect in terms of inverse model optimization effect, followed by MEA and PSO algorithms; the accuracy of the PSO-BPNN, MEA-BPNN, and GA-BPNN inversion models are better than that of the BPNN model, and GA-BPNN is the best salinity inversion model, which achieves R2 of 0.6659, RMSE of 0.0751, and RPD of 2.0211. This approach can effectively solve salinity monitoring accuracy issues of UAV multispectral inversion.

The geophysical properties of snow are essential to study the mountain snow/glacier system and can be used as an indicator for any related hazard. In this study, an attempt has been made to model the geophysical properties of snow, such as dielectric, density, and wetness using the Sentinel–1 dual-polarized SLC product. A state-of-the-art inversion model has been developed using Sentinel–1 derived stokes parameters to estimate snow dielectric and subsequently used to model density and wetness employing Looyega's and Denoth's equations. The proposed inclusion of stokes parameters in the inversion model has significantly predicted the results. The respective modeled and in-situ snow dielectric, density, and wetness show a good coefficient of determination (R2 > 0.7) with 95% confidence. Utilizing the field-measured values, the estimated root mean squared error (RMSE) of snow dielectric, density, and wetness, is 0.26, 0.08 g/cm3, and 0.84, respectively. The comparison of the proposed model with some of the existing models reflects its good efficiency in predicting the snow geophysical parameters. [ABSTRACT FROM AUTHOR]

Copyright of Journal of China University of Petroleum is the property of China University of Petroleum and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cotton plays an essential role in global human life and economic development. However, diseases such as leaf blight pose a serious threat to cotton production. This study aims to advance the existing approach by identifying cotton blight infection and classifying its severity at a higher accuracy. We selected a cotton field in Shihezi, Xinjiang in China to acquire multispectral images with an unmanned airborne vehicle (UAV); then, fifty-three 50 cm by 50 cm ground framed plots were set with defined coordinates, and a photo of its cotton canopy was taken of each and converted to the L*a*b* color space as either a training or a validation sample; finally, these two kinds of images were processed and combined to establish a cotton blight infection inversion model. Results show that the Red, Rededge, and NIR bands of multispectral UAV images were found to be most sensitive to changes in cotton leaf color caused by blight infection; NDVI and GNDVI were verified to be able to infer cotton blight infection information from the UAV images, of which the model calibration accuracy was 84%. Then, the cotton blight infection status was spatially identified with four severity levels. Finally, a cotton blight inversion model was constructed and validated with ground framed photos to be able to explain about 86% of the total variance. Evidently, multispectral UAV images coupled with ground framed cotton canopy photos can improve cotton blight infection identification accuracy and severity classification, and therefore provide a more reliable approach to effectively monitoring such cotton disease damage. [ABSTRACT FROM AUTHOR]

In this study, a two-parameter inversion model is used to improve the accuracy of seawater temperature and salinity inversion. Furthermore, a novel model is proposed to invert the seawater sound velocity, using Brillouin frequency shift and linewidth as independent variables to establish their relationship with the sound velocity. The temperature, salinity, and depth data of the East China Sea collected from the World Ocean Atlas 2018 were used to simulate the seawater sound velocity in the range of 0–100 m at different longitudes and latitudes in different seasons. The results indicate that the simultaneous inversion of the three parameters can be realized using the Brillouin frequency shift and linewidth. The maximum errors of the temperature, salinity, and sound velocity for the inversion model were 0.079 °C, 0.122 ‰, and 0.124 m/s, with relative errors of 0.212, 0.156, and 0.015%, respectively. [ABSTRACT FROM AUTHOR]

The Revell batholith is located within the Western Wabigoon terrane of the Superior Province, Northwestern Ontario, Canada, and is a potential site for a deep geological repository (DGR). This batholith is considered to have favourable geoscientific characteristics for hosting a DGR, including a sufficient volume of relatively homogenous rock. The subsurface geometry of the batholith plays an important role in determining its volume, as well as assessing regional-scale hydraulics, rock mechanics, and glacial stress disturbances on the bedrock, which are other important features and processes that can impact the batholith over the timeframes of concern for long-term storage of used nuclear fuel. Subsurface geometry is complicated to unravel, and surface mapping alone is inadequate to obtain the information at depth. However, gravity, magnetic, or seismic data can be used to enhance understanding by approximating the geometry.This study aims to refine the subsurface geometry and distribution of the Revell batholith from a constrained forward and inverse geophysical model, incorporating high-resolution geophysical data together with a compilation of historic and recent geological field data. The Revell batholith was previously cited as a flat-based pluton with a depth of 1.6 km, where our findings suggest the batholith is deeper than previously thought, with an uneven contact geometry at its base that extends slightly deeper than 3.5 km. Model uncertainties were assessed by varying probabilistic constraints on volume overlap/commonality and shape within GeoModeller™. Results indicate that overall batholith-greenstone contact is generally unchanged when the geological constraints are varied, providing a high degree of confidence that the Revell batholith has a sufficient volume of relatively homogeneous bedrock.

It is essential to study how to obtain precise bathymetric and mapping in inland lakes only from satellite data. In this study, the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) photon point cloud was first denoised by combining the manual interaction (MI) and density-based spatial clustering of applications with noise (DBSCAN) algorithm in QiXiang Co (QXC) and Caiduochaka (CK) on the Tibetan Plateau (TP). Second, we proposed to fit the complete profile depth of the along-track profile and the constructed free profile by using a mathematical function. Finally, the random forest (RF) and linear regression (LR) models were used to obtain bathymetric maps, and bathymetric performance and the benefits of free profile for improving accuracy were verified with the measured depth. The results indicated that the combination of the MI and DBSCAN algorithm could obtain signal photons, and the mean absolute error (MAE) between the fitted free profile water depth and the measured profile water depth was less than 0.90 m. Compared with the original tracks (gt1l, gt2l, and gt3l) in QXC (0–28 m), the MAE of the RF and LR models decreased by 0.35 m and 0.46 m for the validation dataset after adding the free profile. Similarly, the MAE of the RF and LR models decreased by 0.11–0.33 m and 0.13–0.15 m in CK, respectively. This study suggested that this method could be used to obtain the bathymetric map and improve accuracy by fitting profile depth based on ICESat-2 and Sentinel-2 data in shallow water.

Soil total nitrogen (STN) content can be accurately and rapidly estimated to better reflect the response relationship between spectrum and STN content. In this study, an unmanned aerial vehicle (UAV) equipped with a hyperspectral sensor was used to obtain the soil hyperspectral images in farmland. The original spectral reflectance (R) was then transformed into the reciprocal of reflectance (RR), logarithm of reflectance (LR), the first derivative of reflectance (FDR), the first derivative of reciprocal reflectance (FRR), and the first derivative of logarithm of reflectance (FLR). Grey correlation degree and Pearson correlation coefficient were also selected to extract the sensitive band of STN content in each spectrum. The hyperspectral inversion model of STN was finally constructed using the sensitive band, the partial least squares regression (PLSR), ridge regression (RR), and random forest regression (RF). The determination coefficient R², root mean square error RMSE, and mean absolute error MAE were used to evaluate the accuracy of the model. After that, the model with the highest accuracy was selected for the inversion mapping of STN content in the study area. The availability of the model was tested, according to the distribution of STN content. The optimal model was selected to invert and map the STN content. The results showed that: 1) The sensitive band (996-1 003 nm) of the RR spectrum was concentrated in the near-infrared long wave range, according to the gray correlation degree and Pearson correlation coefficient. The sensitive bands in the FRR spectrum (39-459, 469, and 472-1 003 nm), and FLR spectrum (398-459, 463-973, and 978-1 003 nm) were distributed in the visible and near-infrared range. The sensitive bands (615-625, 632, and 666-670 nm) in the FDR spectrum were concentrated mainly in the red range of visible light. 2) Pearson correlation coefficient was used to better reflect the response relationship between spectrum and STN content. The STN inversion model R², RMSE, and MAE were in the range of 0.058-0.693, 0.226-0.477, and 0.171-0.416 g/kg, respectively, in terms of Pearson correlation coefficient. In grey correlation degree, the STN inversion model R², RMSE, and MAE were within the range of 0.693-0.859, 0.123-0.276, and 0.107-0.209 g/kg, respectively. The accuracy of the model with the Pearson correlation coefficient was higher than that with the grey correlation degree, indicating that the Pearson correlation coefficient performed better on the response relationship between spectrum and STN content. 3) The RF-FDR model was used to estimate the STN content in the field. Among the 12 inversion models of STN content, the RF-FDR model shared the highest accuracy, with R² of 0.859, RMSE of 0.143 g/kg, and MAE of 0.114 g/kg. The inversion mapping of STN content showed that the STN content in most areas was in the range of 1.50-2.00 g/kg, according to the RF-FDR model. There was the consistency with the average value of STN content in the 72 soil samples, together with the actual situation of planting in one season, low soil fertility consumption, and annual fertilization. As such, the RF-FDR model can be expected to estimate the STN content in fields. Therefore, the Pearson correlation coefficient can be used to extract the sensitive bands for the soil spectrum from UAV hyperspectral. The inversion model of STN content can be constructed with higher accuracy for the effective estimation of STN content. [ABSTRACT FROM AUTHOR]

The heading stage of rice is a critical period for disease control, such as for panicle blast. The rapid and accurate monitoring of rice growth is of great significance for plant protection operations in large areas for mobilizing resources. For this paper, the canopy multispectral information acquired continuously by an unmanned aerial vehicle (UAV) was used to obtain the heading rate by inversion. The results indicated that the multi-vegetation index inversion model is more accurate than the single-band and single-vegetation index inversion models. Compared with traditional inversion algorithms such as neural network (NN) and support vector regression (SVR), the adaptive boosting algorithm based on ensemble learning has a higher inversion accuracy, with a correlation coefficient (R2) of 0.94 and root mean square error (RMSE) of 0.12 for the model. The study suggests that a more effective inversion model of UAV multispectral remote sensing and heading rate can be built using the AdaBoost algorithm based on the multi-vegetation index, which provides a crop growth information acquisition and processing method for determining the timing of rice tassel control. [ABSTRACT FROM AUTHOR]

The relationship between injection and production wells is a major component of reservoir evaluation and the foundation for establishing an efficient oilfield development plan. The characteristics of impulse responses are considered in an oilfield injection and production system. The production well production rate is treated as a cumulative water injection response of surrounding injection wells, specifically in a system transfer response context. In this study, a variety of impulse response functions are analyzed, and the most applicable impulse response functions, combined with the production characteristics of injection–production wells, are selected. Then, a new injection–production relationship (IPR) equation of state, which is suitable to the actual oilfield situation, is established. Finally, based on the adaptive extended Kalman filter algorithm, the optimal IPR results are achieved by the inversion algorithm. This method has a definite physical meaning, which displays the characteristics of signal attenuation and time delay. The ideal model and actual model are calculated by this method. The results show that this method has strong feasibility and can obtain reliable IPR outcomes for oilfield development. [ABSTRACT FROM AUTHOR]

The Precordillera fold-thrust belt, situated within the Pampean flat-subduction segment (27°–33°S), is characterised by enigmatic transversal structures which extend and influence deformation patterns, the full extent of which is yet to be fully elucidated. The Northern Pie de Palo Lineament represents a key example, and has been proposed to play a pivotal role in the development and structural control of the Precordillera. In any case, this lineament has not been subjected to a comprehensive study, which has led to ongoing debate regarding its structural control, persistence, and morphology. This study was therefore focused on this structure, employing multiple geophysical methodologies, including aeromagnetic and gravimetric techniques. This approach enabled the first visualization of the full extension and fault zone of the North Pie de Palo Lineament, which crosses the entire Precordillera fold-thrust belt in a transverse direction. Consequently, it can be posited that this structure would have exerted a conditioning influence on the thermo-mechanical state of the Andean lithosphere, enabled the uplift of mafic bodies and thus influenced the Neogene deformation of the Precordillera fold and thrust belt. The confirmation and characterization of this major structure open new perspectives on the interaction of deep-seated transversal structures with fold belts during the evolution of the southern central Andes. • The structures transversal to the Andes conditioned the Precordillera. • The pluton was able to take advantage of the weaknesses inherent to the crust. • The 3D inversion model indicated the presence of a high-density body at depth. [ABSTRACT FROM AUTHOR]

In urban areas situated along busy waterways like the Yangtze River, the diesel engines of inland navigation ships emerge as significant contributors to air pollution. Among these vessels, certain high-emission ships exhibit considerably higher levels of nitrogen oxides (NO x ) emissions compared to others. To effectively identify such ships, this study employed a cost-effective ship emission monitoring sensor platform, comprising high-precision gas sensors, automatic identification system receiver, and sensitive meteorological sensors, along the Yangtze River in Wuhan City. By combining multi-source shore-based monitoring data, we identified ship emission signals and proposed a high-emission ship detection method using inverse modeling. Using this method, we successfully detected inland high-emission ships based on two months of monitoring data. Furthermore, the relationship between different ship types, sizes, speeds, and ship NO _x emission rates were investigated. The results of this study are beneficial for strengthening the regulation of high-emission vessels in inland waterways, thereby reducing the adverse impact of ship emissions on the environment and climate. It also encourages the inland shipping industry to adopt more environmentally friendly technologies and fuels, as advocated by the International Maritime Organization.

Hyperspectral technology, with its high spectrum resolution and nanometer continuous spectral information acquisition ability, provide a possibility for rapidly and nondestructive evaluating compost maturity. In this study, the near-infrared spectroscopy (NIRS) analysis techniques was used to analyze quantitatively organic matter (OM) content, total nitrogen (TN) content and carbon-nitrogen (C/N) ratio in compost based on two different composting procedures. In the basis of spectra preprocessing and strategies of variable selection, the nonlinear modeling LBC-siPLS-PLSR for OM, MSC-SPA-PLSR for TN and R-SPA-PLSR for C/N ratio was respectively constructed using partial least squares regression (PLSR). LBC-siPLS-PLSR, MSC-SPA-PLSR and R-SPA-PLSR provided a better prediction capability with root mean square error of prediction, the coefficient of determination for prediction and residual predictive deviation values of 4.061, 0.746 and 2.02 for OM, values of 0.205, 0.65 and 1.71 for TN and values of 1.11, 0.706 and 2.07 for C/N ratio, respectively. These results showed that the NIRS technique could be fitted to each element, using specific spectrum pretreatment, in order to achieve an acceptable accuracy in the prediction.

This paper provides an inverse-model-based iterative learning control (ILC) for the unknown multi-input multi-output (MIMO) nonlinear system with neural network (NN), where a novel gradient adaptive law is used to update the NN weights both hidden and output layers such a faster convergence can be achieved. First, a three-layer NN structure is introduced to observe the MIMO nonlinear system with input–output data, and a new gradient algorithm is proposed to update the unknown parameters of both hidden and output layers. Then, the input dynamic can be obtained with the NN observer, and the inversion-model-based control is designed. Moreover, the ideal inversion control can be obtained based on the reference signal, and the inverse ILC is designed. The stability of the NN observer and the convergence of the inverse-model-based control are analyzed. Finally, a SCARA manipulator MIMO model is simulated to illustrate the correctness of the proposed methods. [ABSTRACT FROM AUTHOR]

The crop leaf color is tightly connected with its meteorological environment. Color gradation skewness-distribution (CGSD) parameters can describe the information of leaf color more accurately, systematically, and comprehensively from five dimensions. We took photographs of pepper growing in the greenhouse at a fixed time every day and observed the meteorological factors. The results showed that the CGSD parameters were significantly correlated with meteorological factors, especially with the accumulated temperature, which showed the strongest correlation. Since the relationship between canopy leaf color and accumulated temperature is nonlinear, the piecewise inversion models were constructed by taking the stationary point of the high-order response model of Gskewness to accumulated temperature as the point of demarcation. The rate of outliers had decreased by 57.72%; moreover, the overall inversion accuracy had increased by 3.31% compared with the linear model directly constructed by the stepwise regression. It was observed that the pepper in the greenhouse had a different response to the same meteorological environmental stimulus before and after the stationary point. This study will provide a new method for constructing crop growth models in future research. [ABSTRACT FROM AUTHOR]

Detection rapidity and model accuracy are the keys to hyperspectral nondestructive testing technology, especially for Pennisetum sinese Roxb (PsR) due to its extremely high adsorptive heavy metal content. The study of the resolution of PsR is conducive to the analysis of the accumulated heavy metal content in its different parts. In this paper, the contents of Cd, Cu and Zn accumulated in the old leaves, young leaves, upper stem, middle stem and lower stem, as well as the hyperspectral data of the corresponding parts, were measured simultaneously in both fresh and dry states. To begin, the spectral data of PsR were preprocessed by using Ensemble Empirical Mode Decomposition-Daubechies3 (EEMD-db3), Savitzky–Golay (SG), Symlet3 (sym3), Symlet5 (sym5), and multiplicative scatter correction (MSC). The 40 samples were divided into 32 training sets and 8 validation sets. The preprocessed spectral data were transformed by the first derivative (FD) and reciprocal logarithm (log(1/R)) to highlight the singularities using binary wavelet decomposition. After screening the significant bands from the correlation curve, the competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were applied to extract the spectral characteristic variables, which were used to establish the partial least-squares (PLS) regression and multiple stepwise linear regression (MSLR) inversion models of Cd, Cu, and Zn contents. Based on EEMD-db3 pretreatment, the inversion model of Zn in the dry (fresh) state had R2 values of 0.884 (0.880), NRMSE values of 0.179 (0.253) and RPD values of 3.191 (3.221), indicating excellent stability and predictive performance. The findings of this study can not only aid in the rapid nondestructive detection of heavy metal adsorption in various parts of PsR, but can also be applied to guide the development and use of animal feed. [ABSTRACT FROM AUTHOR]

Accurate and highly precise gravity gradient data are an important component of, for example, gravity field modeling, seabed topography inversion, and resource exploration. However, high-precision gravity gradient data are difficult to obtain. To address this difficulty, this work introduces the Fourier series expansion method to the modeling of gravity gradient fields. Based on gravity anomalies, the analytic expressions of the gravity gradient tensors have been deduced, which provides a new mathematical method for obtaining gravity gradient data. The expression's derivation and verification processes are as follows. First, these analytic expressions for inverting the gravity gradient based on gravity anomaly data are derived according to the Laplace equation, the boundary value conditions of spherical approximation, and the Fourier series expansion method. Then, global 1' × 1' gravity field data provided by UCSD are used to verify the accuracy of these formulas. Finally, the results are analyzed. The experimental results show that the results obtained based on this inversion formula can sufficiently show the details of gravity gradient changes. The formulas derived in this paper have good computational efficiency in the inversion of regional gravity gradients and provide a new mathematical method for gravity gradient data acquisition. [ABSTRACT FROM AUTHOR]