The spectral characters of hydrocarbons in some oil-bearing strata and soil layers over oil and gas reservoirs in the Junggar Basin and northern Tarim Basin in Xinjiang are compared with those of chemically pure hydro...The spectral characters of hydrocarbons in some oil-bearing strata and soil layers over oil and gas reservoirs in the Junggar Basin and northern Tarim Basin in Xinjiang are compared with those of chemically pure hydrocarbons. The hydrocarbons are characterized by the bi-absorption at 2310nm and 2350nm. Hydrocarbon and radioactive anomalies in oil and gas terrains are found much more widespread than carbonate altcrations. Based on the spectra of heavy hydrocarbons related to oil between 2270nm and 2460nm and refined data treatme nt, remote sensing may hold encouraging promise as a directly prospecting technique for oil and gas resources.展开更多
Visible and near infrared spectroscopy is a non-destructive,green,and rapid technology that can be utilized to estimate the components of interest without conditioning it,as compared with classical analytical methods....Visible and near infrared spectroscopy is a non-destructive,green,and rapid technology that can be utilized to estimate the components of interest without conditioning it,as compared with classical analytical methods.The objective of this paper is to compare the performance of artificial neural network(ANN)(a nonlinear model)and principal component regression(PCR)(a linear model)based on visible and shortwave near infrared(VIS-SWNIR)(400-1000 nm)spectra in the non-destructive soluble solids content measurement of an apple.First,we used multiplicative scattering correction to pre-process the spectral data.Second,PCR was applied to estimate the optimal number of input variables.Third,the input variables with an optimal amount were used as the inputs of both multiple linear regression and ANN models.The initial weights and the number of hidden neurons were adjusted to optimize the performance of ANN.Findings suggest that the predictive performance of ANN with two hidden neurons outperforms that of PCR.展开更多
文摘The spectral characters of hydrocarbons in some oil-bearing strata and soil layers over oil and gas reservoirs in the Junggar Basin and northern Tarim Basin in Xinjiang are compared with those of chemically pure hydrocarbons. The hydrocarbons are characterized by the bi-absorption at 2310nm and 2350nm. Hydrocarbon and radioactive anomalies in oil and gas terrains are found much more widespread than carbonate altcrations. Based on the spectra of heavy hydrocarbons related to oil between 2270nm and 2460nm and refined data treatme nt, remote sensing may hold encouraging promise as a directly prospecting technique for oil and gas resources.
基金Project(No.UTM.J.10.01/13.14/1/127/1 Jld 3(48))supported by the Zamalah Scholarship from the Universiti Teknologi Malaysia
文摘Visible and near infrared spectroscopy is a non-destructive,green,and rapid technology that can be utilized to estimate the components of interest without conditioning it,as compared with classical analytical methods.The objective of this paper is to compare the performance of artificial neural network(ANN)(a nonlinear model)and principal component regression(PCR)(a linear model)based on visible and shortwave near infrared(VIS-SWNIR)(400-1000 nm)spectra in the non-destructive soluble solids content measurement of an apple.First,we used multiplicative scattering correction to pre-process the spectral data.Second,PCR was applied to estimate the optimal number of input variables.Third,the input variables with an optimal amount were used as the inputs of both multiple linear regression and ANN models.The initial weights and the number of hidden neurons were adjusted to optimize the performance of ANN.Findings suggest that the predictive performance of ANN with two hidden neurons outperforms that of PCR.
