An adaptive over/under data combination method

The traditional ＂dephase and sum＂ algorithms for over/under data combination estimate the ghost operator by assuming a calm sea surface. However, the real sea surface is typically rough, which invalidates the calm sea surface assumption. Hence, the traditional ＂dephase and sum＂ algorithms might produce poor-quality results in rough sea conditions. We propose an adaptive over/under data combination method, which adaptively estimates the amplitude spectrum of the ghost operator from the over/under data, and then over/under data combinations are implemented using the estimated ghost operators. A synthetic single shot gather is used to verify the performance of the proposed method in rough sea surface conditions and a real triple over/under dataset demonstrates the method performance.

Research on Data Combination for Phased-Array Ground Penetrating Radar

To resolve the data combination of Phased-array Ground Penetrating Radar (PAGPR), we first build a model of PAGPR and a layered model, and then a new data combination algorithm is presented based on it. This method calculates time delay of multi-receivers, basing on the signal of the nearest receiver, then shifts other signals and adds them up, and gets one signal at last. It has been proved that this method can restrain noise, multiple waves, clutter waves and improve the precision of time location. In the end, an example is given to prove the method's efficiency.

Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging,Laboratory VNIR Spectroscopy and Their Combination

Pedo-spectroscopy has the potential to provide valuable information about soil physical,chemical,and biological properties.Nowadays,wemay predict soil properties usingVNIRfield imaging spectra(IS)such as Prisma satellite data or laboratory spectra(LS).The primary goal of this study is to investigate machine learning models namely Partial Least Squares Regression(PLSR)and Support Vector Regression(SVR)for the prediction of several soil properties,including clay,sand,silt,organic matter,nitrate NO3-,and calcium carbonate CaCO_(3),using five VNIR spectra dataset combinations(%IS,%LS)as follows:C1(0%IS,100%LS),C2(20%IS,80%LS),C3(50%IS,50%LS),C4(80%IS,20%LS)and C5(100%IS,0%LS).Soil samples were collected at bare soils and at the upper(0–30 cm)layer.The data set has been split into a training dataset 80%of the collected data(n=248)and a validation dataset 20%of the collected data(n=61).The proposed PLSR and SVR models were trained then tested for each dataset combination.According to our results,SVR outperforms PLSR for both:C1(0%IS,100%LS)and C5(100%IS,0%LS).For Soil Organic Matter(SOM)prediction,it achieves(R^(2)=0.79%,RMSE=1.42%)and(R^(2)=0.76%,RMSE=1.3%),respectively.The data fusion has improved the soil property prediction.The highest improvement was obtained for the SOM property(R^(2)=0.80%,RMSE=1.39)when using the SVR model and applying the second Combination C2(20% of IS and 80%LS).

Determination of Light Rare Earths by a Spectrophoto-metry Based on the Linear Combination of Multiwavelength Data

The present paper proposes a new method of spectrophotometry based on linear combination of multiwavelength data by means of selecting a set of properly weighted coefficients and combination methods. It is clear that the weighted combination absorbance attained is only in direct proportion to the concentration of the analysed component and independent of coexisting interferents.The accuracy of the analytical results is improved greatly for the analysis of light rare earths with the coexistence of heavy rare earths.The analyti- cal error from the reagent blank and co-coloration of light and heavy rare earths have also been overcome. The greatly improved linearity and additivity of absorbance are obtained.

COMBINATION OF ECMWF REANALYZED DAILY RAINFALL AND PENTAD CMAP IN CHINA

In this study, 16 combinations of the ECMWF (European Centre for Medium Range Weather Forecast) reanalyzed daily rainfall and the pentad CMAP in China for the period 1980-1993(1 May-31 Dec.) were calculated. Correlation analysis was used to roughly evaluate daily rainfall for the whole of China and a combination of RPC (rotated principal component) and wavelet analyses was applied to data on observed and combined daily rainfall to obtain a detailed evaluation of the quality of these combined datasets in 6 selected major rainfall regions of eastern China. The results showed that except for intraweekly fluctuation, the best combination was roughly similar to or accorded well with observation in the aspects of space variation patterns and long period rainfall fluctuations related to monsoon onset and serious meteorologic disasters, indicating that this combination yielded better values of long term daily mean and standard deviation through the pentad CMAP (CPC Merged Analysis of Precipitation), and can also represent rainfall fluctuations through the reanalyzed daily rainfall.

Phylogeny of subclass Scuticociliatia(Protozoa,Ciliophora) using combined data inferred from genetic,morphological,and morphogenetic evidence

Gene sequence-based genealogies of scuticociliates are different from those produced by morphological analyses.For this reason,11 representative scuticociliates and two ambiguously related genera were chosen to test the ability of combined phylogenetic analyses using both gene sequences and morphological/morphogenetic characteristics.Analyses of both the SSrRNA gene sequences and the combined datasets revealed a consistent branching pattern.While the terminal branches and the order level relationships were generally well resolved,the family level relationships remain unresolved.However,two other trees based on ITS1-5.8S-ITS2 region sequences and morphological/morphogenetic characters showed limited information,due to a lack of informative sites in these two datasets.Our data suggest,however,that the combined analysis of morphological/morphogenetic characters and gene sequences did produce some changes to the phylogenetic estimates of this group.

APPLICATION VALUE OF MAGNETIC RESONANCE SEQUENCES IN DIAGNOSIS OF EARLY SPINAL METASTATIC TUMOR

Objective To investigate the clinical value of different magnetic resonance (MR) pulse sequences in diagnosis of spinal metastatic tumor. Methods Fifteen patients with clinically suspected spinal metastatic tumor were included in this study. These patients were with documented primary tumors. Four MR pulse sequences, T1-weighted spin echo (T1WI SE), T2-weighted fast spin echo (T2WI FSE), short time inversion recovery (STIR), and gradient echo 2-D multi echo data imaging combination (GE Me-2D) were used to detect spinal metastasis. Results Fifteen vertebral bodies were entire involvement, 38 vertebral bodies were section involvement, and totally 53 vertebral bodies were involved. There were 19 focal infections in pedicle of vertebral arch, 15 metastases in spinous process and transverse process. Fifty-three vertebral bodies were abnormal in T1WI SE and GE Me-2D, 35 vertebral bodies were found abnormal in T2WI FSE, and 50 vertebral bodies were found abnormal in STIR. The verges of focal signal of involved vertebral bodies were comparatively clear in T1WI SE, comparatively clear or vague in T2WI FSE, vague in STIR, and clear in GE Me-2D.Conclusions GE Me-2D may be the most sensitive technique to detect metastases. So three sequences (T1WI SE, T2WI FSE, GE Me-2D) can demonstrate the early changes of spinal metastasis roundly.

“Second quantitative characterization”and its application in fractured-vuggy carbonate reservoirs

The Ordovician fracture-vug carbonate reservoirs of Tarim Basin,are featured by developed vugs,caves and fractures.The strong heterogeneity results in huge uncertainty when these reservoirs are quantitatively characterized using merely static seismic data.The effective quantitative characterization of the reservoirs has been an urgent problem to be solved.This study creatively proposes the"second quantitative characterization"technique with the combination of dynamic and static data based on the primary static quantitative characterization and fully considering lots of key influence factors when conducting characterization.In this technique,dynamic analysis methods such as well testing,production rate transient analysis,dynamic reserve evaluation and dynamic connectivity evaluation are used to get understandings on this kind of reservoir.These understandings are used as statistical parameters to constrain the inversion of seismic wave impedance to improve the relationship between wave impedance and porosity and determine the fracture-vug morphology,calculate dynamic reserves,and then a more accurate fracture-vugmodel can be selected and used to calculate the oil-water contact inversely based on the results of"second quantitative characterization".This method can lower the uncertainties in the primary quantitative characterization of fracture-vug reservoirs,enhance the accuracy of characterization results significantly,and has achieved good application results in the fracture-vug carbonate reservoirs of Tarim Basin.

Assimilation of Radar and Cloud-to-Ground Lightning Data Using WRF-3DVar Combined with the Physical Initialization Method——A Case Study of a Mesoscale Convective System

Radar data, which have incomparably high temporal and spatial resolution, and lightning data, which are great indicators of severe convection, have been used to improve the initial field and increase the accuracies of nowcasting and short-term forecasting. Physical initialization combined with the three-dimensional variational data assimilation method(PI3 DVarrh) is used in this study to assimilate two kinds of observation data simultaneously, in which radar data are dominant and lightning data are introduced as constraint conditions. In this way, the advantages of dual observations are adopted. To verify the effect of assimilating radar and lightning data using the PI3 DVarrh method, a severe convective activity that occurred on 5 June 2009 is utilized, and five assimilation experiments are designed based on the Weather Research and Forecasting(WRF) model. The assimilation of radar and lightning data results in moister conditions below cloud top, where severe convection occurs;thus, wet forecasts are generated in this study.The results show that the control experiment has poor prediction accuracy. Radar data assimilation using the PI3 DVarrh method improves the location prediction of reflectivity and precipitation, especially in the last 3-h prediction, although the reflectivity and precipitation are notably overestimated. The introduction of lightning data effectively thins the radar data, reduces the overestimates in radar data assimilation, and results in better spatial pattern and intensity predictions. The predicted graupel mixing ratio is closer to the distribution of the observed lightning,which can provide more accurate lightning warning information.