A method of reconstructing 3D model from 2D geological cross-section based on self-adaptive spatial sampling:A case study of Cretaceous McMurray reservoirs in a block of Canada

An orthogonal 2D training image is constructed from the geological analysis results of well logs and sedimentary facies;the 2 D probabilities in three directions are obtained through linear pooling method and then aggregated by the logarithmic linear pooling to determine the 3 D multi-point pattern probabilities at the unknown points,to realize the reconstruction of a 3 D model from 2D cross-section.To solve the problems of reducing pattern variability in the 2 D training image and increasing sampling uncertainty,an adaptive spatial sampling method is introduced,and an iterative simulation strategy is adopted,in which sample points from the region with higher reliability of the previous simulation results are extracted to be additional condition points in the following simulation to improve the pattern probability sampling stability.The comparison of lateral accretion layer conceptual models shows that the reconstructing algorithm using self-adaptive spatial sampling can improve the accuracy of pattern sampling and rationality of spatial structure characteristics,and accurately reflect the morphology and distribution pattern of the lateral accretion layer.Application of the method in reconstructing the meandering river reservoir of the Cretaceous McMurray Formation in Canada shows that the new method can accurately reproduce the shape,spatial distribution pattern and development features of complex lateral accretion layers in the meandering river reservoir under tide effect.The test by sparse wells shows that the simulation accuracy is above 85%,and the coincidence rate of interpretation and prediction results of newly drilled horizontal wells is up to 80%.

Space-time Dynamics of Dendroctonus valens Population in China and Spatial Sampling Technique based on Its Spatial Distribution Pattern

Red turpentine beetle （RTB）, Dendroctongs valens LeConte, is a destructive forest invasive species in China, it mainly attacks Pings tabuliformis and P. bungeana. So far it has spread rapidly to the provinces of Shanxi, Hebei, Henan, Shanxi and Beijing since its first outbreak in Shanxi Province in 1998, and has caused extensive tree mortality. Space-time dynamics of D. valens population and spatial sampling technique based on its spatial distribution pattern were ana- lyzed using geostatistical methods in the pure P. tabuliforis forests and mixedwood stands which were at different damage levels. According to the spatial distribu- tion of D. valeas population, the specific spatial sampling technique was also studied, and then was compared with traditional sampling technique. The spatial sam- piing technique combined with sampling theory and the biological characteristics of D. valens population, which not only could calcnlate the error of the sampling, but also could discuss the optimal sampling number and the optimum size of plot according to different damage levels and different stand types. This helps to explain population expansion and colonization mechanism of D. valens, and to provide a good reference for adopting snitable control measures.

An Artificial Neural Network-Based Response Surface Method for Reliability Analyses of c-φ Slopes with Spatially Variable Soil

This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model;the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties;and finally,an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables.The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability.As a result,the obtained approximate function can be used as an alternative to the specific analysis process in c-φslope reliability analyses.

Comparison analysis of sampling methods to estimate regional precipitation based on the Kriging interpolation methods： A case of northwestern China

The accuracy of spatial interpolation of precipitation data is determined by the actual spatial variability of the precipitation, the interpolation method, and the distribution of observatories whose selections are particularly important. In this paper, three spatial sampling programs, including spatial random sampling, spatial stratified sampling, and spatial sandwich sampling, are used to analyze the data from meteorological stations of northwestern China. We compared the accuracy of ordinary Kriging interpolation methods on the basis of the sampling results. The error values of the regional annual pre-cipitation interpolation based on spatial sandwich sampling, including ME （0.1513）, RMSE （95.91）, ASE （101.84）, MSE （？0.0036）, and RMSSE （1.0397）, were optimal under the premise of abundant prior knowledge. The result of spatial stratified sampling was poor, and spatial random sampling was even worse. Spatial sandwich sampling was the best sampling method, which minimized the error of regional precipitation estimation. It had a higher degree of accuracy compared with the other two methods and a wider scope of application.

Comparison of the local pivotal method and systematic sampling for national forest inventories

Background:The local pivotal method(LPM)utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories(NFIs).Its performance compared to simple random sampling(SRS)and LPM with geographical coordinates has produced promising results in simulation studies.In this simulation study we compared all these sampling methods to systematic sampling.The LPM samples were selected solely using the coordinates(LPMxy)or,in addition to that,auxiliary remote sensing-based forest variables(RS variables).We utilized field measurement data(NFI-field)and Multi-Source NFI(MS-NFI)maps as target data,and independent MS-NFI maps as auxiliary data.The designs were compared using relative efficiency(RE);a ratio of mean squared errors of the reference sampling design against the studied design.Applying a method in NFI also requires a proven estimator for the variance.Therefore,three different variance estimators were evaluated against the empirical variance of replications:1)an estimator corresponding to SRS;2)a Grafström-Schelin estimator repurposed for LPM;and 3)a Matérn estimator applied in the Finnish NFI for systematic sampling design.Results:The LPMxy was nearly comparable with the systematic design for the most target variables.The REs of the LPM designs utilizing auxiliary data compared to the systematic design varied between 0.74–1.18,according to the studied target variable.The SRS estimator for variance was expectedly the most biased and conservative estimator.Similarly,the Grafström-Schelin estimator gave overestimates in the case of LPMxy.When the RS variables were utilized as auxiliary data,the Grafström-Schelin estimates tended to underestimate the empirical variance.In systematic sampling the Matérn and Grafström-Schelin estimators performed for practical purposes equally.Conclusions:LPM optimized for a specific variable tended to be more efficient than systematic sampling,but all of the considered LPM designs were less efficient than the systematic sampling design for some target variables.The Grafström-Schelin estimator could be used as such with LPMxy or instead of the Matérn estimator in systematic sampling.Further studies of the variance estimators are needed if other auxiliary variables are to be used in LPM.

Sampling strategies for estimating forest cover from remote sensing-based two-stage inventories

Background: Remote sensing-based inventories are essential in estimating forest cover in tropical and subtropical countries, where ground inventories cannot be performed periodically at a large scale owing to high costs and forest inaccessibility(e.g. REDD projects) and are mandatory for constructing historical records that can be used as forest cover baselines. Given the conditions of such inventories, the survey area is partitioned into a grid of imagery segments of pre-fixed size where the proportion of forest cover can be measured within segments using a combination of unsupervised(automated or semi-automated) classification of satellite imagery and manual(i.e. visual on-screen)enhancements. Because visual on-screen operations are time expensive procedures, manual classification can be performed only for a sample of imagery segments selected at a first stage, while forest cover within each selected segment is estimated at a second stage from a sample of pixels selected within the segment. Because forest cover data arising from unsupervised satellite imagery classification may be freely available(e.g. Landsat imagery)over the entire survey area(wall-to-wall data) and are likely to be good proxies of manually classified cover data(sample data), they can be adopted as suitable auxiliary information.Methods: The question is how to choose the sample areas where manual classification is carried out. We have investigated the efficiency of one-per-stratum stratified sampling for selecting segments and pixels, where to carry out manual classification and to determine the efficiency of the difference estimator for exploiting auxiliary information at the estimation level. The performance of this strategy is compared with simple random sampling without replacement.Results: Our results were obtained theoretically from three artificial populations constructed from the Landsat classification(forest/non forest) available at pixel level for a study area located in central Italy, assuming three levels of error rates of the unsupervised classification of satellite imagery. The exploitation of map data as auxiliary information in the difference estimator proves to be highly effective with respect to the Horvitz-Thompson estimator,in which no auxiliary information is exploited. The use of one-per-stratum stratified sampling provides relevant improvement with respect to the use of simple random sampling without replacement.Conclusions: The use of one-per-stratum stratified sampling with many imagery segments selected at the first stage and few pixels within at the second stage- jointly with a difference estimator- proves to be a suitable strategy to estimate forest cover by remote sensing-based inventories.

Spatial variation of plant species richness in a sand dune field of northeastern Inner Mongolia, China

Species richness is an important indicator of species diversity. Different sampling intensities will very likely produce different species richness values. Substantial efforts have already been made to explicitly quantify the spatial variability of soil properties in different ecosystems. However, concerns still remain on how to characterize the effect of different sampling intensities on plant species richness within a given region. This study characterized the spatial variability of plant species richness and the species distribution pattern in a 25-hm2 sand dune plot in northeastern Inner Mongolia, China by using an intense sampling method（n=10,000）. We also evaluated the overall effect of information loss associated with the spatial variability and distribution patterns of species richness under various scenarios of sampling intensities（n=10,000 to 289）. Our results showed that semi-variograms of species richness were best described by the spherical and exponential models. As indicated by the nugget/sill ratio, species richness was different in terms of the strength of the spatial relationship. The different spatial metrics of species richness with increasing sampling intensities can represent different responses of the spatial patterns when compared with the reference set（n=10,000）. This study indicated that an appropriate sampling intensity should be taken into account in field samplings for evaluating species biodiversity properly. A sampling intensity of n＆gt;2,500 for species richness yielded satisfactory results to resemble the spatial pattern of the above-quantified reference set（n=10,000） in this sand dune region of China.

Detection of landuse/landcover changes using remotely-sensed data

We evaluated the use of spatial sampling and satellite images to identify deforested areas in Wonju, South Korea. The changes in land cover were identified using a grid of sample points overlaid onto medium and high-resolution remote sensing （RS） satellite images. Deforestation identified in this way （hereafter, RSD） was compared to administrative data on deforestation. We also compared high-resolution satellite images （HR-RSD） and actual deforestation based on categories which were Intergovernmental Panel on Climate Change data. RSD generated by medium-resolution satellite images overesti- mated the amount of deforested area by 1.5-2.4 times the actual deforested area, whereas RSD generated by HR- RSD underestimated the amount of deforested area by 0.4-0.9 times the actual area. The highest degree of matching （90 %） was found in HR-RSD with a grid interval of 500 m and the accuracy of HR-RSD was the highest, at 67 %. The results also revealed that the largest cause of deforestation was the establishment of settlements followed by conversion to cropland and grassland. We conclude that for the identification of deforestation using satellite images, HR-RSD with a grid interval of 500 m is most suitable.

Why Well Spread Probability Samples Are Balanced

When sampling from a finite population there is often auxiliary information available on unit level. Such information can be used to improve the estimation of the target parameter. We show that probability samples that are well spread in the auxiliary space are balanced, or approximately balanced, on the auxiliary variables. A consequence of this balancing effect is that the Horvitz-Thompson estimator will be a very good estimator for any target variable that can be well approximated by a Lipschitz continuous function of the auxiliary variables. Hence we give a theoretical motivation for use of well spread probability samples. Our conclusions imply that well spread samples, combined with the Horvitz- Thompson estimator, is a good strategy in a varsity of situations.

Infestation risk of the intermediate snail host of Schistosomajaponicum in the Yangtze River Basin: improved results by spatial reassessment and a random forest approach

Background:Oncomelania hupensis is only intermediate snail host of Schistosomajaponicum,and distribution of 0.hupensis is an important indicator for the surveillance of schistosomiasis.This study explored the feasibility of a random forest algorithm weighted by spatial distance for risk prediction of schistosomiasis distribution in the Yangtze River Basin in China,with the aim to produce an improved precision reference for the national schistosomiasis control programme by reducing the number of snail survey sites without losing predictive accuracy.Methods:The snail presence and absence records were collected from Anhui,Hunan,Hubei,Jiangxi and Jiangsu provinces in 2018.A machine learning of random forest algorithm based on a set of environmental and climatic variables was developed to predict the breeding sites of the 0.hupensis intermediated snail host of S.japonicum.Different spatial sizes of a hexagonal grid system were compared to estimate the need for required snail sampling sites.The predictive accuracy related to geographic distances between snail sampling sites was estimated by calculating Kappa and the area under the curve(AUC).Results:The highest accuracy(AUC=0.889 and Kappa=0.618)was achieved at the 5 km distance weight.The five factors with the strongest correlation to 0.hupensis infestation probability were:(1)distance to lake(48.9%),(2)distance to river(36.6%),(3)isothermality(29.5%),(4)mean daily difference in temperature(28.1%),and(5)altitude(26.0%).The risk map showed that areas characterized by snail infestation were mainly located along the Yangtze River,with the highest probability in the dividing,slow-flowing river arms in the middle and lower reaches of the Yangtze River in Anhui,followed by areas near the shores of China's two main lakes,the Dongting Lake in Hunan and Hubei and the Poyang Lake in Jiangxi.Conelusions:Applying the machine learning of random forest algorithm made it feasible to precisely predict snail infestation probability,an approach that could improve the sensitivity of the Chinese schistosome surveillance.system.Redesign of the snail surveillance system by spatial bias correction of 0.hupensis infestation in the Yangtze River Basin to reduce the number of sites required to investigate from 2369 to 1747.

Coherent optical adaptive technique improves the spatial resolution of STED microscopy in thick samples

Stimulated emission depletion(STED) microscopy is one of far-field optical microscopy techniques that can provide sub-diffraction spatial resolution. The spatial resolution of the STED microscopy is determined by the specially engineered beam profile of the depletion beam and its power. However, the beam profile of the depletion beam may be distorted due to aberrations of optical systems and inhomogeneity of a specimen's optical properties, resulting in a compromised spatial resolution. The situation gets deteriorated when thick samples are imaged. In the worst case, the severe distortion of the depletion beam profile may cause complete loss of the superresolution effect no matter how much depletion power is applied to specimens. Previously several adaptive optics approaches have been explored to compensate aberrations of systems and specimens. However, it is difficult to correct the complicated high-order optical aberrations of specimens. In this report, we demonstrate that the complicated distorted wavefront from a thick phantom sample can be measured by using the coherent optical adaptive technique. The full correction can effectively maintain and improve spatial resolution in imaging thick samples.

Sample indexed spatial orthogonal frequency division multiplexing

Optical spatial modulation （OSM） is a multiple-transmitter technique that can provide higher data rates with low system complexity as compared with single-input single-output systems. Orthogonal frequency division multiplexing （OFDM） is widely implemented to achieve better spectral efficiency in wireless channels. Asymmetrically clipped optical OFDM （O-OFDM） and DC-biased O-OFDM are two well-known O-OFDM techniques suitable for intensity-modulation direct-detection optical systems. In this work, sample indexed spatial OFDM （SIS-OFDM） is proposed to combine OSM and O-OFDM in a novel way and achieve significant per- formance gain. By assigning time-domain samples of the O-OFDM transmit symbol to different transmitters, SIS-OFDM achieves much better spectral efficiency and reduces computational complexity at the transmitter as compared with previous work that combines OSM with O-OFDM in the frequency domain. We also consider the impact of optical source biasing on overall performance, and the relative performance of imaging receiver （ImR） versus non-imaging receiver （NImR） design for our proposed SIS-OFDM technique. Results indicate that for an Ntx x Nrx multiple-input multiple-output configuration where Nix = N = 4, SIS-OFDM using ImR can achieve up to 135 dB of signal-to-noise ratio gain over comparable system using a NImR. Also, using Nc number of O-OFDM subcarriers provides up to Nsc × log2（Ntx） additional bits per symbol of spectral efficiency over technioues that combine OSM and O-OFDM in the freollencv domain.