Sequential indicator simulation and indicator kriging estimation of 3-dimensional soil textures

Soil Research ◽  
2009 ◽  
Vol 47 (6) ◽  
pp. 622 ◽  
Author(s):  
Y. He ◽  
D. Chen ◽  
B. G. Li ◽  
Y. F. Huang ◽  
K. L. Hu ◽  
...  
Keyword(s):  
Structural Characteristics ◽  
General Pattern ◽  
Regional Scale ◽  
Vertical Direction ◽  
Absolute Error ◽  
Indicator Kriging ◽  
Soil Profiles ◽  
Sequential Indicator Simulation ◽  
Auto Correlation ◽  
Indicator Simulation

The complex distribution characteristics of soil textures at a large or regional scale are difficult to understand with the current state of knowledge and limited soil profile data. In this study, an indicator variogram was used to describe the spatial structural characteristics of soil textures of 139 soil profiles. The profiles were 2 m deep with sampling intervals of 0.05 m, from an area of 15 km2 in the North China Plain. The ratios of nugget-to-sill values (SH) of experimental variograms of the soil profiles in the vertical direction were equal to 0, showing strong spatial auto-correlation. In contrast, SH ratios of 0.48–0.81 in the horizontal direction, with sampling distances of ~300 m, showed weaker spatial auto-correlation. Sequential indicator simulation (SIS) and indicator kriging (IK) methods were then used to simulate and estimate the 3D spatial distribution of soil textures. The outcomes of the 2 methods were evaluated by the reproduction of the histogram and variogram, and by mean absolute error of predictions. Simulated results conducted on dense and sparse datasets showed that when denser sample data are used, complex patterns of soil textures can be captured and simulated realisations can reproduce variograms with reasonable fluctuations. When data are sparse, a general pattern of major soil textures still can be captured, with minor textures being poorly simulated or estimated. The results also showed that when data are sufficient, the reproduction of the histogram and variogram by SIS was significantly better than by the IK method for the predominant texture (clay). However, when data are sparse, there is little difference between the 2 methods.

Assessing spatial distribution and joint uncertainty of TPH-fractions: Indicator kriging and sequential indicator simulation

2007 ◽  
Vol 87 (5) ◽  
pp. 551-563
Author(s):  
Carol Luca ◽  
Bing C Si ◽  
Richard E Farrell
Keyword(s):  
Spatial Distribution ◽  
Petroleum Hydrocarbon ◽  
Indicator Kriging ◽  
Hydrocarbon Contamination ◽  
Contaminated Sites ◽  
Sequential Indicator Simulation ◽  
Maximum Probability ◽  
Dependent Case ◽  
Indicator Simulation ◽  
Independent Case

Petroleum hydrocarbon (PHC) contamination is one of the most common contaminants in soils and remediation of PHC-contaminated sites requires methods for characterizing the spatial distribution of PHC on a site. Few studies have compared the performance of indicator kriging (IK) and sequential indicator simulation (SIS) in site characterization of petroleum-contaminated sites, or the application of these methods given the fraction based guidelines. The objectives of this study were to determine if IK and SIS indicate similar contaminated areas and to examine how the probability of exceeding thresholds changes when multiple fractions are considered simultaneously. An abandoned refinery near Kamsack, Saskatchewan, characterized by clay-textured soils was sampled and analyzed for PHC fractions (F2 and F3). The probability of a location exceeding a fraction’s remediation criteria was determined using IK and SIS. Based on critical probability thresholds, IK indicated a greater area was contaminated by F2 (6.3%) and F3 (0.8%) than SIS (4.5 and 0.6%, respectively). When the remediation criteria for both F2 and F3 were considered simultaneously, “dependent” and “independent” cases were examined. The dependent case assumed perfect correlation and used the maximum probability of either F2 or F3 as the new estimate. The independent case assumed no correlation and evaluated the probability of F2 > 2500 mg kg–1 or F3 > 6600 mg kg–1. The dependent case resulted in a smaller contaminated area than the independent case in both IK and SIS. On this site the differences between the two methods were small, although IK did smooth the distribution. Key words: Sequential indicator simulation, indicator kriging, geostatics, petroleum hydrocarbon contamination, uncertainty

scholarly journals Facies and Petrophysical Modeling of Triassic Chang 6 Tight Sandstone Reservoir, Heshui Oil Field, Ordos Basin, China

Lithosphere ◽  
2021 ◽  
Vol 2021 (Special 1) ◽  
Author(s):  
Khawaja Hasnain Iltaf ◽  
Dali Yue ◽  
Wurong Wang ◽  
Xiaolong Wan ◽  
Shixiang Li ◽  
...  
Keyword(s):  
Indicator Kriging ◽  
Multiple Point ◽  
Low Permeability ◽  
Petrophysical Properties ◽  
Sequential Indicator Simulation ◽  
Tight Sandstone ◽  
Petrophysical Modeling ◽  
Indicator Simulation ◽  
Sandstone Reservoir

Abstract Tight sandstone reservoirs are widely distributed worldwide. The Upper Triassic Chang 6 member of the Yanchang Formation is characterized by low permeability and porosity. The facies model offers a unique approach for understanding the characteristics of various environments also heterogeneity, scale, and control of physical processes. The role of subsurface facies features and petrophysical properties was unclear. Notable insufficient research has been conducted based on facies and petrophysical modeling and that demands to refine the role of reservoir properties. To tackle this problem, a reservoir model is to be estimated using various combinations of property modeling algorithms for discrete (facies) and continuous (petrophysical) properties. Chang 6 member consists of three main facies, i.e., channel, lobe main body, and lobe margin facies. The current research is aimed at comparing the applicability and competitiveness of various facies and petrophysical modeling methods. Further, well-log data was utilized to interpret unique facies and petrophysical models to better understand the reservoir architecture. Methods for facies modeling include indicator kriging, multiple-point geostatistics, surface-based method, and sequential indicator simulation. Overall, the indicator kriging method preserved the local variability and accuracy, but some facies are smoothed out. The surface-based method showed far better results by showing the ability to reproduce the geometry, extent, connectivity, and facies association. The multiple-point geostatistics (MPG) model accurately presented the facies profiles, contacts, geometry, and geomorphological features. Sequential indicator simulation (SIS) honored the facies spatial distribution and input statistical parameters. The porosity model built using sequential Gaussian simulation (SGS) showed low porosity (74% values <2%). Gaussian random function simulation (GRFS) models showed very low average porosity (8%-10%) and low permeability (less than 0.1 mD). These methods indicate that Chang 6 member is a typical unconventional tight sandstone reservoir with ultralow values of petrophysical properties.

scholarly journals Land Use Changes Affecting Soil Organic Matter Accumulation in Topsoil and Subsoil in Northeast Thailand

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Benjapon Kunlanit ◽  
Laksanara Khwanchum ◽  
Patma Vityakon
Keyword(s):  
Land Use ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Agricultural Land ◽  
Soil Depth ◽  
General Pattern ◽  
Clay Fraction ◽  
High Rate ◽  
Soil Profiles ◽  
Land Uses

The objectives of this study were to investigate effects of land use on accumulation of soil organic matter (SOM) in the soil profile (0–100 cm) and to determine pattern of SOM stock distribution in soil profiles. Soil samples were collected from five soil depths at 20 cm intervals from 0 to 100 cm under four adjacent land uses including forest, cassava, sugarcane, and paddy lands located in six districts of Maha Sarakham province in the Northeast of Thailand. When considering SOM stock among different land uses in all locations, forest soils had significantly higher total SOM stocks in 0–100 cm (193 Mg·C·ha−1) than those in cassava, sugarcane, and paddy soils in all locations. Leaf litter and remaining rice stover on soil surfaces resulted in a higher amount of SOM stocks in topsoil (0–20 cm) than subsoil (20–100 cm) in some forest and paddy land uses. General pattern of SOM stock distribution in soil profiles was such that the SOM stock declined with soil depth. Although SOM stocks decreased with depth, the subsoil stock contributes to longer term storage of C than topsoils as they are more stabilized through adsorption onto clay fraction in finer textured subsoil than those of the topsoils. Agricultural practices, notably applications of organic materials, such as cattle manure, could increase subsoil SOM stock as found in some agricultural land uses (cassava and sugarcane) in some location in our study. Upland agricultural land uses, notably cassava, caused high rate of soil degradation. To restore soil fertility of these agricultural lands, appropriate agronomic practices including application of organic soil amendments, return of crop residues, and reduction of soil disturbance to increase and maintain SOM stock, should be practiced.

scholarly journals Estimating the Surface Air Temperature by Remote Sensing in Northwest China Using an Improved Advection-Energy Balance for Air Temperature Model

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Suhua Liu ◽  
Hongbo Su ◽  
Renhua Zhang ◽  
Jing Tian ◽  
Weizhen Wang
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Air Temperature ◽  
Driving Force ◽  
Regional Scale ◽  
Surface Air Temperature ◽  
Absolute Error ◽  
Northwest China ◽  
Absolute Deviation ◽  
Distance Weighting

To estimate the surface air temperature by remote sensing, the advection-energy balance for the surface air temperature (ADEBAT) model is developed which assumes the surface air temperature is driven by the local driving force and the advective driving force. The local driving force produces a local surface air temperature whereas the advective driving force changes it by adding an exotic air temperature. An advection factorfis defined to measure the quantity of the exotic air brought by the advection. Since thefis determined by the advection, this paper improves it to a regional scale by using the Inverse Distance Weighting (IDW) method whereas the original ADEBAT model uses a constant offfor a block of area. Results retrieved by the improved ADEBAT (IADEBAT) model are evaluated and comparison was made with the in situ measurements, with anR2(correlation coefficient) of 0.77, an RMSE (Root Mean Square Error) of 0.31 K, and a MAE (Mean Absolute Error) of 0.24 K. The evaluation shows that the IADEBAT model has higher accuracy than the original ADEBAT model. Evaluations together with at-test of the MAD (Mean Absolute Deviation) reveal that the IADEBAT model has a significant improvement.

The Midway sequence: a Timiskaming-type, pull-apart basin deposit in the western Wawa subprovince, Minnesota

2000 ◽  
Vol 37 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Mark A Jirsa
Keyword(s):  
Structural Characteristics ◽  
Regional Scale ◽  
Volcanic Rocks ◽  
Strike Slip ◽  
Alluvial Fan ◽  
Pull Apart Basin ◽  
Tectonically Active ◽  
History Of ◽  
Fluvial Sedimentation ◽  
Magmatic History

The Midway sequence is an assemblage of subaerially deposited clastic and volcanic rocks that forms a narrow wedge within Neoarchean greenstone of the western Wawa subprovince of the Superior Province. Volcanic conglomerate in the Midway sequence contains clasts of stratigraphically older greenstone, together with clasts of a distinctive hornblende-phyric trachyandesite that is not represented among the older greenstone flows. The trachyandesite forms flows and pyroclastic units that are interbedded with lenticular deposits of volcanic conglomerate in a manner interpreted to indicate approximately coeval volcanism and alluvial fan - fluvial sedimentation within a linear, restricted, and tectonically active depocentre. The Midway sequence unconformably overlies greenstone on one side and is bounded by a regional-scale, strike-slip fault on the other. Structural analyses show that the Midway sequence was deposited after an early, precleavage folding event (D1) in greenstone, but before the regional metamorphic cleavage-forming D2 deformation. Lithologic and structural attributes are consistent with deposition in a strike-slip "pull-apart" basin. The stratigraphic and structural characteristics of the Midway sequence are generally similar to those of the Timiskaming Group and Timiskaming-type rocks in Canada, and more specifically to those of the Shebandowan Group in the Thunder Bay district. This similarity implies that the latest Archean tectonic and magmatic history of the western Wawa subprovince may have been nearly synchronous over great distances.

scholarly journals Regional evapotranspiration from an image-based implementation of the Surface Temperature Initiated Closure (STIC1.2) model and its validation across an aridity gradient in the conterminous US

2018 ◽  
Vol 22 (4) ◽  
pp. 2311-2341 ◽  
Author(s):  
Nishan Bhattarai ◽  
Kaniska Mallick ◽  
Nathaniel A. Brunsell ◽  
Ge Sun ◽  
Meha Jain
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Surface Temperature ◽  
Land Surface ◽  
Surface Energy Balance ◽  
Regional Scale ◽  
Absolute Error ◽  
Error Statistics ◽  
Aridity Gradient ◽  
Balance System

Abstract. Recent studies have highlighted the need for improved characterizations of aerodynamic conductance and temperature (gA and T0) in thermal remote-sensing-based surface energy balance (SEB) models to reduce uncertainties in regional-scale evapotranspiration (ET) mapping. By integrating radiometric surface temperature (TR) into the Penman–Monteith (PM) equation and finding analytical solutions of gA and T0, this need was recently addressed by the Surface Temperature Initiated Closure (STIC) model. However, previous implementations of STIC were confined to the ecosystem-scale using flux tower observations of infrared temperature. This study demonstrates the first regional-scale implementation of the most recent version of the STIC model (STIC1.2) that integrates the Moderate Resolution Imaging Spectroradiometer (MODIS) derived TR and ancillary land surface variables in conjunction with NLDAS (North American Land Data Assimilation System) atmospheric variables into a combined structure of the PM and Shuttleworth–Wallace (SW) framework for estimating ET at 1 km × 1 km spatial resolution. Evaluation of STIC1.2 at 13 core AmeriFlux sites covering a broad spectrum of climates and biomes across an aridity gradient in the conterminous US suggests that STIC1.2 can provide spatially explicit ET maps with reliable accuracies from dry to wet extremes. When observed ET from one wet, one dry, and one normal precipitation year from all sites were combined, STIC1.2 explained 66 % of the variability in observed 8-day cumulative ET with a root mean square error (RMSE) of 7.4 mm/8-day, mean absolute error (MAE) of 5 mm/8-day, and percent bias (PBIAS) of −4 %. These error statistics showed relatively better accuracies than a widely used but previous version of the SEB-based Surface Energy Balance System (SEBS) model, which utilized a simple NDVI-based parameterization of surface roughness (zOM), and the PM-based MOD16 ET. SEBS was found to overestimate (PBIAS = 28 %) and MOD16 was found to underestimate ET (PBIAS = −26 %). The performance of STIC1.2 was better in forest and grassland ecosystems as compared to cropland (20 % underestimation) and woody savanna (40 % overestimation). Model inter-comparison suggested that ET differences between the models are robustly correlated with gA and associated roughness length estimation uncertainties which are intrinsically connected to TR uncertainties, vapor pressure deficit (DA), and vegetation cover. A consistent performance of STIC1.2 in a broad range of hydrological and biome categories, as well as the capacity to capture spatio-temporal ET signatures across an aridity gradient, points to the potential for this simplified analytical model for near-real-time ET mapping from regional to continental scales.

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