scholarly journals Estimating unconsolidated sediment cover thickness by using the horizontal distance to a bedrock outcrop as secondary information

2017 ◽  
Vol 21 (8) ◽  
pp. 4195-4211
Author(s):  
Nils-Otto Kitterød
Keyword(s):  
Cross Validation ◽  
Small Scale ◽  
Horizontal Distance ◽  
Estimation Variance ◽  
Unconsolidated Sediment ◽  
Sediment Cover ◽  
The Cross ◽  
Secondary Information ◽  
Cover Thickness ◽  
Bedrock Outcrop

Abstract. Unconsolidated sediment cover thickness (D) above bedrock was estimated by using a publicly available well database from Norway, GRANADA. General challenges associated with such databases typically involve clustering and bias. However, if information about the horizontal distance to the nearest bedrock outcrop (L) is included, does the spatial estimation of D improve? This idea was tested by comparing two cross-validation results: ordinary kriging (OK) where L was disregarded; and co-kriging (CK) where cross-covariance between D and L was included. The analysis showed only minor differences between OK and CK with respect to differences between estimation and true values. However, the CK results gave in general less estimation variance compared to the OK results. All observations were declustered and transformed to standard normal probability density functions before estimation and back-transformed for the cross-validation analysis. The semivariogram analysis gave correlation lengths for D and L of approx. 10 and 6 km. These correlations reduce the estimation variance in the cross-validation analysis because more than 50 % of the data material had two or more observations within a radius of 5 km. The small-scale variance of D, however, was about 50 % of the total variance, which gave an accuracy of less than 60 % for most of the cross-validation cases. Despite the noisy character of the observations, the analysis demonstrated that L can be used as secondary information to reduce the estimation variance of D.

scholarly journals Estimating sediment thickness by using horizontal distance to outcrop as secondary information

2017 ◽  
Author(s):  
Nils-Otto Kitterød
Keyword(s):  
Cross Validation ◽  
Estimation Error ◽  
Small Scale ◽  
Horizontal Distance ◽  
Estimation Variance ◽  
Sediment Thickness ◽  
Preferential Sampling ◽  
Cross Covariance ◽  
The Cross ◽  
Secondary Information

Abstract. Sediment thickness (D) was estimated utilizing a publically available well database from Norway, GRANADA. General challenges associated with such databases typically involve clustering and bias of the data material due to preferential sampling. However, if information about horizontal distance to the nearest outcrop (L) is included, does the spatial estimation of D improve? This idea was tested comparing two cross-validation results: ordinary kriging (OK) where L was disregarded; and co-kriging (CK) where cross-covariance between D and L was included. The analysis resulted in only minor differences between OK and CK in terms of absolute estimation error, however CK produced more precise results than OK. All observations were declustered and transformed to standard normal probability density functions before estimation and back transformed for the cross-validation analysis. The semivariogram analysis gave correlation lengths for D and L of approx. 10 km and 6 km. These correlations reduce the estimation variance in the cross-validation analysis because more than 50 % of the data material had two or more observations within a radius of 5 km. The small-scale variance of D, however, was about 50 % of the total variance, which gave an accuracy of less than 60 % for most of the cross-validation cases. Despite of the noisy character in the data material, the analysis demonstrates that L can be used as a secondary information to reduce the estimation variance of D.

scholarly journals Comparison and synergy of stratospheric ozone measurements by satellite limb sounders and the ground-based microwave radiometer SOMORA

2007 ◽  
Vol 7 (15) ◽  
pp. 4117-4131 ◽  
Author(s):  
K. Hocke ◽  
N. Kämpfer ◽  
D. Ruffieux ◽  
L. Froidevaux ◽  
A. Parrish ◽  
...  
Keyword(s):  
Measurement Errors ◽  
Cross Validation ◽  
Stratospheric Ozone ◽  
Microwave Radiometer ◽  
Measurement Techniques ◽  
Horizontal Distance ◽  
Ground Station ◽  
Difference Field ◽  
The Cross ◽  
Satellite Ground Station

Abstract. Stratospheric O3 profiles obtained by the satellite limb sounders Aura/MLS, ENVISAT/MIPAS, ENVISAT/GOMOS, SAGE-II, SAGE-III, UARS/HALOE are compared to coincident O3 profiles of the ground-based microwave radiometer SOMORA in Switzerland. Data from the various measurement techniques are within 10% at altitudes below 45 km. At altitudes 45–60 km, the relative O3 differences are within a range of 50%. Larger deviations at upper altitudes are attributed to larger relative measurement errors caused by lower O3 concentrations. The spatiotemporal characteristics of the O3 differences (satellite – ground station) are investigated by analyzing about 2300 coincident profile pairs of Aura/MLS (retrieval version 1.5) and SOMORA. The probability density function of the O3 differences is represented by a Gaussian normal distribution. The dependence of the O3 differences on the horizontal distance between the sounding volumes of Aura/MLS and SOMORA is derived. While the mean bias (Aura/MLS – SOMORA) is constant with increasing horizontal distance (up to 800 km), the standard deviation of the O3 differences increases from around 8 to 11% in the mid-stratosphere. Geographical maps yield azimuthal dependences and horizontal gradients of the O3 difference field around the SOMORA ground station. Coherent oscillations of O3 are present in the time series of Aura/MLS and SOMORA (e.g., due to traveling planetary waves). Ground- and space-based measurements often complement one another. We discuss the double differencing technique which allows both the cross-validation of two satellites by means of a ground station and the cross-validation of distant ground stations by means of one satellite. Temporal atmospheric noise in the geographical ozone map over Payerne is significantly reduced by combination of the data from SOMORA and Aura/MLS. These analyses illustrate the synergy of ground-based and space-based measurements.

scholarly journals Comparison and synergy of stratospheric ozone measurements by satellite limb sounders and the ground-based microwave radiometer SOMORA

2007 ◽  
Vol 7 (2) ◽  
pp. 5053-5098 ◽  
Author(s):  
K. Hocke ◽  
N. Kämpfer ◽  
D. Ruffieux ◽  
L. Froidevaux ◽  
A. Parrish ◽  
...  
Keyword(s):  
Measurement Errors ◽  
Cross Validation ◽  
Stratospheric Ozone ◽  
Microwave Radiometer ◽  
Measurement Techniques ◽  
Horizontal Distance ◽  
Ground Station ◽  
Difference Field ◽  
The Cross ◽  
Satellite Ground Station

Abstract. Stratospheric O3 profiles obtained by the satellite limb sounders Aura/MLS, ENVISAT/MIPAS, ENVISAT/GOMOS, SAGE-II, SAGE-III, UARS/HALOE are compared to coincident O3 profiles of the ground-based microwave radiometer SOMORA in Switzerland. Data from the various measurement techniques are within 10% at altitudes below 45 km. At altitudes 45–60 km, the relative O3 differences are within a range of 50% Larger deviations at upper altitudes are attributed to larger relative measurement errors caused by lower O3 concentrations. The spatiotemporal characteristics of the O3 differences (satellite – ground station) are investigated by analyzing about 5000 coincident profile pairs of Aura/MLS (retrieval version 1.5) and SOMORA. The probability density function of the O3 differences is represented by a Gaussian normal distribution (except for profile pairs around the stratopause at noon). The dependence of the O3 differences on the horizontal distance between the sounding volumes of Aura/MLS and SOMORA is derived. While the mean bias (Aura/MLS – SOMORA) is constant with increasing horizontal distance (up to 800 km), the standard deviation of the O3 differences increases from around 8 to 12% in the mid-stratosphere. Geographical maps yield azimuthal dependences and horizontal gradients of the O3 difference field around the SOMORA ground station. Coherent oscillations of O3 are present in the time series of Aura/MLS and SOMORA (e.g., due to traveling planetary waves). Ground- and space-based measurements often complement one another. We introduce the double differencing technique which allows both the cross-validation of two satellites by means of a ground station and the cross-validation of distant ground stations by means of one satellite. Temporal atmospheric noise in the geographical ozone map over Payerne is significantly reduced by combination of the data from SOMORA and Aura/MLS. These analyses illustrate the synergy between ground-based and space-based measurements.

scholarly journals Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 591
Author(s):  
Manasavee Lohvithee ◽  
Wenjuan Sun ◽  
Stephane Chretien ◽  
Manuchehr Soleimani
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Cross Validation ◽  
Ant Colony ◽  
Reference Image ◽  
X Ray ◽  
X Ray Computed ◽  
Hyperparameter Selection ◽  
The Cross ◽  
Optimal Set

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.

Mapping Arctic clam abundance using multiple datasets, models, and a spatially explicit accuracy assessment

2019 ◽  
Vol 76 (7) ◽  
pp. 2349-2361
Author(s):  
Benjamin Misiuk ◽  
Trevor Bell ◽  
Alec Aitken ◽  
Craig J Brown ◽  
Evan N Edinger
Keyword(s):  
Spatial Autocorrelation ◽  
Cross Validation ◽  
Predictive Accuracy ◽  
Accuracy Assessment ◽  
Small Scale ◽  
Suitable Habitat ◽  
Distribution Models ◽  
Management Tools ◽  
Seabed Topography ◽  
Leave One Out

Abstract Species distribution models are commonly used in the marine environment as management tools. The high cost of collecting marine data for modelling makes them finite, especially in remote locations. Underwater image datasets from multiple surveys were leveraged to model the presence–absence and abundance of Arctic soft-shell clam (Mya spp.) to support the management of a local small-scale fishery in Qikiqtarjuaq, Nunavut, Canada. These models were combined to predict Mya abundance, conditional on presence throughout the study area. Results suggested that water depth was the primary environmental factor limiting Mya habitat suitability, yet seabed topography and substrate characteristics influence their abundance within suitable habitat. Ten-fold cross-validation and spatial leave-one-out cross-validation (LOO CV) were used to assess the accuracy of combined predictions and to test whether this was inflated by the spatial autocorrelation of transect sample data. Results demonstrated that four different measures of predictive accuracy were substantially inflated due to spatial autocorrelation, and the spatial LOO CV results were therefore adopted as the best estimates of performance.

scholarly journals Application of a fast and efficient algorithm to assess landslide-prone areas in sensitive clays in Sweden

2015 ◽  
Vol 15 (12) ◽  
pp. 2703-2713 ◽  
Author(s):  
C. Melchiorre ◽  
A. Tryggvason
Keyword(s):  
Optimal Algorithm ◽  
Model Performance ◽  
Small Scale ◽  
Depth Information ◽  
Computationally Efficient ◽  
Cross Sectional ◽  
Bedrock Depth ◽  
The Cross ◽  
Intelligent Filtering ◽  
Filtering Procedure

Abstract. We refine and test an algorithm for landslide susceptibility assessment in areas with sensitive clays. The algorithm uses soil data and digital elevation models to identify areas which may be prone to landslides and has been applied in Sweden for several years. The algorithm is very computationally efficient and includes an intelligent filtering procedure for identifying and removing small-scale artifacts in the hazard maps produced. Where information on bedrock depth is available, this can be included in the analysis, as can information on several soil-type-based cross-sectional angle thresholds for slip. We evaluate how processing choices such as of filtering parameters, local cross-sectional angle thresholds, and inclusion of bedrock depth information affect model performance. The specific cross-sectional angle thresholds used were derived by analyzing the relationship between landslide scarps and the quick-clay susceptibility index (QCSI). We tested the algorithm in the Göta River valley. Several different verification measures were used to compare results with observed landslides and thereby identify the optimal algorithm parameters. Our results show that even though a relationship between the cross-sectional angle threshold and the QCSI could be established, no significant improvement of the overall modeling performance could be achieved by using these geographically specific, soil-based thresholds. Our results indicate that lowering the cross-sectional angle threshold from 1 : 10 (the general value used in Sweden) to 1 : 13 improves results slightly. We also show that an application of the automatic filtering procedure that removes areas initially classified as prone to landslides not only removes artifacts and makes the maps visually more appealing, but it also improves the model performance.

scholarly journals Comparative Analysis of Different Spatial Interpolation Methods Applied to Monthly Rainfall as Support for Landscape Management

2021 ◽  
Vol 11 (20) ◽  
pp. 9566
Author(s):  
Tommaso Caloiero ◽  
Gaetano Pellicone ◽  
Giuseppe Modica ◽  
Ilaria Guagliardi
Keyword(s):  
New Zealand ◽  
Spatial Interpolation ◽  
Cross Validation ◽  
Landscape Management ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
The Other ◽  
Geostatistical Methods ◽  
The Cross ◽  
Almost All

Landscape management requires spatially interpolated data, whose outcomes are strictly related to models and geostatistical parameters adopted. This paper aimed to implement and compare different spatial interpolation algorithms, both geostatistical and deterministic, of rainfall data in New Zealand. The spatial interpolation techniques used to produce finer-scale monthly rainfall maps were inverse distance weighting (IDW), ordinary kriging (OK), kriging with external drift (KED), and ordinary cokriging (COK). Their performance was assessed by the cross-validation and visual examination of the produced maps. The results of the cross-validation clearly evidenced the usefulness of kriging in the spatial interpolation of rainfall data, with geostatistical methods outperforming IDW. Results from the application of different algorithms provided some insights in terms of strengths and weaknesses and the applicability of the deterministic and geostatistical methods to monthly rainfall. Based on the RMSE values, the KED showed the highest values only in April, whereas COK was the most accurate interpolator for the other 11 months. By contrast, considering the MAE, the KED showed the highest values in April, May, June and July, while the highest values have been detected for the COK in the other months. According to these results, COK has been identified as the best method for interpolating rainfall distribution in New Zealand for almost all months. Moreover, the cross-validation highlights how the COK was the interpolator with the best least bias and scatter in the cross-validation test, with the smallest errors.

scholarly journals Quantifying methane point sources from fine-scale (GHGSat) satellite observations of atmospheric methane plumes

2018 ◽  
Author(s):  
Daniel J. Varon ◽  
Daniel J. Jacob ◽  
Jason McKeever ◽  
Dylan Jervis ◽  
Berke O. A. Durak ◽  
...  
Keyword(s):  
Wind Speed ◽  
Methane Emissions ◽  
Point Sources ◽  
Small Scale ◽  
Atmospheric Methane ◽  
Flux Method ◽  
Pixel Resolution ◽  
Cross Sectional ◽  
Source Rate ◽  
The Cross

Abstract. Anthropogenic methane emissions originate from a large number of relatively small point sources. The planned GHGSat satellite fleet aims to quantify emissions from individual point sources by measuring methane column plumes over selected ~ 10 × 10 km2 domains with ≤ 50 × 50 m2 pixel resolution and 1–5 % measurement precision. Here we develop algorithms for retrieving point source rates from such measurements. We simulate a large ensemble of instantaneous methane column plumes at 50 × 50 m2 pixel resolution for a range of atmospheric conditions using the Weather Research and Forecasting model (WRF) in large eddy simulation (LES) mode and adding instrument noise. We show that standard methods to infer source rates by Gaussian plume inversion or source pixel mass balance are prone to large errors because the turbulence cannot be properly parameterized on the small scale of instantaneous methane plumes. The integrated mass enhancement (IME) method, which relates total plume mass to source rate, and the cross-sectional flux method, which infers source rate from fluxes across plume transects, are better adapted to the problem. We show that the IME method with local measurements of the 10-m wind speed can infer source rates with error of 0.07–0.17 t h−1 + 5–12 % depending on instrument precision (1–5 %). The cross-sectional flux method has slightly larger errors (0.07–0.26 t h−1 + 8–12 %) but a simpler physical basis. For comparison, point sources larger than 0.5 t h−1 contribute more than 75 % of methane emissions reported to the U.S. Greenhouse Gas Reporting Program. Additional error applies if local wind speed measurements are not available, and may dominate the overall error at low wind speeds. Low winds are beneficial for source detection but not for source quantification.

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