Prediction of High Resolution Data from a Coded Low Resolution Grid within the Context of SVC

2006 ◽  
Author(s):  
Andrew Segall
Keyword(s):  
High Resolution ◽  
Low Resolution ◽  
High Resolution Data ◽  
Resolution Data

The influence of resolution on scale-dependent clustering in fracture spacing data

2016 ◽  
Vol 4 (3) ◽  
pp. T387-T394 ◽  
Author(s):  
Ankur Roy ◽  
Atilla Aydin ◽  
Tapan Mukerji
Keyword(s):  
High Resolution ◽  
Spatial Data ◽  
Reservoir Characterization ◽  
Data Capture ◽  
Low Resolution ◽  
Data Set ◽  
Bootstrap Analysis ◽  
High Resolution Data ◽  
Fracture Spacing ◽  
Resolution Data

It is a common practice to analyze fracture spacing data collected from scanlines and wells at various resolutions for the purposes of aquifer and reservoir characterization. However, the influence of resolution on such analyses is not well-studied. Lacunarity is a parameter that is used for multiscale analysis of spatial data. In quantitative terms, at any given scale, it is a function of the mean and variance of the distribution of masses captured by a gliding a window of that scale (size) across any pattern of interest. We have described the application of lacunarity for delineating differences between scale-dependent clustering attributes of data collected at different resolutions along a scanline. Specifically, we considered data collected at different resolutions from two outcrop exposures, a pavement and a cliff section, of the Cretaceous turbititic sandstones of the Chatsworth Formation widely exposed in southern California. For each scanline, we analyzed data from low-resolution aerial or ground photographs and high-resolution ground measurements for scale-dependent clustering attributes. High-resolution data show larger values of scale-dependent lacunarity than their respective low-resolution counterparts. We further performed a bootstrap analysis for each data set to test for the significance of such clustering differences. We started with generating 300 realizations for each data set and then ran lacunarity analysis on them. It was seen that lacunarity for higher resolution data set lay significantly outside the upper 90th percentile values, thus proving that higher resolution data are distinctly different from random and fractures are clustered. We have therefore postulated that lower resolution data capture fracture zones that had relatively uniform spacing, whereas higher resolution data capture thin and short splay joints and sheared joints that contribute to fracture clustering. Such findings have important implications in terms of understanding organization of fractures in fracture corridors, which in turn is critical for modeling and upscaling exercises.

scholarly journals Correction and Accuracy of High- and Low-Resolution CTD Data from Animal-Borne Instruments

2019 ◽  
Vol 36 (5) ◽  
pp. 745-760 ◽  
Author(s):  
Lia Siegelman ◽  
Fabien Roquet ◽  
Vigan Mensah ◽  
Pascal Rivière ◽  
Etienne Pauthenet ◽  
...  
Keyword(s):  
High Resolution ◽  
Mass Effect ◽  
Thermal Mass ◽  
Low Resolution ◽  
Data Logger ◽  
High Resolution Data ◽  
Reconstructed Temperature ◽  
Rapid Temperature ◽  
Satellite Transmission ◽  
Resolution Data

AbstractMost available CTD Satellite Relay Data Logger (CTD-SRDL) profiles are heavily compressed before satellite transmission. High-resolution profiles recorded at the sampling frequency of 0.5 Hz are, however, available upon physical retrieval of the logger. Between 2014 and 2018, several loggers deployed on elephant seals in the Southern Ocean have been set in continuous recording mode, capturing both the ascent and descent for over 60 profiles per day during several months, opening new horizons for the physical oceanography community. Taking advantage of a new dataset made of seven such loggers, a postprocessing procedure is proposed and validated to improve the quality of all CTD-SRDL data: that is, both high-resolution profiles and compressed low-resolution ones. First, temperature and conductivity are corrected for a thermal mass effect. Then salinity spiking and density inversion are removed by adjusting salinity while leaving temperature unchanged. This method, applied here to more than 50 000 profiles, yields significant and systematic improvements in both temperature and salinity, particularly in regions of rapid temperature variation. The continuous high-resolution dataset is then used to provide updated accuracy estimates of CTD-SRDL data. For high-resolution data, accuracies are estimated to be of ±0.02°C for temperature and ±0.03 g kg−1 for salinity. For low-resolution data, transmitted data points have similar accuracies; however, reconstructed temperature profiles have a reduced accuracy associated with the vertical interpolation of ±0.04°C and a nearly unchanged salinity accuracy of ±0.03 g kg−1.

Using high resolution data to reveal depth-dependent mechanisms that drive land subsidence: The Venice coast, Italy

2009 ◽  
Vol 474 (1-2) ◽  
pp. 271-284 ◽  
Author(s):  
L. Tosi ◽  
P. Teatini ◽  
L. Carbognin ◽  
G. Brancolini
Keyword(s):  
High Resolution ◽  
Land Subsidence ◽  
High Resolution Data ◽  
Resolution Data
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