HIGH-RESOLUTION SPECTRAL RECONSTRUCTION: METHOD AND APPLICATIONS FOR CONTAMINATION MEASUREMENT, DIGITAL SAMPLING, AND CONTINUITY ASSESSMENT

2020 ◽  
Author(s):  
Christopher Michael Jones ◽  
◽  
Yngve B Johansen ◽  
Artur Kotwicki ◽  
Cameron Rekully ◽  
...  
Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1369
Author(s):  
Ling Jiang ◽  
Yang Hu ◽  
Xilin Xia ◽  
Qiuhua Liang ◽  
Andrea Soltoggio ◽  
...  

The scarcity of high-resolution urban digital elevation model (DEM) datasets, particularly in certain developing countries, has posed a challenge for many water-related applications such as flood risk management. A solution to address this is to develop effective approaches to reconstruct high-resolution DEMs from their low-resolution equivalents that are more widely available. However, the current high-resolution DEM reconstruction approaches mainly focus on natural topography. Few attempts have been made for urban topography, which is typically an integration of complex artificial and natural features. This study proposed a novel multi-scale mapping approach based on convolutional neural network (CNN) to deal with the complex features of urban topography and to reconstruct high-resolution urban DEMs. The proposed multi-scale CNN model was firstly trained using urban DEMs that contained topographic features at different resolutions, and then used to reconstruct the urban DEM at a specified (high) resolution from a low-resolution equivalent. A two-level accuracy assessment approach was also designed to evaluate the performance of the proposed urban DEM reconstruction method, in terms of numerical accuracy and morphological accuracy. The proposed DEM reconstruction approach was applied to a 121 km2 urbanized area in London, United Kingdom. Compared with other commonly used methods, the current CNN-based approach produced superior results, providing a cost-effective innovative method to acquire high-resolution DEMs in other data-scarce regions.


2021 ◽  
Author(s):  
Akhil Kallepalli ◽  
Daan Stellinga ◽  
Ming-Jie Sun ◽  
Richard Bowman ◽  
Enzo Rotunno ◽  
...  

Abstract Transmission electron microscopes (TEM) achieve high resolution imaging by raster scanning a focused beam of electrons over the sample and measuring the transmission to form an image. While a TEM can achieve a much higher resolution than optical microscopes, they face challenges of damage to samples during the high energy processes involved. Here, we explore the possibility of applying computational ghost imaging techniques adapted from the optical regime to reduce the total, required illumination intensity. The technological lack of the equivalent high-resolution, optical spatial light modulator for electrons means that a different approach needs to be pursued. Using the optical equivalent, we show that a simple six-needle charged device to modulate the illuminating beam, alongside a novel reconstruction method to handle the resulting highly non-orthogonal patterns, is capable of producing images comparable in quality to a raster-scanned approach with much lower peak intensity.


2021 ◽  
Author(s):  
Bin Dai ◽  
◽  
Christopher Jones ◽  
Jimmy Price ◽  
Darren Gascooke ◽  
...  

Downhole fluid analysis has the potential to resolve ambiguity in very complex reservoirs. Downhole fluid spectra contain a wealth of information to fingerprint a fluid and help to assess continuity. Commonly, a narrowband spectrometer with limited number of channels is used to acquire optical spectra of downhole fluid. The spectral resolution of this type of spectrometer is low due to limited number of narrowband channels. In this paper, we demonstrate a new type, compressive sensing (CS) based broadband spectrometer that provides accurate and high-resolution spectral measurement. Several specially designed broadband filters are used to simplify the mechanical, electrical, optical, and computational construction of a spectrometer, therefore provides measurement of fluid spectrum with high signal-to-noise ratio, robustness, and a broader spectral range. The compressive sensing spectrometer relies on reconstruction technique to compute the optical spectrum. Based on a large spectral database, containing more than 10000 spectra of various fluids at different temperature and pressure conditions, which were collected using conventional high resolution spectrometer in a lab, the basis functions of the optical spectra of three types of fluids (water, oil and gas/condensate) can be extracted. The reconstruction algorithm first classifies the fluid into one of three fluid types based on multichannel CS spectrometer measurements, the optical spectrum is reconstructed by using linear combination of the basis functions of corresponding fluid type, with weighting coefficients determined by minimizing the difference between calculated detector responses and measured detector responses across multiple optical channels. The reconstructed data may then be used for purposes such as contamination measurement, fluid property trends for reservoir continuity assessment, and digital sampling. Digital sampling is the process of extrapolating clean fluid properties from formation fluids not physically sampled. The reconstruction spectrum covers wavelengths from 500 nm to 3300 nm, which is a wider spectral region than has historically been accessible to formation testers. The expanded wavelength range allows access of the mid-infrared spectral region for which synthetic drilling-fluid components typically have higher optical absorbance. This reconstruction spectra may allow contamination to be directly determined. This paper will discuss the CS optical spectrometer design, fluid classification and spectral reconstruction algorithm. In addition, the applicability of the technique to fluid continuity assessment, sample contamination assessment and digital sampling will also be discussed.


2012 ◽  
Vol 220-223 ◽  
pp. 2754-2757
Author(s):  
Pan Li He ◽  
Bo Yang Wang ◽  
Xiao Xia Liu ◽  
Xiao Wei Han

Super-resolution image reconstruction has been one of the most active research fields in recent years. In this paper, a new super-resolution algorithm is proposed to the problem of obtaining a high-resolution image from several low- resolution images that have been sub sampled. In the image registration, the paper puts forward an improved search strategies improving registration accuracy. In the MAP algorithm, the threshold parameters of solving the optimal value, making the estimated value of the optimal high-resolution images, so that the reconstructed image is better. The results of the experiments indicate that the proposed algorithm can not only make an automatic choice of the parameter and get the high resolution reconstruction image expected, but also can preserve the edges and details of the image effectively.


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