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8 results for “CSEM”
Towards an open-source landscape for 3D CSEM modelling
<p>Accompanying data to journal article</p> <blockquote> <p>Werthmüller, D., R. Rochlitz, O. Castillo-Reyes, and L. Heagy, 2021, Towards an open-source landscape for 3D CSEM modelling: Geophysical Journal International; ggab238, DOI: <a href="https://doi.org/10.1093/gji/ggab238">10.1093/gji/ggab238</a>.</p> </blockquote> <ul> <li>Official article: <a href="https://doi.org/10.1093/gji/ggab238">https://doi.org/10.1093/gji/ggab238</a></li> <li>GitHub repo: <a href="https://github.com/swung-research/3d-csem-open-source-landscape">https://github.com/swung-research/3d-csem-open-source-landscape</a></li> <li>arXiv.org: <a href="https://arxiv.org/abs/2010.12926">https://arxiv.org/abs/2010.12926</a></li> </ul> <p>The Marlim R3D model can be found at:</p> <ul> <li>Original, fine resistivity model: <a href="https://doi.org/10.5281/zenodo.400233">https://doi.org/10.5281/zenodo.400233</a></li> <li>Upscaled computational model: <a href="https://doi.org/10.5281/zenodo.3748491">https://doi.org/10.5281/zenodo.3748491</a></li> <li>CSEM data set: <a href="https://doi.org/10.5281/zenodo.1256786">https://doi.org/10.5281/zenodo.1256786</a></li> <li>Noise-free CSEM data set: <a href="https://doi.org/10.5281/zenodo.1807134">https://doi.org/10.5281/zenodo.1807134</a></li> </ul>
Marlim R3D (MR3D) - The full azimuth CSEM dataset
<p>Marlim R3D (MR3D) is an open-source realistic geoelectric model for CSEM simulations of the post-salt turbiditic reservoirs at the Brazilian offshore margin.<br> Here we make available the complete CSEM dataset, with inline and broadside geometries, of two simulated surveys on the MR3D model. We computed the six electromagnetic field components (Ex, Ey, Ez, Hx, Hy, and Hz) at six frequencies in the 0.125–1.25 Hz range.<br> The full azimuth CSEM data were simulated for a total of 500 receivers located on the irregular seafloor of the MR3D model. For each receiver, sources were situated along 45 tow lines having a spacing of 1000 m. Towlines were positioned along 25<br> EW lines and 20 NS lines for each receiver. Figure~\ref{fig:mapa} shows the receiver positions and towlines superimposed on the bathymetry of the MR3D model.<br> Each EW towline has a length of 42 km, and each NS towline has a length of 47 km. Both directions are extending 11 km away from the first and last receivers at each towline. Sources positions were spaced at 100 m along each towline for a total of 201 source positions along each towline. The source is a 275 m long horizontal electrical dipole (HED), oriented parallel to the towline.<br> The output dataset was generated in the current industry netCDF-based data format.<br> You can follow the updates of the marlim project at:<br> https://www.researchgate.net/project/Marlim-R3D-A-realistic-model-for-mCSEM-simulations</p>
CSEM-MISD - CSEM's Multi-Illumination Surface Defect Detection Dataset
<p>In automated surface visual inspection, it is often necessary to capture the inspected part under many different illumination conditions to capture all the defects. To address this issue, at <a href="http://www.csem.ch/">CSEM</a> we have acquired a real-world multi-illumination defect segmentation dataset, called CSEM-MISD and we release it for research purposes to benefit the community.</p> <p>The dataset consists of three different types of metallic parts -- washers, screws, and gears. Parts were captured in a half-spherical <a href="https://register.epo.org/application?number=EP13197867">light-dome system</a> that filtered out all the ambient light and successively illuminated it from 108 distinct illumination angles. Each 12 illumination angles share the same elevation level and the relative azimuthal difference between the adjacent illumination angles on the same level is 30 degrees. For more details, please read Sections 3 and 4 of our paper.</p> <p>The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.</p> <p>We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.</p> <p>This dataset is challenging mainly because:</p> <ul> <li>each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space;</li> <li>the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors;</li> <li>the annotations are not very precise because the exact extent of defect contours is always subjective;</li> <li>the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. </li> </ul> <p>The dataset is organized as follows:</p> <ul> <li>each sample resides in the Test, Train, or Unannotated directory;</li> <li>each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks;</li> <li>each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels;</li> <li>Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12).</li> <li>Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.</li> </ul> <p>We provide data loaders implemented in python at the project's <a href="https://github.com/DawyD/illumination-preserving-rotations">repository</a>.</p> <p>If you find our dataset useful, please cite our paper:</p> <blockquote> <p>Honzátko, D., Türetken, E., Bigdeli, S. A., Dunbar, L. A., & Fua, P. (2021). Defect segmentation for multi-illumination quality control systems. <em>Machine vision and Applications</em>.</p> </blockquote>
Chesley et al., 2023 - Gofar Oceanic Transform Fault CSEM data from fault-perpendicular profiles (collected 2022)
<p>This repository contains processed and edited controlled-source electromagnetic amplitude and phase data from the Gofar oceanic transform fault with corresponding bathymetry files. The files beginning "dataFile.." are the amplitudes and phases and the files beginning "topo..." are the bathymetry information. Each profile in this repository crosses the fault approximately perpendicularly.</p>
CSEM data and inversion result
<p>The files contain information about the CSEM data and inversion results presented in the GJI paper.</p>
Marlim R3D (MR3D) - The full azimuth noise-free CSEM dataset
<p>Marlim R3D (MR3D) is an open-source realistic geoelectric model for CSEM simulations of the post-salt turbiditic reservoirs at the Brazilian offshore margin.<br> Here we make available the complete noise-free CSEM dataset, with inline and broadside geometries, of two simulated surveys on the MR3D model. We computed the six electromagnetic field components (Ex, Ey, Ez, Hx, Hy, and Hz) at six frequencies in the 0.125–1.25 Hz range.<br> The output dataset was generated in the current industry netCDF-based data format.<br> You can follow the updates of the marlim project at:<br> https://www.researchgate.net/project/Marlim-R3D-A-realistic-model-for-mCSEM-simulations</p>
cSEMS vs. NEPS in Stricture of Biliary Anastomosis After Liver Transplantation
ClinicalTrials.gov study NCT01393067. IPD Sharing: Not stated. Countries: 3. Publications: 4.
Prospective Evaluation of the Covered Self-expandable Metal Stents (CSEMS) for Incomplete Biliary Stone Clearance
ClinicalTrials.gov study NCT01475864. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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