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6 results for “seismic interpretation”
A Benchmark Dataset for Semi-Automatic Seismic Interpretation Based on a New Zealand's Seismic Survey
<p>Open access to curated datasets positively impacts on scientific research of machine learning and deep learning techniques. It is a fact that benchmarks and public datasets prepared for data science assist researchers interested in evaluating, testing, and building new data-driven methodologies for specific domain areas.</p> <p>In geosciences, there has been a remarkable growth of public datasets arranged to address machine learning challenges related to the oil and gas industry, particularly for reserves exploration and data interpretation. </p> <p>For these reasons, we present the Taranaki dataset, which is a collection of seismic horizons interpreted for a seismic stratigraphic interpretation study in the Taranaki Basin, offshore New Zealand. This data comprises fourteen seismic horizons that mark stratigraphic discordances in the Tui-3D seismic dataset. We annotated five seismic horizons on 33 inline sections and nine horizons on 19 crossline sections.</p> <p>Besides, we present the results of a series of experiments that compare a method of interpolation and a method of deep learning for seismic segmentation. The deep learning experiments evaluated the result of different image tile sizes to train the model, which is presented separately in this dataset. </p> <p>Finally, we evaluated both methodologies to interpret the horizons of this dataset in selected seismic sections. Also, we assessed the absolute error of each method with the ground truth interpretations proposed in this dataset.</p>
A foundation model enpowered by a multi-modal prompt engine for universal seismic geobody interpretation across surveys
<p>A multi-type geobody dataset for training SAG model, including channel, paloekarst, salt body, and so on.</p> <p>A foundation model enpowered by a multi-modal prompt engine for universal seismic geobody interpretation across surveys (<a href="https://arxiv.org/abs/2409.04962">[2409.04962] A foundation model enpowered by a multi-modal prompt engine for universal seismic geobody interpretation across surveys (arxiv.org)</a>)</p> <p> </p>
cigKast: A data of 3D synthetic seismic volumes with labeled paleokarsts for deep-learning-based paleokarst interpretation
<p>cigKarst is a dataset created by the <a href="http://cig.ustc.edu.cn/">Computational Interpretation Group (CIG)</a> for the deep-learning-based peleokarst interpretation in 3D seismic images, <a href="http://cig.ustc.edu.cn/xinming/list.htm" target="_blank" rel="noopener">Xinming Wu</a> is the main contributor to the dataset.</p> <p>This dataset contains 120 pairs of synthetic 3D seismic images and the corresponding label images with the ground truth of the paleokarst systems simulated in the seismic images. More detail of building this dataset is discussed in the paper published at the journal of JGR Solid Earth:</p> <p><strong>Wu, X.</strong>, S. Yan, J. Qi, and H. Zeng, 2020, Deep learning for characterizing paleokarst collapse features in 3D seismic images. <strong>JGR, Solid Earth</strong>, Vol. 125(9), 1-23, e2020JB019685. <a href="http://cig.ustc.edu.cn/_upload/tpl/05/cd/1485/template1485/papers/wu2020karst.pdf">[PDF]</a>. doi: 10.1029/2020JB019685</p> <p>Below are some brief description of the dataset:</p> <p>1) The "seismic.zip" contains 120 3D seismic images, each image is with the dimension of 256X256X256;</p> <p> 2) The "karst.zip" contains 120 3D label images of the karsts. Each label image is with the same dimension of 256X256X256. The values in a label image are set with ones in the karst areas while zeros elsewhere, which is why the compressed label images in the karst.zip is much smaller than the seismic images compressed in the seismic.zip</p>
Temporal Seismic Velocity Changes Associated with the Mw 6.1, May 2008 Ölfus Doublet, South Iceland: a Joint Interpretation from dv/v and GPS. Cubuk-Sabuncu-etal-Dataset
<p>The dataset for the article "Temporal Seismic Velocity Changes Associated with the Mw 6.1, May 2008 Ölfus Doublet, South Iceland: a Joint Interpretation from dv/v and GPS" by Cubuk-Sabuncu et al. is provided.</p> <p>The weather dataset is now included in version 2.</p>
Seismic interpretation of key stratigraphic and structural surfaces, and crustal faults within the Galicia 3-D reflection survey
<p>This repository contains all the seismic interpretations utilized for the analysis in the article "<em>Origin of serpentinization patterns beneath the S-reflector detachment fault in the Galicia margin, offshore Spain</em>". </p> <p>The "<em>Surfaces</em>" file contains the CPS-3 shape files of the major stratigraphic and structural surfaces (seafloor, base of post-rift sedimentary strata, base of pre/syn-rift sedimentary strata, base of crystalline basement, S-reflector detachment fault, Moho). The "<em>Faults</em>" file contains the interpretations of the major crustal faults overlying the S-reflector detachment. The "<em>Data</em>" file contains the spatial boundary of where the S-reflector is the crust-mantle boundary, the P-wave velocities of Schuba et al. (2019) and calculated degree of serpentinization (Schuba et al., submitted) based on Christensen's (2004) 200 MPa/200<sup>o</sup>C serpentinite compilation study. </p> <p>All seismic interpretations were carried out on Petrel<sup>TM</sup> versions 2015 and 2017. The seismic reflection volume that was interpreted can be found at https://doi.org/10.1594/IEDA/500151.</p> <p> </p> <p>References: </p> <ul> <li>Christensen, N.I. (2004). Serpentinites, Peridotites, and Seismology. <em>International Geology Review</em>, <em>46</em>(9), 795-816. https://doi.org/10.2747/0020-6814.46.9.795</li> <li>Schuba, C.N., Schuba, J.P. Gray, G.G., and Davy, R.G., (2019). Interface targeted velocity estimation using machine learning. <em>Geophysical Journal International</em>, <em>218</em>(1), 45-56. https://doi.org/10.1093/gji/ggz142</li> <li>Schuba, C.N., Gray, G.G., Morgan, J.K., Schuba, J.P., and Sawyer, D.S., (submitted). Interface targeted velocity estimation using machine learning. <em>Geochemistry, Geophysics, Geosystems.</em></li> </ul>
cigChannel: A large-scale 3D seismic dataset with labeled paleochannels for advancing deep learning in seismic interpretation
<h2>Assorted channel subset of the cigChannel dataset (V1.0)</h2> <h2>Expansion package of folded and faulted structures</h2> <div> <p>This version includes the last 100 samples (400 samples in total) of the assorted channel subset. Each sample features folded and faulted structures. Explanations of the uploaded zip files are listed below: </p> </div> <ul> <li>Assorted_Channel_Ip_xx-xx.zip: Seismic impedance volumes of sample No.xx to No.xx.</li> <li>Assorted_Channel_Label_xx-xx.zip: Binary-class label volumes of sample No.xx to No.xx, where the value 0 represents the background (non-channel areas) and 1 represents the channel.</li> <li>Assorted_Channel_Seismic_xx-xx.zip: Seismic (amplitude) volumes of sample No.xx to No.xx.</li> </ul> <h3>Portal to the assorted channel subset (main package, the first 300 samples):</h3> <ul> <li>Assorted channel subset: <a href="https://doi.org/10.5281/zenodo.11044512" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11044512</a></li> </ul> <h3>Portals to the other subsets:</h3> <ul> <li>Submarine canyon subset: <a href="https://doi.org/10.5281/zenodo.11079950" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11079950</a></li> <li>Meandering channel subset: <a href="https://doi.org/10.5281/zenodo.11078794" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11078794</a></li> <li>Tributary channel network subset: <a href="https://doi.org/10.5281/zenodo.11073030" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11073030</a></li> </ul>
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