Articles | Volume 17, issue 2
https://doi.org/10.5194/essd-17-595-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-595-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
10 Feb 2025
Data description paper |
| 10 Feb 2025
| 10 Feb 2025
cigFacies: a massive-scale benchmark dataset of seismic facies and its application
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
Xiaoming Sun
Institute of Advanced Technology, University of Science and Technology of China, Hefei, China
Mingcai Hou
Institute of Sedimentary Geology, Chengdu University of Technology, Chengdu, China
State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu, China
Hang Gao
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
Guangyu Wang
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
Hanlin Sheng
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
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Wenbin Tang, Ji Zhou, Jin Ma, Ziwei Wang, Lirong Ding, Xiaodong Zhang, and Xu Zhang
Earth Syst. Sci. Data, 16, 387–419, https://doi.org/10.5194/essd-16-387-2024, https://doi.org/10.5194/essd-16-387-2024, 2024
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Arno Zang, Peter Niemz, Sebastian von Specht, Günter Zimmermann, Claus Milkereit, Katrin Plenkers, and Gerd Klee
Earth Syst. Sci. Data, 16, 295–310, https://doi.org/10.5194/essd-16-295-2024, https://doi.org/10.5194/essd-16-295-2024, 2024
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Quetzalcoatl Rodríguez-Pérez and F. Ramón Zúñiga
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Iman R. Kivi, Auregan Boyet, Haiqing Wu, Linus Walter, Sara Hanson-Hedgecock, Francesco Parisio, and Victor Vilarrasa
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Claudia Finger, Marco P. Roth, Marco Dietl, Aileen Gotowik, Nina Engels, Rebecca M. Harrington, Brigitte Knapmeyer-Endrun, Klaus Reicherter, Thomas Oswald, Thomas Reinsch, and Erik H. Saenger
Earth Syst. Sci. Data, 15, 2655–2666, https://doi.org/10.5194/essd-15-2655-2023, https://doi.org/10.5194/essd-15-2655-2023, 2023
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Jia-Hao Li, Zhao-Liang Li, Xiangyang Liu, and Si-Bo Duan
Earth Syst. Sci. Data, 15, 2189–2212, https://doi.org/10.5194/essd-15-2189-2023, https://doi.org/10.5194/essd-15-2189-2023, 2023
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The Advanced Very High Resolution Radiometer (AVHRR) is the only sensor that has the advantages of frequent revisits (twice per day), relatively high spatial resolution (4 km at the nadir), global coverage, and easy access prior to 2000. This study developed a global historical twice-daily LST product for 1981–2021 based on AVHRR GAC data. The product is suitable for detecting and analyzing climate changes over the past 4 decades.
Konstantinos Michailos, György Hetényi, Matteo Scarponi, Josip Stipčević, Irene Bianchi, Luciana Bonatto, Wojciech Czuba, Massimo Di Bona, Aladino Govoni, Katrin Hannemann, Tomasz Janik, Dániel Kalmár, Rainer Kind, Frederik Link, Francesco Pio Lucente, Stephen Monna, Caterina Montuori, Stefan Mroczek, Anne Paul, Claudia Piromallo, Jaroslava Plomerová, Julia Rewers, Simone Salimbeni, Frederik Tilmann, Piotr Środa, Jérôme Vergne, and the AlpArray-PACASE Working Group
Earth Syst. Sci. Data, 15, 2117–2138, https://doi.org/10.5194/essd-15-2117-2023, https://doi.org/10.5194/essd-15-2117-2023, 2023
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We examine the spatial variability of the crustal thickness beneath the broader European Alpine region by using teleseismic earthquake information (receiver functions) on a large amount of seismic waveform data. We compile a new Moho depth map of the broader European Alps and make our results freely available. We anticipate that our results can potentially provide helpful hints for interdisciplinary imaging and numerical modeling studies.
Muhammad Rizwan Asif, Nikolaj Foged, Thue Bording, Jakob Juul Larsen, and Anders Vest Christiansen
Earth Syst. Sci. Data, 15, 1389–1401, https://doi.org/10.5194/essd-15-1389-2023, https://doi.org/10.5194/essd-15-1389-2023, 2023
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To apply a deep learning (DL) algorithm to electromagnetic (EM) methods, subsurface resistivity models and/or the corresponding EM responses are often required. To date, there are no standardized EM datasets, which hinders the progress and evolution of DL methods due to data inconsistency. Therefore, we present a large-scale physics-driven model database of geologically plausible and EM-resolvable subsurface models to incorporate consistency and reliability into DL applications for EM methods.
Médéric Gravelle, Guy Wöppelmann, Kevin Gobron, Zuheir Altamimi, Mikaël Guichard, Thomas Herring, and Paul Rebischung
Earth Syst. Sci. Data, 15, 497–509, https://doi.org/10.5194/essd-15-497-2023, https://doi.org/10.5194/essd-15-497-2023, 2023
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We produced a reanalysis of GNSS data near tide gauges worldwide within the International GNSS Service. It implements advances in data modelling and corrections, extending the record length by about 7 years. A 28 % reduction in station velocity uncertainties is achieved over the previous solution. These estimates of vertical land motion at the coast supplement data from satellite altimetry or tide gauges for an improved understanding of sea level changes and their impacts along coastal areas.
Michal Kruszewski, Gerd Klee, Thomas Niederhuber, and Oliver Heidbach
Earth Syst. Sci. Data, 14, 5367–5385, https://doi.org/10.5194/essd-14-5367-2022, https://doi.org/10.5194/essd-14-5367-2022, 2022
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The authors assemble an in situ stress magnitude and orientation database based on 429 hydrofracturing tests that were carried out in six coal mines and two coal bed methane boreholes between 1986 and 1995 within the greater Ruhr region (Germany). Our study summarises the results of the extensive in situ stress test campaign and assigns quality to each data record using the established quality ranking schemes of the World Stress Map project.
Andrea Rovida, Andrea Antonucci, and Mario Locati
Earth Syst. Sci. Data, 14, 5213–5231, https://doi.org/10.5194/essd-14-5213-2022, https://doi.org/10.5194/essd-14-5213-2022, 2022
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EPICA is the 1000–1899 catalogue compiled for the European Seismic Hazard Model 2020 and contains 5703 earthquakes with Mw ≥ 4.0. It relies on the data of the European Archive of Historical Earthquake Data (AHEAD), both macroseismic intensities from historical seismological studies and parameters from regional catalogues. For each earthquake, the most representative datasets were selected and processed in order to derive harmonised parameters, both from intensity data and parametric catalogues.
Suqin Zhang, Changhua Fu, Jianjun Wang, Guohao Zhu, Chuanhua Chen, Shaopeng He, Pengkun Guo, and Guoping Chang
Earth Syst. Sci. Data, 14, 5195–5212, https://doi.org/10.5194/essd-14-5195-2022, https://doi.org/10.5194/essd-14-5195-2022, 2022
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The Sheshan observatory has nearly 150 years of observation history, and its observation data have important scientific value. However, with time, these precious historical data face the risk of damage and loss. We have carried out a series of rescues on the historical data of the Sheshan observatory. New historical datasets were released, including the quality-controlled absolute hourly mean values of three components (D, H, and Z) from 1933 to 2019.
Guoyu Li, Wei Ma, Fei Wang, Huijun Jin, Alexander Fedorov, Dun Chen, Gang Wu, Yapeng Cao, Yu Zhou, Yanhu Mu, Yuncheng Mao, Jun Zhang, Kai Gao, Xiaoying Jin, Ruixia He, Xinyu Li, and Yan Li
Earth Syst. Sci. Data, 14, 5093–5110, https://doi.org/10.5194/essd-14-5093-2022, https://doi.org/10.5194/essd-14-5093-2022, 2022
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A permafrost monitoring network was established along the China–Russia crude oil pipeline (CRCOP) route at the eastern flank of the northern Da Xing'anling Mountains in Northeast China. The resulting datasets fill the gaps in the spatial coverage of mid-latitude mountain permafrost databases. Results show that permafrost warming has been extensively observed along the CRCOP route, and local disturbances triggered by the CRCOPs have resulted in significant permafrost thawing.
Alessandro Cicoira, Samuel Weber, Andreas Biri, Ben Buchli, Reynald Delaloye, Reto Da Forno, Isabelle Gärtner-Roer, Stephan Gruber, Tonio Gsell, Andreas Hasler, Roman Lim, Philippe Limpach, Raphael Mayoraz, Matthias Meyer, Jeannette Noetzli, Marcia Phillips, Eric Pointner, Hugo Raetzo, Cristian Scapozza, Tazio Strozzi, Lothar Thiele, Andreas Vieli, Daniel Vonder Mühll, Vanessa Wirz, and Jan Beutel
Earth Syst. Sci. Data, 14, 5061–5091, https://doi.org/10.5194/essd-14-5061-2022, https://doi.org/10.5194/essd-14-5061-2022, 2022
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This paper documents a monitoring network of 54 positions, located on different periglacial landforms in the Swiss Alps: rock glaciers, landslides, and steep rock walls. The data serve basic research but also decision-making and mitigation of natural hazards. It is the largest dataset of its kind, comprising over 209 000 daily positions and additional weather data.
Xiaoli Chang, Huijun Jin, Ruixia He, Yanlin Zhang, Xiaoying Li, Xiaoying Jin, and Guoyu Li
Earth Syst. Sci. Data, 14, 3947–3959, https://doi.org/10.5194/essd-14-3947-2022, https://doi.org/10.5194/essd-14-3947-2022, 2022
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Based on 10-year observations of ground temperatures in seven deep boreholes in Gen’he, Mangui, and Yituli’he, a wide range of mean annual ground temperatures at the depth of 20 m (−2.83 to −0.49 ℃) and that of annual maximum thawing depth (about 1.1 to 7.0 m) have been revealed. This study demonstrates that most trajectories of permafrost changes in Northeast China are ground warming and permafrost degradation, except that the shallow permafrost is cooling in Yituli’he.
Alice C. Frémand, Julien A. Bodart, Tom A. Jordan, Fausto Ferraccioli, Carl Robinson, Hugh F. J. Corr, Helen J. Peat, Robert G. Bingham, and David G. Vaughan
Earth Syst. Sci. Data, 14, 3379–3410, https://doi.org/10.5194/essd-14-3379-2022, https://doi.org/10.5194/essd-14-3379-2022, 2022
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This paper presents the release of large swaths of airborne geophysical data (including gravity, magnetics, and radar) acquired between 1994 and 2020 over Antarctica by the British Antarctic Survey. These include a total of 64 datasets from 24 different surveys, amounting to >30 % of coverage over the Antarctic Ice Sheet. This paper discusses how these data were acquired and processed and presents the methods used to standardize and publish the data in an interactive and reproducible manner.
Cited articles
Chen, L., Lu, Y.-C., Guo, T.-L., and Deng, L.-S.: Growth characteristics of Changhsingian (Late Permian) carbonate platform margin reef complexes in Yuanba gas Field, northeastern Sichuan Basin, China, Geol. J., 47, 524–536, 2012. a
Duan, Y., Zheng, X., Hu, L., and Sun, L.: Seismic facies analysis based on deep convolutional embedded clustering, Geophysics, 84, IM87–IM97, 2019. a
Dunham, M., Malcolm, A., and Welford, J.: Toward a semisupervised machine learning application to seismic facies classification, in: EAGE 2020 Annual Conference & Exhibition Online, 2020, 1–5, European Association of Geoscientists & Engineers, 2020. a
Fensel, D., Simsek, U., Angele, K., Huaman, E., Kärle, E., Panasiuk, O., Toma, I., Umbrich, J., and Wahler, A.: Knowledge graphs, Springer, https://doi.org/10.1007/978-3-030-37439-6, 2020. a
Gao, H., Wu, X., Sun, X., and Hou, M.: cigFacies datasets: the massive-scale benchmark dataset of seismic facies, Zenodo [data set], https://doi.org/10.5281/zenodo.10777460, 2024a. a, b, c
Gao, H., Wu, X., Sun, X., and Hou, M.: cigFacies codes: cigFaciesNet for data generation and model training, Zenodo [code], https://doi.org/10.5281/zenodo.13150879, 2024b. a, b
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C.: Improved training of wasserstein gans, ArXiv [preprint], 30, https://doi.org/10.48550/arXiv.1803.01541, 2017. a
He, K., Zhang, X., Ren, S., and Sun, J.: Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 26 June–1 July 2016, Las Vegas, USA, 770–778, https://doi.org/10.48550/arXiv.1512.03385, 2016. a
Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., Melo, G. D., 75 Gutierrez, C., Kirrane, S., Gayo, J. E. L., Neumaier, S., Polleres, A., Navigli, R., Ngomo, A.-C. N., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., and Zimmermann, A.: Knowledge graphs, ACM Computing Surveys (Csur), 54, 1–37, https://doi.org/10.1145/3447772, 2021. a
Hu, X., Xu, Y., Ma, X., Zhu, Y., Ma, C., Li, C., Lü, H., Wang, X., Zhou, C., and Wang, C.: Knowledge System, Ontology, and Knowledge Graph of the Deep-Time Digital Earth (DDE): Progress and Perspective, J. Earth Sci., 34, 1323–1327, 2023. a
Jia, C. Z., Zhao, W. Z., Zou, C. N., Feng, Z. Q., Yuan, X. J., Chi, Y. L., Tao, S. Z., and Xue, S. H.: Geological Theory and Exploration Technology for Lithostratigraphic Hydrocarbon Reservoirs, Pet. Explor. Develop., 34, 257–272, 2007. a
Karras, T., Aila, T., Laine, S., and Lehtinen, J.: Progressive Growing of GANs for Improved Quality, Stability, and Variation, ArXiv [preprint], abs/1710.10196, https://doi.org/10.48550/arXiv.1710.10196, 2017. a
Li, J., Wu, X., Ye, Y., Yang, C., Hu, Z., Sun, X., and Zhao, T.: Unsupervised contrastive learning for seismic facies characterization, Geophysics, 88, WA81–WA89, 2023. a
Liu, J., Dai, X., Gan, L., Liu, L., and Lu, W.: Supervised seismic facies analysis based on image segmentation, Geophysics, 83, O25–O30, 2018. a
Liu, M., Jervis, M., Li, W., and Nivlet, P.: Seismic facies classification using supervised convolutional neural networks and semisupervised generative adversarial networks, Geophysics, 85, O47–O58, 2020. a
Ma, C., Kale, A. S., Zhang, J., and Ma, X.: A knowledge graph and service for regional geologic time standards, Geosci. Front., 14, 101453, https://doi.org/10.1016/j.gsf.2022.101453, 2023. a
Mitchum Jr., R. M., Vail, P. R., and Sangree, J. B.: Seismic stratigraphy and global changes of sea level: Part 6. Stratigraphic interpretation of seismic reflection patterns in depositional sequences: Section 2. Application of seismic reflection configuration to stratigraphic interpretation, AAPG Bulletin, 26, 117–133, https://doi.org/10.1306/M26490C8, 1977a. a, b
Mitchum Jr., R. M., Vail, P. R., and Thompson III, S.: Seismic stratigraphy and global changes of sea level: Part 2. The depositional sequence as a basic unit for stratigraphic analysis: Section 2. Application of seismic reflection configuration to stratigraphic interpretation, AAPG Bulletin, 26, 53–62, https://doi.org/10.1306/M26490C4, 1977b. a, b
Paulheim, H.: Knowledge graph refinement: A survey of approaches and evaluation methods, Semantic web, 8, 489–508, 2017. a
Puzyrev, V. and Elders, C.: Unsupervised seismic facies classification using deep convolutional autoencoder, Geophysics, 87, IM125–IM132, 2022. a
Qi, J., Lin, T., Zhao, T., Li, F., and Marfurt, K.: Semisupervised multiattribute seismic facies analysis, Interpretation, 4, SB91–SB106, 2016. a
Qian, F., Yin, M., Liu, X.-Y., Wang, Y.-J., Lu, C., and Hu, G.-M.: Unsupervised seismic facies analysis via deep convolutional autoencoders, Geophysics, 83, A39–A43, 2018. a
Sangree, J. and Widmier, J.: Seismic stratigraphy and global changes of sea level: Part 9. Seismic interpretation of clastic depositional facies: Section 2. Application of seismic reflection configuration to stratigraphic interpretation, AAPG Bulletin, 62, 752–771, https://doi.org/10.1306/C1EA4E46-16C9-11D7-8645000102C1865D, 1977. a
Sheriff, R.: Inferring stratigraphy from seismic data, AAPG Bulletin, 60, 528–542, 1976. a
Tan, L., Liu, H., Tang, Y., Luo, B., Zhang, Y., Yang, Y., Liao, Y., Du, W., and Yang, X.: Characteristics and mechanism of Upper Permian reef reservoirs in the eastern Longgang Area, northeastern Sichuan Basin, China, Petroleum, 6, 130–137, 2020. a
Wrona, T., Pan, I., Gawthorpe, R. L., and Fossen, H.: Seismic facies analysis using machine learning, Geophysics, 83, O83–O95, 2018. a
Wu, X. and Fomel, S.: Least-squares horizons with local slopes and multigrid correlations, Geophysics, 83, IM29–IM40, 2018. a
Xu, G. and Haq, B. U.: Seismic facies analysis: Past, present and future, Earth-Sci. Rev., 224, 103876, https://doi.org/10.1016/j.earscirev.2021.103876, 2022. a, b, c
Xu, G., Xie, G., Long, K., and Song, X.: Sedimentary features and exploration targets of Middle Permian reservoirs in the SW Sichuan Basin, Natural Gas Industry B, 2, 415–420, 2015. a
Zhang, H., Chen, T., Liu, Y., Zhang, Y., and Liu, J.: Automatic seismic facies interpretation using supervised deep learning, Geophysics, 86, IM15–IM33, 2021. a
Zhang, L., Hou, M., Chen, A., Zhong, H., Ogg, J. G., and Zheng, D.: Construction of a fluvial facies knowledge graph and its application in sedimentary facies identification, Geosci. Front., 14, 101521, https://doi.org/10.1016/j.gsf.2022.101521, 2023. a
Zhao, T.: Seismic facies classification using different deep convolutional neural networks, in: SEG International Exposition and Annual Meeting, 14–19 October 2018, Anaheim, California, USA, SEG–2018, SEG, https://doi.org/10.1190/segam2018-2997085.1, 2018. a
Zhao, T., Li, F., and Marfurt, K. J.: Seismic attribute selection for unsupervised seismic facies analysis using user-guided data-adaptive weights, Geophysics, 83, O31–O44, 2018. a
Zhou, C., Wang, H., Wang, C., Hou, Z., Zheng, Z., Shen, S., Cheng, Q., Feng, Z., Wang, X., Lv, H., Fan, J., Hu, X., Hou, M., and Zhu, Y.: Geoscience knowledge graph in the big data era, Sci. China Earth Sci., 64, 1105–1114, 2021. a
Short summary
We propose three strategies for field seismic data curation, knowledge-guided synthesization, and generative adversarial network (GAN)-based generation to construct a massive-scale, feature-rich, and high-realism benchmark dataset of seismic facies and evaluate its effectiveness in training a deep-learning model for automatic seismic facies classification.
We propose three strategies for field seismic data curation, knowledge-guided synthesization,...
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