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all geography including DSDP/ODP Sites and Legs
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Africa
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East Africa
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Zimbabwe
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Western Europe
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Belgium
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Norway
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United Kingdom
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Indian Ocean
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Indian Ocean Islands
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Mediterranean Sea
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Hawaii
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Pacific Ocean
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West Pacific
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Journal Article
Integrating unsupervised learning and transformer for missing log prediction Available to Purchase
Journal: Geophysics
Publisher: Society of Exploration Geophysicists
Published: 23 June 2025
Geophysics (2025) D85–D100.
..., it significantly improved model performance in the absence of actual lithology labels. This provided a practical solution for real exploration situations where real lithology data is difficult to obtain. By integrating the unsupervised clustering method with the transformer deep-learning architecture, we...
Early Publication
Journal Article
Signal separation using unsupervised learning with local uncorrelated constraints: Applications to ground-roll contaminated data sets Available to Purchase
Journal: Geophysics
Publisher: Society of Exploration Geophysicists
Published: 08 May 2025
Geophysics (2025) V313–V323.
... by advancements in artificial intelligence (AI) technologies, we innovatively explore a signal separation method using unsupervised learning (USL) with local uncorrelated constraints and apply it to ground-roll contaminated data sets. We establish one neural network (NN) comprising two parallel branches...
Early Publication
Journal Article
Bridging Supervised and Unsupervised Learning to Build Volcano Seismicity Classifiers at Kilauea Volcano, Hawaii Available to Purchase
Journal: Seismological Research Letters
Publisher: Seismological Society of America
Published: 02 January 2024
Seismological Research Letters (2024) 95 (3): 1849–1857.
.... Here, we build supervised learning models to discriminate volcano tectonic events (VTs), long‐period events (LPs), and hybrid events in Kilauea by training with pseudolabels from unsupervised clustering. We test three different supervised models, and all of them achieve >93% accuracy. We apply...
Includes: Supplemental Content
Journal Article
Iterative deblending using unsupervised learning with double-deep neural networks Available to Purchase
Journal: Geophysics
Publisher: Society of Exploration Geophysicists
Published: 10 April 2023
Geophysics (2023) 88 (3): V187–V205.
... in conventional data processing procedures. Therefore, simultaneous source data need to be deblended to obtain the conventional shot record. Under densely sampled sources, we have developed a novel unsupervised deep learning (UDL) method based on the double-deep neural networks for iterative inversion deblending...
Series: Course Notes Series
Publisher: Society of Exploration Geophysicists
Published: 30 March 2023
DOI: 10.1190/1.9781560803898.ch4
EISBN: 9781560803898
Journal Article
Contrasting faulting styles of salt domes and volcanoes: Can unsupervised learning techniques differentiate fault styles? Available to Purchase
Journal: Interpretation
Publisher: Society of Exploration Geophysicists
Published: 27 January 2023
Interpretation (2023) 11 (1): C1–C13.
... intrusions also are essential to interpret. Volcanic cones to the untrained eye (or an unsupervised learning algorithm) worldwide have been easily mistaken for carbonate buildups ( Klarner et al., 2006 ) and remnant sandstone erosions ( Stoppa, 2012 ). Volcanic geomorphologies on the surface are evident...
Journal Article
Anatomy of Continuous Mars SEIS and Pressure Data from Unsupervised Learning Available to Purchase
Salma Barkaoui, Philippe Lognonné, Taichi Kawamura, Éléonore Stutzmann, Léonard Seydoux, Maarten V. de Hoop, Randall Balestriero, John‐Robert Scholz, Grégory Sainton, Matthieu Plasman, Savas Ceylan, John Clinton, Aymeric Spiga, Rudolf Widmer‐Schnidrig, Francesco Civilini, W. Bruce Banerdt
Publisher: Seismological Society of America
Published: 09 November 2021
Bulletin of the Seismological Society of America (2021) 111 (6): 2964–2981.
... analysis techniques, including autocorrelation. Cataloging these events has so far been made with specific algorithms and operator’s visual inspection. We investigate here the continuous data with an unsupervised deep‐learning approach built on a deep scattering network. This leads to the successful...
Includes: Supplemental Content
Journal Article
Unsupervised Learning Used in Automatic Detection and Classification of Ambient‐Noise Recordings from a Large‐ N Array Available to Purchase
Journal: Seismological Research Letters
Publisher: Seismological Society of America
Published: 13 November 2019
Seismological Research Letters (2020) 91 (1): 370–389.
...Michał Chamarczuk; Yohei Nishitsuji; Michał Malinowski; Deyan Draganov Abstract We present a method for automatic detection and classification of seismic events from continuous ambient‐noise (AN) recordings using an unsupervised machine‐learning (ML) approach. We combine classic and recently...
Journal Article
Deep carbonate reservoir characterization using multiseismic attributes: A comparison of unsupervised machine-learning approaches Available to Purchase
Journal: Geophysics
Publisher: Society of Exploration Geophysicists
Published: 05 January 2024
Geophysics (2024) 89 (2): B65–B82.
... the application of supervised machine learning (ML) for seismic reservoir characterization. Unsupervised learning methods, in contrast, explore the internal structure of data and extract low-dimensional features of geologic interest from seismic data without the need for labels. We compare various unsupervised...
Journal Article
Deterministic versus unsupervised machine learning approach for facies modeling within the Late Devonian Duvernay Formation, Western Canada Sedimentary Basin, Alberta Available to Purchase
Journal: AAPG Bulletin
Publisher: American Association of Petroleum Geologists
Published: 01 October 2024
AAPG Bulletin (2024) 108 (10): 1941–1955.
... algorithms are developed for facies prediction in wells lacking core control. In contrast, stochastic unsupervised learning analyzes and automatically clusters recurring well log data associations without calibration to core observations. Using petrophysical and core description data collected from the Late...
Journal Article
Waveform embedding: Automatic horizon picking with unsupervised deep learning Available to Purchase
Journal: Geophysics
Publisher: Society of Exploration Geophysicists
Published: 08 May 2020
Geophysics (2020) 85 (4): WA67–WA76.
...Yunzhi Shi; Xinming Wu; Sergey Fomel ABSTRACT Picking horizons from seismic images is a fundamental step that could critically impact seismic interpretation quality. We have developed an unsupervised approach, waveform embedding, based on a deep convolutional autoencoder network to learn...
Journal Article
Improving the Signal‐to‐Noise Ratio of Seismological Datasets by Unsupervised Machine Learning Available to Purchase
Journal: Seismological Research Letters
Publisher: Seismological Society of America
Published: 22 May 2019
Seismological Research Letters (2019) 90 (4): 1552–1564.
... that is based on an unsupervised machine‐learning technique. We leverage the unsupervised learning philosophy of the autoencoding method to adaptively learn the seismic signals from the noisy observations. This could potentially enable us to better represent the true seismic‐wave components. To mitigate...
Journal Article
Denoising distributed acoustic sensing data using unsupervised deep learning Available to Purchase
Journal: Geophysics
Publisher: Society of Exploration Geophysicists
Published: 13 June 2023
Geophysics (2023) 88 (4): V317–V332.
... and residual connections to attenuate various complex noises in real DAS data. The network is designed to learn the features of useful reflection signals and remove various noises in an unsupervised way, therefore enjoying the convenience of label-free processing. Our network uses several encoders and decoders...
Journal Article
Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning Open Access
Chengping Chai, Omar Marcillo, Monica Maceira, Junghyun Park, Stephen Arrowsmith, James O. Thomas, Joshua Cunningham
Journal: The Seismic Record
Publisher: Seismological Society of America
Published: 30 January 2025
The Seismic Record (2025) 5 (1): 64–72.
... have emerged to explore the seismic wavefield in these complex environments. We applied two unsupervised machine learning algorithms to analyze continuous seismic data collected from an industrial facility in Texas, United States. The Uniform Manifold Approximation and Projection for Dimension...
Includes: Supplemental Content
Journal Article
An unsupervised deep-learning method for porosity estimation based on poststack seismic data Available to Purchase
Journal: Geophysics
Publisher: Society of Exploration Geophysicists
Published: 10 November 2020
Geophysics (2020) 85 (6): M97–M105.
...Runhai Feng; Thomas Mejer Hansen; Dario Grana; Niels Balling ABSTRACT We propose to invert reservoir porosity from poststack seismic data using an innovative approach based on deep-learning methods. We develop an unsupervised approach to circumvent the requirement of large volumes of labeled data...
Journal Article
Unsupervised contrastive learning for seismic facies characterization Available to Purchase
Journal: Geophysics
Publisher: Society of Exploration Geophysicists
Published: 03 October 2022
Geophysics (2023) 88 (1): WA81–WA89.
... to leverage unsupervised contrastive learning to automatically analyze seismic facies. To obtain a stable result, we use 3D seismic cubes instead of seismic traces or their variants as inputs of networks to improve lateral consistency. In addition, we treat seismic attributes as geologic constraints and feed...
Journal Article
Unsupervised Deep Feature Learning for Icequake Discrimination at Neumayer Station, Antarctica Available to Purchase
Journal: Seismological Research Letters
Publisher: Seismological Society of America
Published: 02 January 2024
Seismological Research Letters (2024) 95 (3): 1834–1848.
...Louisa Kinzel; Tanja Fromm; Vera Schlindwein; Peter Maass Abstract Unsupervised machine learning methods are gaining attention in the seismological community as more and larger datasets of continuous waveforms are collected. Recently, contrastive learning for unsupervised feature learning has shown...
Includes: Supplemental Content
Journal Article
Uncertainty assessment in unsupervised machine-learning methods for deepwater channel seismic facies using outcrop-derived 3D models and synthetic seismic data Available to Purchase
Journal: Interpretation
Publisher: Society of Exploration Geophysicists
Published: 24 June 2024
Interpretation (2024) 12 (3): T303–T319.
...Karelia La Marca; Heather Bedle; Lisa Stright; Kurt Marfurt Abstract Unsupervised machine-learning (ML) techniques have been widely applied to analyze seismic reflection data, including the identification of seismic facies and structural features. However, interpreting the resulting clusters often...
Journal Article
Combining DAS Ambient Seismic Noise Interferometry and Unsupervised Machine Learning for 2D Imaging of an Urban Subsurface Available to Purchase
Journal: Seismological Research Letters
Publisher: Seismological Society of America
Published: 24 June 2025
Seismological Research Letters (2025)
... with this procedure, which highlights the need for an automated method to identify and remove low‐quality VSGs. To achieve this, we employ unsupervised clustering (Viens and Iwata, 2020), a machine learning method for dividing a dataset into groups with similar characteristics (Steinbach et al. , 2004...
Early Publication
Includes: Supplemental Content
Journal Article
Unsupervised boundary analysis of potential field data: A machine learning method Available to Purchase
Journal: Geophysics
Publisher: Society of Exploration Geophysicists
Published: 10 April 2023
Geophysics (2023) 88 (3): G57–G65.
...Carmine Cutaneo; Andrea Vitale; Maurizio Fedi ABSTRACT We develop a boundary analysis method, called unsupervised boundary analysis (UBA), based on machine learning algorithms applied to potential fields. Its main purpose is to create a data-driven process yielding a good estimate of the source...
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