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unsupervised learning

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Journal Article
Journal: Geophysics
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...
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Journal Article
Journal: Geophysics
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...
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Journal Article
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...
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Journal Article
Journal: Geophysics
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...
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Book Chapter

Series: Course Notes Series
Published: 30 March 2023
DOI: 10.1190/1.9781560803898.ch4
EISBN: 9781560803898
Journal Article
Journal: Interpretation
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...
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Journal Article
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...
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Journal Article
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...
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Journal Article
Journal: Geophysics
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...
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Journal Article
Journal: AAPG Bulletin
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...
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Journal Article
Journal: Geophysics
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...
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Journal Article
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...
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Journal Article
Journal: Geophysics
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...
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Journal Article
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...
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Journal Article
Journal: Geophysics
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...
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Journal Article
Journal: Geophysics
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...
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Journal Article
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...
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Journal Article
Journal: Interpretation
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...
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Journal Article
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...
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Journal Article
Journal: Geophysics
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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