Principles of seismic stratigraphy and seismic geomorphology I: Extracting geologic insights from seismic data

Posamentier, Henry W.; Paumard, Victorien; Lang, Simon

doi:10.1016/j.earscirev.2022.103963

Cited by 32 publications

(5 citation statements)

References 137 publications

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“…We used GeoEast and the paradigm for seismic data processing. Seismic interpretation is carried out by identifying key seismic reflectors, which may be continuous or discontinuous [55]. Through the continuous reflectors and logging data, the depth and thickness of the strata can be effectively identified, and the regional structure can be determined [56].…”

Section: Methods and Datamentioning

confidence: 99%

Tectonic Features of the Wufeng–Longmaxi Formation in the Mugan Area, Southwestern Sichuan Basin, China, and Implications for Shale Gas Preservation

et al. 2022

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Shale gas resources in mainland China and its commercial exploitation has been widely focused on the Wufeng–Longmaxi Formation organic-matter-rich shale in the Sichuan Basin. However, whether southwestern margin of the Sichuan Basin can produce high-quality shale gas has not been well resolved, which might be related to the poor understanding of the relationship between Cenozoic tectonic deformation and shale gas preservation. To answer the aforementioned scientific question, we conducted a detailed work in the Mugan area to show geologic structures and gas contents in the area through seismic profiles and geochemistry analysis. Specifically, the stable Mugan syncline shows a high gas content (>2.6 m3/t measured at three boreholes D1, D2, and D3), whereas its periphery presents a poor gas content (about 0.6 m3/t measured at two boreholes X1 and Y1). Moreover, oblique fracture density and dissolved pores are much higher at boreholes X1 and Y1 than that at the other three boreholes. We propose an opposite-verging thrust fault model to explain the different gas contents and tectonic features in the Mugan area, which might indicate that regions in the southwestern Sichuan Basin with similar tectonic and stratigraphic characteristics as those in the Mugan syncline are likely to produce high-yield shale gas. This finding provides new insights into the exploration theory of shale gas in the Tibetan Plateau.

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Section: Methods and Datamentioning

confidence: 99%

Tectonic Features of the Wufeng–Longmaxi Formation in the Mugan Area, Southwestern Sichuan Basin, China, and Implications for Shale Gas Preservation

et al. 2022

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“…Such geomorphologies are present in BBR (Figure 8f; James et al, 1976), which is therefore not an exact analogue for Ridge 1. Additionally, Ridge 1 is continuous and there are no indicators of sediment flow, such as inter-platform seaway, reef passes or slope debris, in contrast to other reefs described from seismic data (e.g., Courgeon et al, 2016;Posamentier et al, 2010Posamentier et al, , 2022Schlager, 2005). Drowned reefal ridges described in the literature tend to be only a few 100's metres or a few kilometres long (e.g., Jorry et al, 2016;Khanna et al, 2017;Mallarino et al, 2021;Rovere et al, 2018) or to be composed of joint and isolated pinnacles (Abbey et al, 2011), and as such are dissimilar to Ridge 1.…”

Section: F I G U R Ementioning

confidence: 99%

Differentiating reefal ridges from relict coastal ridges: Lessons from the seismic geomorphologic study of buried Miocene buildups (North West Shelf, Australia)

et al. 2023

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Linear buildups formed in tropical carbonate environments are often interpreted as bioconstructed reefs. Nevertheless, coastal processes can also form extensive sedimentary ridges exhibiting buildup morphologies. This study investigates two Miocene ridges developed along the Australian North West Shelf using 3D seismic and well data. Ridge 1 is ca. 30 m thick and >60 km long, and it is made of foraminiferal pack‐grainstones. It protects a lagoon with pinnacle morphologies. Ridge 2 is ca. 150 m thick and >80 km long. It is composed of quartz sand forming lobes. Both ridges have a continuous curvilinear front and are in a mid‐shelf setting. They mimic the modern Australian coastline. It is then proposed that Ridge 1 is either: (1) a barrier reef developed on a drowned shoreline, or (2) stacked carbonate aeolianites and beachrocks acting as a barrier. Ridge 2 is interpreted as stacked deltaic sands. This study demonstrates that lithified and buried coastal features of carbonate and siliciclastic nature can form extensive ridges exhibiting buildup morphologies. It is proposed that ridges formed by stacked coastal features are overall continuous with a curvilinear front, while reefal ridges are more discontinuous and exhibit deeper and more stable passes.

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“…A robust evaluation and metrics section provides a thorough evaluation using various metrics such as the Silhouette (Shutaywi & Kachouie, 2021) and Davies-Bouldin (Posamentier, Paumard, & Lang, 2022) scores, offering a quantitative and objective assessment of the methodologies employed. The in-depth comparative analysis contrasts the integrated computational approach with traditional seismic analysis methods, highlighting the improvements and advancements offered by the former.…”

Section: Introductionmentioning

confidence: 99%

Advanced Seismic Data Analysis: Comparative study of Machine Learning and Deep Learning for Data Prediction and Understanding

Airlangga

2024

Brilliance

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This study delves into the application of machine learning (ML) and deep learning (DL) techniques for the analysis of seismic data, aiming to identify and categorize patterns and anomalies within seismic events. Using a robust dataset, we applied three distinct clustering approaches: K-Means, DBSCAN, and an Autoencoder-based method, each offering unique perspectives on the data. K-Means clustering provided a fundamental partitioning of the data into five predefined clusters, facilitating the identification of broad seismic patterns. DBSCAN, a density-based clustering algorithm, offered insights into the spatial distribution and density of seismic events, adeptly pinpointing anomalies and outliers that signify unusual seismic activity. The Autoencoder, leveraging deep learning, excelled in capturing complex and non-linear relationships within the data, revealing subtle patterns not immediately apparent through traditional methods. The effectiveness of these clustering techniques was quantitatively evaluated using the Silhouette Score and the Davies-Bouldin Score, alongside visual assessments through PCA and t-SNE for dimensionality reduction. The results indicated that while K-Means provided clear partitioning, DBSCAN excelled in outlier detection, and the Autoencoder offered a balanced approach with its nuanced analysis capabilities. Our comprehensive analysis underscores the significance of employing a multi-methodological approach in seismic data analysis, as each method contributes uniquely to the understanding of seismic events. The insights gained from this study are valuable for enhancing predictive models and improving disaster risk management strategies in seismology. Future research directions include the integration of additional seismic features, validation against larger datasets, and the development of hybrid models to further refine the predictive accuracy of seismic event analysis.

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Principles of seismic stratigraphy and seismic geomorphology I: Extracting geologic insights from seismic data

Cited by 32 publications

References 137 publications

Tectonic Features of the Wufeng–Longmaxi Formation in the Mugan Area, Southwestern Sichuan Basin, China, and Implications for Shale Gas Preservation

Tectonic Features of the Wufeng–Longmaxi Formation in the Mugan Area, Southwestern Sichuan Basin, China, and Implications for Shale Gas Preservation

Differentiating reefal ridges from relict coastal ridges: Lessons from the seismic geomorphologic study of buried Miocene buildups (North West Shelf, Australia)

Advanced Seismic Data Analysis: Comparative study of Machine Learning and Deep Learning for Data Prediction and Understanding

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