Morphometric scaling relationships in submarine channel–lobe systems

Pettinga, Luke A.; Jobe, Zane R.; Shumaker, Lauren E.; Howes, Nick

doi:10.1130/g45142.1

Cited by 25 publications

(37 citation statements)

References 16 publications

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“…The channels extending westward from the West Mariana Ridge and from the Anatahan segment of the Mariana backarc also become wider (as much as 3-5 km wide) as they transition out onto the sedimented plain. Measured relationships plotted in Pettinga et al (2018) suggest that the Mariana-scale channels could easily produce lobes ranging up to 100 m thick like those we describe at Site U1438 (Fig. 4).…”

Section: Insights From Modern Analoguessupporting

confidence: 58%

“…In the modern Mariana backarc, west of Anatahan Island (Chadwick et al, 2005) and the West Mariana Ridge (Gardner, 2010), the quality of bathymetric data decreases, so we cannot rule out the presence of depositional lobes in these settings. Several studies have amassed statistics on lobe-channel relationships for deep-sea fan systems from around the globe that allow for prediction of lobe size and thickness based on channel width (e.g., Pettinga et al, 2018). The channels extending westward from the West Mariana Ridge and from the Anatahan segment of the Mariana backarc also become wider (as much as 3-5 km wide) as they transition out onto the sedimented plain.…”

Section: Insights From Modern Analoguesmentioning

confidence: 99%

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Intra-oceanic submarine arc evolution recorded in an ~1-km-thick rear-arc succession of distal volcaniclastic lobe deposits

et al. 2021

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International Ocean Discovery Program (IODP) Expedition 351 drilled a rear-arc sedimentary succession ~50 km west of the Kyushu-Palau Ridge, an arc remnant formed by rifting during formation of the Shikoku Basin and the Izu-Bonin-Mariana arc. The ~1-km-thick Eocene to Oligocene deep-marine volcaniclastic succession recovered at Site U1438 provides a unique opportunity to study a nearly complete record of intra-oceanic arc development, from a rear-arc perspective on crust created during subduction initiation rather than supra-subduction seafloor spreading. Detailed facies analysis and definition of depositional units allow for broader stratigraphic analysis and definition of lobe elements. Patterns in gravity-flow deposit types and subunits appear to define a series of stacked lobe systems that accumulated in a rear-arc basin. The lobe subdivisions, in many cases, are a combination of a turbidite-dominated subunit and an overlying debris-flow subunit. Debris flow–rich lobe-channel sequences are grouped into four, 1.6–2 m.y. episodes, each roughly the age range of an arc volcano. Three of the episodes contain overlapping lobe facies that may have resulted from minor channel switching or input from a different source. The progressive up-section coarsening of episodes and the increasing channel-facies thicknesses within each episode suggest progressively prograding facies from a maturing magmatic arc. Submarine geomorphology of the modern Mariana arc and West Mariana Ridge provide present-day examples that can be used to interpret the morphology and evolution of the channel (or channels) that fed sediment to Site U1438, forming the sequences interpreted as depositional lobes. The abrupt change from very thick and massive debris flows to fine-grained turbidites at the unit III to unit II boundary reflects arc rifting and progressive waning of turbidity current and ash inputs. This interpretation is consistent with the geochemical record from melt inclusions and detrital zircons. Thus, Site U1438 provides a unique record of the life span of an intra-oceanic arc, from inception through maturation to its demise by intra-arc rifting and stranding of the remnant arc ridge.

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Section: Insights From Modern Analoguessupporting

confidence: 58%

Section: Insights From Modern Analoguesmentioning

confidence: 99%

Intra-oceanic submarine arc evolution recorded in an ~1-km-thick rear-arc succession of distal volcaniclastic lobe deposits

et al. 2021

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“…We do not, however, include channels in the quantitative analysis of natural data presented below, which focuses exclusively on the lobe-shaped bodies. The dimensions of channels have recently been analysed in relation to the dimensions of their concomitant lobes (Pettinga et al 2018), and the dimensions we have used in the models are compatible with the analysis of Pettinga et al…”

Section: Parameterisation Approach and Parameter Definitionsmentioning

confidence: 92%

Hierarchical parameterization and compression-based object modelling of high net:gross but poorly amalgamated deep-water lobe deposits

Manzocchi

Zhang

Haughton

et al. 2020

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Deep-water lobe deposits are arranged hierarchically and can be characterized by high net:gross ratios but poor sand connectivity due to thin, but laterally extensive, shale layers. This heterogeneity makes them difficult to represent in standard full-field object-based models, since the sands in an object-based model are not stacked compensationally and become connected at a low net:gross ratio. The compression algorithm allows the generation of low-connectivity object-based models at high net:gross ratios, by including the net:gross and amalgamation ratios as independent input parameters. Object-based modelling constrained by the compression algorithm has been included in a recursive workflow, permitting the generation of realistic models of hierarchical lobe deposits. Representative dimensional and stacking parameters collected at four different hierarchical levels have been used to constrain a 250 m-thick, 14 km2 model that includes hierarchical elements ranging from 20 cm-thick sand beds to more than 30 m-thick lobe complexes. Sand beds and the fine-grained units are represented explicitly in the model, and the characteristic facies associations often used to parameterize lobe deposits are emergent from the modelling process. The model is subsequently resampled without loss of accuracy for flow simulation, and results show clearly the influence of the hierarchical heterogeneity on drainage and sweep efficiency during a water-flood simulation.

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“…However, due to its simplicity, this scheme provides less information about sedimentary facies, sedimentary structures, and other characteristics useful for inferring depositional environment and dimensions of architectural elements (Cf. Mutti and Normark, 1987;Deptuck et al, 2008;Pettinga et al, 2018;Shumaker et al, 2018). We tested the number of lithology classes using unsupervized machine-learning techniques (e.g., k-means clustering) of the CCL data, and found that five classes is approaching the upper limit of appropriate number of clusters when comparing the silhouette scores.…”

Section: Labeling Of Core Data-lithologymentioning

confidence: 99%

Centimeter-Scale Lithology and Facies Prediction in Cored Wells Using Machine Learning

2021

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Machine-learning algorithms have been used by geoscientists to infer geologic and physical properties from hydrocarbon exploration and development wells for more than 40 years. These techniques historically utilize digital well-log information, which, like any remotely sensed measurement, have resolution limitations. Core is the only subsurface data that is true to geologic scale and heterogeneity. However, core description and analysis are time-intensive, and therefore most core data are not utilized to their full potential. Quadrant 204 on the United Kingdom Continental Shelf has publicly available open-source core and well log data. This study utilizes this dataset and machine-learning models to predict lithology and facies at the centimeter scale. We selected 12 wells from the Q204 region with well-log and core data from the Schiehallion, Foinaven, Loyal, and Alligin hydrocarbon fields. We interpreted training data from 659 m of core at the sub-centimeter scale, utilizing a lithology-based labeling scheme (five classes) and a depositional-process-based facies labeling scheme (six classes). Utilizing a “color-channel-log” (CCL) that summarizes the core image at each depth interval, our best performing trained model predicts the correct lithology with 69% accuracy (i.e., the predicted lithology output from the model is the same as the interpreted lithology) and predicts individual lithology classes of sandstone and mudstone with over 80% accuracy. The CCL data require less compute power than core image data and generate more accurate results. While the process-based facies labels better characterize turbidites and hybrid-event-bed stratigraphy, the machine-learning based predictions were not as accurate as compared to lithology. In all cases, the standard well-log data cannot accurately predict lithology or facies at the centimeter level. The machine-learning workflow developed for this study can unlock warehouses full of high-resolution data in a multitude of geological settings. The workflow can be applied to other geographic areas and deposit types where large quantities of photographed core material are available. This research establishes an open-source, python-based machine-learning workflow to analyze open-source core image data in a scalable, reproducible way. We anticipate that this study will serve as a baseline for future research and analysis of borehole and core data.

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Morphometric scaling relationships in submarine channel–lobe systems

Cited by 25 publications

References 16 publications

Intra-oceanic submarine arc evolution recorded in an ~1-km-thick rear-arc succession of distal volcaniclastic lobe deposits

Intra-oceanic submarine arc evolution recorded in an ~1-km-thick rear-arc succession of distal volcaniclastic lobe deposits

Hierarchical parameterization and compression-based object modelling of high net:gross but poorly amalgamated deep-water lobe deposits

Centimeter-Scale Lithology and Facies Prediction in Cored Wells Using Machine Learning

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