Mass wasting on the Mississippi River subaqueous delta

Maloney, J. M.; Bentley, Samuel J.; Xu, Kehui; Obelcz, J.; Georgiou, Ioannis Y.; Jafari, Navid H.; Miner, Michael D.

doi:10.1016/j.earscirev.2019.103001

Cited by 27 publications

(41 citation statements)

References 97 publications

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“…Coastal zones that experience high sedimentation are more susceptible to develop slope stability issues, as the weight of the newly added sediment increases shear stress which can lead to slope failure and trigger powerful downslope flows in already unstable area even with a very low gradient slope (Coleman and Garrison 1977;Clare et al 2016;Maloney et al 2018Maloney et al , 2020. Additionally, newly deposited sediment lacks the consolidated cohesive strength of older sediment, compounding the problem of an already unstable seafloor (Obelcz et al 2017).…”

Section: Discussionmentioning

confidence: 99%

Oceanic sediment accumulation rates predicted via machine learning algorithm: towards sediment characterization on a global scale

2020

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Observed vertical sediment accumulation rates (n = 1031) were gathered from ~ 55 years of peer reviewed literature. Original methods of rate calculation include long-term isotope geochronology (14C, 210Pb, and 137Cs), pollen analysis, horizon markers, and box coring. These observations are used to create a database of global, contemporary vertical sediment accumulation rates. Rates were converted to cm year−1, paired with the observation’s longitude and latitude, and placed into a machine learning–based Global Predictive Seabed Model (GPSM). GPSM finds correlations between the data and established global “predictors” (quantities known or estimable everywhere, e.g., distance from coastline and river mouths). The result, using a k-nearest neighbor (k-NN) algorithm, is a 5-arc-minute global map of predicted benthic vertical sediment accumulation rates. The map generated provides a global reference for vertical sedimentation from coastal to abyssal depths. Areas of highest sedimentation, ~ 3–8 cm year−1, are generally river mouth proximal coastal zones draining relatively large areas with high maximum elevations and with wide, shallow continental shelves (e.g., the Gulf of Mexico and the Amazon Delta), with rates falling exponentially towards the deepest parts of the oceans. The exception is Oceania, which displays significant vertical sedimentation over a large area without draining the large drainage basins seen in other regions. Coastal zones with relatively small drainage basins and steep shelves display vertical sedimentation of ~ 1 cm year−1, which is limited to the near shore when compared with shallow, wide margins (e.g., the western coasts of North and South America). Abyssal depth rates are functionally zero at the time scale examined (~ 10−4 cm year−1) and increase one order of magnitude near the Mid-Atlantic Ridge and at the Galapagos Triple Junction.

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Section: Discussionmentioning

confidence: 99%

Oceanic sediment accumulation rates predicted via machine learning algorithm: towards sediment characterization on a global scale

2020

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“…As Mississippi Delta sediment accumulated over tens of millions of years, heat and pressure turned organic material into oil and gas, which rose through the water-saturated sediments until reaching a porous and permeable sand unit capped with an impermeable layer such as clay, forming hydrocarbon reservoirs. The subsurface is complex, rendered yet more so because the masses of river-derived sand are encased in muds so soft and plastic that the sheer weight of overlying sediment makes the sedimentary deposits deform, causing subsidence, slumping, and underwater landslides (Maloney et al, 2020). During the Anthropocene, these landslides pose hazards to oil and gas infrastructure that has increasingly developed in areas prone to such slope instabilities (Chaytor et al, 2020).…”

Section: The Mississippi River As a Modelmentioning

confidence: 99%

Geological evolution of the Mississippi River into the Anthropocene

Russell

Waters

Himson

et al. 2021

The Anthropocene Review

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The Mississippi River maintains commercial and societal networks of the USA along its >3700 km length. It has accumulated a fluvial sedimentary succession over 80 million years. Through the last 11,700 years of the Holocene Epoch, the wild river shaped the landscape, models of which have become classic in geological studies of ancient river strata. Studies of the river were led by the need to develop infrastructure and to search for hydrocarbons, through which, these models have become quite sophisticated. However, whilst the models demonstrate how the wild river behaves, a monumental shift in fundamental controls on the entire fluvial system, broadly coinciding with the proposed mid-20th century onset of the Anthropocene Epoch, has generated new geological patterns that are becoming globally ubiquitous, and which the Mississippi River typifies. As such, whilst classic Holocene river models may be compared to human-modified systems such as the Lower Mississippi River (and others worldwide), locally the models may now only directly apply to its fossilized components preserved in the sub-surface. Such river models need adapting to better understand the present dynamics, and future evolution of these landscapes.

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“…Depth change on the MRDF is primarily controlled by three factors: (1) seabed movement within mudflow zones, (2) self‐weight sediment compaction, and (3) accretion via river plume sediment deposition (Maloney et al, 2020). The latter two factors are relatively invariant over the ~100 km 2 prediction domain (Figure 1b) and produce an order of magnitude less depth change (decimeters/year vs. meters/year) than instability‐driven seabed movement over decadal timescales (Keller et al, 2017; Figure 2 in Obelcz et al, 2017; Tornqvist et al, 2008).…”

Section: Study Site and Datamentioning

confidence: 99%

“…Depth change on the MRDF is primarily controlled by three factors: (1) seabed movement within mudflow zones, (2) self-weight sediment compaction, and (3) accretion via river plume sediment deposition (Maloney et al, 2020). The latter two factors are relatively invariant over the~100 km 2 prediction domain ( Figure 1b…”

Section: Depth Change Observationsmentioning

confidence: 99%

Machine Learning Augmented Time‐Lapse Bathymetric Surveys: A Case Study From the Mississippi River Delta Front

Obelcz

Wood

Phrampus

et al. 2020

Geophysical Research Letters

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The subaqueous Mississippi River Delta Front is prone to seabed instabilities >1 m of vertical bathymetric change per year, but the ability to predict the location and magnitude of instability-driven depth change is limited. Here we demonstrate that data-driven geospatial models can predict MRDF depth change from a small amount (1% of full coverage) of training data. We predict depth change at 100 m 2 resolution between 2005 and 2017 over a~100 km 2 area. Models trained on~1% of full-coverage depth change data produce comparable and relatively low average predicted depth change errors (1-2 cm). K-nearest neighbors best reproduce the spatial variability of depth change and can interpolate and extrapolate from training data. This approach has immediate applications for geohazard monitoring on the MRDF and other geologically similar settings and can be applied in other settings if the drivers of depth change variance are well known.Plain Language Summary Many river deltas worldwide have unstable seabeds that move vertically and laterally on yearly intervals, threatening seafloor infrastructure such as communication cables and pipelines. A common method to measure seabed movement is with repeat bathymetric surveys, which are expensive and require considerable expertise. Here we introduce a new method that greatly reduces the resurvey data required to assess seabed instability: using geospatial models to estimate depth change where it is not measured. We test three different models trained on a small amount (1% of complete resurvey coverage) of Mississippi River Delta Front (MRDF) depth change data and find the predicted depth change is comparably accurate (1-2 cm error) for all methods. We recommend the K-nearest neighbors machine learning method of the three, as it produces the most geologically realistic estimates and can predict depth change from multiple types of resurvey data. This method is applicable to MRDF-like deltas and can be modified to predict depth change in different geologic environments as well.

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Mass wasting on the Mississippi River subaqueous delta

Cited by 27 publications

References 97 publications

Oceanic sediment accumulation rates predicted via machine learning algorithm: towards sediment characterization on a global scale

Oceanic sediment accumulation rates predicted via machine learning algorithm: towards sediment characterization on a global scale

Geological evolution of the Mississippi River into the Anthropocene

Machine Learning Augmented Time‐Lapse Bathymetric Surveys: A Case Study From the Mississippi River Delta Front

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