The efficacy of kinematic indicators in a complexly deformed Mass Transport Deposit: Insights from the deepwater Taranaki Basin, New Zealand

Omeru, Tuviere; Cartwright, Joseph Albert

doi:10.1016/j.marpetgeo.2019.04.037

Cited by 15 publications

(8 citation statements)

References 30 publications

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“…Apart from the presence of rafted blocks in some of the deposits, headwall and toe domains are not seen in these MTDs. Moreover, Omeru and Cartwright, (2019) showed that the entire length and extent of these MTDs are not covered by the Romney 3D survey. Despite these challenges, it is likely that all the MTDs (MTD 1, 2, 3, 5) are composed of two or more deposits that have coalesced to form Mass Transport Complexes (MTC).…”

Section: Discussionmentioning

confidence: 99%

“…In earlier works by Nwoko et al (2020a) and Omeru and Cartwright (2019), the convolute fabrics in the western part of MTD 3 were interpreted as thrust-fold belt/pressure ridges. This work, however, demonstrates that these fabrics are associated with shear bands and deformation of the rafted blocks (Figures 9,10,12 and 14).…”

Section: Kinematic Indicators Definitions Referencesmentioning

confidence: 99%

“…The MTDs (1-5) discussed in this work were previously studied for their internal characteristics, upper topographies and implication on deepwater sedimentation (cf. Bull et al, 2019;Bull et al, 2020;Nwoko et al, 2020aNwoko et al, , 2020bOmeru & Cartwright, 2019;Rusconi, 2017). However, this work differs from these earlier works has it focused on detailed analyses of the BSZ features and their use as geomorphologic tools for interpreting the likely flow types during mass movement.…”

Section: Interpreted Mtds and Their Associated Seismic Faciesmentioning

confidence: 99%

“…These BSZ may range from a few millimetres to several tens of metres in thickness (Alves & Lourenço, 2010; Sobiesiak et al., 2018) but yet are ancient archives of geomorphologic and kinematic features such as headwall scarp (Frey Martinez et al., 2005; Prior et al., 1984), lateral margins (Gawthorpe & Clemmey, 1985; Martinsen, 1994), ramps and flats (Lucente & Pini, 2003; Omosanya & Alves, 2013a; Trincardi & Argnani, 1990), grooves (Eruteya et al., 2020; Gee et al., 2005, 2006; Nissen et al., 1999; Prior et al., 1984), scours (Moscardelli et al., 2006) and monkey fingers/cat‐claw scours (Moscardelli et al., 2006). As geomorphologic features, kinematic indicators allow the overall sense of direction (Frey Martinez et al., 2005; Gawthrope & Clemmey, 1985; Trincardi & Argnani, 1990), magnitude (Moscardelli et al., 2006; Strachan, 2002), dominant flow mechanism (Sobiesiak et al., 2018) and mode of transport of submarine mass movements to be deciphered (Alsop & Marco, 2012; Bull et al., 2009; Moscardelli et al., 2006; Omeru & Cartwright, 2019; Sobiesiak et al., 2017; Strachan, 2002; Woodcock et al, 1979).…”

Section: Introductionmentioning

confidence: 99%

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Geomorphological characterization of basal flow markers during recurrent mass movement: A case study from the Taranaki Basin, offshore New Zealand

Kumar

Omosanya²,

Eruteya

et al. 2021

Basin Research

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This work examines the mode of basal to substrate interaction, and flow dynamics of recurrent mass wasting events from a high-quality 3D seismic reflection data. The Taranaki Basin, offshore New Zealand offers a unique environment to understand these processes, as the Neogene succession of the area preserves vertical stacks of mass transport deposits (MTDs) from the Miocene to Pliocene. The approach used here combines seismic interpretation of the basal shear zones (BSZs) of the MTD, seismic attribute analyses and colour rendering of both RMS amplitude and energy gradient maps. The five mass transport deposits are characterized into blocky-MTDs consisting of moderate to high amplitude and variably deformed rafted blocks, and chaotic masses composed of slides and debris flow deposits. Classic examples of kinematic indicators at the BSZ of the MTDs akin to free-slip flow processes such as liquefaction, hydroplaning and shear wetting are convolute flow fabrics and basal shear zone cut-offs (fractures). Striations, grooves, mega scours, U-and V-shaped scours, substrate erosion, monkey fingers (peel backs), substrate deformation and shearing are associated with no-slip flows, suggesting that the mass movements efficiently interacted with the underlying substrate. Importantly, the intersection of different kinematic indicators along the BSZs of all the MTDs suggests an overlap of flow regimes, flow overprinting and transformation during mass movement. Although basal tooling by rafted blocks seems dominant during remobilization of the blocky MTDs, the presence of other kinematic indicators signifies combined mechanisms involving both free-flow and no-slip processes during their translation. The classification scheme evaluated here innovatively shows mass movements habitually occur through a combination of flow mechanisms rather than an independent flow regime.

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Section: Discussionmentioning

confidence: 99%

Section: Kinematic Indicators Definitions Referencesmentioning

confidence: 99%

Section: Interpreted Mtds and Their Associated Seismic Faciesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Geomorphological characterization of basal flow markers during recurrent mass movement: A case study from the Taranaki Basin, offshore New Zealand

Kumar

Omosanya²,

Eruteya

et al. 2021

Basin Research

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“…Tectonic evolution of the basin includes extension during the Late Cretaceous to Early Eocene, followed by compression during the Late Eocene and back-arc extension from the Late Miocene to Recent 32 . Towards the end of Miocene, the basin accumulated large sediment influx, known as the Giant Foresets, deposited in shelf to basin succession 27 , 33 − 35 . MTDs are widespread within this basin and have been recognised throughout the offshore in TB.…”

Section: Introductionmentioning

confidence: 99%

A machine learning tool for interpretation of Mass Transport Deposits from seismic data

Kumar

Sain

2020

Sci Rep

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Machine learning is a tool that allows machines or intelligent systems to learn and get equipped to solve complex problems in predicting reliable outcome. the learning process consists of a set of computer algorithms that are employed to a small segment of data with a view to speed up realistic interpretation from entire data without extensive human intervention. Here we present an approach of supervised learning based on artificial neural network to automate the process of delineating structural distribution of Mass Transport Deposit (MTD) from 3D reflection seismic data. The responses, defined by a set of individual attributes, corresponding to the MTD, are computed from seismic volume and amalgamated them into a hybrid attribute. this generated new attribute, called as MTD Cube meta-attribute, does not only define the subsurface architecture of MTD distinctly but also reduces the human involvement thereby accelerating the process of interpretation. the system, after being fully trained, quality checked and validated, automatically delimits the structural geometry of MTDs within the Karewa prospect in northern Taranaki Basin off New Zealand, where MtDs are evidenced. Mass Transport Deposits (MTDs), occurring in different tectonic and depositional settings, are defined as gravity induced slope failure deposits that include creeps, slides, slumps and debris flows 1−6. These deposits are internally deformed and associated with variable shape and size. During slope failure, masses tend to flow downslope over a shearing surface, called the basal shear surface (BSS) that forms the base of the MTDs. BSS preserves the record of all erosional and deformational activities experienced by these deposits or masses during their translation. Their interpretation is crucial, as such deposits during translation over the instable slope may lead to several catastrophic submarine events e.g., landslides, tsunamis, avalanches and thus possess precursory threats for subsea installations 7−15. Several authors 16−25 attempted to study the MTDs in order to understand their evolution, geomorphic character and possible trigger mechanisms responsible for slope failure. The use of modern techniques e.g., reflection seismic (2D/3D), side scan sonar, bathymetry etc. added value for their detailed investigation. In reflection seismic, the MTDs are first identified by mapping their top and BSS, and then interpreted from cross-sections and attribute maps 23−27. For this, the seismic attributes such as the root-mean square (RMS) amplitude, dip magnitude and coherency have been used for the interpretation of this geologic feature 23−26. Though the single attribute technology has been successful in interpreting MTDs from seismic data, several authors 28−29 demonstrated the downside of such approach, where a single attribute hardly ever responds to a particular geological target (see sections "Initial Interpretation" and "From Seismic Attributes to Meta-attributes" in the Supplementary Note for detailed explanation). The Taranaki Basin (TB) is a we...

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Deepwater sedimentation units

Rotzien¹,

Hernández-Molina²,

Fonnesu³

et al. 2022

Deepwater Sedimentary Systems

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The efficacy of kinematic indicators in a complexly deformed Mass Transport Deposit: Insights from the deepwater Taranaki Basin, New Zealand

Cited by 15 publications

References 30 publications

Geomorphological characterization of basal flow markers during recurrent mass movement: A case study from the Taranaki Basin, offshore New Zealand

Geomorphological characterization of basal flow markers during recurrent mass movement: A case study from the Taranaki Basin, offshore New Zealand

A machine learning tool for interpretation of Mass Transport Deposits from seismic data

Deepwater sedimentation units

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