Deposition and preservation of tephra in marine sediments at the active Hikurangi subduction margin

Hopkins, Jenni L.; Wysoczanski, Richard J.; Orpin, Alan R.; Howarth, Jamie; Strachan, Lorna J.; Lunenburg, Ryan; McKeown, Monique; Ganguly, Aratrika; Twort, Emily; Camp, Sian

doi:10.1016/j.quascirev.2020.106500

Cited by 25 publications

(47 citation statements)

References 59 publications

(116 reference statements)

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“…The timing of these eruptions is determined by radiometric dating techniques and the "geochemical fingerprint" of a tephra can be analysed to match it to a particular eruption, without having to radiometrically date every occurrence of said tephra (Shane, 2000). Tephra deposits can be grouped into four lithofacies types; primary deposits, volcaniclastic-rich turbidites, blebs or pods of volcaniclastic-rich material, and complex deposits, a combination of the previous three lithofacies (Hopkins et al, 2020). Primary deposits are ideal, as they represent the direct accumulation of tephra via airfall on the ocean surface, which settles over hours to days onto the sea floor.…”

Section: Tephrochronologymentioning

confidence: 99%

“…These can be made up of material from a single eruption or multiple eruptions. The former can provide a maximum age for the deposit, while the latter can be more complex, but a maximum age can be assigned using the age of the youngest event, if the events can be identified from the geochemical signature (Hopkins et al, 2020).…”

Section: Tephrochronologymentioning

confidence: 99%

“…The LDA model was applied to data from a core (TAN1613-27) containing primary Taupō tephra, as identified by Hopkins et al (2020). The non-destructive data from these cores was applied to the model, treated as a test set.…”

Section: Radiocarbon Datingmentioning

confidence: 99%

“…To assess if the background sediment identified by the LDA models can produce reliable radiocarbon ages, samples targeted around the Taupō eruption isochron, dated at ~1718 cal. yr. BP (Hopkins et al, 2020) were examined to test if these sediments could produce reliable radiocarbon ages. A core from a canyon-proximal core site, TAN1613-27, was examined for this purpose.…”

Section: Application Of the Approach For Radiocarbon Dating Backgrounmentioning

confidence: 99%

“…yr. BP (Figure 5.33), older than the Taupō tephra below it, which is dated at ~1718 cal. yr. BP (Hopkins et al, 2020).…”

Section: Radiocarbon Datingmentioning

confidence: 99%

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Distinguishing Turbidite Tails from Background Sedimentation on the Hikurangi Margin and the Implications for Turbidite Paleoseismology

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The Hikurangi margin is one of the largest sources of seismic and tsunami hazards in New Zealand, but there is still much that remains unknown about previous ruptures on the subduction interface. Turbidite paleoseismology has the potential to increase the spatial density and temporal extent of paleoearthquake records. However, it is heavily reliant on temporal correlation of turbidites, and thus, requires them to be precisely dated. Typically, ages are obtained using radiocarbon dating of pelagic foraminifera from background sediments deposited between turbidites. This dating method requires background sedimentation to be accurately distinguished from the fine-grained tails of turbidites. Along the southern and central Hikurangi Margin, background sedimentation and turbidite tails have proven difficult to distinguish from one another. Here, a quantitative approach is developed to distinguish turbidite tails and background sediments using machine learning. This study utilizes a natural experiment generated by the M W 7.8 Kaikōura earthquake, which caused the deposition of co-seismic turbidites at locations both proximal and distal to active canyon systems. The 2016 turbidite could be recognised due to its stratigraphic position at core tops. Turbidites and background sediments were independently identified using 210 Pb activity profiles to identify gradual accumulation. Additionally, foraminiferal assemblages were used to identify transported material. The physical and geochemical properties of the sediments were then analysed using non-destructive (computed tomography density, magnetic susceptibility, micro X-ray fluorescence derived geochemistry) and destructive (grain size, carbonate content, organic content) techniques, to develop a quantitative definition of turbidite tails and background sediments. The destructive datasets were then compared to the non-destructive data that acts as a proxy for these analyses because the latter are rapidly generated at high resolutions down core and are now routinely acquired in most turbidite paleoseismology studies. It was determined that there was a statistically significant correlation between the destructive data and the non-destructive proxies, such that the non-destructive data could be used as a viable alternative to the time consuming destructive analyses. The machine learning technique, Linear Discriminant Analysis (LDA), successfully distinguishes background sediment and turbidite tails in areas where they are visually indistinguishable. The LDA model shows that in cores distal from active canyon systems, background sediment and turbidite tails are more distinct than in cores proximal to active canyon systems. Difference between canyon-proximal and distal sites may be due to the impact of weak bottom currents that are inferred to be acting on the background sedimentation processes along this margin. This study shows that quantitative identification of background sediments and turbidite tails is possible, and could allow for more robust identification and dating of turbidites globally, which is of paramount importance for the effective application of turbidite paleoseismology.

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Section: Tephrochronologymentioning

confidence: 99%

Section: Tephrochronologymentioning

confidence: 99%

Section: Radiocarbon Datingmentioning

confidence: 99%

Section: Application Of the Approach For Radiocarbon Dating Backgrounmentioning

confidence: 99%

“…yr. BP (Figure 5.33), older than the Taupō tephra below it, which is dated at ~1718 cal. yr. BP (Hopkins et al, 2020).…”

Section: Radiocarbon Datingmentioning

confidence: 99%

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Distinguishing Turbidite Tails from Background Sedimentation on the Hikurangi Margin and the Implications for Turbidite Paleoseismology

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Using pollen in turbidites for vegetation reconstructions

McDonald,

Strachan,

Holt

et al. 2024

J Quaternary Science

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Turbidites, deposited by sub‐aqueous gravity flows, are common in sedimentary archives worldwide and present a unique challenge and opportunity when reconstructing past vegetation through pollen analysis. When sampling pollen from a sediment core for palaeovegetation records, it is common practice to target background sediments (i.e. pelagic sediment) and avoid sampling turbidites, as they are presumed to portray a misleading picture of past vegetation. This assumption stems from our limited understanding of pollen abundance and distribution through turbidites, meaning that palynologists overlook deposits that could potentially be used to reconstruct past vegetation and climate. We present pollen assemblage and sedimentological data from four recent (<150 years) deep marine turbidite deposits from the Hikurangi Subduction Margin, Aotearoa‐New Zealand, with the aim of understanding the abundance and distribution of pollen in fine‐grained turbidites. We find that pollen is diluted in the bases of turbidites, but despite this dilution, the proportions of different pollen taxa remain consistent through each turbidite. These results confirm that pollen can be sampled from turbidites for palaeovegetation reconstructions and that sampling the fine‐grained upper parts of turbidites will provide the best pollen recovery.

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