How to estimate the paleocurrent velocities from turbidites: some examples of the Plio-Pleistocene Kakegawa Group.

雄介, 久保; 富士雄, 増田; 哲弥, 酒井

doi:10.5575/geosoc.101.925

Cited by 5 publications

(9 citation statements)

References 22 publications

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“…Compared with the methods proposed in previous studies, the present method of inverse analysis is at a great advantage for reconstructing the paleo-flow velocities of ancient deposits. Previously, researchers derived the flow velocities of turbidity currents based on the critical velocity of suspended sediments [Kubo et al, 1995[Kubo et al, , 1998]. Such a method, however, can only be used to estimate the minimum flow velocity, and Hiscott [1994] pointed out that the results are quite different from the actual paleo-flow velocities of turbidity currents.…”

Section: Advantages Of the Methods Proposed In This Studymentioning

confidence: 99%

“…A remaining problem in this field of research is the quantitative understanding of the developmental process of turbidity currents at natural scales, which is essential to estimate the distribution of ancient turbidites from the limited amounts of information provided by cores and seismic profiles [e.g., Takano, 2016]. Although several in-situ measurements of turbidity currents on deep sea floors have been reported [e.g., Shepard, 1963;Inman et al, 1976;Dengler et al, 1984;Xu et al, 2004;Vangriesheim et al, 2009;Arai et al, 2013;Cooper et al, 2013;Clarke, 2016], hydraulic conditions of turbidity currents such as flow velocity and sediment concentration, however, remain unclear [Kubo et al, 1995;Falcini et al, 2009;Lesshafft et al, 2011]; conducting such in-situ measurements is quite difficult because of their highly destructive nature and infrequent occurrences [Naruse and Olariu, 2008;Talling et al, 2015]. The in-situ measurements cost and it is easer to observe ancient turbidites than turbidity currents in fieldwork.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, there have been several attempts to reconstruct the hydraulic conditions of ancient turbidity currents from geologic records [Stow and Bowen, 1980;van Tassell, 1981;Bowen et al, 1984;Komar, 1985;Hiscott, 1994;Allen, 1991;Kubo et al, 1995Kubo et al, , 1998Baas et al, 2000;Falcini et al, 2009;Lesshafft et al, 2011]. Initially, the critical velocities of particle motion inferred from turbidites were used for estimating the paleo-flow conditions of turbidity currents [Komar, 1985;Kubo et al, 1995Kubo et al, , 1998]. However, these methods based on critical velocity estimate only the minimum values of flow velocity, because deposition can occur even at velocities much higher than the critical velocity of particle motion when the capacity of sediment transport in a turbidity current is exceeded by the volume flux of sediment [Hiscott, 1994].…”

Section: Introductionmentioning

confidence: 99%

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Inverse analysis to reconstruct hydraulic conditions of non-steady turbidity currents based on multiple grain-size classes

Nakao¹,

Naruse²

2017

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This study proposes a new method of inverse analysis of ancient turbidites to represent non-steady turbidity currents and account for multiple grain-size classes. The forward model employed in this study is based on the shallow water equation, and the initial conditions of flows are assumed as a lock-exchange type. To obtain a solution to the inverse problem, a genetic algorithm is employed to determine the optimal initial conditions. The present method successfully estimated the true initial conditions of the turbidity currents from the artificial data sets of deposits created by the forward model. The method is also applied to a turbidite bed of the Kiyosumi Formation. The results of the inverse analysis yield solutions that fits well with the observed data of the individual turbidite, and provide estimates of the flow velocity, flow thickness and sediment concentration of the turbidity current. The flow thickness and velocity when the turbidity current reached the downstream end of the study area were reconstructed to be 334.6 m, 0.98 m/s, respectively.

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Section: Advantages Of the Methods Proposed In This Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Inverse analysis to reconstruct hydraulic conditions of non-steady turbidity currents based on multiple grain-size classes

Nakao¹,

Naruse²

2017

Preprint

View full text Add to dashboard Cite

show abstract

“…A remaining problem in this field of research is the quantitative understanding of the developmental process of turbidity currents at natural scales, which is essential to estimate the distribution of ancient turbidites from the limited amounts of information provided by cores and seismic profiles (Takano, 2016). Although several in-situ measurements of turbidity currents on deep sea floors have been reported (Shepard, 1965;Inman et al, 1976;Dengler et al, 1984;Xu et al, 2004;Vangriesheim et al, 2009;Arai et al, 2013;Andrieux et al, 2013), hydraulic conditions of turbidity currents such as flow velocity and sediment concentration, however, remain unclear (Kubo, 1995;Falcini et al, 2009;Lesshafft et al, 2011); conducting such in-situ measurements is quite difficult because of their highly destructive nature and infrequent occurrences (Naruse and Olariu, 2008;Talling et al, 2015). The in-situ measurements cost and it is easer to observe ancient turbidites than turbidity currents in fieldwork.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, there have been several attempts to reconstruct the hydraulic conditions of ancient turbidity currents from geologic records (Stow and Bowen, 1980;van Tassell, 1981;Bowen et al, 1984;Komar, 1985;Allen, 1991;Hiscott, 1994;Kubo, 1995;Kubo et al, 1998;Baas et al, 2000;Naruse and Olariu, 2008;Falcini et al, 2009;Lesshafft et al, 2011). Previous reconstructions of turbidity currents from their deposits were mostly based on the concepts of the capacity or competence of a flow (Dorrell et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Inverse Analysis to Reconstruct Hydraulic Conditions of Non-steady Turbidity Currents: Application to an Ancient Turbidite of the Kiyosumi Formation of the Awa Group, Boso Peninsula, Central Japan

Nakao¹,

Naruse²,

Tokuhashi³

2020

Preprint

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This study proposes a new method of inverse analysis from ancient turbidites to the non-steady turbidity currents with consideration of multiple grain-size classes. The forward model employed in this study is based on the shallow water equation, and the initial condition of flows are assumed to be the lock-exchange type condition. To obtain a solution of the inverse problem, this study employed the genetic algorithm for finding the optimized initial conditions. The present method successfully estimated the true given initial conditions of the turbidity currents from the artificial data sets of deposits created by the calculation of the forward model. The author also applied the method to the turbidite bed in the Kiyosumi Formation. As a result of inverse analysis, the obtained solution fits well to the observed data of the individual turbidite, providing estimates of the flow velocity, the flow thickness and the sediment concentration of the turbidity current. The flow thickness and velocity when the turbidity current reached at the downstream end of the study area were reconstructed to be 334.6 m, 0.98 m/s respectively at the location of the downstream end. Result of our analysis is the first example of reconstructing a reasonable conditions of the turbidity current from an ancient turbidite observed in the field, and the method is expected to be applied in various regions in the future.

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Inverse modeling of turbidity currents using artificial neural network: verification for field application

Naruse¹,

Nakao²

2020

Preprint

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Abstract. Although in situ measurements observed on modern frequently occurring turbidity currents have been performed, the flow characteristics of turbidity currents that occur only once every hundreds of years and deposit turbidites over a large area have not yet been elucidated. In this study, we propose a method for estimating the paleo-hydraulic conditions of turbidity currents from ancient turbidites by using machine learning. In this method, we hypothesize that turbidity currents result from suspended sediment clouds that flow down a steep slope in a submarine canyon and into a gently sloping basin plain. Using inverse modeling, we reconstruct seven model input parameters including the initial flow depth, the sediment concentration and the basin slope. Repeated numerical simulation using one-dimensional shallow water equations under various input parameters generates a dataset of the characteristic features of turbidites. This artificial dataset is then used for supervised training of a deep learning neural network (NN) to produce an inverse model capable of estimating paleo-hydraulic conditions from data of the ancient turbidites. Only 3,500 datasets are needed to train this inverse model. The performance of the inverse model is tested using independently generated datasets. Consequently, the NN successfully reconstructs the flow conditions of the test datasets. In addition, the proposed inverse model is quite robust to random errors in the input data. Judging from the results of subsampling tests, inversion of turbidity currents can be conducted if an individual turbidite can be correlated over 10 km at approximately 1 km intervals. These results suggest that the proposed method can sufficiently analyze field-scale turbidity currents.

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How to estimate the paleocurrent velocities from turbidites: some examples of the Plio-Pleistocene Kakegawa Group.

Cited by 5 publications

References 22 publications

Inverse analysis to reconstruct hydraulic conditions of non-steady turbidity currents based on multiple grain-size classes

Inverse analysis to reconstruct hydraulic conditions of non-steady turbidity currents based on multiple grain-size classes

Inverse Analysis to Reconstruct Hydraulic Conditions of Non-steady Turbidity Currents: Application to an Ancient Turbidite of the Kiyosumi Formation of the Awa Group, Boso Peninsula, Central Japan

Inverse modeling of turbidity currents using artificial neural network: verification for field application

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