Relationship between mn nodule abundance and other geological factors in the northeastern pacific: Application of gis and probability method

Ko, Young-Tak; Lee, Saro; Kim, Jonguk; Kim, Ki-Hyune; Jung, Mee-Sook

doi:10.1007/bf03022420

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…High-resolution bathymetry enables computing bathymetric derivatives like slope and rugosity with a similarly high resolution. These derivatives play an important role in predicting Mn nodules' distribution and abundance (Craig, 1979;Kodagali, 1988;Skornyakova and Murdmaa, 1992;Kodagali andSudhakar, 1993, Sharma andKo et al, 2006). However, a small number of recent studies have investigated this role on an AUV scale (Okazaki and Tsune, 2013;Peukert et al, 2018a;Alevizos et al, 2018).…”

Section: Auv Hydroacoustic Mappingmentioning

confidence: 99%

“…High-resolution quantitative predictive mapping of the distribution and abundance of manganese nodules (Mn nodules) is of interest for both the deep-sea mining industry and scientific fields such as marine geology, geochemistry, and ecology. The distribution and abundance of Mn nodules are affected by several factors such as local bathymetry (Craig, 1979;Kodagali, 1988;Kodagali and Sudhakarand, 1993;Sharma and Kodagali, 1993), sedimentation rate (Glasby, 1976;Frazer and Fisk, 1981;von Stackelberg and Beiersdorf, 1991;Skornyakova and Murdmaa, 1992), availability of nucleus material (Glasby, 1973), and bottom current strength (Frazer and Fisk, 1981;Skornyakova and Murdmaa, 1992). As a consequence, the distribution and abundance of Mn nodules is heterogeneous (Craig, 1979;Frazer and Fisk, 1981;Kodagali, 1988;Kodagali and Sudhakar, 1993;Kodagali and Chakraborty, 1999;Kuhn et al, 2011), even on fine scales of 10 to 1000 m (Peukert et al, 2018a;Alevizos et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Quantitative mapping and predictive modeling of Mn nodules' distribution from hydroacoustic and optical AUV data linked by random forests machine learning

et al. 2018

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Abstract. In this study, high-resolution bathymetric multibeam and optical image data, both obtained within the Belgian manganese (Mn) nodule mining license area by the autonomous underwater vehicle (AUV) Abyss, were combined in order to create a predictive random forests (RF) machine learning model. AUV bathymetry reveals small-scale terrain variations, allowing slope estimations and calculation of bathymetric derivatives such as slope, curvature, and ruggedness. Optical AUV imagery provides quantitative information regarding the distribution (number and median size) of Mn nodules. Within the area considered in this study, Mn nodules show a heterogeneous and spatially clustered pattern, and their number per square meter is negatively correlated with their median size. A prediction of the number of Mn nodules was achieved by combining information derived from the acoustic and optical data using a RF model. This model was tuned by examining the influence of the training set size, the number of growing trees (ntree), and the number of predictor variables to be randomly selected at each node (mtry) on the RF prediction accuracy. The use of larger training data sets with higher ntree and mtry values increases the accuracy. To estimate the Mn-nodule abundance, these predictions were linked to ground-truth data acquired by box coring. Linking optical and hydroacoustic data revealed a nonlinear relationship between the Mn-nodule distribution and topographic characteristics. This highlights the importance of a detailed terrain reconstruction for a predictive modeling of Mn-nodule abundance. In addition, this study underlines the necessity of a sufficient spatial distribution of the optical data to provide reliable modeling input for the RF.

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Section: Auv Hydroacoustic Mappingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative mapping and predictive modeling of Mn nodules' distribution from hydroacoustic and optical AUV data linked by random forests machine learning

et al. 2018

View full text Add to dashboard Cite

show abstract

“…High-resolution bathymetry enables computing bathymetric derivatives like slope and rugosity with a similarly high resolution. These derivatives play an important role in predicting Mn-nodules' distribution and abundance (Craig, 1979;Kodagali, 1988;Skornyakova and Murdmaa, 1992; Kodagali andSudhakar, 1993, Sharma &Ko et al, 2006). Until now, few studies have examined the correlations between Mn-nodules and bathymetry, its derivatives, and backscatter response in AUV scale (Okazaki and Tsune, 2013;Peukert et al, 2018a;Alevizos et al, 2018) promoting such data as key for predictive modelling of Mn-Nodule occurrence (e.g.…”

Section: Introductionmentioning

confidence: 99%

Quantitative mapping and predictive modelling of Mn-nodules' distribution from hydroacoustic and optical AUV data linked by Random Forests machine learning

Gazis¹,

Schoening²,

Alevizos³

et al. 2018

Preprint

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Abstract. In this study, high-resolution bathymetric multibeam and optical image data, both obtained within the Belgian manganese (Mn) nodule mining license area by the autonomous underwater vehicle (AUV) Abyss, were combined in order to create a predictive Random Forests (RF) machine learning model. AUV bathymetry reveals small-scale terrain variations, allowing slope estimations and calculation of bathymetric derivatives such as slope, curvature, and ruggedness. Optical AUV imagery provides quantitative information regarding the distribution (number and median size) of Mn-nodules. Within the area considered in this study, Mn-nodules show a heterogeneous and spatially clustered pattern and their number per square meter is negatively correlated with their median size. A prediction of the number of Mn-nodules was achieved by combining information derived from the acoustic and optical data using a RF model. This model was tuned by examining the influence of the training set size, the number of growing trees (ntree) and the number of predictor variables to be randomly selected at each RF node (mtry) on the RF prediction accuracy. The use of larger training data sets with higher ntree and mtry values increases the accuracy. To estimate the Mn-nodule abundance, these predictions were linked to ground truth data acquired by box coring. Linking optical and hydro-acoustic data revealed a non-linear relationship between the Mn-nodule distribution and topographic characteristics. This highlights the importance of a detailed terrain reconstruction for a predictive modelling of Mn-nodule abundance. In addition, this study underlines the necessity of a sufficient spatial distribution of the optical data to provide reliable modelling input for the RF.

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Spatial Modeling of Deep-Sea Ferromanganese Nodules With Limited Data Using Neural Networks

Vishnu

Kalyan

Ganesan

2018

IEEE J. Oceanic Eng.

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Relationship between mn nodule abundance and other geological factors in the northeastern pacific: Application of gis and probability method

Cited by 9 publications

References 16 publications

Quantitative mapping and predictive modeling of Mn nodules' distribution from hydroacoustic and optical AUV data linked by random forests machine learning

Quantitative mapping and predictive modeling of Mn nodules' distribution from hydroacoustic and optical AUV data linked by random forests machine learning

Quantitative mapping and predictive modelling of Mn-nodules' distribution from hydroacoustic and optical AUV data linked by Random Forests machine learning

Spatial Modeling of Deep-Sea Ferromanganese Nodules With Limited Data Using Neural Networks

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