the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
Abstract. Spatial predictions of total organic carbon (TOC) concentrations and stocks are crucial for understanding marine sediments’ role as a significant carbon sink in the global carbon cycle. In this study, we present a geospatial prediction of TOC concentrations and stocks at a 5 x 5 arc minute grid scale, using a deep learning model — a novel machine learning approach based on a new compilation of over 22,000 global TOC measurements and a new set of predictors, such as seafloor lithologies, grain size distribution, and an alpha-chlorophyll satellite data. In our study, we compared the predictions and discuss the limitations from various machine learning methods. Our findings reveal that the neural network approach outperforms methods such as k Nearest Neighbors and random forests, which tend to overfit to the training data, especially in highly heterogeneous and complex geological settings. We provide estimates of mean TOC concentrations and total carbon stock in both continental shelves and deep sea settings across various marine regions and oceans. Our model suggests that the upper 10 cm of oceanic sediments harbors approximately 171 Pg of TOC stock and has a mean TOC concentration of 0.68 %. Furthermore, we introduce a standardized methodology for quantifying predictive uncertainty using Monte Carlo dropout and present a map of information gain, that measures the expected increase in model knowledge achieved through in-situ sampling at specific locations which is pivotal for sampling strategy planning.
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Status: open (until 30 Jul 2024)
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CEC1: 'Comment on egusphere-2024-1360', Juan Antonio Añel, 20 Jun 2024
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Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".
https://www.geoscientific-model-development.net/policies/code_and_data_policy.html
You have archived your code on a git repository hosted by GEOMAR. However, this is not a suitable repository for scientific publication. Therefore, please, publish your code in one of the appropriate repositories (check our policy for examples), and reply to this comment with the relevant information (link and DOI). Manuscripts no compliant with the code and data policy can not be published in Discussions. Therefore, the current situation with your manuscript is irregular.In this way, if you do not fix this problem, we will have to reject your manuscript for publication in our journal.
Also, you must include in a potentially reviewed version of your manuscript the modified 'Code Availability' section, adding the link and DOI of the code.
Juan A. Añel
Geosci. Model Dev. Executive EditorCitation: https://doi.org/10.5194/egusphere-2024-1360-CEC1 -
AC1: 'Reply on CEC1', Naveenkumar Parameswaran, 21 Jun 2024
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Dear Dr. Juan A. Añel
Thank you for your comment. The code has been published and archived using the Zenodo integration with Github.
1. The Zenodo archive can be found at the following URL : https://doi.org/10.5281/zenodo.12206146
2. DOI for the code is 10.5281/zenodo.12206145
3. The Github repository can be found at the following URL: https://github.com/paramnav/nn-toc/tree/nn-toc-v1
As suggested, this has to be updated in the manuscript. Since it is not allowed to submit the revised manuscript here, it will be updated in the next possible revision upload of the manuscript.
Thank you again and kind regards,
Naveenkumar Parameswaran et. al.Citation: https://doi.org/10.5194/egusphere-2024-1360-AC1 -
CEC2: 'Reply on AC1', Juan Antonio Añel, 21 Jun 2024
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Dear authors,
Many thanks for addressing this issue. Now we can consider it solved.
Regards,
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/egusphere-2024-1360-CEC2
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CEC2: 'Reply on AC1', Juan Antonio Añel, 21 Jun 2024
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AC1: 'Reply on CEC1', Naveenkumar Parameswaran, 21 Jun 2024
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RC1: 'Comment on egusphere-2024-1360', Taylor Lee, 24 Jun 2024
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General:
This paper builds off previous literatures by using a different machine learning method (neural networks) to generate a global prediction of seafloor total organic carbon. The paper primarily differs from other papers in the machine learning approach that is utilized. This paper will also release a new higher density observational dataset and discusses carbon stocks over regional areas in more detail than other literatures. Since a major highlight of this paper is the comparison of NN to other ML methods, the paper needs some significant work on technical details in this manner. This work does not achieve any higher resolution or spatial coverage than other more recent works, it also utilizes very old datasets which should be noted in the manuscript. Inevitably, the best predictions are a result of the best features and observational datasets not always the ML algorithm used. Special care in discussing this in the manuscript is vitally important.
Overall, this paper is worthy of publication. In my opinion, the paper is worthy not so much for the ML and technical approach used (as previously mentioned the method is always problem-specific) but mostly due to the discussion of carbon stocks and trends that may be revealed in final predictions. I think most of these trends are likely apparent in any of the three discussed ML algorithms. Potentially making these points the highlight of the paper would strength the paper and lead to a heavily cited paper.
Technical (specific) notes: See attached pdf for comments.
Grammar: See attached pdf for comments.
Other: Saving this data in a more universal format (.xyz, netCDF, etc) would be advantageous for others to use. .npy files are not always suitable for everyone. Further, something to understand specifically what these files are would be useful. Some of the short hand is difficult to interpret (e.g., prediction_map_TOC_supervised_men_CS_noconstraint.npy, prediction_map_TOC_noconstraint_supervised_men_DO.npy). I assume CS for continental shelf and DO for deep ocean but being explicit would be best.
Citation: https://doi.org/10.5194/egusphere-2024-1360-RC1 -
RC2: 'Reply on RC1', Taylor Lee, 24 Jun 2024
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See attached for PDF, I do not think it attached to the previous.
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RC2: 'Reply on RC1', Taylor Lee, 24 Jun 2024
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Data sets
Dataset for the Global Prediction Of Total Organic Carbon In Marine Sediments Using Deep Neural Networks (nn-toc) Naveenkumar Parameswaran et al. https://zenodo.org/records/11186224
Interactive computing environment
Global Prediction Of Total Organic Carbon In Marine Sediments Using Deep Neural Networks (nn-toc). Naveenkumar Parameswaran et al. https://doi.org/10.3289/SW_3_2024
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