EGU23-15684, updated on 13 May 2024
EGU General Assembly 2023
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Semi-supervised feature-based learning for prediction of Mass Accumulation Rate of sediments

Naveenkumar Parameswaran1,2, Everardo Gonzalez1, Ewa Bur­wicz-Ga­ler­ne3, David Greenberg4, Klaus Wallmann1, and Malte Braack2
Naveenkumar Parameswaran et al.
  • 1GEOMAR Helmholtz Zentrum Kiel, Department of Marine Biogeochemistry, Kiel, Germany
  • 2Department of Applied Mathematics, Christian Albrecht University, Kiel, Germany
  • 3MARUM: Centre for Marine Environmental Sciences, University of Bremen, Bremen, Germany
  • 4Helmholtz Zentrum Hereon, Geesthacht, Germany

Mass accumulation rates of sediments[g/cm2/yr] or sedimentation rates[cm/yr] on the seafloor are important to understand various benthic properties, like the rate of carbon sequestration in the seafloor and seafloor geomechanical stability. Several machine learning models, such as random forests, and k-Nearest Neighbours have been proposed for the prediction of geospatial data in marine geosciences, but face significant challenges such as the limited amount of labels for training purposes, skewed data distribution, a large number of features etc. Previous model predictions show deviation in the global sediment budget, a parameter used to determine a model's predicitve validity, revealing the lack of accurate representation of sedimentation rate by the state of the art models. 

Here we present a semi-supervised deep learning methodology to improve the prediction of sedimentation rates, making use of around 9x106  unlabelled data points. The semi-supervised neural network implementation has two parts: an unsupervised pretraining using an encoder-decoder network. The encoder with the optimized weights from the unsupervised training is then taken out and fitted with layers that lead to the target dimension. This network is then fine-tuned with 2782 labelled data points, which are observed sedimentation rates from peer-reviewed sources. The fine-tuned model then predicts the rate and quantity of sediment accumulating on the ocean floor, globally.

The developed semi-supervised neural network provide better predictions than supervised models trained only on labelled data. The predictions from the semi-supervised neural network are compared with that of the supervised neural network with and without dimensionality reduction(using Principle Component Analysis).

How to cite: Parameswaran, N., Gonzalez, E., Bur­wicz-Ga­ler­ne, E., Greenberg, D., Wallmann, K., and Braack, M.: Semi-supervised feature-based learning for prediction of Mass Accumulation Rate of sediments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15684,, 2023.

Corresponding supplementary materials formerly uploaded have been withdrawn.