Machine learning techniques applied to sediment core scanning data in the framework of sedimentological and paleoceanographical investigations

Studying the mother Earth to understand and predict its system process facilitates the evolution of science and human society toward a progressive and sustainable stage. One of the key research materials is sediment archive, which records various short-and long-term historical information, such as climatic, biological, geomorphological and human activity variations. With the progress in geoscience and computer science, this thesis presents three interdisciplinary approaches to solve practical problems in sedimentological and paleoceanographical investigations.
The first study compiles different core scanning data (magnetic susceptibility, X-ray computed tomography, elemental profiles, digital photography) to characterize a priori classified sediment core sections recovered from the Wadden Sea region. The results confirm that the description of human-recognized sediment facies can be reproduced using the scanning data, which gives a promising hint to a further step: automatic sediment facies classification.
The second study increases the data scale by covering more core sections and sediment facies to develop a machine learning (ML) model that classifies sediments into facies by reading elemental profiles acquired from X-ray fluorescence (XRF) core scanning. A series of feature engineering and ML algorithms are tested to find the optimal solution. As a result, a simple but powerful model is proposed to simulate sedimentologists’ observational behavior and have promising performance (78% accuracy), which is supported by a tailor-made evaluation involving sedimentary knowledge. Furthermore, the model can highlight critical sections of sediments requiring sedimentologists’ expertise. This provides an increased capability of classification without losing accuracy.
The third study focuses on obtaining cost-efficient high-resolution bulk chemistry measurement (CaCO3 and total organic carbon) by quantifying XRF spectra using ML. This novel approach of using XRF spectra directly and enhanced regression power of ML eliminates manual bias and increases input information. Meanwhile, the previous limitation of data coverage is lifted by including multi-regional data (high latitude sectors of Pacific Ocean). The outcome is carefully evaluated using cross-validation, test set, and case study, with R2 of CaCO3: 0.96 and TOC: 0.78 from the test set.
In conclusion, ML increases the capability of data analysis and automation. Sediment core scanning provides thorough but rapid measurement in high spatial resolution. This thesis offers generalizable methodological blueprints for integrating these two techniques to release the wealth of sedimentary information from the shackles of expensive and observer-dependent data in the past.