Date of Award


Degree Type


Degree Name

Doctor of Philosophy in Ocean Engineering


Ocean Engineering

First Advisor

Kathryn Moran


The Integrated Ocean Drilling Program (IODP) is the world’s largest collaborative research initiative for the study of the ocean seafloor, subseafloor, and their application to earth system science. The processing of recovered cores from the subseafloor by IODP has been standardized to maintain constant, reproducible results and to make the measurements available to the scientific community using a worldwide- web based database. The study of physical properties is a basic requirement for the study of marine sediments as they form a foundation data set for most of earth system dynamic studies.

This thesis focuses on solving four different scientific problems using analyses and modeling of marine sediments based on physical properties among other IODP data. The four manuscripts presented here are applied cases that use marine sediment physical properties to understand various aspects of past and future climate change. The main objectives of this dissertation are: validation of the design and development steps of a new seafloor observatory that will measure physical properties over time in IODP boreholes; dimensioning of this system based on the physical properties previously measured at the deployment site; development of a classification system for the major types of carbonate sediments used to reconstruct past sea level rise; and reconstructing past Pacific Ocean maximum bottom water salinities during the Last Glaciation Maximum (LGM) based on physical properties of marine sediments. These objectives were addressed by using multivariate statistics, machine learning techniques and a one dimensional diffusion model.

A new modular borehole observatory, named SCIMPI (Simple Cabled Instrument for Measuring Parameters In situ) was successfully developed and tested for deployment. This system is equipped to take time series measurements of temperature, pressure and electrical resistivity in the geological formation where it is emplaced. The characteristics of the system and the testing process are presented in Chapter 1.

In Chapter 2, the modular design of the first SCIMPI prototype is dimensioned using a clustering approach based on physical properties from the deployment site. The multivariate statistics applied show direct relation with the geological formation and gas hydrate dynamics at the site.

In the third Chapter of this dissertation, physical properties are related to characteristics of marine carbonates that, in turn, are indicative of their lithological type. Three different models were tested: Linear Discriminant Analysis, Random Forest and, Support Vector Machines. This study demonstrates a strong nonlinear relationship between physical properties and carbonate lithotypes. The results show that machine learning models can help identify lithologies, thus aiding the selection of sample locations, core-log correlations, and processing of these rare core materials.

Finally, pore fluid chloride concentration profiles from six Pacific Ocean deep sea sites were used to reconstruct past salinities. This study expands the spatial coverage of salinity reconstructions during the Last Glacial Maximum (LGM) to the equatorial Pacific and North and South Pacific Gyres. These reconstructed LGM chloride concentrations of deep Pacific bottom water are ~4% greater than today’s values. This is consistent with the view that the deep ocean density structure was primarily controlled by salinity variations during the LGM.



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