Building a tectonic plate: Water, magma and faults in the oceanic lithosphere

Lead Research Organisation: Imperial College London
Department Name: Earth Science and Engineering

Abstract

At mid-ocean ridges, tectonic plates are pulled apart and the Earth's mantle slowly rises and is partially molten, producing magma that rises and solidifies to form new crust, the outer low-density layer of the Earth. However, on some ridge sections where magma supply is low, the production of magma by mantle melting is less efficient and creation of new crust cannot keep up with the stretching of the tectonic plates. Here faulting brings mantle rocks to the surface and produces anomalous "non-volcanic" seabed, containing rocks from the Earth's mantle. These mantle rocks are chemically modified by contact with sea water penetrating cracks and fractures. Circulating water becomes assimilated in the structure of the rocks, modifying the minerals that compose it. This process often produces a family of minerals called serpentinites and is thus called serpentinization. At the same time the mantle rocks transfer heat and chemicals to the hydrothermal fluids, which are transported to the seabed and escape into the ocean at hydrothermal vent sites.

The chemicals released near the vents can include precious metals and trace elements and give rise to valuable mineral deposits. The extreme physical conditions of high pressure, high temperature and high acidity, sustain unique biological communities that are thought to represent the closest present day analogue of the conditions that led to the development of early life on Earth. Hydrothermal processes in non-volcanic crust represent an important gateway for energy and chemical exchange between the solid Earth and the oceans, but its deep structure and formation mechanisms are still poorly understood. What is the composition of non-volcanic crust? How widespread is it in the World's oceans? How much water does it assimilate?

This project aims to produce an integrated model of accretion and hydration of the oceanic lithosphere at slow-spreading ridges and to characterize the interaction between magma, faults, and hydrothermal fluids. My study will focus on the Rainbow area of the Mid-Atlantic Ridge, a ridge section where the tectonic stretching and magmatic input vary rapidly in space, providing a complete picture of the different conditions encountered along the global mid-ocean ridge system. I will use full-waveform seismic tomography, a geophysical imaging technique which uses the entire record of the seismic oscillations, and joint geophysical inversion, to reconstruct a detailed and complete representation of the rock properties beneath the seabed. I will combine these constraints with rock physics and automated rock classification aided by machine learning to estimate composition, porosity, melt content and hydration. My work will have implications for the energy and chemical exchange between the solid Earth and the oceans, and for the recycling of chemicals in the deep Earth.

Planned Impact

The main impact goals of this project are to:
- Develop and publish robust methodologies and associated software and documentation to estimate important geological parameters from geophysical models, including porosity, composition, hydration and temperature of rocks in the subsurface.
- Advance the state of the art in the geophysical imaging and characterization of fluids at depth. The study of fluids in the solid Earth system is of great importance for understanding and estimating geohazards and georesources. The study of hydrothermal fluid flow through fracture networks has direct bearing on the exploitation of geothermal energy and on the identification and extraction of hydrocarbons, therefore my research will impact on the UK's capability to identify and exploit alternative energy sources and keep up with energy demand in a changing World.
- Develop a conceptual model of the accretion, composition and hydration of non-volcanic oceanic lithosphere. The presence of water can drastically affect the mechanical properties of the lithosphere and can have important consequences on the seismogenic behaviour and the potential for large earthquakes, with direct societal and economic implications.
- Advance our understanding of what governs the location, size, vigour and longevity of submarine hydrothermal systems. This is particularly important at present, because of the growing interest in the exploitation of associated sulphide deposits for commercial extraction of valuable metals such as copper, gold, silver and zinc. My research will have implications on the global distribution of hydrothermal vent fields and therefore on the distribution of mineral deposits and on the environments in which they form. Hydrothermal vent fields are also important because they host unique biological communities which have evolved to scrape a living by extracting energy from chemical reactions in extreme temperature and pressure conditions. The study of organisms found on black smoker vent fields has led to the discovery of new chemical compounds with interesting and useful properties for medical applications. By providing new constraints on the evolution and driving mechanism of vent fields my research will help understand the conditions under which these ecosystems become established.

The main non-academic stakeholders who will benefit from my research are:
- Government organizations and research institutions interested in seabed exploration and resources, including the British Geological Survey (BGS) and the Marine Management Organization (MMO).
- Policy stakeholders including the International Seabed Authority, which governs the exploitation of offshore mineral resources.
- The seabed mining industry. This nascent sector includes companies involved in the exploration of seabed mineral resources and developing commercially viable extraction projects. Ensuring that this is done in a way that minimizes the impact on the environment is of great importance.
- The geophysical exploration industry. The ICL FullWave group has strong links with several major companies in the sector.
- The geothermal energy sector.
- Other companies that operate in the World's oceans including related to offshore construction and seabed cable installation;
- Companies offering submersible dives to paying customers. Hydrothermal vents on mid-ocean ridges are a popular destination for deep-sea tourism.
- Associations and organizations interested in the preservation of seabed ecosystems.
- Publics interested in deep-sea ecosystems.

Publications

10 25 50
 
Description We used a novel imaging technique to detect the magma intrusions providing the heat that drives the Rainbow hydrothermal field. Previously, researchers had postulated that such intrusions must exist, but our study provides the first direct evidence.
Exploitation Route The analysis is being refined and prepared for publication in a scientific journal
Sectors Energy,Environment

 
Description I have started an outreach project to illustrate and communicate volcano and hydriothermal system science to the general public. I am working with a professional illustrator to create a set of illustrations representing the magmatic and hydrothermal systems of active volcanoes.
First Year Of Impact 2022
Sector Creative Economy,Education,Culture, Heritage, Museums and Collections
Impact Types Cultural,Societal

 
Title Oceanic crust density model 
Description New model of the density distribution along a section of the mid Atlantic ridge from joint inversion of seismic and gravity data. 
Type Of Material Computer model/algorithm 
Year Produced 2019 
Provided To Others? No  
Impact Better understanding of oceanic core complexes and detachment faulting. Starting model underpinning further research 
 
Title P-wave velocity and density models of Montserrat 
Description P-wave velocity model and density model obtained by joint inversion of seismic traveltimes and gravity anomalies as described in Paulatto et al. (2019).The traveltime data come from the SEA-CALIPSO experiment and are described in Paulatto et al. (2012). The gravity data are a compilation of land and marine gravity data. The code used for the inversion (jif3D, https://sourceforge.net/projects/jif3d/) is described in Moorkamp et al. (2010). The authors welcome reuse of the models and data. For any questions contact Michele Paulatto (m.paulatto@imperial.ac.uk).
Included files:monji_ttcx_1d0dd.nc = traveltime datasetmonji_ga5_grad_f500.nc = gravity anomaly datasetresult.tomo.inv.nc = output slowness fileresult.grav.inv.nc = ourput density anomaly fileresult.dens.inv.nc = output density file (density anomaly + reference 1d density model)paperzsections_2_4_6.pdf = plots of horizontal cross-sectionspvsection_23_23.5.pdf = plots of vertical cross-sections

Coordinates and unitsThe model coordinates are defined over a local Cartesian coordinate system with origin at longitude -62.4 degrees, latitude 16.5 degrees. Easting, northing and depth are in units of metres from the origin point. Depth is referenced to an elevation of 2000 m above mean sea level and is positive downward (zero Depth corresponds to 2000 m elevation). The Slowness field is in units of s/m and the density is in units of g/cm 3.
Northing bounds: 0 / 50000Easting bounds: 0 / 45000Depth bounds: 0 / 14500 (-2000 / 12500 )
Reading the modelsThe contents can be loaded in Python using the netcdf4 library. See example code snippet below.
from netCDF4 import Dataseta = Dataset('result.tomo.inv.nc', mode='r')northing = a.variables['Northing'][:]easting = a.variables['Easting'][:]depth = a.variables['Depth'][:]slowness = a.variables[Slowness][:,:,:]a.close()
b = Dataset('result.dens.inv.nc', mode='r')northing = b.variables['Northing'][:]easting = b.variables['Easting'][:]depth = b.variables['Depth'][:]density = b.variables[Density][:,:,:]b.close()
c = Dataset('result.grav.inv.nc', mode='r')northing = c.variables['Northing'][:]easting = c.variables['Easting'][:]depth = c.variables['Depth'][:]density_a = c.variables[Density][:,:,:]c.close()
 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? Yes  
URL https://figshare.com/articles/dataset/P-wave_velocity_and_density_models_of_Montserrat/13260491
 
Title P-wave velocity and density models of Montserrat 
Description P-wave velocity model and density model obtained by joint inversion of seismic traveltimes and gravity anomalies as described in Paulatto et al. (2019).The traveltime data come from the SEA-CALIPSO experiment and are described in Paulatto et al. (2012). The gravity data are a compilation of land and marine gravity data. The code used for the inversion (jif3D, https://sourceforge.net/projects/jif3d/) is described in Moorkamp et al. (2010). The authors welcome reuse of the models and data. For any questions contact Michele Paulatto (m.paulatto@imperial.ac.uk).
Included files:monji_ttcx_1d0dd.nc = traveltime datasetmonji_ga5_grad_f500.nc = gravity anomaly datasetresult.tomo.inv.nc = output slowness fileresult.grav.inv.nc = ourput density anomaly fileresult.dens.inv.nc = output density file (density anomaly + reference 1d density model)paperzsections_2_4_6.pdf = plots of horizontal cross-sectionspvsection_23_23.5.pdf = plots of vertical cross-sections

Coordinates and unitsThe model coordinates are defined over a local Cartesian coordinate system with origin at longitude -62.4 degrees, latitude 16.5 degrees. Easting, northing and depth are in units of metres from the origin point. Depth is referenced to an elevation of 2000 m above mean sea level and is positive downward (zero Depth corresponds to 2000 m elevation). The Slowness field is in units of s/m and the density is in units of g/cm 3.
Northing bounds: 0 / 50000Easting bounds: 0 / 45000Depth bounds: 0 / 14500 (-2000 / 12500 )
Reading the modelsThe contents can be loaded in Python using the netcdf4 library. See example code snippet below.
from netCDF4 import Dataseta = Dataset('result.tomo.inv.nc', mode='r')northing = a.variables['Northing'][:]easting = a.variables['Easting'][:]depth = a.variables['Depth'][:]slowness = a.variables[Slowness][:,:,:]a.close()
b = Dataset('result.dens.inv.nc', mode='r')northing = b.variables['Northing'][:]easting = b.variables['Easting'][:]depth = b.variables['Depth'][:]density = b.variables[Density][:,:,:]b.close()
c = Dataset('result.grav.inv.nc', mode='r')northing = c.variables['Northing'][:]easting = c.variables['Easting'][:]depth = c.variables['Depth'][:]density_a = c.variables[Density][:,:,:]c.close()
 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? Yes  
URL https://figshare.com/articles/dataset/P-wave_velocity_and_density_models_of_Montserrat/13260491/1
 
Description Ecuador project 
Organisation University of Côte d'Azur
Country France 
Sector Academic/University 
PI Contribution I was invited to be project partner in a research expedition to image the Ecuador subduction margin. I could not join the cruise because of COVID restrictions. I have access to the data collected and am planning an undergraduate research project to work on the data.
Collaborator Contribution The lead the field campaign to collect the data and are leading additional efforts to secure funding from the ANR (French research agency)
Impact None yet
Start Year 2019
 
Description Joint inversion 
Organisation Ludwig Maximilian University of Munich (LMU Munich)
Country Germany 
Sector Academic/University 
PI Contribution Provided dataset, computing resources and geological framework.
Collaborator Contribution Provided access to source code and expertise on geophysical data inversion.
Impact MSci dissertation. Paper in preparation. Plans for more shared undergraduate and postgraduate student supervision in 2019. Plans for further code development in collaboration.
Start Year 2018
 
Description Rainbow 
Organisation University of Hawaii
Department Institute of Geophysics and Planetology
Country United States 
Sector Academic/University 
PI Contribution New analysis of data from Mid-ocean ridge
Collaborator Contribution Provided data, previous models and expertise
Impact No outputs yet
Start Year 2018
 
Description Rainbow 
Organisation Woods Hole Oceanographic Institution
Country United States 
Sector Charity/Non Profit 
PI Contribution New analysis of data from Mid-ocean ridge
Collaborator Contribution Provided data, previous models and expertise
Impact No outputs yet
Start Year 2018
 
Description Rock physics 
Organisation Claude Bernard University Lyon 1 (UCBL)
Country France 
Sector Academic/University 
PI Contribution Still in planning stages
Collaborator Contribution Still in planning stages
Impact No outputs yet
Start Year 2019
 
Description Rock physics 
Organisation National Oceanography Centre
Department Marine Geoscience
Country United Kingdom 
Sector Charity/Non Profit 
PI Contribution Still in planning stages
Collaborator Contribution Still in planning stages
Impact No outputs yet
Start Year 2019
 
Title Software library for automated clustering of geophysical models 
Description Software library for testing and validation of clustering algorithms and their application to automated facies classification. Software developed in Python 3 and supported by Jupyter notebooks. The code was developed as part of a student summer project and will be further expanded in the following years. 
Type Of Technology Software 
Year Produced 2019 
Open Source License? Yes  
Impact - Training of postgraduate students - Methodological advancement in geological applications of unsupervised machine learning 
URL https://github.com/msc-acse/acse-9-independent-research-project-wafflescore
 
Description Tutorial on geophysical exploration of the Oceanic Lithosphere 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Undergraduate students
Results and Impact Tutorial developed with data from the fellowship. Delivered to undergraduate students at Imperial College London.
Impact: training of future scientists
Year(s) Of Engagement Activity 2018,2019,2020