P2: Interpretation of gravity and gravity changes in light of uplift processes

Supervisors

Prof. Roland Pail, Prof. Hans-Peter Bunge

Advisors (int.)

Prof. Martin Werner (TUM)

Advisors (ext.)

Dr. Sia Ghelichkhan (ANU Canberra)

Description

Static and temporal gravity models reflect mass distribution and mass transport processes in the Earth system, and therefore, they are important constraints for geodynamic models (PC IV). Global and regional gravity models will be determined from past and current satellite gravity data and complementary ground data. From combined static gravity field models, which will be computed rigorously (based on the solution of fully occupied normal equation matrices) applying high-performance computing techniques, specific fingerprints of the plate, plume and isostatic mode will be derived. They also represent important constraints for modelling dynamic topography. Geoid trends reflect signatures of all three uplift modes. Measured trends can be used as important constraint for their modelling.

One of the greatest methodological challenges is the separation of very strong near-surface signals (related to processes of land hydrology, cryosphere, atmosphere and ocean) from rather weak mantle signals, which, however, might be distinguished by their space-time behavior. We will investigate and adapt modern algorithms of machine-learning to encounter this challenge. As a starting point we will start with standard models of computer vision and will study their performance in an end-to-end simulation environment. In order to properly consider the time behavior, we will investigate Bayesian structural time series models and adapt them to our purpose. By this, for the first time deep-learning algorithms will be applied to tackle the separation problem.

Main objectives

• Computation of combined static geoid models incl. realistic error estimates in terms of variance-covariance matrices as constraints for modelling dynamic topography
• Determination of contemporary geoid rates and error estimates from current satellite gravity mission data
• Projection of achievable performance of future double-pair satellite missions, which are expected in the upcoming years
• Separation of near-surface signals from mantle signals by means of pattern recognition algorithms and machine-learning techniques

RTG coupling

Input:

• Satellite and ground gravity data (with special focus on test areas)
• Predicted geoid rates from adjoint geodynamic model (P6) as input for full-fledged end-to-end simulation studies
• Geometric change rates from P1, P3 and P4 as prior and complementary information on uplift processes
• Requirement from P9 regarding uncertainty quantification

Output:

• Combined static gravity field models tailored to test areas (P1, P8, P10), including uncertainty quantification (P9).
• Geoid rates from temporal gravity models (P1, P4, P8, P10) including uncertainty quantification (P9).
• Predicted geoid rates from future gravity field missions (to P1) including uncertainty quantification (P9).
• Spatial patterns related to plume, plate and isostatic mode (all other Ps).