Streamlining Multi-Data Geophysical Inference with BayesBridge

The Earth's interior structure must be inferred from geophysical observations collected at the surface. Compared to just a few decades ago, the amount of geophysical data available today is voluminous and growing exponentially. Dense seismic networks like USArray, AlpArray, and AusArray now enable joint inversions of various geophysical data types to maximise subsurface resolution at scales ranging from local to continental. However, the practical application of joint inversions faces several challenges:

• Various geophysical techniques typically probe different scales and depths, complicating the choice of an appropriate discretisation for the Earth's interior.
• Different geophysical observables may respond to physical properties that are not directly related (e.g., density and electrical conductivity), making the construction of self-consistent parameterisations a non-trivial task.
• Without a comprehensive understanding of noise characteristics, standard methods require assigning weights to different data sets, yet robust choices remain elusive.

Capable of overcoming these recognised challenges and allowing estimates of model uncertainty, probabilistic inversions have grown in popularity in the geosciences over the last few decades, and have been successfully applied to specific modelling problems. Here, we present BayesBridge, a user-friendly Python package for generalised transdimensional and hierarchical Bayesian inference. Computationally optimised through Cython, our software offers multi-processing capabilities and runs smoothly on both standard computers and computer clusters. As opposed to existing software libraries, BayesBridge provides high-level functionalities to define complex parameterisations, with prior probabilities (defined by uniform, Gaussian, or custom density functions) that may or may not be dependent on depth and/or geographic coordinates. By default, BayesBridge employs reversible-jump Markov chain Monte Carlo for sampling the posterior probability, with the option of parallel tempering, but its low-level features enable effortless implementations of arbitrary sampling criteria. Utilising object-oriented programming principles, BayesBridge ensures that each component of the inversion -- such as the discretisation, the physical properties to be inferred, and the data noise -- is a self-contained unit. This design facilitates the seamless integration of various forward solvers and data sets, promoting the use of multiple data types in geophysical inversions.