Hamiltonian Monte Carlo to Characterize Induced Earthquakes: Application to a M_L 3.4 Event in the Groningen Gas Field and the Role of Prior

The Hamiltonian Monte Carlo algorithm is known to be highly efficient when sampling high-dimensional model spaces due to Hamilton's equations guiding the sampling process. For weakly non-linear problems, linearizing the forward problem enhances this efficiency. This study integrates this linearization with geological prior knowledge for...

An efficient probabilistic workflow for estimating induced earthquake parameters in 3D heterogeneous media

We present an efficient probabilistic workflow for the estimation of source parameters of induced seismic events in three-dimensional heterogeneous media. Our workflow exploits a linearized variant of the Hamiltonian Monte Carlo (HMC) algorithm. Compared to traditional Markov chain Monte Carlo (MCMC) algorithms, HMC is highly efficient in...