Preprints
https://doi.org/10.5194/se-2021-156
https://doi.org/10.5194/se-2021-156
 
09 Feb 2022
09 Feb 2022
Status: this preprint is currently under review for the journal SE.

Efficient probabilistic inversion for induced earthquake parameters in 3D heterogeneous media

La Ode Marzujriban Masfara1, Thomas Cullison2, and Cornelis Weemstra1,3 La Ode Marzujriban Masfara et al.
  • 1Delft University of Technology, Stevinweg 1, 2628 CN, Delft, The Netherlands
  • 2Utrecht University, Heidelberglaan 8, 3584 CS Utrecht, The Netherlands
  • 3Royal Netherlands Meteorological Institute, Utrechtseweg 297, 3730 AE, De Bilt, The Netherlands

Abstract. 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 sampling high-dimensional model spaces. Through a linearization of the forward problem around the prior mean (i.e., the "best" initial model), this efficiency can be further improved. We show, however, that this linearization leads to a performance in which the output of an HMC chain strongly depends on the quality of the prior; in particular, because not all (induced) earthquake model parameters have a linear relationship with the recordings observed at the surface. To mitigate the importance of an accurate prior, we integrate the linearized HMC scheme into a workflow that (i) allows for a weak prior through linearization around various (initial) centroid locations, (ii) is able to converge to the mode containing the model with the (global) minimum misfit by means of an iterative HMC approach, and (iii) uses variance reduction as a criterion to include the output of individual Markov chains in the estimation of the posterior probability. Using a three-dimensional heterogeneous subsurface model of the Groningen gas field, we simulate an induced earthquake to test our workflow. We then demonstrate the virtue of our workflow by estimating the event's centroid (three parameters), moment tensor (six parameters), and the earthquake's origin time. We find that our workflow is able to recover the posterior probability of these source parameters rather well, even when the prior model information is inaccurate, imprecise, or both inaccurate and imprecise.

La Ode Marzujriban Masfara et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on se-2021-156', Andreas Fichtner, 08 Apr 2022
  • RC2: 'Comment on se-2021-156', Tom Kettlety, 08 Apr 2022

La Ode Marzujriban Masfara et al.

La Ode Marzujriban Masfara et al.

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Short summary
Induced earthquakes are natural phenomena on which the events are associated with human activities. Although the magnitudes of these events are mostly smaller than tectonic events, in some cases, the magnitudes can be high enough to damage buildings near the event's location. To study these (high magnitudes) induced events, we developed a workflow on which the recorded data from an earthquake is used to describe the source and monitor the area for other (potentially high magnitude) earthquakes.