Static and quasi-static inversion of fault slip during laboratory earthquakes

SUMMARY Inferring the spatiotemporal distribution of slip during earthquakes remains a significant challenge due to the high dimensionality and ill-posed nature of the inverse problem. As a result, finite-source inversions typically rely on simplified assumptions. Moreover, in the absence of ground-truth measurements, the performance of inversion methods can only be evaluated through synthetic tests. Laboratory earthquakes offer a valuable alternative by providing ‘simulated real data’ and ground truth observations under controlled conditions, enabling a more reliable evaluation of source inversion procedures. In this study, we present static and quasi-static slip inversion results from data recorded during laboratory earthquakes. Each event is instrumented with 20 accelerometers along the fault, and the recorded acceleration data are used to invert for the slip history. We consider two different types of Green’s functions (${\bf GF}$): simplistic ${\bf GF}$ assuming a homogeneous elastic half-space and realistic ${\bf GF}$ computed by finite element modelling of the experimental setup. The inversion results are then compared to direct observations of fault slip and rupture velocity obtained independently during the experiments. Our results show that, regardless of the ${\bf GF}$ used, the inversions fit well with the data and result in small formal uncertainties of model parameters. However, only the inversion with realistic ${\bf GF}$ yields slip distributions consistent with the true fault slip measurements and successfully recovers the distribution of rupture velocity along the fault. These findings emphasize the critical role of ${\bf GF}$ selection in accurately resolving slip dynamics and highlight an important distinction in Bayesian inversion: while posterior uncertainty quantification is essential, it does not guarantee accuracy, especially if forward modelling uncertainties are not properly accounted for. Thus, confidence in inversion results must be paired with careful modelling choices to ensure physical reliability.