Seismic Inversion Techniques for Earthquake Source Characterization

Seismic inversion encompasses a suite of mathematical and computational methods designed to reconstruct the geometry, slip distribution and dynamic evolution of an earthquake source from observed ground motion or surface deformation. Data inputs range from broadband seismic waveforms and teleseismic body waves to space-geodetic measurements such as GPS and interferometric synthetic aperture radar (InSAR). Linear inversion schemes permit rapid recovery of moment tensors or distributed slip under the assumption of small perturbations, while non-linear approaches iteratively refine fault geometry and rheological parameters. Bayesian frameworks have been adopted to quantify epistemic and observational uncertainties, yielding posterior probability distributions rather than single best-fit models. Recent advances in high-performance computing, automated data processing chains and robust uncertainty quantification have greatly enhanced both the speed and reliability of source characterisation. These developments underpin improved seismic hazard assessment, tsunami warning systems and a more nuanced understanding of fault mechanics across diverse tectonic settings.

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Automated inversion pipelines now enable real-time modelling of fault geometry and slip distribution from InSAR displacement fields. A fully automatic processing chain selects optimal radar interferograms, conducts non-linear fault parameter inversion and linear slip estimation, and delivers emergency-phase solutions for a global suite of M5.5–8.2 events. In parallel, a Bayesian inversion framework for multiple geodetic data sets has demonstrated rapid retrieval of posterior distributions for source parameters and uncertainties via Markov chain Monte Carlo sampling, supporting operational deployment in volcano observatories and rapid-response tectonic studies. Complementary work has addressed the impact of uncertain fault geometry on inversion results by embedding epistemic errors in the misfit covariance matrix. Sensitivity analyses to fault dip and position perturbations show that including geometry uncertainties yields more robust slip models and mitigates shallow-slip deficits for large megathrust and continental earthquakes.

Seismic Inversion Techniques for Earthquake Source Characterization publication trend

The graph below shows the total number of articles in seismic inversion techniques for earthquake source characterization across all publications each year (not limited to Nature Index journals).

Technical terms

Seismic inversion: Mathematical procedure for inferring earthquake source parameters from observed seismic or geodetic data.

Linear inversion: Technique assuming a linear relationship between observations and model parameters, often applied to moment tensor and slip distribution under small perturbations.

Non-linear inversion: Iterative method accounting for non-linear relationships between data and model parameters, essential for resolving complex fault geometry and slip.

Bayesian framework: Statistical approach that treats model parameters as random variables and estimates their posterior probability distributions, incorporating prior information and data uncertainty.

Markov chain Monte Carlo (MCMC): Sampling algorithm used in Bayesian inversion to explore posterior distributions of model parameters.

Covariance matrix: Mathematical representation of uncertainties in data and model parameters, used to weight misfits and include epistemic errors in inversion.

Interferometric Synthetic Aperture Radar (InSAR): Satellite-based remote-sensing technique that measures ground deformation by comparing radar phase signals acquired before and after an earthquake.

References

1. Automatic seismic source modeling of InSAR displacements. International Journal of Applied Earth Observation and Geoinformation (2023).
2. Inversion of Surface Deformation Data for Rapid Estimates of Source Parameters and Uncertainties: A Bayesian Approach. Geochemistry Geophysics Geosystems (2018).
3. Accounting for uncertain fault geometry in earthquake source inversions – I: theory and simplified application. Geophysical Journal International (2018).