Hypocenter Inversion Eval

hypocenter-inversion-eval

HypoSVI: Hypocenter inversion with Stein variational inference and Physics Informed Neural Networks — Smith et al. (2021) (arXiv:2101.03271, 2021)

What this evaluates

Evaluates the accuracy and uncertainty quantification of a physics-informed neural network for locating earthquake hypocenters using synthetic seismic arrival times.

Datasets

• Synthetic Seismic Array — total ?; splits: test (-1); repo https://github.com/Ulvetanna/HypoSVI

Metrics

• location uncertainty (primary) — range: km | percent
  • Distance between the recovered hypocenter and the true hypocenter, assessed alongside whether the true location falls within the predicted 95% posterior uncertainty contour.

Input / output format

Input: Synthetic phase arrival times at fixed elevation (0 km) with 0.01s picking uncertainty, paired with station coordinates.

Output: Predicted hypocenter coordinates (latitude, longitude, depth) and a posterior distribution representing location uncertainty.

Scoring recipe

def score(predictions, gold):
    pred_loc = predictions['hypocenter']
    true_loc = gold['true_hypocenter']
    dist = euclidean_distance(pred_loc, true_loc)
    contour = predictions['95pct_contour']
    coverage = true_loc in contour
    return {'distance_error_km': dist, 'coverage_rate': coverage}

Common pitfalls

• Synthetic tests use regularly spaced stations and a simplified velocity model, which may overestimate performance on real, clustered seismic networks.
• The evaluation relies on visual/contour comparison rather than a standardized numerical metric like RMSE, making cross-study comparison difficult.

Evidence (verbatim from paper)

The recovered optimal hypocentre and location uncertainty are then compared with the true earthquake locations and an expected 95 percentile contour from a the solution of a grid-search inversion.

Citation

@misc{smith2021hyposvi,
  title={HypoSVI: Hypocenter inversion with Stein variational inference and Physics Informed Neural Networks},
  author={Smith et al. (2021)},
  year={2021},
  note={arXiv:2101.03271}
}

• arXiv: 2101.03271