The Influence of Variations in Prior Information and Design Criteria on Optimal Designs of Seismic Source Location Surveys
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ID: 323728
2026
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Abstract
Summary Estimating accurate locations of seismic sources requires appropriately designed surveys. Optimal designs maximise location accuracy or any other desired criterion, given a budget of equipment, time, or other resources. In this study, we empirically assess how variations in prior information and design criteria affect the performance of optimised seismic networks using both real and synthetic data from the Cuolm da Vi slope instability. We benchmark network designs using 95 controlled explosions recorded by 878 sensors. We explore how prior information about source locations and velocity models (and their uncertainties), data correlations, signal-to-noise ratio reduction with distance, optimisation methods, and design criteria influence the quality of optimised receiver layouts. We find that designs optimised using fully non-linear Bayesian experimental design methods consistently outperform non-optimised designs, with median improvements of approximately 25% in source localisation accuracy for our real data test case. The effectiveness of optimising designs increases with the number of receivers, up to around 6 to 10 in an area of ∼0.3 km2, after which the marginal gain drops off. Our results highlight the importance of accurately modelling the prior distribution of source locations, and of accounting for at least one of either attenuation effects or velocity uncertainties when designing seismic networks. Optimised designs are robust to reasonable variations in design parameters, particularly the number of events used to sample the prior distribution, information gain estimation methods, and optimisation approaches. This work provides practical guidance for deploying optimal seismic networks in complex geological settings, where accurate source locations are essential for hazard monitoring.
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| Authors | Dominik Strutz, Tjeerd Kiers, Cédric Schmelzbach, Hansruedi Maurer, Andrew Curtis |
| Journal | geophysical journal international |
| Year | 2026 |
| DOI |
10.1093/gji/ggag301
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| URL | |
| Keywords | Keywords not found |
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