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Article Dans Une Revue Geophysical Research Letters Année : 2023

Weighing Geophysical Data With Trans‐Dimensional Algorithms: An Earthquake Location Case Study

Nicola Piana Agostinetti
Alberto Malinverno
Christina Dahner
  • Fonction : Auteur
Savka Dineva
Eduard Kissling

Résumé

Measured scientific data make possible a quantitative analysis of observations (e.g., a seismometer can record seismic waves, which are only felt by humans as transient phenomena). Scientific data are routinely processed before making inferences on the spatio-temporal distribution of physical quantities and/or physical processes (e.g., arrival times for seismic P-waves are extracted from continuous seismic recordings to infer the position of a seismic source). Processing steps can be necessary to remove spurious data (e.g., arrival times from seismic sensors that are not synchronized), but also to enhance data to better represent the most relevant signal for the problem being investigated (e.g., seismic waveforms may be filtered in the frequency domain before picking relative arrival times by cross-correlation (VanDecar & Crosson, 1990), for a clear identification of phases and for removing noise-site-effect interferences with targeted signal wavelet). Geo-scientific data are especially challenging, because they are generally used to make inferences on physical quantities which are not directly measurable, but need to be estimated by solving an inverse problem (Tarantola, 2005), where processed measurements (e.g., P-wave arrival times or maximum wavelet amplitudes) are combined with hypotheses about the physics of the system (e.g., models of seismic wave propagation in the rock volume or seismic energy released by source). In this case, data processing typically includes selecting a subset of the data that is most relevant for the problem at hand (e.g., by removing arrival times for P-waves that do not travel directly from source to receiver). Additionally, seemingly less accurate data are often excluded or apriori downweighted to make them less influential in the final solution (e.g., arrival times recorded at distant seismic sensors that are likely to show larger effect of influence by attenuation or scattering along the ray-path). These data processing steps are usually based on expert opinion, but expert decisions made a priori before solving the inverse problem can be somewhat arbitrary and bias the inversion results.
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Dates et versions

hal-04313022 , version 1 (28-11-2023)

Identifiants

Citer

Nicola Piana Agostinetti, Alberto Malinverno, Thomas Bodin, Christina Dahner, Savka Dineva, et al.. Weighing Geophysical Data With Trans‐Dimensional Algorithms: An Earthquake Location Case Study. Geophysical Research Letters, 2023, 50, ⟨10.1029/2023gl102983⟩. ⟨hal-04313022⟩
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