Mb Magnitude Station-station Spatial Correlations and Station Mb Biases
Description:
Station mb’s from ISC (2000-2020) and NEIC (2017-2021) catalogs are examined for station effects (station amplitude biases). Analyses include a General Linear Model (GLM) as well as statistics of station residuals w.r.t. the network average. Residuals were examined versus epicentral distance, network magnitude and with time. Consistent station biases are observed in both catalogs with regional spatial correlation and consistent over time. That is, most stations systematically report a magnitude higher or lower w.r.t. the network average. The station biases exhibit spatial patterns that correlate to geophysical/tectonic provinces and may contain valuable geophysical information. A global station-station correlation structure may be extracted. That is, mb’s reported by two stations within 100 km will generally have a correlation coefficient >50%. While the mb network standard deviation is generally 0.35-0.4 units, any two stations less than 100 km apart will likely report station-station standard deviation 0.17-0.2. Station biases for most sites are remarkably stable over time with events from multiple source regions. This implies the leading bias mechanism is the local mantle-crust response. The distribution of station biases closely resembles the distribution of network residuals. Past work in world-wide explosion seismology observed reciprocal relationships between regional station mb biases and explosion teleseismic mb biases. Consequently network mb magnitudes may exhibit regional baseline biases due to these mantle-crustal responses.
Session: Advances in Reliable Earthquake Source Parameter Estimation [Poster]
Type: Poster
Date: 4/16/2025
Presentation Time: 08:00 AM (local time)
Presenting Author: Keith
Student Presenter: No
Invited Presentation:
Poster Number: 30
Authors
Keith McLaughlin Presenting Author Corresponding Author mclaughlin0kl@me.com Leidos, Inc. |
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Mb Magnitude Station-station Spatial Correlations and Station Mb Biases
Category
Advances in Reliable Earthquake Source Parameter Estimation