Cross-validation of independent ultra-low-frequency magnetic recording systems for active fault studies

Abstract When working with ultra-low-frequency (ULF) magnetic datasets, as with most geophysical time-series data, it is important to be able to distinguish between cultural signals, internal instrument noise, and natural external signals with their induced telluric fields. This distinction is commo...

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Bibliographic Details
Main Authors: Can Wang, Chen Bin, Lilianna E. Christman, Jonathan M. G. Glen, Simon L. Klemperer, Darcy K. McPhee, Karl N. Kappler, Tom E. Bleier, J. Clark Dunson
Format: Article
Language:English
Published: SpringerOpen 2018-04-01
Series:Earth, Planets and Space
Subjects:
Online Access:http://link.springer.com/article/10.1186/s40623-018-0823-7
Description
Summary:Abstract When working with ultra-low-frequency (ULF) magnetic datasets, as with most geophysical time-series data, it is important to be able to distinguish between cultural signals, internal instrument noise, and natural external signals with their induced telluric fields. This distinction is commonly attempted using simultaneously recorded data from a spatially remote reference site. Here, instead, we compared data recorded by two systems with different instrumental characteristics at the same location over the same time period. We collocated two independent ULF magnetic systems, one from the QuakeFinder network and the other from the United States Geological Survey (USGS)-Stanford network, in order to cross-compare their data, characterize data reproducibility, and characterize signal origin. In addition, we used simultaneous measurements at a remote geomagnetic observatory to distinguish global atmospheric signals from local cultural signals. We demonstrated that the QuakeFinder and USGS-Stanford systems have excellent coherence, despite their different sensors and digitizers. Rare instances of isolated signals recorded by only one system or only one sensor indicate that caution is needed when attributing specific recorded signal features to specific origins.
ISSN:1880-5981