Determining OBS Clock Drift Using Seismic Interferometry
Determining clock errors in Ocean Bottom Seismometers (OBSs) is crucial for accurate seismic data recording. This study presents a method using ambient seismic noise and seismic interferometry to correct clock drift in OBSs. The program developed employs Python/Fortran to analyze seismic data directly, estimating clock errors for each OBS station and identifying linear drift patterns. The open-source program will be available on GitHub soon.
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Determining OBS clock drift Determining OBS clock drift using ambient seismic noise using ambient seismic noise David Naranjo David Naranjo Supervised by: Supervised by: Philippe Jousset Jousset, , Dr. Dr. Kees Dr. Dr.Laura Laura Parisi Parisi, , Dr. Dr.Philippe Kees Weemstra Weemstra, and Prof. , and Prof. Sigurj n Sigurj nJ nsson J nsson
Clock errors in OBSs Clock errors in OBSs: : OBSs suffer from clock drift (timing of the recorded data deviates from the actual time). Accurate timing is critical for most applications in seismology. We built an open-source Python/Fortran program to correct these errors. The program uses seismic interferometry to correct the errors using the data directly. 2
What is ambient noise What is ambient noise and seismic and seismic interferometry? interferometry? Seismic interferometry principle: Cross- correlating signals create new virtual seismic responses (Wapenaar, 2010). Shift Ambient seismic noise is defined as the continuous noise stream that is being recorded. (Modified from Wapenaar et al., 2010) 3
Calculating the clock error Calculating the clock error The equation that describes clock error for a given instrument is: (Modified from Wapenaar et al., 2010) Shift Shift We can therefore build a system of equations of the form: For more details see Weemstra et al. (2021) 4
Data Data- -set and seismic network set and seismic network In the context of a European geothermal project named IMAGE, a temporal seismic network was installed (http://www.image-fp7.fr/Pages/default.aspx). Seismic data was acquired between April 2014 until August 2015 (1.5 years) 25 OBSs + 30 on-land stations. 5 Data can be retrieved from https://geofon.gfz-potsdam.de/doi/network/4L/2014
Results Results 6
Conclusions Conclusions We estimated the clock errors of each OBS station. We found a linear drift for each of the OBSs. The open-source program will be available next month on GitHub. 7
Thank you very much for Thank you very much for your attention your attention Muchas gracias! Muchas gracias! 8
Acknowledgments Acknowledgments The data used in this project was provided by the IMAGE project (Integrated Methods for Advanced Geothermal Exploration). Thanks to Laura Parisi, Kees Weemstra, Philippe Jousset, and Sigurj n J nsson. Thanks to KAUST, Delft University of Technology, and the GFZ - Potsdam. This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sk odowska- Curie grant agreement No 956965. 9
References References Wapenaar, K. & Fokkema, J., 2006. Green s function representations for seismic interferometry, Geophysics, 71(4), SI33 SI46. Weemstra, Cornelis & de Laat, Janneke & Verdel, Arie & Smets, Pieter. (2020). Systematic recovery of instrumental timing and phase errors using interferometric surface waves retrieved from large-N seismic arrays. Geophysical Journal International. 224. 10.1093/gji/ggaa504. Stehly, L., Campillo, M. & Shapiro, N.M., 2006. A study of the seismic noise from its long-range correlation properties, J. geophys. Res., 111(B10), B10306, doi:10.1029/2005JB004237. 10
