EGU22-7844, updated on 28 Mar 2022
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Performance of Deep Learning pickers in routine network processing applications

Luis M. Fernandez-Prieto1, José Enrique García2, Antonio Villaseñor1, Verónica Sanz3, Jean-Baptiste Ammirati4, Eduardo Díaz5, and Carmen García2
Luis M. Fernandez-Prieto et al.
  • 1Institute of Marine Sciences (ICM), CSIC, Barcelona, Spain
  • 2Instituto de Física Corpuscular (IFIC), Universitat de Valencia-CSIC, Valencia, Spain
  • 3Departament de Fisica Teorica and IFIC, Universitat de Valencia-CSIC, Burjassot, Spain
  • 4Departamento de Geología, Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Chile
  • 5Centro Geofísico de Canarias, Instituto Geográfico Nacional, Santa Cruz de Tenerife, Spain

In recent years there have been a great progress in earthquake detection and picking arrival times of P and S phases using Deep Learning algorithms. However, the general adoption of these methods for the routine processing of monitoring networks has been held back by factors such as the availability of well documented software, computational resources, and a gap in knowledge of these methods. We have analyzed recent available Deep Learning pickers, comparing the results against data picked by a human operartor and against non-Deep Learning programs. We have used data recorded in several locations, with different characteristics and triggering mechanisms, such as volcanic eruptions, induced seismicity and local eartquakes, recorded using different types of instruments. We have found that the Deep Learning algorithms are able to achieve results comparables to a human operator, and several times better than a classical program, specially in data with a low signal to noise ratio. They are very efficient at ignoring large amplitude transient noise and at picking S waves, a task that is often difficult even for experienced analysts, and they require very few parameters to tune (often only the probability threshold) so an in-depth knowledge of neural networks is not required. (This research has been funded by  Spanish Ministry of Science and Innovation MICINN/AEI/10.13039/501100011033 grants CGL2017-88864-R and PRE2018-084986).

How to cite: Fernandez-Prieto, L. M., García, J. E., Villaseñor, A., Sanz, V., Ammirati, J.-B., Díaz, E., and García, C.: Performance of Deep Learning pickers in routine network processing applications, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7844,, 2022.