Trabajo presentado en la 3rd International Conference on Coupled Processes in Fractured Geological Media: "Observation, Modeling, and Application" (Coufrac 2022), celebrada en Berkeley (USA) entre el 14 y el 16 de noviembre de 2022., Pressure buildup is the standard, but often insufficient, explanation for induced seismicity, , especially when it comes to delayed effects. Coupled processes may play relevant roles, so that they must be accounted for to find the mechanisms triggering induced seismicity, especially when it comes to delayed effects. We illustrate the importance of coupled processes in induced seismicity through the case of the Underground Gas Storage (UGS) project of Castor, Spain, where, cushion gas injection induced hundreds of events, with maximum magnitudes of 4.1, leading to the cancellation of the project. Gas injection lasted for 15 days, and the largest earthquakes occurred 17 days after the stop of injection. Injection overpressures had dissipated by the time of the largest events. The gas was injected at 1.7 km depth, while the induced seismicity occurred at depths ranging from 4 to 10 km. These characteristics of the induced seismicity at Castor pose a challenge on explaining its causes. Coupled processes provide a plausible explanation when combining poromechanical stresses, buoyancy, and shear-slip stress transfer. If coupled processes had been considered in the assessment of the induced seismicity at Castor, the induced earthquakes at Castor could have been anticipated and managed., V.V. acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme through the Starting Grant GEoREST (www.georest.eu) under grant agreement No. 801809. A.V. acknowledges funding from Spanish Ministry of Science and Innovation grant CGL2017-88864-R. IDAEA-CSIC and ICM-CSIC are Centres of Excellence Severo Ochoa (Spanish Ministry of Science and Innovation Grants CEX2018-000794-S and CEX2019-000928-S, respectively, funded by MCIN/AEI/ 10.13039/501100011033)., With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2018-000794-S) y (CEX2019-000928-S), Peer reviewed

European Geosciences Union (EGU) General Assembly, 23-27 May 2022, Vienna, Austria, 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, Peer reviewed