The initial months of the projects will be mainly devoted to an in-depth study of earthquake seismology, fault mechanics, and state-of-the-art machine learning techniques. The student will conduct a Bibliographic research and receive training in programming tools (python, machine learning, data analysis). The student will also become acquainted with protocols and techniques of rock mechanics laboratory experiments.
My the mid- to end-of-year, a first series of broad-band seismic network data from public databases (IRIS, EPOS) will be sourced, focussing on earthquake sequences on major intra-plate and inter-plate faults. Short duration (minutes to hours) laboratory experiments will be conducted on experimental earthquake faults. The existing CNN will be trained and tested on these data, and its efficiency will be compared with the results obtained on the Japanese earthquake series, to evaluate the portabilty and generality of the method to different tectonic environments.
The second year will be devoted to the design and systematic testing of alternative version of the CNN algorithm, and to broaden the database on which the algorithms are being tested. The most accurate and efficient type of algorithms will be selected, and expanded into a full analytic machine-learning suite. The accuracy will be cast in terms of probability of forecasting successfully within a given time, space and magnitude window, versus over-prediction (cry wolf effect). The data will be analysed to determine what type of signal or combination of signals triggers the detection of a precursor. This will be interpreted in terms of a changes in the slip dynamics in the fault system in the preparatory phase of an earthquake, and integrated in a model of the earthquake cycle. Time should be allowed to finalise the publication and dissemination of main results, and to the write-up of the PhD manuscript.
The work started in the second year will be pursued, with the additional testing of the developed analytical tools. The practical implementation of the algorithm as a possible probabilistic forecasting tool for earthquake rupture will be investigated. Automatic download of waveform data and meta data from a regional network in a target active seismic area will be performed using query software (e.g. ObsPyLoad) and fed into a real-time, continuously running 24h algorithm, with an aim at the possible integration in Earthquake Early Warning protocols. The forecasting accuracy algorithm will also be tested on data from in induced-seismicity sites (e.g., borehole injection operations for geothermal energy), with possible implication for the current Ã¢â‚¬Å“traffic-lightÃ¢â‚¬Â safety systems. Processing of regional seismic data will also be implemented with the aim to augment the number of detected events and provide an estimate of magnitude and location. magnitude. The student and the supervisory team will start to organise the results obtained into publishable form, with the aim to disseminate them through articles in scientific journals, conferences, seminars, web pages.
Time should be allowed to finalise and confirm the tests, to the publication and dissemination of main results, and to the write-up of the PhD manuscript.