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21-44. Improving short-term induced and natural seismicity forecasts with machine learning


Closing Date: November 1, 2022

This Research Opportunity will be filled depending on the availability of funds. All application materials must be submitted through USAJobs by 11:59 pm, US Eastern Standard Time, on the closing date.

Please communicate with individual Research Advisor(s) on the right to discuss project ideas and answer specific questions about the Research Opportunity.

How to Apply


Many features of earthquake sequences are not captured well by current earthquake forecasting models, including 1) large inter-sequence variations in aftershock productivity, 2) correlations between aftershock locations and background seismicity, stress changes, and physical properties of the crust, 3) changes in the size distribution of earthquakes (e.g., b-value), which may inform whether or not an earthquake is a foreshock to a larger event, 4) variability/correlations of Omori parameters describing aftershock decay, and 5) swarms, induced seismicity, and other transients. Capturing patterns within these signals requires new approaches, as does finding ways to use the substantial catalog information below the magnitude of completeness.  The confluence of newly available, modern high-resolution seismic catalogs – which contain orders of magnitude more data than conventional catalogs – and recent advances in supervised and unsupervised machine learning, particularly deep learning techniques, provide a unique opportunity to mine seismic catalogs for deeper levels of earthquake predictability than have previously been found. 

The goal of this opportunity is to develop machine-learning approaches to improve earthquake forecasting capabilities – namely, to better predict the rate, locations, and sizes of earthquakes, and to supplement or potentially replace the empirical epidemic-type aftershock sequence (ETAS) models currently being used by the USGS for operational earthquake forecasting.  These approaches could also be used to improve seismic hazard forecasts, addressing a gap in providing hazard forecasts in regions with rapidly evolving seismicity.  In their proposal, applicants should describe methods and architectures amenable to extracting relevant features from catalogs of earthquake times and locations, and to generating stochastic representations of future activity.   

We welcome applications that focus on natural seismicity and/or induced seismicity.  Induced seismicity, in particular, is an area where current seismic forecast methods are ill-suited – as are natural earthquake swarms that are driven by time-varying physical processes such as fluid injection, magmatic intrusion, or slow-slip events. Induced sequences could provide good opportunities to develop and test forecasts for sequences with known driving processes and may be better captured by developing forecasts on shorter timescales, e.g., forecasts with durations of weeks to months, or by developing forecasts that can explicitly account for time-varying background seismicity rates.  

We encourage applicants to consider ways to use other types of geophysical data such as fault information, geodetic data, or in the case of induced seismicity, fluid injection data and geomechanical information, in their model. Applicants should discuss their plans for model validation and testing, and comparisons to conventional forecasting methods (e.g., ETAS). 

Interested applicants are strongly encouraged to contact the Research Advisor(s) early in the application process to discuss project ideas. 


Beroza, G.C., Segou, M. and Mostafa Mousavi, S., Machine learning and earthquake forecasting—next steps, Nat. Commun. 12, 4761 (2021), doi: 10.1038/s41467-021-24952-6 

Proposed Duty Station(s): Pasadena, California; Moffett Field, California; Seattle, Washington; Golden Colorado 

 Computer science, geophysics, seismology, statistics, or related fields (candidates holding a Ph.D. in other disciplines, but with extensive knowledge and skills relevant to the Research Opportunity may be considered). 

Qualifications:  Applicants must meet one of the following qualifications: Research Geophysicist, Research Computer Scientist, or Research Statistician.   

(This type of research is performed by those who have backgrounds for the occupations stated above.  However, other titles may be applicable depending on the applicant's background, education, and research proposal. The final classification of the position will be made by the Human Resources specialist.)

Human Resources Office Contact:  Audrey Tsujita, 916-278-9395,

Apply Here