DeepQuake: Artificial Intelligence for Earthquake Forecasting Using Fine-Grained Climate Data (Proposals Track) Spotlight

Yash Narayan (The Nueva School)

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Abstract

Earthquakes are one of the most catastrophic natural disasters, making accurate, fine-grained, and real-time earthquake forecasting extremely important for the safety and security of human lives. In this work, we propose DeepQuake, a deep learning model for fine-grained earthquake forecasting using time-series data of the horizontal displacement of Earth’s surface measured from continuously operating Global Positioning System (cGPS) data between 2007 to 2019. Recent studies using cGPS data have established a link between transient deformation within earth’s crust including Earth’s elastic response to climate variables such as hydrologic loads, temperature gradients, and atmospheric pressure. DeepQuake’s physics-based pre-processing algorithm extracts relevant features including the x,y, and z components of strain in the earth’s crust, feeding it into an LSTM model to predict key earthquake variables such as the time, location, magnitude, and depth of a future earthquake. Initial results across California, including the Napa Valley and Long Valley Caldera regions, show promising correlations between cGPS data and the earthquake catalog ground truth for a given location and time