Short-term earthquake forecasting using electromagnetic and geoacoustic observations via contrastive learning

Different observations provide earthquake-related information from various perspectives, and effectively leveraging them is essential for enhancing forecasting. Existing data-driven methods primarily rely on concatenation, directly aligning features (e.g., the absolute mean) extracted from different observations side by side. However, this naive approach ignores that each observation reflects a different aspect of the same physical process and inadequately explores cross-observation interactions. To address these issues, we propose CL4EF, a contrastive learning framework that leverages electromagnetic and geoacoustic observations for earthquake forecasting. Specifically, we introduce the synchronous response consistency hypothesis, assuming that different observations within the same time window should respond consistently to the same physical process. Following this hypothesis, we design a contrastive loss that attracts observation pairs from the same station and time window (positives) and repulses others (negatives), enabling cross-observation interaction modeling for the downstream forecasting task. Experimental results demonstrate that CL4EF achieves state-of-the-art performance, improving AUC by 22%. The spatial distribution of forecast probabilities reveals alignment with active fault zones, suggesting the model’s capacity to extract meaningful information for earthquake forecasting. As a result, this study contributes a scalable approach for integrating heterogeneous observations in geosciences and offers new insights into short-term earthquake forecasting.