Real-Time Prediction of S-Wave Accelerograms from P-Wave Signals Using LSTM Networks with Integrated Fragility-Based Structural Damage Alerts for Induced Seismicity

Early warning of structural damage from induced seismic events requires rapid and reliable ground motion forecasting. This study presents a novel real-time framework that couples a deep learning approach with structural fragility assessment to generate immediate damage alerts following the onset of seismic shaking. Long Short-Term Memory (LSTM) neural networks are employed to predict full S-wave accelerograms from initial P-wave inputs, trained and tested on accelerometric records from induced seismicity scenarios. The predicted S-wave motion is then used as input for a suite of fragility curves in real time to estimate the probability of structural damage for masonry buildings typical in rural areas of geothermal platforms. The proposed method captures both the temporal evolution of shaking and the structural response potential, offering critical seconds of lead time for automated decision-making systems. Results demonstrate high predictive accuracy of the LSTM model and effective early classification of structural risk. This integrated system provides a practical tool for early warning or rapid response in regions experiencing anthropogenic seismicity, such as those affected by geothermal operations.