A HYBRID MACHINE LEARNING APPROACH FOR REAL-TIME FORECASTING OF MINUTE-LEVEL SEISMIC SIGNAL AMPLITUDES
Real-time interpretation of seismic waveform data is essential for monitoring signal dynamics and detecting abnormal activity. However, forecasting seismic amplitudes remains challenging because seismic signals are highly variable, noisy, and non-stationary. This study presents an automated, gap-aware real-time forecas...