  Amongst the available technologies for earthquake research, remote sensing
has been commonly used due to its unique features such as fast imaging and wide
image-acquisition range. Nevertheless, early studies on pre-earthquake and
remote-sensing anomalies are mostly oriented towards anomaly identification and
analysis of a single physical parameter. Many analyses are based on singular
events, which provide a lack of understanding of this complex natural
phenomenon because usually, the earthquake signals are hidden in the
environmental noise. The universality of such analysis still is not being
demonstrated on a worldwide scale. In this paper, we investigate physical and
dynamic changes of seismic data and thereby develop a novel machine learning
method, namely Inverse Boosting Pruning Trees (IBPT), to issue short-term
forecast based on the satellite data of 1,371 earthquakes of magnitude six or
above due to their impact on the environment. We have analyzed and compared our
proposed framework against several states of the art machine learning methods
using ten different infrared and hyperspectral measurements collected between
2006 and 2013. Our proposed method outperforms all the six selected baselines
and shows a strong capability in improving the likelihood of earthquake
forecasting across different earthquake databases.
