Different methods have been studied to predict earthquakes, but the results are still far from optimal. Due to their seemingly dynamic and unpredictable nature, it has been very hard to find data correlating with earthquakes happening. But recently, various research has been done using neural networks, and some has suggested that it could extract valuable information from preceding seismic data. To get a better sense of how seismic data can contain this information, we need to look at how long before an earthquake seismic precursor signals can exist. This paper uses an LSTM model to perform binary classification of the task: ”Given the seismic wave recordings of N stations during T seconds, will an earthquake happen after H seconds?” By varying the parameter H and studying its effect on the prediction accuracy of the NN, results suggest that sensitive information is very present in the seismic data 10 to 15 minutes before a low-magnitude (less than 2.5 on Richters scale) earthquake strikes. We aim to open the way for further research about precursor-based earthquake prediction using neural networks, showing that LSTM can be a good option. We also hope for further research to dig deeper in understanding what the signals in the seismic data are to further improve earthquake prediction.