Background Seismic signals are useful for earthquake detection and classification. Therefore, various artificial intelligence (AI) models have been used with seismic signals to develop automated earthquake detection systems. Our primary goal is to present an accurate feature engineering model for earthquake detection and classification using seismic signals.

Material and model We have used a public dataset in this work containing three categories: (1) noise, (2) P waves, and (3) S waves. P and S waves are used to define earthquakes. We have presented two applied use cases using this dataset: (i) earthquake detection and (ii) wave classification. In this work, a new textural feature extractor has been presented by using a graph pattern similar to a butterfly. Thus, this feature extraction function is named Butterfly pattern (BFPat). We have created a new feature engineering architecture by deploying BFPat, statistics, and wavelet packet decomposition (WPD) functions. The recommended BFPat and statistics have been applied to the wavelet bands created by WPD and the raw seismic signals. Multilevel features have been extracted from both frequency and space domains. The used dataset contains signals with three channels. Using these three channels, seven signals have been created. Seven feature vectors have been created from 7 input signals used in this study. The most meaningful/informative features from the generated feature set are then selected using the iterative neighborhood component analysis feature selector method. Seven chosen feature vectors have been considered as inputs of the two shallow classifiers: k nearest neighbors (kNN) and support vector machine (SVM). A total of 14 (=7 × 2) results have been obtained in the classification phase. A majority voting process was applied in the last phase to choose the best results and improve the classification performance.

Results We have presented two use cases for our new BFPat method in this work to obtain superior results. Our model reached an accuracy of 99.58% in detecting the earthquake detection and 93.13% accuracy in 3-class classifications of waves.

Conclusions Our recommended model has achieved over 90% classification performance for both cases. Also, we have presented the most valuable channel and combinations in our work. Our developed system is ready to be tested with a bigger database