# 3D Convolution Recurrent Neural Networks for Multi-Label Earthquake Magnitude Classification +
## 3 Data and Methods
### Properties of Dataset  
A worldwide earthquake database was published recently, encompassing 1.2 million seismic waveforms obtained from different major networks globally. The data comprises earthquakes that occurred between January 1984 and August 2018. The data catalog is named STEAD, and it was recently made opensource to expedite research in this area. The data is split up into two subsets: local quakes and seismological noise. Typically, earthquakes are monitored, and their position is estimated using measurements from seismometers acquired in east-west, north-south, and vertical directions. Generally, the seismic waveforms often contain different lengths of pre-event data before the primary wave (P-wave); however, the seismic waveforms in the given dataset [29] are well aligned and do not affect the feature extraction process. Nearly 1,050,000 three-component seismological events are included in the database. Each one-minute-long waveform comprises 6000 samples of seismic activity. We extract the vertical component from the Stanford database for the presented work as the model is intended to be installed on an inexpensive personal seismometer equipped with a vertical component sensor designed to detect ground motion in an urban region with strong seismic noise.  
Each waveform in the presented dataset comprises 32 properties, making it suitable for multi-class and multi-label-related research. A major part of the database consists of earthquake waveforms with a magnitude of \(<\)2.5 (on the Richter scale), which is indicated as an attribute named'source magnitude.' To perform this research, we filter the recorded waveforms into several classes by utilizing the features as explained in STEAD. The Stanford database has distinctive waveforms: ones recognized automatically by algorithms and, secondly, manually picked by seismic stations. These are referred to as 'automatic' and'manual' picks. We examined the signals recorded by seismographs to train the algorithms, i.e.,'manual' selections. Training and testing sets have distinct waveforms with no overlapping signals. The'source magnitude' and 'earthquakes' attributes are used to distinguish waveforms and are separated into several categories. We ensure consistency by detrending (i.e., mean shifted) the data, using a band-passed filter from 1-45 Hz, and resampling the signals at 100 Hz. We built our dataset from the Stanford earthquake database using the characteristics indicated above. Our experiment used 93,144 waveforms, 65,208 for learning the model, and 27,936 for testing. The training and test sets are composed of 70% and 30%, respectively. We defined five classes of earthquakes and constructed the dataset necessary to conduct multi-class classification. Additionally, we labeled the signals with the numerical values '(0-1)', '(1-2)', '(2-3)', '(3-4)', '(4-8)', and 'Non-earthquake', where '(0-1)' means we include all the earthquakes with magnitude greater than zero and less than equal to one in this category. The same procedure is valid for all the other categories. The complete distribution of earthquake categories and accompanying training and test sets are shown in Table 1.