# 3D Convolution Recurrent Neural Networks for Multi-Label Earthquake Magnitude Classification +
###### Abstract  
We examine a classification task in which signals of naturally occurring earthquakes are categorized ranging from minor to major, based on their magnitude. Generalized to a single-label classification task, most prior investigations have focused on assessing whether an earthquake's magnitude falls into the minor or large categories. This procedure is often not practical since the tremor it generates has a wide range of variation in the neighboring regions based on the distance, depth, type of surface, and several other factors. We present an integrated 3-dimensional convolutional recurrent neural network (3D-CNN-RNN) trained to classify the seismic waveforms into multiple categories based on the problem formulation. Recent studies demonstrate using artificial intelligence-based techniques in earthquake detection and location estimation tasks with progress in collecting seismic data. However, less work has been performed in classifying the seismic signals into single or multiple categories. We leverage the use of a benchmark dataset comprising of earthquake waveforms having different magnitude and present 3D-CNN-RNN, a highly scalable neural network for multi-label classification problems. End-to-end learning has become a conventional approach in audio and image-related classification studies. However, for seismic signals classification, it has yet to be established. In this study, we propose to deploy the trained model on personal seismometers to effectively categorize earthquakes and increase the response time by leveraging the data-centric approaches. For this purpose, firstly, we transform the existing benchmark dataset into a series of multi-label examples. Secondly, we develop a novel 3D-CNN-RNN model for multi-label seismic event classification. Finally, we validate and evaluate the learned model with unseen seismic waveforms instances and report whether a specific event is associated with a particular class or not. Experimental results demonstrate the superiority and effectiveness of the proposed approach on unseen data using the multi-label classifier. multi-label classification; earthquake signal detection; 3D CNN; GRU; log-Mel spectrogram; disaster response +
Footnote †: _Applied_  
earthquake recognition as a single-label task: determining whether a seismic signal belongs to an earthquake or a seismic noise. Nevertheless, it is often not natural to assume earthquake detection as a binary classification task. There are primarily two explanations for this inappropriateness. To begin, the limitation of addressing earthquake detection as a binary classification task is incompleteness; a single earthquake label may not accurately describe the earthquake category for a particular seismic wave. For example, it will be a subjective value, but it can represent a human impression of the earthquake better than the physical value of magnitude. Secondly, it also demonstrates uncertainty, i.e., the class boundaries among many earthquake categories are ambiguous essentially. We see from many reported earthquakes worldwide; the series of effects from an earthquake depends on the shallowness and duration of an earthquake and its magnitude. Sometimes, two earthquakes slightly differ between their magnitudes but demonstrate the same effect on the ground. When referring to an intermediate earthquake condition, it is difficult to identify whether the earthquake category is \"minor\" or \"slightly major\" in terms of its effect on the ground. Currently, available earthquake datasets do not provide information about the intensity or felt reports. For this reason, we have converted the multiclass problem to a multi-label problem where each earthquake waveform can have multiple categories simultaneously, i.e., for example, when an earthquake happens, it has one value generally known as the magnitude that describes the size. However, we can have many intensity values distributed around the epic center and located in different geographic areas for this particular earthquake.