## 3 Methodology
### Model Description  
In this approach, we design jointly neural network-based feature extraction, ensemble learning, and sophisticated data preprocessing to construct a robust framework for microtremor event classification. The approach leverages unsupervised and supervised learning with imbalance dataset processing of relevant seismic features, enabling the classification of seismic events into their respective categories with high accuracy while preventing the model from overfitting to establish generalizability. Figure 1: Seismic data processing and HVSR feature extraction workflow. Figure 2 first takes raw seismic features in three filtered component vectors: East (E), North (N), and Vertical (Z). First, we unify vector sizes by padding or trimming them to some fixed length; in this approach, we choose 4. This ensures that the model treats every vector identically, regardless of whatever original length variability existed in the data. The pad_or_trim operationdoes this by adding zeros to the end of shorter vectors or truncating longer ones to make all inputs to the data identical in several dimensions before further processing. Finally, padded vectors for each seismic component are concatenated into one combined feature vector. This concatenation, itself, forms the basis of the neural network input, where each instance is represented as a flattened vector of the concatenated East, North, and Vertical components, enabling the model to learn complex relationships among them. Figure 2: The proposed approach. We use a standard scaler for feature data normalization by transforming the data to a standard normal distribution with zero mean and unit variance, making neural network training very stable. We use an autoencoder, an unsupervised neural network usually trained for practical compression of an input, to extract a compressed representation of seismic data. At its core, are the autoencoder model comprises the following components: an encoder, a mapping that represents the input to a lower dimensionality latent space, and a decoder that reconstructs a compressed representation. It captures structural features from the input and generates embeddings containing significant information about the input. Figure 3 depicts the non-conventional approach to imbalanced data, with an encoder of a dense-layer rectified linear activation function (ReLU) that extracts features and compresses the input into a 16-dimensional latent representation; this models the complex and subtle relationships necessary for the identification of rare events of liquefaction. The use of a 16-dimensional latent space provides an acceptable compromise between compression and information preservation. Through exploratory trials, we discovered that fewer dimensions (e.g., <10) led to the loss of crucial information, whereas more dimensions showed modest performance benefits but increased complexity. Thus, 16 dimensions provide an adequate ability to capture crucial geophysical diversity without overfitting. BN helps prevent overfitting to more frequent features, allowing the model to learn how to detect less common patterns. The decoder rebuilds the input data from this compact embedding with a sigmoid output layer to ensure the reconstruction remains within the range of [0, 1]. However, the reconstruction loss is not directly related to liquefaction identification, leading the encoder to learn essential representations from the data. This is paramount in an imbalanced dataset, where the autoencoder pays more attention to the rare instances of liquefaction without bias towards the other classes, which occur more frequently. The increased ability to capture anomalies enhances the detection of liquefaction, thereby improving risk assessment and disaster preparedness in geophysical research. The encoder part of the autoencoder consists of an array of dense layers, each followed by batch normalization for stabilization of learning and to prevent overfitting.