## 3 Neural Network Architecture Options  
Neural networks are computational models that draw inspiration from the structure and functioning of the human brain. These models comprise interconnected layers of artificial neurons (known also as nodes), which process information by emulating the communication between biological neurons. As data traverse the network, each neuron processes and transforms the input, progressively constructing a hierarchy of increasingly intricate features. During training, the network fine-tunes its weights and biases through a process known as learning (by means of an algorithmic procedure like backpropagation), which minimizes the discrepancy between its predictions and the actual output. Through iterative search optimization, neural networks acquire the ability to discern intricate patterns, generalize from the training data, and effectively tackle complex tasks like image recognition, natural language processing, and decision making. The problem at hand can be characterized as a deterministic regression task. However, due to the specific nature of the system inputs, employing a popular generative adversarial network (GAN) architecture was deemed unsuitable. GANs generate outputs that are non-deterministic, meaning that the same input can yield different outputs each time the model is used. This behavior arises from the stochastic processes utilized by GANs, such as incorporating random noise during sample generation. Furthermore, the output of GANs is highly reliant on factors like training data, hyperparameter selection, and the training process itself. While GANs can produce impressive and realistic results, they do not offer a unique and definitive solution to a given problem. Even when considering the subclass of GANs known as conditional generative adversarial networks (CGANs), which can be trained to generate samples conditioned on specific input information, the results were still not deterministic. As a result, an alternative approach was pursued using recurrent neural networks (RNNs), specifically the long short-term memory (LSTM) architecture. LSTMs can mitigate the vanishing gradient problem often encountered in traditional RNNs. Another  
Figure 4: Earthquakes adopted for generating the training–validation sets; starting from ID 1 to ID 9 (**a**–**i**). option considered for the regression task was to employ convolutional neural networks (CNNs). However, considering the substantial size of the dataset (multiple gigabytes), the limitations of the LSTM in comparison to CNNs were taken into careful consideration. * Computationally expensive: LSTMs are computationally expensive compared to CNNs, as they require a more complex architecture and involve more computations. This can make them more challenging to train and deploy, especially in real-time applications. * Limited parallelization: LSTMs are less parallelizable compared to CNNs, as the computations in LSTMs are sequential and depend on the output of previous time steps. This can limit their scalability and make them less suitable for high-performance computing applications. In the context of the proposed neural network architecture, CNN layers were selected as a fundamental component. Convolutional networks (e.g., [PERSON] et al. [3; 4; 5; 6]), often referred to as convolutional neural networks (CNNs), were chosen due to their specialized nature in handling data with grid-like structures. Such grid-like data examples encompass time-series data, which can be conceptualized as a 1D grid with regularly spaced time interval samples, as well as image data, which can be visualized as a 2D grid composed of pixels. Convolutional networks have exhibited remarkable success in practical applications, and the term \"convolutional neural network\" reflects their utilization of a mathematical operation known as convolution.