## 2 Data Preparation and Methodology
### Model Construction  
The CNN is applied for both onset time picking and microseismic waveform detection. As shown in Figure 4, its basic structure can be logically divided into three separate parts: encoder, decoder, and representation. The encoder maps the input data to a hidden representation through an activation function, and the decoder transforms the hidden representation into an estimation of the input by the activation function. The above process includes feature extraction and estimation output of the input data. To solve the onset time picking and detection of microseismic waveforms, a PDNetwork is constructed by connecting two networks through convolutional operators. The microseismic waveform, which is the input vector of the onset time picking section, is firstly reshaped from (30,000, 1) into (32,768, 1) through a zero-padding procedure for the encoding process. The operation is adopted to avoid the lack of microseismic waveform information in the encoding process and to ensure that the information and features can be reconstructed entirely in the decoding process. Subsequently, the reshaped vector is transformed into new layers through a series of encoding operators, comprising a convolution, rectified linear unit (ReLU) activation, and batch normalization [37]. A stride of 2 is also alternately employed to gradually shrink the feature space and enhance the computational efficiency during the encoding procedure. For the convolution calculation, a larger kernel corresponds to a broader receptive field, enabling the capture of richer non-local features of the microseismic waveform. However, the utilization of a large convolution kernel can lead to a significant increase in computational time, which severely restricts the depth and potential of a neural network. Therefore, the kernel size of the convolution and deconvolution layers is set to a constant of 3 throughout the entire work, except in the first and second layers of the detection section of the PDNetwork. The corresponding feature maps in the encoding and decoding processes are concatenated, thereby enhancing the convergence of training and facilitating the reconstruction of waveform information with high fidelity [38]. In the penultimate layer of the onset time picking section, a sigmoid binary activation function is used to generate the score map of the microseismic waveform. Subsequently, in the last layer of the onset time picking section, the score map with a shape of (32,768, 1) is reshaped into (30,000, 1) to facilitate further processing. Based on a comprehensive consideration of the computational time, in order to better obtain the feature of the score map of the microseismic waveform, the kernel sizes of the first and  
Figure 3: Score map of various waveforms: (**a**) a microseismic waveform with an accurate onset time picking; (**b**) a score map of the onset time picking; (**c**) noise waveforms; (**d**) a score map of the noise waveform. second convolution layers are set to 7 and 5, respectively. After a series of convolution operations, the Global MaxPooling function is employed to maintain the salient features while diminishing the spatial dimension of the feature map in the penultimate layer of the detection section. Subsequently, fully connected layers are employed to perform high-level reasoning and map the learned features to the probability of the required classes from the last step. A softmax activation function is used in the last layer of the network to output a vector of the predicted probabilities of each class. The Adam optimizer for the learning rate decay is utilized for weight updates, while a cross-entropy loss function is used to minimize the predicted error and ensure that the model converges. The early stopping operation is used to prevent model overfitting. The learning rate is specifically set to 0.005, and the batch size is configured to 32. All the experiments in this study are conducted on a PC with an i7-11700F CPU, an NVIDIA GeForce 3060 Ti GPU, and 64 GB of RAM.