# Research on Seismic Signal Denoising Model Based on DnCNN Network
## 3 Dataset and Evaluation Metrics  
### Dataset and Data Preprocessing  
A large amount of seismic data is required to train and test the model. The dataset used in this paper is the STEAD (Stanford Earthquake Dataset--STEAD), which is a high-quality, large-scale, and global dataset of local seismic and nonseismic signals recorded by seismic instruments. The seismic event signals contained in the dataset are labeled with the labels required for the training of various deep learning networks [17]. Examples of seismic event signals and nonseismic signals in the dataset are shown in Figures 3-5.  
Figure 2: Structure diagram of seismic signal denoising model based on DnCNN network.  
pack_azimuth_deg 280.5 coda_and_sample 3114.0 network_code TA p_arrivix_sample 500.0 p_status manual p_travest_seq 17.26 p_weight_6.5 receiver_edge 195c receiver_elevation 150.0 receiver_latitude 32.8889 receiver_longitude -117.1851 receiver_type bin - s_arrival_sample 1678.0 s_status manual s_weight 6.5 s_arb_d 137.2808076 42. 38.59999847] source_depth_max 12.66 source_depth_uncertainty_max None source_distance_deg 0.92 source_distance_lm 161.87 source_error_seq 8.127 source_gap 48.096 source_period_uncertainty_km 3.0397 source_id 8994123 source_latitude 32.725 source_ longitude 116.348 source_magnitude 3.6 source_magnitude_Author None source_magnitude_type 21 source_constant_statistic_file_dig_rake None source_origin_time 280.11-48 36.12:12.44 source_origin_interval 35, see 2.27 trace_category earthquake_local trace_same 195c.TA,2806118361223.EV trace_start_time 2806-11-83 16:12:124.700000  
For the purposes of training and evaluating our network, we require seismic signals that include noise. In this study, these noisy signals were artificially created by combining  
Figure 4: Labels corresponding to seismic events.  
Figure 5: Examples of noise in the dataset.  
Figure 3: Examples of seismic event signals contained in the dataset.  
pristine seismic signals with noise. Given the challenge of acquiring truly clean seismic signals in the field, we chose 82,380 seismic event signals from the STEAD dataset, which have a high signal-to-noise ratio (SNR), to serve as our proxy for clean signals. Additionally, we selected 199,760 nonseismic signals from STEAD to act as our noise dataset. The synthesis of noisy seismic signals was achieved by blending these clean signal samples with noise samples in various ways.  
To bolster our dataset of noisy seismic signals, we employed a random combination method. This involved randomly shifting each clean seismic signal, arbitrarily selecting a noise sample, arbitrarily adjusting its amplitude, and then superimposing this noise onto the clean signal to produce a range of seismic signals with varying SNRs. To enhance the network's training convergence, the synthesized noisy seismic signals were normalized, as detailed by the normalization Formula (5), which refers to the amplitude value of the seismic event signal. This normalization step is crucial for maintaining the integrity of the signal features during the training process.  
\[v_{i}=\frac{v_{i}}{\max\mid v_{i}\mid} \tag{5}\]  
Each signal within our dataset was uniformly segmented to a duration of 30 s, with any excess length being truncated from the end. This 30-s window was chosen, as it sufficiently captures the primary waveform of high SNR seismic events within the initial 30 s. The dataset was then randomly partitioned into three subsets--the training set, the validation set, and the test set--in the ratios of 80% (65,904), 10% (8238), and 10% (8238), respectively. The training set was utilized for model training, the validation set for tuning the model's hyperparameters, and the test set for evaluating the model's denoising performance. This systematic division ensured a robust evaluation of the model's capabilities across different phases of development and testing.