the clean seismic data were taken as the training labels of the resnet18 in matlab

Assuming that you have already acquired the seismic data and labeled it, you can use the following steps to preprocess the data before feeding it to ResNet18 in Matlab:

1. Load the data into Matlab using appropriate signal processing functions.
2. Apply any necessary preprocessing steps such as filtering, noise removal, or feature extraction. This step is critical to remove any artifacts or noise present in the data that could bias the training process.
3. Normalize the data to have zero mean and unit variance. This step is crucial for the ResNet18 to train effectively, avoiding the vanishing gradient problem.
4. Split the dataset into training, validation, and test sets, using a suitable ratio.
5. Convert the labels to categorical data using the categorical() function in Matlab.

Here's a sample code snippet to perform the above steps:

main.m
% Load the data
load('seismic_data.mat')

% Preprocessing steps
data = preprocess_data(seismic_data);

% Normalize the data
data_mean = mean(data);
data_std = std(data);
data_norm = (data - data_mean)./data_std;

% Split the dataset into training, validation, and test sets
[trainInd,valInd,testInd] = dividerand(size(data_norm,2),0.7,0.15,0.15);

% Convert the labels to categorical data
labels = categorical(labels);

% Define the ResNet18 architecture
net = resnet18;

% Train the network using the normalized data and categorical labels
[net,info] = trainNetwork(data_norm(trainInd,:),labels(trainInd),net);
612 chars
23 lines

Note: The above code is just a sample, and you may need to modify it based on your specific requirements.