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

To clean seismic data for training labels of resnet in MATLAB, you can follow these general steps:

1. Load the seismic data into MATLAB.
2. Preprocess the data to remove any noise and artifacts that may interfere with the training process.
3. Segment the data into smaller, meaningful units to facilitate the training process.
4. Normalize the data to ensure consistency and improve the accuracy of the training.
5. Convert the data into a format that can be used as training labels for the resnet network.

Here is an example code for cleaning and preprocessing seismic data for use as training labels:

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

% Preprocessing
clean_data = denoise(data);
artifact_free_data = remove_artifacts(clean_data);

% Segmentation
segmented_data = segment(artifact_free_data);

% Normalization
normalized_data = normalize(segmented_data);

% Conversion
training_labels = convert_to_resnet_input(normalized_data);

% save the training labels
save('training_labels.mat','training_labels');
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Note that the specific details of each step will depend on the characteristics of the seismic data and the specific requirements of the resnet network.