Z. Yang, H. Li, X. Tuo, L. Li and J. Wen, "Unsupervised Clustering of Microseismic Signals Using a Contrastive Learning Model," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-12, 2023, Art no. 5903212, doi: 10.1109/TGRS.2023.3240728.
Link: https://doi.org/10.1109/TGRS.2023.3240728
Clone the project into your local system
git clone https://github.com/yangzhen-cdut/Unsupervised-Clustering.git
cd Unsupervised-Clustering
The recommended requirements for TSCC are specified as follows:
- Python 3.7
- torch==1.8.1
- scipy==1.7.3
- numpy==1.21.6
- pandas==1.3.5
- scikit_learn==0.24.2
- matplotlib==3.5.2
- bottleneck==1.3.4
- seaborn==0.11.2
The dependencies can be installed by:
pip install -r requirements.txtNote that you should have CUDA installed for running the code.
To train TSCC on a microseismic dataset, run the following command:
python run.py <dataset_name> <dataset_name> --pretraining_epoch <pretraining_epoch> --batch-size <batch_size> --MaxIter <MaxIter> --repr-dims <repr_dims>
After training, the trained encoder of pre-training phase, the trained encoder of fine-tuning phase and clustering centers can be found in ./<dataset_name>_Pretraining_phase, ./<dataset_name>_Finetuning_phase, ./<dataset_name>_Centers.
To evaluate TSCC on a microseismic dataset, run the following command:
python evaluation.py <dataset_name> <dataset_name> --pretraining_epoch <pretraining_epoch> --batch-size <batch_size> --MaxIter <MaxIter> --repr-dims <repr_dims>
There are two examples are given in evaluation.py: eval_with_real_data and eval_with_synthetic_data. You can call those two functions directly, and the output of representations can be found in ./Eval_Representations.npy and ./Eval_Syn_Representations.npy, respectively.
Latent representations of synthetic waveforms
Latent representations of real microseismic waveforms
Tpyical microseismic waveforms
Tpyical noise waveforms
Classification performance comparison of supervised classifier using raw time series and the features (R) generated by the TSCC.
| Methods | Time series | Feature R | ||||
|---|---|---|---|---|---|---|
| ACC (%) | NMI (%) | AUPRC (%) | ACC (%) | NMI (%) | AUPRC (%) | |
| Linear | 71.59 | 15.38 | 66.47 | 99.11 | 92.63 | 98.71 |
| KNN | 90.58 | 55.55 | 87.65 | 98.13 | 86.62 | 97.36 |
| SVM | 97.65 | 83.91 | 99.81 | 98.94 | 91.56 | 99.91 |
| TSCC | 98.07 | 86.26 | 97.15 | -- | -- | -- |