Thickness Distribution Prediction for Tectonically Deformed Coal with a Deep Belief Network: A Case Study
Abstract
:1. Introduction
2. Methods
2.1. Dimensionality Reduction
2.2. Restricted Boltzmann Machine
2.3. The DBN Prediction Model
3. Synthetic Example
3.1. Synthetic Data Preparing
3.2. Dimensional Reduction and TDC Prediction
3.3. Prediction Comparison
4. Case Study
4.1. General Settings
4.2. Cross-Validation
4.3. Prediction
4.4. Discussions
4.4.1. Influences of Burial Depth and Coalbed Thickness
4.4.2. Influences of Fault Development
4.4.3. Prediction Comparison
5. Conclusions
- (1)
- The predicted TDC thicknesses with SVM, ELM, and DBN models were accurate and stable, while the DBN model provided the best results considering the prediction accuracy and stability. However, it is worthy of further studies of this model on the TDC thickness prediction in some other areas.
- (2)
- Both synthetic and measured seismic attributes had more or less correlation with each other. PCA could transform the high dimensional seismic attributes into low dimensional PCs and simplify the complexity of TDC-thickness prediction.
- (3)
- The burial depth, coalbed thickness, and fault development were the main influence factors for the TDC thickness in the study area. This observation was consistent with the known regional characteristics of TDC development.
Author Contributions
Funding
Conflicts of Interest
References
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Attr1 | Attr2 | Attr3 | Attr4 | Attr5 | Attr6 | Attr7 | Attr8 | Attr9 | Attr10 | Attr11 | |
---|---|---|---|---|---|---|---|---|---|---|---|
Attr1 | 1.00 | −0.18 | −0.17 | 0.09 | 0.10 | 0.10 | 0.09 | 0.09 | 0.08 | 0.08 | 0.15 |
Attr2 | −0.18 | 1.00 | 0.99 | −0.76 | −0.78 | −0.78 | −0.77 | −0.77 | −0.75 | −0.75 | −0.83 |
Attr3 | −0.17 | 0.99 | 1.00 | −0.84 | −0.85 | −0.85 | −0.85 | −0.84 | −0.83 | −0.83 | −0.90 |
Attr4 | 0.09 | −0.76 | −0.84 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.98 |
Attr5 | 0.10 | −0.78 | −0.85 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.98 |
Attr6 | 0.10 | −0.78 | −0.85 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.98 |
Attr7 | 0.09 | −0.77 | −0.85 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.98 |
Attr8 | 0.09 | −0.77 | −0.84 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.98 |
Attr9 | 0.08 | −0.75 | −0.83 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.98 |
Attr10 | 0.08 | −0.75 | −0.83 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.98 |
Attr11 | 0.15 | −0.83 | −0.90 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 1.00 |
PCs | Eigenvalues | Variance Contributions | Cumulative Contributions |
---|---|---|---|
PC1 | 3.332 | 0.937 | 0.937 |
PC2 | 0.177 | 0.050 | 0.986 |
PC3 | 0.042 | 0.012 | 0.998 |
PC4 | 0.006 | 0.002 | 1.000 |
Well Name | Inline | Xline | Depth (m) | Coalbed Thickness (m) | TDC Thickness (m) |
---|---|---|---|---|---|
L44 | 375 | 345 | 926.8 | 10.6 | 8.1 |
L50 | 321 | 151 | 813.0 | 11.8 | 8.4 |
91-5 | 224 | 209 | 671.2 | 13.0 | 6.6 |
2002-4 | 239 | 311 | 723.9 | 14.1 | 11.6 |
2002-5 | 309 | 248 | 795.0 | 12.7 | 9.1 |
2010-11 | 325 | 413 | 877.3 | 11.1 | 8.2 |
2012-1 | 118 | 565 | 643.5 | 7.4 | 5.3 |
2014-5 | 376 | 177 | 930.7 | 11.0 | 8.6 |
L43 | 297 | 327 | 807.9 | 10.7 | 8.7 |
06-4 | 51 | 249 | 457.8 | 7.4 | 4.3 |
91-2 | 101 | 284 | 566.0 | 11.5 | 8.5 |
92-8 | 56 | 494 | 592.8 | 6.6 | 4.5 |
94-2 | 134 | 235 | 600.4 | 3.8 | 3.8 |
91-1 | 52 | 298 | 458.6 | 8.4 | 5.9 |
92-2 | 20 | 471 | 589.6 | 7.6 | 4.8 |
94-5 | 15 | 442 | 582.9 | 8.7 | 5.4 |
2002-3 | 239 | 405 | 760.4 | 11.5 | 5.7 |
94-1 | 167 | 128 | 505.6 | 9.8 | 4.7 |
94-3 | 138 | 70 | 448.6 | 16.2 | 7.7 |
99-1 | 161 | 404 | 673.6 | 11.0 | 4.4 |
Attr1 | Attr2 | Attr3 | Attr4 | Attr5 | Attr6 | Attr7 | Attr8 | Attr9 | Attr10 | Attr11 | |
---|---|---|---|---|---|---|---|---|---|---|---|
Attr1 | 1.00 | 0.06 | 0.05 | −0.06 | 0.01 | 0.00 | 0.01 | 0.01 | 0.01 | −0.04 | 0.04 |
Attr2 | 0.06 | 1.00 | 0.93 | −0.06 | −0.04 | −0.03 | −0.01 | 0 | −0.01 | 0.17 | 0.38 |
Attr3 | 0.05 | 0.93 | 1.00 | 0.10 | −0.05 | −0.03 | −0.01 | −0.01 | −0.02 | 0.36 | 0.05 |
Attr4 | −0.06 | −0.06 | 0.10 | 1.00 | −0.02 | 0.01 | 0.02 | 0.01 | −0.01 | 0.94 | −0.50 |
Attr5 | 0.01 | −0.04 | −0.05 | −0.02 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | −0.04 | 0.04 |
Attr6 | 0 | −0.03 | −0.03 | 0.01 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0 | 0.02 |
Attr7 | 0.01 | −0.01 | −0.01 | 0.02 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.01 | 0.01 |
Attr8 | 0.01 | 0 | −0.01 | 0.01 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.01 | 0.02 |
Attr9 | 0.01 | −0.01 | −0.02 | −0.01 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | −0.01 | 0.03 |
Attr10 | −0.04 | 0.17 | 0.36 | 0.94 | −0.04 | 0 | 0.01 | 0.01 | −0.01 | 1.00 | −0.51 |
Attr11 | 0.04 | 0.38 | 0.05 | −0.50 | 0.04 | 0.02 | 0.01 | 0.02 | 0.03 | −0.51 | 1.00 |
PCs | Eigenvalues | Variance Contributions | Cumulative Contributions |
---|---|---|---|
PC1 | 0.087 | 0.603 | 0.603 |
PC2 | 0.038 | 0.265 | 0.868 |
PC3 | 0.010 | 0.071 | 0.938 |
PC4 | 0.005 | 0.036 | 0.974 |
PC5 | 0.003 | 0.020 | 0.995 |
PC6 | 0.001 | 0.005 | 0.999 |
PC7 | 0 | 0 | 1.000 |
Well Number | Well Name | True TDC Thickness (m) | SVM Model | ELM Model | DBN Model | |||
---|---|---|---|---|---|---|---|---|
Predicted Thickness (m) | Absolute Errors (m) | Predicted Thickness (m) | Absolute Errors (m) | Predicted Thickness (m) | Absolute Errors (m) | |||
1 | L44 | 8.1 | 8.36 | 0.26 | 9.02 | 0.92 | 7.61 | 0.49 |
2 | L50 | 8.4 | 9.20 | 0.80 | 9.18 | 0.78 | 9.03 | 0.63 |
3 | 91-5 | 6.6 | 6.21 | 0.39 | 7.23 | 0.63 | 6.64 | 0.04 |
4 | 2002-4 | 11.6 | 10.41 | 1.19 | 10.53 | 1.07 | 10.58 | 1.02 |
5 | 2002-5 | 9.1 | 10.96 | 1.86 | 7.69 | 1.41 | 8.12 | 0.98 |
6 | 2010-11 | 8.2 | 8.50 | 0.30 | 8.15 | 0.05 | 7.47 | 0.73 |
7 | 2012-1 | 5.3 | 6.30 | 1.00 | 6.24 | 0.94 | 5.89 | 0.59 |
8 | 2014-5 | 8.6 | 8.34 | 0.26 | 8.40 | 0.20 | 8.06 | 0.54 |
9 | L43 | 8.7 | 9.40 | 0.70 | 9.27 | 0.57 | 9.10 | 0.40 |
10 | 06-4 | 4.3 | 4.38 | 0.08 | 6.83 | 2.53 | 4.85 | 0.55 |
11 | 91-2 | 8.5 | 7.45 | 1.05 | 7.60 | 0.90 | 7.53 | 0.97 |
12 | 92-8 | 4.5 | 4.94 | 0.44 | 5.20 | 0.70 | 5.71 | 1.21 |
13 | 94-2 | 3.8 | 6.93 | 3.13 | 2.69 | 1.11 | 4.56 | 0.76 |
14 | 91-1 | 5.9 | 6.05 | 0.15 | 5.66 | 0.24 | 6.16 | 0.26 |
15 | 92-2 | 4.8 | 5.77 | 0.97 | 5.26 | 0.46 | 6.73 | 1.93 |
16 | 94-5 | 5.4 | 6.10 | 0.70 | 6.03 | 0.63 | 6.11 | 0.71 |
17 | 2002-3 | 5.7 | 5.11 | 0.59 | 6.35 | 0.65 | 6.21 | 0.51 |
18 | 94-1 | 4.7 | 6.50 | 1.80 | 6.71 | 2.01 | 6.32 | 1.62 |
19 | 94-3 | 7.7 | 6.95 | 0.75 | 7.31 | 0.39 | 7.72 | 0.02 |
20 | 99-1 | 4.4 | 5.92 | 1.52 | 5.24 | 0.84 | 5.21 | 0.81 |
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Wang, X.; Chen, T.; Xu, H. Thickness Distribution Prediction for Tectonically Deformed Coal with a Deep Belief Network: A Case Study. Energies 2020, 13, 1169. https://doi.org/10.3390/en13051169
Wang X, Chen T, Xu H. Thickness Distribution Prediction for Tectonically Deformed Coal with a Deep Belief Network: A Case Study. Energies. 2020; 13(5):1169. https://doi.org/10.3390/en13051169
Chicago/Turabian StyleWang, Xin, Tongjun Chen, and Hui Xu. 2020. "Thickness Distribution Prediction for Tectonically Deformed Coal with a Deep Belief Network: A Case Study" Energies 13, no. 5: 1169. https://doi.org/10.3390/en13051169