A3Mark Evaluation Results - Version 1

We compute quality measures with respect to the four ground truth seismic cubes depending on their discrete or continuous amplitude values. The metrics are summarized below, for more precise mathematical details, please refer to our paper [1].

Discrete group:

  • precision: Percentage of true positives of extracted edges/faults in attributes to all extracted edges/faults.
  • recall: Percentage of true positives of extracted edges/faults in attributes to all ground truth edges/faults.
  • rmsDistance: Root mean square weighted by the nonlinear distance (sigmf([1:1:30],[0.5 10])) between edges/faults in attributes and their closest edges/faults in ground truth. Extracted edges/faults using the seismic attributes are treated differently depending on their distance to the closest object in the ground truth.

Continuous group:

  • recall: Percentage of true positives of extracted attributes to ground truth.
  • rms: Root mean square between attributes and ground truth.
  • rmsDiscontinuity: Root mean square between attributes and ground truth without including the edges/faults area. This originates from the fact that most structural attributes (except edge attributes) are undefined at (or very near) edges or discontinuities. This is because derivatives are undefined at discontinuities.

The threshold for a true positive is different for each ground truth category. For each continuous group, the threshold D is 20% of the difference between the maximum and minimum of its ground truth. If the submitted seismic attribute for edge/fault is continuous, not discrete, they will be discretized according to the top 20% rule.

We have computed their varieties, both in 3D and 2D, as well as in different dip regions [0°, 90°] and [0°, 45°]. In the 3D version, the quality measures are computed over the entire seismic cube, whereas in 2D they are first computed on every inline, crossline, and time section of the cube, followed by a mean operation. A caveat of the above metrics is that they do not discriminate between attributes at different dip angle ranges. However, in practice, it is very hard to image structural dips beyond 45 degrees for most types of seismic data. Therefore, more quality metrics (named *_LowDip), which account only for the dip in the 0-45 degrees range, are developed.

Submit and evaluate your own attributes.

Curvature_K1
Attribute Name Updated Average Train
N/F
Train
N/R
Train
N/C
Test 1
N/F
Test 1
N/R
Test 1
N/C
Test 2
N/F
Test 2
N/R
Test 2
N/C
DGB_OD_Curvature_Harishidayat 2016/11/17 0.0015 0.0053 0.0004 0.0006 0.0052 0.0004 0.0006 0.0012 0.0001 0.0001
SLB_Petrel_3D curvature_Xing 2016/12/11 0.4052 0.6773 0.1888 0.4679 0.6705 0.1892 0.4615 0.5585 0.1032 0.3301
SLB_Petrel_Consistent curvature_Xing 2016/12/11 0.9547 0.9723 0.9926 0.9701 0.9721 0.9926 0.9700 0.8909 0.9506 0.8812
Attribute Name Updated Average Train
N/F
Train
N/R
Train
N/C
Test 1
N/F
Test 1
N/R
Test 1
N/C
Test 2
N/F
Test 2
N/R
Test 2
N/C
DGB_OD_Curvature_Harishidayat 2016/11/17 0.7476 0.7377 0.7521 0.7520 0.7381 0.7526 0.7523 0.7383 0.7527 0.7528
SLB_Petrel_3D curvature_Xing 2016/12/11 0.4534 0.2372 0.5639 0.4924 0.2902 0.5655 0.5170 0.3106 0.5721 0.5314
SLB_Petrel_Consistent curvature_Xing 2016/12/11 0.0031 0.0033 0.0021 0.0038 0.0033 0.0021 0.0038 0.0033 0.0021 0.0038
Attribute Name Updated Average Train
N/F
Train
N/R
Train
N/C
Test 1
N/F
Test 1
N/R
Test 1
N/C
Test 2
N/F
Test 2
N/R
Test 2
N/C
DGB_OD_Curvature_Harishidayat 2016/11/17 0.7515 0.7419 0.7563 0.7562 0.7424 0.7568 0.7565 0.7416 0.7560 0.7561
SLB_Petrel_3D curvature_Xing 2016/12/11 0.4174 0.1961 0.5728 0.4923 0.1902 0.5647 0.4902 0.1897 0.5666 0.4942
SLB_Petrel_Consistent curvature_Xing 2016/12/11 0.0030 0.0032 0.0019 0.0038 0.0032 0.0020 0.0037 0.0032 0.0020 0.0037
Attribute Name Updated Average Train
N/F
Train
N/R
Train
N/C
Test 1
N/F
Test 1
N/R
Test 1
N/C
Test 2
N/F
Test 2
N/R
Test 2
N/C
DGB_OD_Curvature_Harishidayat 2016/11/17 0.7469 0.7369 0.7513 0.7512 0.7373 0.7518 0.7515 0.7376 0.7520 0.7521
SLB_Petrel_3D curvature_Xing 2016/12/11 0.3898 0.2016 0.4920 0.4114 0.2514 0.4944 0.4302 0.2757 0.5038 0.4478
SLB_Petrel_Consistent curvature_Xing 2016/12/11 0.0027 0.0029 0.0019 0.0033 0.0029 0.0019 0.0033 0.0029 0.0019 0.0033
Attribute Name Updated Average Train
N/F
Train
N/R
Train
N/C
Test 1
N/F
Test 1
N/R
Test 1
N/C
Test 2
N/F
Test 2
N/R
Test 2
N/C
DGB_OD_Curvature_Harishidayat 2016/11/17 0.7482 0.7382 0.7527 0.7525 0.7390 0.7534 0.7531 0.7385 0.7529 0.7530
SLB_Petrel_3D curvature_Xing 2016/12/11 0.3350 0.1412 0.4771 0.3849 0.1415 0.4731 0.3860 0.1433 0.4764 0.3910
SLB_Petrel_Consistent curvature_Xing 2016/12/11 0.0025 0.0027 0.0018 0.0031 0.0026 0.0018 0.0030 0.0027 0.0018 0.0031
Attribute Name Updated Average Train
N/F
Train
N/R
Train
N/C
Test 1
N/F
Test 1
N/R
Test 1
N/C
Test 2
N/F
Test 2
N/R
Test 2
N/C
DGB_OD_Curvature_Harishidayat 2016/11/17 0.0017 0.0059 0.0005 0.0006 0.0058 0.0005 0.0006 0.0013 0.0001 0.0001
SLB_Petrel_3D curvature_Xing 2016/12/11 0.3471 0.5178 0.2830 0.3894 0.5177 0.2824 0.3884 0.3737 0.1407 0.2309
SLB_Petrel_Consistent curvature_Xing 2016/12/11 0.9884 0.9979 0.9984 0.9978 0.9980 0.9984 0.9979 0.9696 0.9735 0.9638
Attribute Name Updated Average Train
N/F
Train
N/R
Train
N/C
Test 1
N/F
Test 1
N/R
Test 1
N/C
Test 2
N/F
Test 2
N/R
Test 2
N/C
DGB_OD_Curvature_Harishidayat 2016/11/17 0.6882 0.6749 0.6906 0.6896 0.6782 0.6938 0.6932 0.6809 0.6965 0.6963
SLB_Petrel_3D curvature_Xing 2016/12/11 0.2083 0.1898 0.1974 0.2079 0.2011 0.2083 0.2186 0.2084 0.2155 0.2279
SLB_Petrel_Consistent curvature_Xing 2016/12/11 0.0014 0.0013 0.0013 0.0015 0.0013 0.0013 0.0015 0.0013 0.0013 0.0015
Attribute Name Updated Average Train
N/F
Train
N/R
Train
N/C
Test 1
N/F
Test 1
N/R
Test 1
N/C
Test 2
N/F
Test 2
N/R
Test 2
N/C
DGB_OD_Curvature_Harishidayat 2016/11/17 0.6780 0.6658 0.6817 0.6804 0.6681 0.6840 0.6829 0.6693 0.6852 0.6847
SLB_Petrel_3D curvature_Xing 2016/12/11 0.0587 0.0248 0.0710 0.0823 0.0237 0.0700 0.0814 0.0237 0.0701 0.0816
SLB_Petrel_Consistent curvature_Xing 2016/12/11 0.0010 0.0009 0.0009 0.0011 0.0009 0.0009 0.0011 0.0009 0.0009 0.0011
Attribute Name Updated Average Train
N/F
Train
N/R
Train
N/C
Test 1
N/F
Test 1
N/R
Test 1
N/C
Test 2
N/F
Test 2
N/R
Test 2
N/C
DGB_OD_Curvature_Harishidayat 2016/11/17 0.6759 0.6622 0.6783 0.6773 0.6645 0.6806 0.6801 0.6695 0.6851 0.6853
SLB_Petrel_3D curvature_Xing 2016/12/11 0.2235 0.2074 0.2220 0.2284 0.2181 0.2303 0.2375 0.2113 0.2244 0.2318
SLB_Petrel_Consistent curvature_Xing 2016/12/11 0.0021 0.0022 0.0023 0.0024 0.0022 0.0022 0.0024 0.0016 0.0016 0.0018
Attribute Name Updated Average Train
N/F
Train
N/R
Train
N/C
Test 1
N/F
Test 1
N/R
Test 1
N/C
Test 2
N/F
Test 2
N/R
Test 2
N/C
DGB_OD_Curvature_Harishidayat 2016/11/17 0.6729 0.6612 0.6777 0.6758 0.6624 0.6790 0.6766 0.6636 0.6795 0.6801
SLB_Petrel_3D curvature_Xing 2016/12/11 0.0724 0.0402 0.0825 0.0910 0.0407 0.0831 0.0912 0.0432 0.0861 0.0933
SLB_Petrel_Consistent curvature_Xing 2016/12/11 0.0009 0.0007 0.0009 0.0010 0.0007 0.0009 0.0010 0.0008 0.0011 0.0012
Ground Truth vs Attribute Result (Snapshots from different view angles. Please single click the table cell above.)