Name
Title | ||
|---|---|---|
Efficient Data-Driven Geologic Feature Detection from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm. |
Abstract | ||
|---|---|---|
Conventional seismic techniques for detecting the subsurface geologic features are challenged by limited data coverage, computational inefficiency and subjective human factors. We developed a novel data-driven geological feature characterization approach based on pre-stack seismicmeasurements. Our characterization method employs an efficient and accuratemachine learning method to extract useful subsurface geologic features automatically. Specifically, we use kernel ridge regression to account for the nonlinear relationship between seismic data and geological features. We further employ kernel tricks to avoid the explicit nonlinear mapping and infinite dimension of feature space. However, the conventional kernel ridge regression can be computationally prohibitive because of the large volume of seismic measurements. We employ a data reduction technique in combination with the conventional kernel ridge regression method to improve the computational efficiency and reduce memory usage. In particular, we utilize a randomized numerical linear algebra technique, named Nystrom method, to effectively reduce the dimensionality of the feature space without compromising the information content required for accurate characterization. We provide thorough computational cost analysis to show the efficiency of our new geological feature characterization methods. We validate the performance of our method in characterizing geologic fault zones because faults play an important role in various subsurface applications. Our numerical examples demonstrate that our new characterization method significantly improves the computational efficiency while maintaining comparable accuracy. Interestingly, we show that our method yields a speedup ratio on the order of similar to 10(2) to similar to 10(3) in a multicore computational environment. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1093/gji/ggy385 | GEOPHYSICAL JOURNAL INTERNATIONAL |
Keywords | Field | DocType |
Inverse Theory,Numerical Solutions,Computational Seismology,Neural Networks | Feature vector,Data-driven,Feature detection,Kernel ridge regression,Algorithm,Curse of dimensionality,Artificial intelligence,Geology,Cost analysis,Numerical linear algebra,Machine learning,Data reduction | Journal |
Volume | Issue | ISSN |
215 | 3 | 0956-540X |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Youzuo Lin | 1 | 23 | 7.93 |
| Shusen Wang | 2 | 129 | 15.99 |
| Jayaraman J. Thiagarajan | 3 | 247 | 42.17 |
| G. D. Guthrie | 4 | 1 | 1.43 |
| David Coblentz | 5 | 7 | 1.22 |