Reflection Seismic Data Processing by Use of Neural Network
| Project/Area Number |
07651146
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
資源開発工学
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| Research Institution | KYOTO UNIVERSITY |
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Principal Investigator |
ASHIDA Yuzuru Kyoto Univ., Fac.of Eng., Professor, 工学研究科, 教授 (60184165)
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| Co-Investigator(Kenkyū-buntansha) |
WATANABE Toshiki Kyoto Univ., Fac.of Eng., Instructor, 工学研究科, 助手 (50210935)
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| Project Period (FY) |
1995 – 1996
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| Project Status |
Completed (Fiscal Year 1996)
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| Budget Amount *help |
¥2,100,000 (Direct Cost: ¥2,100,000)
Fiscal Year 1996: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 1995: ¥1,400,000 (Direct Cost: ¥1,400,000)
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| Keywords | Neural Network / Reflection Seismology / Data Processing |
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| Research Abstract |
The present reasearch proposed an algorithm for data processing of reflection seismic data using of neural networks. A neural network algorithm was applied to the reading of arrival time of first break signal, the recognition of waveform in seismic trace and the automatic picking of result of constant velocity scan among the various data processing techniques. A layred network with the correct answer, so called, teacher's signal, in training period by the error back propagation algorithm was used.
The general procedure of processing of reflection seismic data by use of neural network is as follows.
1) Constitution of the most suitable network for the target processing.
2) Setting of the weight values to all units in the layrs and the teacher's signal.
3) Calculation of the output signals from the output layr by activating the network.
4) Estimation of learning signal from the energy of errors between the actual output signal and the teacher's signal.
5) Calculation of the change of weight values by using learning signal so as to minimize the energy of errors between the actual signal and the teacher's signal.
6) Steps 3) to 5) are repeated till the errors fall into the designated limitation or the designated learning count is reached.
As a result of model studies, it was determined that the proposed algorithm performed the readings of arrival time of first break signal, the waveform recognition and the automatic picking of velocity analysis result with good accuracy.
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Report
(3 results)
Research Products
(18 results)
