The value of information from horizontal distributed acoustic sensing compared to multicomponent geophones via machine learning
| Autor: | Ivan Lim Chen Ning, Michael Morphew, Samir Jreij, Whitney Trainor-Guitton |
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| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
Artificial neural network Renewable Energy Sustainability and the Environment Computer science Mechanical Engineering Acoustics Real-time computing Energy Engineering and Power Technology Geophone Distributed acoustic sensing 010502 geochemistry & geophysics 01 natural sciences Value of information Fuel Technology Data acquisition Geochemistry and Petrology Reliability (statistics) Geology 0105 earth and related environmental sciences |
| Zdroj: | SEG Technical Program Expanded Abstracts 2020. |
| Popis: | Faults play an important role in recharging many geothermal reservoirs, and seismic information can image the locations of these faults. The value of information (VOI) metric is used to objectively quantify and compare the value of two types of seismic receiver data via a machine learning approach. The demonstrated VOI methodology is novel by including spatial models from seismic data and obtaining the information statistics from machine learning. Our two-dimensional numerical experiments compare images created from sparsely spaced (80 m), two-component geophone sampling to high spatial resolution (1 m), single-component DAS. We used a three-fold cross validation of a U-Net convolutional neural networks to achieve average classification statistics. The results suggest that when horizontal sources are utilized, geophones and DAS identify reflectors and non-reflectors at roughly the same rate. The average F1 score for horizontal DAS is 0.939 and 0.931 for geophones. For images created from a vertical source, DAS performed marginally better (F1 = 0.919) than geophones (F1 = 0.877). Our transferrable methodology can provide guidance on which acquisition scenarios can improve images of important structures in the subsurface and present an efficient method for obtaining reliability statistics from high-dimensional, spatial data. |
| Databáze: | OpenAIRE |
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