Optimising Time-Lapse Seismic Data Processing: Stage 2C of the CO2CRC Otway Project Case Study

2019 ◽  
Author(s):  
Dmitry Popik ◽  
Roman Pevzner ◽  
Stanislav Glubokovskikh ◽  
Valeriya Shulakova ◽  
Sasha Ziramov
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Time Lapse ◽  
Seismic Data Processing ◽  
Processing Stage

SC-PSNET: A deep neural network for automatic P- and S-phase detection and arrival-time picker using 1C recordings

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. U87-U98
Author(s):  
Jing Zheng ◽  
Jerry M. Harris ◽  
Dongzhuo Li ◽  
Badr Al-Rumaih
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Seismic Data ◽  
Arrival Time ◽  
Time Lapse ◽  
S Phase ◽  
Training Data ◽  
Phase Detector ◽  
Phase Detection ◽  
Seismic Data Processing

It is important to autopick an event’s arrival time and classify the corresponding phase for seismic data processing. Traditional arrival-time picking algorithms usually need 3C seismograms to classify event phase. However, a large number of borehole seismic data sets are recorded by arrays of hydrophones or distributed acoustic sensing elements whose sensors are 1C and cannot be analyzed for particle motion or phase polarization. With the development of deep learning techniques, researchers have tried data mining with the convolutional neural network (CNN) for seismic phase autopicking. In the previous work, CNN was applied to process 3C seismograms to detect phase and pick arrivals. We have extended this work to process 1C seismic data and focused on two main points. One is the effect of the label vector on the phase detection performance. The other is to propose an architecture to deal with the challenge from the insufficiency of training data in the coverage of different scenarios of [Formula: see text] ratios. Two novel points are summarized after this analysis. First, the width of the label vector can be designed through signal time-frequency analysis. Second, a combination of CNN and recurrent neural network architecture is more suitable for designing a P- and S-phase detector to deal with the challenge from the insufficiency of training data for 1C recordings in time-lapse seismic monitoring. We perform experiments and analysis using synthetic and field time-lapse seismic recordings. The experiments show that it is effective for 1C seismic data processing in time-lapse monitoring surveys.

scholarly journals Purposeless repeated acquisition time-lapse seismic data processing

2008 ◽  
Vol 5 (1) ◽  
pp. 31-36 ◽  
Author(s):  
Jingye Li ◽  
Xiaohong Chen ◽  
Wei Zhao ◽  
Yunpeng Zhang
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Time Lapse ◽  
Acquisition Time ◽  
Seismic Data Processing ◽  
Repeated Acquisition

Study on Tunnel Seismic Detection Applied in Coal Mine

2014 ◽  
Vol 602-605 ◽  
pp. 1757-1760
Author(s):  
Yun Bing Hu ◽  
Yao Wang ◽  
Yan Qing Wu ◽  
Zi Xuan Liu ◽  
Peng Hui Xian
Keyword(s):  
Data Processing ◽  
Coal Mine ◽  
Seismic Data ◽  
Data Interpretation ◽  
Seismic Data Processing ◽  
Mine Field ◽  
Qinshui Basin ◽  
Hazard Detection ◽  
Seismic Detection

The idea of applying tunnel seismic advance exploration in coal mine geo-hazard detection is conducted in this paper. We successfully made a mine advance and side detection simultaneously with one-time shot seismic data by splitting the field seismic data using radon transformation, and we preceded mine seismic data processing and data interpretation was done to explain the phenomenon. We also introduce a case study in this paper which was carried out at Qinshui basin Yangmei No.5 mine field, and the detection results basically matched with out-crops, illustrating that our method can be one reference in mine geo-hazard detection.

A review on time-lapse seismic data processing and interpretation

2015 ◽  
Vol 19 (2) ◽  
pp. 375-392 ◽  
Author(s):  
Phung K. T. Nguyen ◽  
Myung Jin Nam ◽  
Chanho Park
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Time Lapse ◽  
Seismic Data Processing

Interpretation Guided Seismic Data Processing of a Large 3D OBC Survey With Simultaneous Shooting: A Case Study from Offshore Abu Dhabi

2018 ◽  
Author(s):  
Subhrankar Paul ◽  
Carlos Manuel Barajas ◽  
Konrad Piotr Gawron ◽  
Oleg Khakimov ◽  
Shraddha Chatterjee ◽  
...  
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Abu Dhabi ◽  
Seismic Data Processing

scholarly journals A Case Study of Amplitude-Preserved Seismic Data Processing

2017 ◽  
Vol 54 (3) ◽  
pp. 253-267
Author(s):  
Dong Kwon Lee ◽  
Sung Oh Cho ◽  
Myung Jin Nam ◽  
Bosung Lim ◽  
Hai Soo Yoo
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Seismic Data Processing
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