scholarly journals Microseismic Event Detection Using Multiple Geophone Arrays in Southwestern Utah

2018 ◽  
Vol 89 (5) ◽  
pp. 1660-1670 ◽  
Author(s):  
Andy J. Trow ◽  
H. Zhang ◽  
A. S. Record ◽  
K. A. Mendoza ◽  
K. L. Pankow ◽  
...  
Keyword(s):  
Event Detection ◽  
Microseismic Event

Automated Microseismic Event Detection and Location by Continuous Spatial Mapping

2005 ◽  
Author(s):  
Julian Edmund Drew ◽  
H. David Leslie ◽  
Philip Neville Armstrong ◽  
Gwenola Michard
Keyword(s):  
Event Detection ◽  
Spatial Mapping ◽  
Microseismic Event

Introduction to this special section: Distributed acoustic sensing

2020 ◽  
Vol 39 (11) ◽  
pp. 775-775
Author(s):  
Tieyuan Zhu ◽  
Ariel Lellouch ◽  
Kyle T. Spikes
Keyword(s):  
Event Detection ◽  
Special Section ◽  
Unique Problem ◽  
Fracture Characterization ◽  
Acoustic Sensing ◽  
Distributed Acoustic Sensing ◽  
Work Done ◽  
Microseismic Event

Distributed acoustic sensing (DAS) has seen many advances in recent years with many different applications. This special section contains six papers featuring applications that vary from microseismic event detection to subsea applications to surface deployments, fracture characterization, and well irregularity identification. Each paper introduces a unique problem and then poses the use of DAS in an appropriate way to solve the problem. Special sections on DAS are relatively frequent in the literature currently, so these papers are a snapshot of the work done with the technology. We hope you enjoy these publications.

On the importance of benchmarking algorithms under realistic noise conditions

2020 ◽  
Vol 221 (1) ◽  
pp. 504-520
Author(s):  
Claire Birnie ◽  
Kit Chambers ◽  
Doug Angus ◽  
Anna L Stork
Keyword(s):  
Event Detection ◽  
Gaussian Noise ◽  
Moment Tensor ◽  
Synthetic Data ◽  
Data Sets ◽  
Signal To Noise ◽  
Noise Data ◽  
Recorded Data ◽  
Imaging Algorithms ◽  
Microseismic Event

SUMMARY Testing with synthetic data sets is a vital stage in an algorithm’s development for benchmarking the algorithm’s performance. A common addition to synthetic data sets is White, Gaussian Noise (WGN) which is used to mimic noise that would be present in recorded data sets. The first section of this paper focuses on comparing the effects of WGN and realistic modelled noise on standard microseismic event detection and imaging algorithms using synthetic data sets with recorded noise as a benchmark. The data sets with WGN underperform on the trace-by-trace algorithm while overperforming on algorithms utilizing the full array. Throughout, the data sets with realistic modelled noise perform near identically to the recorded noise data sets. The study concludes by testing an algorithm that simultaneously solves for the source location and moment tensor of a microseismic event. Not only does the algorithm fail to perform at the signal-to-noise ratios indicated by the WGN results but the results with realistic modelled noise highlight pitfalls of the algorithm not previously identified. The misleading results from the WGN data sets highlight the need to test algorithms under realistic noise conditions to gain an understanding of the conditions under which an algorithm can perform and to minimize the risk of misinterpretation of the results.

A method for microseismic event detection based on fourth-order cross-cumulants

2015 ◽  
Author(s):  
W. B. Wang* ◽  
B. J. Li ◽  
G. Q. Sheng ◽  
D. S. Zhou ◽  
J. Chen
Keyword(s):  
Fourth Order ◽  
Event Detection ◽  
Microseismic Event

Full-waveform based microseismic event detection and signal enhancement: An application of the subspace approach

2014 ◽  
Vol 62 (6) ◽  
pp. 1406-1431 ◽  
Author(s):  
Fuxian Song ◽  
Norm R. Warpinski ◽  
M. Nafi Toksöz ◽  
H. Sadi Kuleli
Keyword(s):  
Event Detection ◽  
Signal Enhancement ◽  
Full Waveform ◽  
Microseismic Event
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