scholarly journals Rapid Learning of Earthquake Felt Area and Intensity Distribution with Real-time Search Engine Queries

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hengshu Zhu ◽  
Ying Sun ◽  
Wenjia Zhao ◽  
Fuzhen Zhuang ◽  
Baoshan Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Search Engine ◽  
Intensity Distribution ◽  
Rapid Learning ◽  
Felt Area

Transient electromagnetic search engine for real-time imaging

Geophysics ◽  
2017 ◽  
Vol 82 (5) ◽  
pp. E277-E285 ◽  
Author(s):  
Jide Nosakare Ogunbo ◽  
Jie Zhang ◽  
Xiong Zhang
Keyword(s):  
Real Time ◽  
Search Engine ◽  
Solution Space ◽  
Inversion Method ◽  
Model Parameters ◽  
Transient Electromagnetic ◽  
Model Solutions ◽  
Resistivity Model ◽  
2D Resistivity ◽  
Engine Method

To image the resistivity distribution of the subsurface, transient electromagnetic (TEM) surveying has been established as an effective geophysical method. Conventionally, an inversion method is applied to resolve the model parameters from the available measurements. However, significant time and effort are involved in preparing and executing an inversion and this prohibits its use as a real-time decision-making tool to optimize surveying in the field. We have developed a search engine method to find approximate 1D resistivity model solutions for circular central-loop configuration TEM data in real time. The search engine method is a concept used for query searches from large databases on the Internet. By extension, approximate solutions to any input TEM data can be found rapidly by searching a preestablished database. This database includes a large number of forward simulation results that represent the possible model solutions. The database size is optimized by the survey depth of investigation and the sensitivity analysis of the model layers. The fast-search speed is achieved by using the multiple randomized [Formula: see text]-dimensional tree method. In addition to its high speed in finding solutions, the search engine method provides a solution space that quantifies the resolutions and uncertainties of the results. We apply the search engine method to find 1D model solutions at different data points and then interpolate them to a pseudo-2D resistivity model. We tested the method with synthetic and real data.

Microseismic search engine for real-time estimation of source location and focal mechanism

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. KS169-KS182 ◽  
Author(s):  
Xiong Zhang ◽  
Jie Zhang
Keyword(s):  
Hydraulic Fracturing ◽  
Real Time ◽  
Search Engine ◽  
Focal Mechanism ◽  
Input Data ◽  
Web Search ◽  
Solution Space ◽  
Web Search Engine ◽  
Fracturing Process ◽  
Microseismic Events

Similar to a web search engine, we have developed a microseismic search engine that can estimate an event location and the focal mechanism in less than a second to monitor the hydraulic fracturing process. The method was extended from a real-time earthquake monitoring approach for seismological applications. We first calculate the full waveforms of all possible microseismic events over a 3D grid with a known velocity model for a given acquisition geometry to create a database. We then index and rank all of the seismic waveforms in the database by following the characteristics of the phase and amplitude of the waveform through a computer fast search technology, specifically, the multiple randomized k-dimensional tree method. When a microseismic event occurs, the approximate best matches to the entry waveform are found immediately by comparing the characteristic features between the input data and the database. The method returns not just one but a series of solutions, similar to a web search engine. Thus, we can obtain a solution space that delineates the resolution and confidence level of the results. Also similar to a web search engine, the microseismic search engine does not require any input parameter or processing experience; thus, the solutions are the same for any user. Numerical tests suggest that the waveform search approach is insensitive to random and correlated noises. However, if the correlation values between the input data and best matches in the database are too low, suggesting unreliable results, the solution may be rejected automatically by applying a preset threshold. We have applied the method to real data, and found great potential for the routine real-time monitoring of microseismic events during hydraulic fracturing.

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