• Geophysics and Geophysical Exploration
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• v.20 no.1
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• pp.1-11
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• 2017

In order to improve the accuracy of microseismic epicenter location through the inversion techniques using P and S wave first arrivals, field experiments of microseismic monitoring were performed using borehole 3-component geophones. The direction of epicenter was estimated from the hodograms of P-wave first arrivals through the weight drop experiments in which the $\times$ component of 3-component geophone was aligned to the magnetic north. The picking of S wave first arrival was possible in the polarization filtered data even if S waves are difficult to be identified in raw data. The inversion technique using only P wave first arrival times can often converge to the local minimum when the initial values for epicenter are largely apart from the true epicenter, so that the correct solution can not be found. To solve this problem, the epicenter determination method using differences between P and S wave arrival times was used to estimate proper initial values of epicenter. The inversion result using only P-wave first arrival times which started from the estimated initial values showed the improved accuracy of the epicenter location.

• The Journal of Engineering Geology
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• v.19 no.2
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• pp.139-144
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• 2009

The microseismic monitoring system with wide range of frequency has been operating in real time and it is remotely monitored at indoor and on-site for one year. This system was constructed and established in order to secure the safe and effective operation of the KAERI Underground Research Tunnel(KURT). For one year monitoring work, total 14 events were recorded in the vicinity of the KURT, and the majority of events are regarded as ultramicroseismic earthquake and artificial impacts around the tunnel. The major event is the magnitude 3.4 earthquake which was centered around Gongju city, Chungnam Province. It means that there is no significant evidence of high frequency microseismic event, which is associated with fracture initiation and/or propagation in the rock mass and shotcrete. Three components sensor was applied in order to analyze and define the direction of vibration as well as an epicenter of microseismic origin, and also properly designed and installed in a small borehole. This monitoring system is able to predict the location and timing of fracturing of rock mass and rock fall around an undreground openings as well as analysis on safety of various kinds of engineering structures such as nuclear facilities and other structures.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.177-188
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• 2022

Recently, the application of distributed acoustic sensors (DAS), which can replace geophones and seismometers, has significantly increased along with interest in micro-seismic monitoring technique, which is one of the CO₂ storage monitoring techniques. A significant amount of temporally and spatially continuous data is recorded in a DAS monitoring system, thereby necessitating fast and accurate data processing techniques. Because event detection and seismic phase picking are the most basic data processing techniques, they should be performed on all data. In this study, a machine learning-based P, S wave phase picking algorithm was developed to compensate for the limitations of conventional phase picking algorithms, and it was modified using a transfer learning technique for the application of DAS data consisting of a single component with a low signal-to-noise ratio. Our model was constructed by modifying the convolution-based EQTransformer, which performs well in phase picking, to the ResUNet structure. Not only the global earthquake dataset, STEAD but also the augmented dataset was used as training datasets to enhance the prediction performance on the unseen characteristics of the target dataset. The performance of the developed algorithm was verified using K-net and KiK-net data with characteristics different from the training data. Additionally, after modifying the trained model to suit DAS data using the transfer learning technique, the performance was verified by applying it to the DAS field data measured in the Pohang Janggi basin.

• Geophysics and Geophysical Exploration
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• v.19 no.1
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• pp.1-10
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• 2016

The hypocenter distribution of microseismic events generated by hydraulic fracturing for shale gas development provides essential information for understanding characteristics of fracture network. In this study, we evaluate how inaccurate velocity models influence the inversion results of two widely used location programs, hypoellipse and hypoDD, which are developed based on an iterative linear inversion. We assume that 98 stations are densely located inside the circle with a radius of 4 km and 5 artificial hypocenter sets (S0 ~ S4) are located from the center of the network to the south with 1 km interval. Each hypocenter set contains 25 events placed on the plane. To quantify accuracies of the inversion results, we defined 6 parameters: difference between average hypocenters of assumed and inverted locations, $d_1$; ratio of assumed and inverted areas estimated by hypocenters, r; difference between dip of the reference plane and the best fitting plane for determined hypocenters, ${\theta}$; difference between strike of the reference plane and the best fitting plane for determined hypocenters, ${\phi}$; root-mean-square distance between hypocenters and the best fitting plane, $d_2$; root-mean-square error in horizontal direction on the best fitting plane, $d_3$. Synthetic travel times are calculated for the reference model having 1D layered structure and the inaccurate velocity model for the inversion is constructed by using normal distribution with standard deviations of 0.1, 0.2, and 0.3 km/s, respectively, with respect to the reference model. The parameters $d_1$, r, ${\theta}$, and $d_2$ show positive correlation with the level of velocity perturbations, but the others are not sensitive to the perturbations except S4, which is located at the outer boundary of the network. In cases of S0, S1, S2, and S3, hypoellipse and hypoDD provide similar results for $d_1$. However, for other parameters, hypoDD shows much better results and errors of locations can be reduced by about several meters regardless of the level of perturbations. In light of the purpose to understand the characteristics of hydraulic fracturing, $1{\sigma}$ error of velocity structure should be under 0.2 km/s in hypoellipse and 0.3 km/s in hypoDD.