High geo-stresses and complicated geological conditions are two typical characteristics in the traffic tunnel of the tailrace surge chamber at the Shuangjiangkou hydropower station, Southwest China. Rock failures such as spalling and rockbursts induced by excavation were encountered repeatedly in this tunnel. To evaluate the stability of rock mass of the tunnel during excavation, the high-precision microseismic (MS) monitoring technology was introduced. Through analysing the tempo-spatial distribution characteristics of MS events, the main damage areas and their influencing factors were studied. The rock fracturing mechanisms of MS clusters were revealed based on the source parameters of MS events. A three-dimensional model was established to obtain the stress and deformation characteristics of rock mass. Results indicated that the MS events mainly occurred between July 10 and 26, 2017 and the energy release was higher during this period. The main damage areas of rock mass were located at the Stake K0 + 110m–K0 + 170m. Weak structures and multi-cavern effect aggravated rock mass damage subjected to excavation-unloading. Tensile failure was the main fracturing type of rock mass. Rock mass damage influenced by weak structures was especially prominent. The numerical simulation results are consistent with the MS monitoring results. The results could provide significant references for safety evaluation and construction design of similar tunnels.

High-precision seismic fault detection and fault surface extraction (or grouping) are critical steps in reservoir characterization. In this article, basing on the gradually changing characteristics of the target faults in the adjacent 2-D seismic fault profiles, we propose an efficient and high-precision automatic fault surface grouping (EHFG) method for complex 3-D seismic fault data; and it is realized using pairs of adjacent 2-D seismic fault profiles without human intervention. EHFG comprises of high-precision fault separation (HFS) and high-precision fault labeling (or naming) (HFL), in which HFS aims to separate the positive-slope and negative-slope faults in the seismic fault detection result and ultimately obtain the high-precision positive-slope and negative-slope faults; HFL aims to label the separated positive-slope and negative-slope faults and ultimately obtain the corresponding high-precision fault surface grouping results. An actual 3-D seismic fault data example demonstrates that EHFG is a good potential technique for fault surface grouping.

Highly accurate microseismic (MS) localization is the basis for rock damage assessment and disaster warning. The engineering background noise mixed in the MS signal seriously affects the subsequent analysis of the MS signal. A noise reduction method of singular spectral analysis-complementary ensemble empirical mode decomposition-wavelet threshold (CEEMD-WT-SSA) is proposed. The complementary ensemble empirical mode decomposition (CEEMD), complementary ensemble empirical mode decomposition-wavelet threshold (CEEMD-WT) and the proposed method are respectively used for denoise the noisy Ricker wavelet. The signal-to-noise ratio of the denoised signal by the proposed method is 56.77% and 37.88% higher than that of CEEMD and CEEMD-WT methods. Moreover, an adaptive artificial bee colony (ABC) algorithm is applied for MS source location. The time to quantile difference is introduced as the objective function. The blast positioning test results prove that the proposed method improves the positioning accuracy by 44.12% and 47.64% particle swarm optimization (PSO) algorithm and simulated annealing particle swarm optimization (SA-PSO) algorithm, respectively. The MS positions of underground caverns reveal that the calculated cluster of MS events using the adaptive ABC algorithm are more concentrated at the structural plane and appearance deformation failure location and in good agreement with field survey and routine monitoring data.