Microseismic monitoring plays an essential role for reservoir characterization and earthquake disaster monitoring and early warning. The accuracy of the subsurface velocity model directly affects the precision of event localization and subsequent processing. It is challenging for traditional methods to realize efficient and accurate microseismic velocity inversion due to the low signal-to-noise ratio of field data. Deep learning can efficiently invert the velocity model by constructing a mapping relationship from the waveform data domain to the velocity model domain. The predicted and reference values are fitted with mean square error as the loss function. To reduce the feature mismatch between the synthetic and real microseismic data, data augmentation is also performed using correlation and convolution operations. Moreover, a hybrid training strategy is proposed by combining synthetic and augmented data. By testing real microseismic data, the results show that the Unet is capable of high-resolution and robust velocity prediction. The data augmentation method complements more high-frequency components, while the hybrid training strategy fully combines the low-frequency and high-frequency components in the data to improve the inversion accuracy.

Current extensive seismicity in southern Sichuan Basin is ascribed to the reactivation of pre-existing faults, as a result of prolonged fluid injection for salt mining and shale gas development, respectively. However, the structural framework of the region remains poorly understood. Here, we apply V_p/V_sconsistency-constrained double-difference seismic tomography to high quality phase data from 36,314 earthquakes jointly recorded by our local array and a regional seismic network to determine high-resolution velocity models. Earthquake relocations reveal shallow hypocenters for the M_s>5.0 earthquakes and two distinct seismogenic zones corresponding to the salt mine and shale gas regions, with most induced seismic events forming widespread lineaments some of which extend to the basement and are remarkably similar to the fault and fracture trends interpreted on reflection seismic and outcrops, respectively. Our 3-D crustal velocity analyses show that seismicity beneath the Changing salt mining area is associated with a combination of relatively low V_p/V_s(1.6–1.74) and high V_p/V_s(1.75–1.86) expressions, while most of small earthquakes within the Xingwen shale gas block are associated with relatively high V_p/V_svalues (1.77–1.87), indicating the earthquakes in these two areas are caused by unique inducing mechanisms. The two moderately strong 2018 Xingwen M_s5.7 and 2019 Gongxian M_s5.3 earthquakes in the Xingwen shale gas block are located around low V_p/V_s. zones, suggesting they could be structurally controlled. In comparison, the 2019 Changning M_s6.0 earthquake in the Changning salt mining area is associated with high V_p/V_s. expression, suggesting its occurrence is related to fluid injections. In addition, top of the crystalline early Neoproterozoic (pre-Sinian) Sichuan basement is characterized by the 6.5 km/s V_pcontour, which is new for earthquake tomographic studies in the region. Combined with outcrop analysis, we are able to construct a structural framework for induced seismicity in southern Sichuan basin, which unravels the structural architecture of induced seismicity.

Microseismic event back-azimuth is an indispensable parameter for source localization in downhole microseismic monitoring, and the accurate orientation of horizontal components of downhole seismic receivers is vital for reliably determining the event back-azimuth. Variation in the monitoring data quality may jeopardize the accuracy of receiver orientation which will further affect the event back-azimuth estimation. To mitigate this issue, we proposed a new probabilistic method based on P-wave polarization analysis for receiver orientation and event back-azimuth estimation. The algorithm constructs the von Mises distribution function using the polarization angle and corresponding rectilinearity of the P-wave, then determines the target angle using the maximum of the probability function. The receiver having the highest rectilinearity from the active-source event is used to quantify a reliable absolute orientation angle, and the relative orientation angles are calculated by the probability distributions based on the measurement angle differences and the associated averages of rectilinearity from all events. After receiver orientation, the P-wave polarization angles with different rectilinearity values are applied to construct the probability distribution functions to estimate the event back-azimuths. By using high-quality events and multi-receiver recordings, our methodology can greatly reduce the unintentional error in receiver orientation and increase event back-azimuth accuracy. We investigate the feasibility and reliability of the proposed method using both synthetic and field data. The synthetic data results demonstrate that, compared to the conventional methods, the proposed method can minimize the variance of the receiver orientation angle and back-azimuth estimation. The weighted standard deviation analysis demonstrates that the proposed method can reduce the orientation error and improve the event back-azimuth accuracy in the field dataset.

With the development of shale gas in the Changning-Zhaotong play in the southern Sichuan basin of China, which is the largest shale gas prospect in China, the frequency and magnitude of earthquakes in this region have increased significantly in recent years. For example, a M5.7 earthquake occurred on December 16, 2018, and a M5.3 earthquake on January 6, 2019 in addition to many M4.0+ earthquakes in this area. Some studies argue the large magnitude earthquakes are triggered by hydraulic fracturing in for the local shale gas development, which commenced in 2011. The frequency of the earthquake occurrence has been on steady increase in the past few years that local residents often reported felt quakes. To further understand the correlation between the shale gas development and local seismic activity, we conducted a two-phase dense array seismic monitoring with about 200 Zland 3C and SmartSolo 3C 5 Hz seismic nodes, from late February to early May, 2019 for a period of 70 days. The survey consists of roughly 340 deployments at 240 sites, with an average interstation distance of 1.5 km, covering 500 km2 in total. We have processed seismic records from late February to early April, 2019 (phase I), and picked some 600,000 P- and S-wave arrival times from 4385 detected local earthquakes. The earthquake hypocenters and the subsurface velocity structure of the Changning-Zhaotong area are inverted for using the double-difference tomography method. The relocation results show that the majority of hypocenters were located at depths ranging from 1.0km to 4.0km, in the proximity of the horizontal hydraulic fracturing wells. The tomographic results (< 3 km) correlate well with the known surface geological units, and most earthquakes occurred along the velocity discontinuities, likely characterizing a large hidden fault which, interestingly, is where the January 2019 M5.3 occurred. Our study is very important for understanding the seismic potentials in this area, and should provide useful information for the shale gas development in this region and other areas in China with similar geological, tectonic and stress conditions.

Coal mining disrupts the stress equilibrium of the surrounding rock mass, and the rock mass cracks as a result of the changes in the stress field, releasing strain energy and causing microseismic events. Monitoring seismicitiy during coal mining is critical for ensuring safe production and preventing geological disasters. In this study, we deployed 29 surface seismic nodes above an underground coal mine in the Yulin region of Shaanxi Province, China, to monitor the mining operation for 665 hours. A large number of microseismic events have been detected from continuous monitoring data, and analyzed using event clustering, source location, and mechanism estimate. The results show that (1) the frequency and intensity of microseismic events are related to underground mining working conditions; (2) the temporal and spatial locations of the microseismic sources may be utilized for real-time tracking the location of the underground coal mining face; and (3) three rupture mechanisms of tension rupture, implosion rupture, and shear rupture reflect the triggering mechanisms of the coal pillar failure, roof breakage and movement and fault slip.

Seismic data acquired in the presence of mechanical vibrations or power facilities may be contaminated by strong interferences, significantly decreasing the data signal-to-noise ratio (S/N). Conventional methods, such as the notch filter and time-frequency transform method, are usually inadequate for suppressing non-stationary interference noises, and may distort effective signals if overprocessing. In this study, we propose a method for eliminating mechanical vibration interferences in seismic data. In our method, we extended the variational mode extraction (VME) technique to a multivariate form, called multivariate variational mode extraction (MVME), for synchronous analysis of multitrace seismic data. The interference frequencies are determined via synchrosqueezing-based time-frequency analysis of process recordings; their corresponding modes are extracted and removed from seismic data using MVME with optimal balancing factors. We used synthetic data to investigate the effectiveness of the method and the influence of tuning parameters on processing results, and then applied the method to field datasets. The results have demonstrated that, compared with the conventional methods, the proposed method could effectively suppress the mechanical vibration interferences, improve the S/Ns and enhance polarization analysis of seismic signals.