ALBERT

Description: A dense seismological array and profile reveal the deep structure across the Longmenshan from the Songpan-Ganzi terrane of the Tibetan plateau to the Sichuan basin. Receiver function and tomographic images reveal that the Pengguan Complex which cores the Longmenshan in the region where the Ms. 8 Wenchuan earthquake of 2008 occurred, is marked by high velocities in the upper 15 km of the crust. At about 15 km depth both P- and S-wave velocities decrease at a flat-lying boundary around which the aftershock hypocentres of the Wenchuan earthquake are concentrated. Thus, this boundary may be a faulted interface or detachment, marking the base of the Pengguan Complex. Moho depths change significantly in going from the Tibetan plateau to the Sichuan basin. At the location of the dense profile a Moho step occurs, located about 50 km NW of the surface trace of the Yingxiu-Beichuan fault (YBF). The boundary at about 15 km depth below the Pengguan Complex seems to deepen at around the Wenchuan-Maoxian fault (WMF) by about 3 km and merge to the NW with another interface at about 18 km depth. This interface, NW of the WMF, which correlates with the top of a zone of high conductivity is interpreted to represent the top of the Tibetan mid-crustal low velocity, high conductivity zone. The tomographic image indicates that the boundary between the low velocities of the Songpan-Ganzi terrane and the high velocities of the Sichuan basin in the middle and lower crust occurs NW of the surface trace of the YBF. Thus, it is proposed that a zone extending from the WMF at about 15 km depth to the Moho step about 25 km further NW marks the boundary between the Tibetan plateau and the Sichuan basin in the middle and lower crust.

Description: An Ms6.9 earthquake occurred in Menyuan, Qinghai, China on Jan 7th, 2022. This earthquake destructed the high-speed railway passing through the epicentral area and rise concerns on the seismic hazard of the Tianzhu Seismic gap, which is about 150 km long. Two DAS arrays were deployed with about 40km long dark fiber optic cable for aftershock monitoring and subsurface imaging. Three automatic methods were utilized to scan earthquake signals on the continuous data including: a neural network, template-matching method, and a hybrid method integrating array-detection and template-matching. All of three methods reported more than 80% aftershocks in the routine catalog and up to 50% newly detected events. For the template-matching method, using aftershocks occurred in the first two-day as templates provides about 91% catalog events and 50% additional events. The other two methods do not depend on the prior information, which means quicker response. The fault-related signal is also observed on the seismic wavefields of earthquakes, which helps to identify blind faults in the Menyuan basin. Ambient noise tomography method was used to construct 2D Vs profile along one cable. Strong lateral variation emerges and faults can be identified on the NCF COG gather. Such high-resolution shallow structure model and earthquake catalog provides additional information of seismic hazard in the Menyuan region.

Description: Microseismic monitoring is an important tool to characterize the reservoirs and delineate the growth of small-scale fractures. In addition, microseismic events are crucial for describing detailed fault geometries, stress changes, and spatial-temporal evolution of seismogenic activities. Deep learning has been extensively and successfully utilized for seismic event detection and phase picking. In this work, we propose an integrated workflow of waveform denoising, event detection, and seismic phase detection based on convolutional neural network (CNN) and unsupervised clustering, aiming at identifying and classifying microseismic P- and S-wave arrivals accurately. First, we preprocess (e.g., bandpass filter) the continuous waveforms and extract statistical features, and then feed the features into CNN for deep feature extraction and learning. The microseismic phase detection is then performed using clustering from waveform feature distance (similarity), which is retrieved from both statistical features of waveforms and the deep features extracted by CNN, yielding microseismic P- and S-wave arrival detections. We use synthetic data with different source mechanisms and signal-to-noise ratios, along with field microseismic data collected from the Hengill Geothermal area in Iceland, to verify the effectiveness of the proposed workflow in detecting weak microseismic phases. The refined phase detections also improved the resolution of stacking-based source imaging.