Xiangfang Zeng

Blind fault mapping requires dense observations over large areas, and distributed acoustic sensing (DAS) provides a viable solution using dark fibers. Seismic attenuation is sensitive to fault‐related structure but relies on reliable amplitude measurements. In DAS data, amplitudes are distorted by non‐attenuation effects, including non‐uniform coupling and small‐scale heterogeneity, limiting their direct use for attenuation imaging. Here, we propose an attenuation imaging method based on ambient noise cross‐correlation functions, in which double virtual shot gathers are used to separate attenuation‐related and non‐attenuation‐related amplitude contributions. Using 6 hr of noise data, we obtain an attenuation coefficient profile along a 7.2‐km DAS array with sub‐kilometer resolution. The image reveals three strong‐attenuation zones, two of which are likely associated with fault‐related structures characterized by strong scattered waves, while the third is more likely associated with a low‐velocity structure. These results highlight the potential of DAS‐based ambient‐noise attenuation imaging for large‐scale, high‐resolution fault exploration.

Rapid response to destructive earthquakes is an important application of the distributed acoustic sensing technology (DAS), which can exploit existing telecom fiber‐optic cables as ultra‐dense DAS arrays. This technique significantly reduces deployment time and records large volume waveforms. However, the large stream of DAS data necessitates efficient earthquake detection methods. We propose a new DAS earthquake detection approach that hybridizes the Array Beam Detection (ABD) and Template Matched Filter (TMF) methods. This approach is applied to detect aftershocks of the 2022 Menyuan Mw6.6 earthquake recorded by a linear DAS array with the dark fiber. The continuous DAS records are scanned by the ABD method first. During this process, if the energy of the ABD‐detected event exceeds a specific threshold, the event is added to the template library, and the TMF is then used to re‐scan the records within a designated time window. The ABD method detects 69.4% of the events in the network catalog. Our new approach detects 89.4% of the catalog events, representing a 20% increase in the overall detection rate. Of the newly detected events, our approach identifies 42.4% more than the ABD method. Notably, the magnitudes of 95.7% of these newly detected events are estimated to be below M2.0, indicating an enhanced monitoring capability. However, precise event locations remain challenging with the linear DAS array.

The Ebao Basin, which locates along the northwest segment of the Qilian‐Haiyuan fault, has experienced several destructive earthquakes in past decades. A 10 km long distributed acoustic sensing (DAS) array was deployed with an existing telecom fiber‐optic cable after the 2022 Mw6.6 Menyuan earthquake, which destroyed high‐speed railways and highways in this region. The DAS array continuously recorded ambient noise and earthquake signals during the 5‐day deployment period. Noise cross‐correlation functions (NCFs) were calculated, and the Rayleigh wave signal clearly emerged on the NCFs. The phase velocities of the fundamental mode between 2.5 and 17 Hz were measured with the modified Frequency‐Bessel transform method. The S‐wave velocity structure along the cable, extending to a depth of 200 m, was constructed using the surface wave tomography method. It consists of three blocks, with velocity variations of up to 12%. P‐wave delay times of regional earthquakes support the lateral variation among the three blocks. Both Vs30 and site amplification predicted with our S‐wave velocity model correlate well with the ground motion induced by local earthquakes. Precursor and coda waves on the NCFs reveal four possible faults, and fault zone scattered waves are also observed on the record of local earthquakes.

Earthquake occurrence depth in the crust is related to stress, temperature, and brittle–ductile transition, which is also near the transition depth of the upper to lower crust. The composition variation between the upper and lower crust causes remarkable changes of rheological properties and variation in stress distribution. Clarifying the detailed stress distribution in the upper and lower crust is crucial for understanding the brittle–ductile transition and the stress environment of the seismogenic zone. The Southeastern Tibetan Plateau (SETP), with wide spread of active strike−slip faults and clustered earthquakes, provides a natural field for investigating the relationships between crustal stresses, deformation behaviors, and earthquake mechanics. By considering the rheological properties of granite and anorthite, this paper established stress models with different boundary depths (15, 20 and 25 km) between the upper and lower crust along the Anninghe, Xianshuihe, and Longmenshan Faults in the SETP with a horizontal strain of 6 × 10⁻⁴ extracted from in situ stress data. The stress model with different geothermal gradients and a boundary depth of 20 km between the upper and lower crust suggests two distinct types of the brittle–ductile transition below these three faults. Simultaneously, the stress model can account for the continuity of earthquake depth distribution below the Longmenshan Fault and the seismic gap below the Anninghe and Xianshuihe Faults. The continuity of earthquake depth distribution or seismic gap below these three faults can be explained by their different geothermal gradients. These findings provide new insights for understanding the stress environment of the seismogenic zone in the SETP. Our model reveals the relationships between differential stress, seismicity, brittle–ductile transition, and boundary depth of the upper and lower crust in the continental crust, and connects the multiple observations from geophysics and geology. Furthermore, our model provides insights for studying multiple processes in the continental crust, such as crustal deformation, fault slip, and earthquake occurring.

In the nearshore area, ocean current display intricate complexities due to interactions among tide, river, and coastline, which makes accurate current modeling challenging. Continuous in situ observation with high spatial and temporal resolution helps to better understand the dynamics of these currents. In this study, we used a 10‐km long submarine fiber‐optic cable with distributed acoustic sensing technology to record seismic signals associated with ocean waves. The current velocity and water depth were obtained from the velocity dispersion using frequency‐wave number analysis matched against theoretical ocean wave propagation equations. The results show remarkable agreement with observation of a nearby current meter, confirming the dominance of tidal currents as well as a small‐scale residual current. The temporal variation of water depth is consistent with observation by a nearby tidal gauge. This study demonstrates the potential of using submarine fiber‐optic cable for long‐term, high‐resolution, near real‐time nearshore current monitoring.

Distributed acoustic sensing (DAS) technology, combined with existing telecom fiber-optic cable, has shown great potential in earthquake monitoring. The template matching algorithm (TMA) shows good detection capabilities but depends on heavy computational cost and diverse template events. We developed a program named HD-TMA (high-efficiency DAS template matching algorithm), which accelerates computation by 40 times on the central processing unit platform and 2 times on the graphic processing unit platform. For linear DAS array data, we introduced a fast arrival-picking algorithm based on the Hough transform to pick the time window of template waveform. The HD-TMA was successfully applied to the 2022 Ms 6.9 Menyuan earthquake aftershock sequence recorded by a DAS array, and the DAS data result was compared with a collocated short-period seismometer data’s result. Two optimization strategies were discussed based on this data set. (1) Using signal-to-noise ratio in choosing the location and aperture of the subarray and the time window of the template waveform. (2) Considering the decrease in template events’ marginal utility, we proposed applying a neural network to build a template event library, followed by the HD-TMA scanning. Such strategies can effectively reduce computational cost and improve detection capability.

Studying seismic sources and wave propagation in ice plates can provide valuable insights into understanding various processes, such as ice structure dynamics, migration, fracture mechanics and mass balance. However, the harsh environment makes it difficult to conduct in situ dense seismic observations. Consequently, our understanding of the dynamic changes within the ice sheet remains insufficient. We conducted a seismic experiment using a distributed acoustic sensing (DAS) array on a frozen lake, exciting water vibrations through underwater airgun shots. By employing an artificial intelligence method, we were able to detect seismic events that include both high-frequency icequakes and low-frequency events. The icequakes clustered along ice fractures and their activity correlated with local temperature variations. The waveforms of low-frequency events exhibit characteristics of flexural-gravity waves, which offers insights into the properties of the ice plate. Our study demonstrates the effectiveness of an DAS array as an in situ dense seismic network for investigating the internal failure process and dynamic deformation of ice plates such as the ice shelf, which may contribute to an enhanced comprehension and prediction of ice shelf disintegration.

Seismicity induced by fluid injection including wastewater injection, hydrofracking, and enhanced geothermal system (EGS) site production draws public attention. Dense arrays have been deployed to improve monitoring capability. In March 2016, the PoroTomo experiment deployed an 8.6-kilometer-long fiber-optic cable for Distributed Acoustic Sensing (DAS) in the geothermal field at Brady Hot Springs, Nevada, covering an area of 1.5 by 0.5 km. The goal was to assess an integrated technology for characterizing and monitoring temporal changes in the rock mechanical properties of an EGS reservoir in three dimensions. We applied a neural network designed for earthquake detection called ADE-Net2 to the DAS dataset to detect seismic events in continuous records. We were able to detect a total of 90 earthquakes, which included 21 events that had not been reported by a previous template-matching study. Additionally, we were able to successfully detect almost all of the active source signals, with only seven events being missed. We used the STA/LTA method to pick arrivals and a clustering method to remove outliers. We initially tried a standard event location algorithm, but the low signal-to-noise ratio resulted in significant picking uncertainty that is up to ∼0.5 s, leading to large location uncertainty. Therefore, we developed a new location method based on the similarity between the theoretical travel-time curve and picked moveout. A grid search scheme was adopted to find the optimal point at which the travel-time curve is most similar to the picked one. Most newly detected earthquakes locate southwest of the DAS array, where five earthquakes were reported by a local seismic network. The plant began shutting down at 19:15 UTC on the 13^th of March, and most earthquakes occurred on the 14th of March, indicating a relationship between the seismicity and the pressure changes caused by the shutdown of the plant. The pressure changes at epicenters obtained from a simplified model range from 71 to 157 kPa, exceeding a typical earthquake trigger threshold of 10 kPa.