Hao Lv

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.

The study of seismic sources and wave propagation in ice plate is helpful to understand the structure, migration, fracture mechanics, mass balance and other processes. However, due to extreme environment, in-situ dense seismic observations are rare and the dynamic changes of the ice plate remain poorly understood. We conduct a seismic experiment with distributed acoustic sensing array on a frozen lake. We excite water vibrations by under-water airgun shots. With an artificial intelligence method, we detected seismic signals including high frequency icequakes and low frequency events. Icequakes cluster along the fractures and correlate with the local temperature variation. The flexural-gravity wave reveals the property of the ice plate. Our study demonstrates the utility of DAS array as an in-situ dense seismic network in illuminating the internal failure process and dynamic deformation of ice plate such as ice shelf, which contributes to understanding and prediction of disintegrations of ice shelves.

The distributed acoustic sensing (DAS) technology that has emerged in recent years, combined with existing optical cables for earthquake monitoring, has the advantages of low deployment cost and dense sampling capability and has been used in various environments. However, the massive data generated by DAS are difficult to process manually, and there is an urgent need for efficient automatic earthquake detection algorithm. The deep neural networks trained with manually processed earthquake data in the past decade have been introduced. However, available manually processed earthquake DAS data are very limited, which makes it difficult to train neural networks. A convolutional neural network called “APM-DAS-earthquake network (ADE-Net) ” was built and trained with only five earthquakes’ records in Tangshan, China. The neural network detected about 2.56 times earthquakes on a DAS dataset than the permanent seismic network’s catalog with an accuracy rate of 80.4% and a recall rate of 93.18%. Results of tests with synthetic datasets and another real dataset suggest the neural network works well with a variety of earthquake location, magnitude, type, and array geometry. The computational time cost with a PC for 10-s data is less than 2.5-s data, which demonstrates its feasibility on time-sensitive applications, for example, earthquake early warning.