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Articles in press have been peer-reviewed and accepted, which are not yet assigned to volumes /issues, but are citable by Digital Object Identifier (DOI).
A Review of the Wave Gradiometry Method for Seismic Imaging
Chuntao Liang, Feihuang Cao, Zhijin Liu, Yingna Chang
Abstract Full HTML(0) PDF[20175KB](0)
As dense seismic arrays at different scales are deployed, the techniques to make full use of array data with low computing cost become increasingly needed. The wave gradiometry method (WGM) is a new branch in seismic tomography, which utilizes the spatial gradients of the wavefield to determine the phase velocity, wave propagation direction, geometrical spreading, and radiation pattern. Seismic wave propagation parameters obtained using the WGM can be further applied to invert 3D velocity models, Q values, and anisotropy at lithospheric (crust and/or mantle) and smaller scales (e.g., industrial oilfield or fault zone). Herein, we review the theoretical foundation, technical development, and major applications of the WGM, and compared the WGM with other commonly used major array imaging methods. Future development of the WGM is also discussed.
Topography of the 660-km Discontinuity within the Izu–Bonin Subduction Zone and Evidence of Slab Penetration near the Bonin Super Deep Earthquake (~680 km)
Gang Hao
Abstract Full HTML(7) PDF[12540KB](0)
The Izu–Bonin subduction zone in the Northwest Pacific is an ideal location for understanding mantle dynamics such as cold lithosphere subduction. The slab produces a lateral thermal anomaly, inducing local topographic changes at the boundary of a post-spinel phase transformation, considered to be the origin of the ‘660-km discontinuity.’ In this study, the short-period (1~2 Hz) S-to-P conversion phase S660P was used to obtain the fine-scale structure of the discontinuity. More than 100 earthquakes that occurred from the 1980s to the 2020s and were recorded by high-quality seismic arrays in the United States and Europe were analyzed. A discontinuity in the ambient mantle with an average depth of ~670 km was found beneath the 300–400-km event zone in the northern Bonin region near 33°N. Meanwhile, the ‘660-km discontinuity’ has been pushed upward, away from the slab, possibly because of a hot upwelling mantle plume. In the central part of the subduction zone, the 660-km discontinuity is depressed to an average depth of 690 ± 5 km within the slab at approximately 150 km below the coldest slab core, indicating a 300 ± 100 °C cold anomaly estimated using a post-spinel transformation Clapeyron slope of −2.0 ± 1.0 MPa/K. In southern Bonin near 28°N, the discontinuity was found to be further depressed at an average depth of 695 ± 5 km below the deepest event and with a focal depth of ~550 km. The discontinuity is located where the slab bends abruptly to become sub-horizontal toward the west-southwest. Near the zone of the isolated Bonin Super Deep Earthquake, which occurred at ~680 km on May 30, 2015, the discontinuity is depressed to ~700 km, suggesting a near-vertical penetrating slab and an S-to-P conversion in the coldest slab core, where a large low-temperature anomaly should exist.
Quality influencing factors of dispersion curves from short period dense arrays based on a convolutional neural network across the north section of the Xiaojiang fault area
Si Chen, Rui Gao, Zhanwu Lu, Yao Liang, Wei Cai, Lifu Cao, Zilong Chen, Guangwen Wang
Abstract Full HTML(16) PDF[8410KB](4)
The number of dispersion curves increases significantly when the scale of a short-period dense array increases. Owing to a substantial increase in data volume, it is important to quickly evaluate dispersion curve quality as well as select the available dispersion curve. Accordingly, this study quantitatively evaluated dispersion curve quality by training a convolutional neural network model for ambient noise tomography using a short-period dense array. The model can select high-quality dispersion curves that exhibit a ≤ 10% difference between the results of manual screening and the proposed model. In addition, this study established a dispersion curve loss function by analyzing the quality of the dispersion curve and the corresponding influencing factors, thereby estimating the number of available dispersion curves for the existing observation systems. Furthermore, a Monte Carlo simulation experiment is used to illustrates the station-pair interval distance probability density function, which is independent of station number in the observational system with randomly deployed stations. The results suggested that the straight-line length should exceed 15 km to ensure that loss rate of dispersion curves remains < 0.5, while maintaining the threshold ambient noise tomography accuracy within the study area.
A review of the influencing factors on teleseismic traveltime tomography
Yang Pan, Shaolin Liu, Dinghui Yang, Wenshuai Wang, Xiwei Xu, Wenhao Shen, Mengyang Li
 doi: 10.1016/j.eqs.2022.12.006
Abstract Full HTML(96) PDF[15061KB](44)
Teleseismic traveltime tomography is an important tool for investigating the crust and mantle structure of the Earth. The imaging quality of teleseismic traveltime tomography is affected by many factors, such as mantle heterogeneities, source uncertainties and random noise. Many previous studies have investigated these factors separately. An integral study of these factors is absent. To provide some guidelines for teleseismic traveltime tomography, we discussed four main influencing factors: the method for measuring relative traveltime differences, the presence of mantle heterogeneities outside the imaging domain, station spacing and uncertainties in teleseismic event hypocenters. Four conclusions can be drawn based on our analysis. (1) Comparing two methods, i.e., measuring the traveltime difference between two adjacent stations (M1) and subtracting the average traveltime of all stations from the traveltime of one station (M2), reveals that both M1 and M2 can well image the main structures; while M1 is able to achieve a slightly higher resolution than M2; M2 has the advantage of imaging long wavelength structures. In practical teleseismic traveltime tomography, better tomography results can be achieved by a two-step inversion method. (2) Global mantle heterogeneities can cause large traveltime residuals (up to about 0.55 s), which leads to evident imaging artifacts. (3) The tomographic accuracy and resolution of M1 decrease with increasing station spacing when measuring the relative traveltime difference between two adjacent stations. (4) The traveltime anomalies caused by the source uncertainties are generally less than 0.2 s, and the impact of source uncertainties is negligible.
Joint inversion of Rayleigh group and phase velocities for S-wave velocity structure of the 2021 MS6.0 Luxian earthquake source area, China
Wei Xu, Pingping Wu, Dahu Li, Huili Guo, Qiyan Yang, Laiyu Lu, Zhifeng Ding
Abstract Full HTML(12) PDF[13800KB](3)
On September 16, 2021, a MS6.0 earthquake struck Luxian County, one of the shale gas blocks in the Southeastern Sichuan Basin, China. To understand the seismogenic environment and its mechanism, we inverted a fine three-dimensional S-wave velocity model from ambient noise tomography using data from a newly deployed dense seismic array around the epicenter, by extracting and jointly inverting the Rayleigh phase and group velocities in the period of 1.6–7.2 s. The results showed that the velocity model varied significantly beneath different geological units. The Yujiasi syncline is characterized by low velocity at depths of ~ 3.0–4.0 km, corresponding to the stable sedimentary layer in the Sichuan Basin. The eastern and western branches of the Huayingshan fault belt generally show high velocity in the NE–SW direction, excepting several local low-velocity zones. The Luxian MS6.0 earthquake epicenter is located at the boundary between the high- and low-velocity zones, and the earthquake sequences expand eastward from the epicenter at depths of 3.0–5.0 km. Integrated with the velocity variations around the epicenter, distribution of aftershock sequences, and focal mechanism solution, it is speculated that the seismogenic mechanism of the main shock might be interpreted as the reactivation of pre-existing faults by hydraulic fracturing.
Generalization of PhaseNet in Shandong and its Application to the Changqing M4.1 Earthquake Sequence
Zonghui Dai, Lianqing Zhou, Junhao Qu, Xia Li
Abstract Full HTML(30) PDF[8554KB](7)
Waveforms of seismic events, extracted from January 2019 to December 2021 were used to construct a test dataset to investigate the generalizability of PhaseNet in the Shandong region. The results show that errors in the picking of seismic phases (P- and S-waves) had a broadly normal distribution, mainly concentrated in the ranges of -0.4~0.3 s and -0.4~0.8 s, respectively. These results were compared with those published in the original PhaseNet article and were found to be approximately 0.2~0.4 s larger. PhaseNet had a strong generalizability for P- and S-wave picking for epicentral distances of less than 120 km and 110 km, respectively. However, the phase recall rate decreased rapidly when these distances were exceeded. Furthermore, the generalizability of PhaseNet was essentially unaffected by magnitude. The M4.1 earthquake sequence in Changqing, Shandong Province, China, that occurred on February 18, 2020, was adopted as a case study. PhaseNet detected more than twice the number of earthquakes in the manually obtained catalog. This further verified that PhaseNet has strong generalizability in the Shandong region, and a high-precision earthquake catalog was constructed. According to these precise positioning results, two earthquake sequences occurred in the study area, and the southern cluster may have been triggered by the northern cluster. The focal mechanism solution, regional stress field, and the location results of the northern earthquake sequence indicated that the seismic force of the earthquake was consistent with the regional stress field.
Upper crustal deformation characteristics in the northeastern Tibetan plateau and its adjacent areas revealed by GNSS and anisotropy data
Shuyu Li, Yuan Gao, Honglin Jin
Abstract Full HTML(36) PDF[10754KB](4)
The northeastern part of the Tibetan Plateau is a region where different tectonic blocks collide and intersect, and large earthquakes are frequent. Global Navigation Satellite System (GNSS) readings show that tectonic deformation in this region is strong and manifests as non-uniform deformation associated with tectonic features. S-wave splitting studies of near-field seismic data show that seismic anisotropy parameters can also reveal the upper crustal medium deformation beneath the reporting station. In this paper, we summarize the surface deformation from GNSS observations and crustal deformation from seismic anisotropy data in the northeastern Tibetan Plateau. By comparing the principal compressive strain direction with the fast S-wave polarization direction of near-field S-wave splitting, we analyzed deformation and its differences in surface and upper crustal media in the northeastern Tibetan Plateau and adjacent areas. The principal compressive strain direction derived from GNSS is generally consistent with the polarization direction of fast S-waves, but there are also local tectonic regions with large differences between them, which reflect the different deformation mechanisms of regional upper crustal media. The combination of GNSS and seismic anisotropy data can reveal the depth variation characteristics of crustal deformation and deepen understanding of three-dimensional crustal deformation and the deep dynamical mechanisms underlying it.
Simulating the strong ground motion of the 2022 MS6.8 Luding Earthquake, Sichuan, China
Libao ZHANG, Lei Fu, Aiwen LIU, Su CHEN
Abstract Full HTML(61) PDF[20275KB](11)
Stochastic finite-fault simulations are effective for simulating ground motions and are widely used in engineering to determine the impacts of ground motion and develop relevant predictive equations. In this study, the source, path, and site amplification coefficient of western Sichuan Province, China, and stochastic finite-fault simulations were used to simulate the acceleration time histories, Fourier amplitude spectra, and 5% damped response spectra of 28 strong-motion stations with rupture distances within 300 km of the 2022 MS6.8 Luding earthquake. The results showed that for soil stations at rupture distances < 50 km, the simulated values were higher than those observed at > 6 Hz. Historical seismic records revealed nonlinear effects in near-field soil stations, which reduced site amplification in the high frequency section. For soil stations at rupture distances of 50-200 km, the simulated values were consistent with observation records at > 0.1 Hz. However, at distances greater than 200 km, the simulated values of the high-frequency portion of the far-field stations were significantly lower than those observed. As the far-field stations are located in the northeastern section of the Longmen Shan fault zone, and the propagation path of the seismic waves passed through the low-velocity anomaly area of this fault zone, generalized inversion technique was used to recalculate the quality factor (Q(f)) based on the data from strong-motion stations on the Western Sichuan Plateau near the Longmen Shan fault zone. The new Q(f) significantly improved the simulation results, indicating that lateral heterogeneity in the crustal velocity structure significantly impacted the simulation results.
Medium-to-short-term monitoring of seismic precursors and prediction of the 2022 MS6.8 Luding earthquake
Yan Xue, Jie Liu, Rui Yan, Huaizhong Yu, Zhiwei Zhang, Zhengyi Yuan, Xiaotao Zhang, Tiebao Zhang
Abstract Full HTML(21) PDF[7811KB](3)
An Accessible Strong-Motion Dataset (PGA, PGV, and Site vS30) of 2022 M6.8 Luding, China Earthquake
Jian Zhou, Nan Xi, Chuanchuan Kang, Li Li, Kun Chen, Xin Tian, Chao Wang, Jifeng Tian
 doi: 10.1016/j.eqs.2023.01.001
Abstract Full HTML(154) PDF[7734KB](37)
A M6.8 earthquake occurred on 5th September 2022 in Luding county, Sichuan, China, at 12: 52 Beijing Time (4:52 UTC). We complied a dataset of PGA, PGV, and site vS30 of 73 accelerometers and 791 Micro-Electro-Mechanical System (MEMS) sensors within 300 km of the epicenter. The inferred vS30 of 820 recording sites were validated. The study results show that: 1) The maximum horizontal PGA and PGV reaches 634.1 gal and 71.1 cm/s respectively. 2) Over 80% of records are from soil sites. 3) The vS30 proxy model of Zhou J et al. (2022) is superior than that of Wald and Allen (2007) and performs well in the study area. The dataset was compiled in a flat file that consists the information of strong-motion instruments, the strong-motion records, and the vS30 of the recording sites. The dataset is available at https://www.seismisite.net.
Rupture process and aftershock mechanisms of the 2022 Luding M6.8 earthquake in Sichuan, China
Zhigao Yang, Danqing Dai, Yong Zhang, Xuemei Zhang, Jie Liu
 doi: 10.1016/j.eqs.2022.09.001
Abstract Full HTML(606) PDF[3199KB](286)
On September 5, 2022, a strong earthquake of M6.8 occurred in Luding County (102.08°E, 29.59°N), Sichuan, China, with a focal depth of 16km, from the rapid earthquake information released by China Earthquake Networks Center:It is of great importance to quickly determine the source parameters of an earthquake sequence for earthquake rescue, disaster assessment and scientific research. Near-field seismic observations play a key role in the fast and reliable determination of source parameters. A large number of broadband and strong motion stations newly built by the National Intensity Rapid Report and Early Warning Project of China Earthquake Administration provide valuable near-field real-time observation data. Based on these near-field observations and traditional mid- and far-field seismic waveform data, we can use the waveform fitting method to determine the focal mechanism solutions of the mainshock and M≥3.0 aftershocks, and to quickly invert the rupture process of the mainshock. Combined with the focal mechanism solution of the main shock and the regional tectonic background, it is inferred that the M6.8 earthquake is associated with the Xianshuihe fault. The focal mechanism solutions of aftershocks show that there are obvious differences in focal mechanisms of three earthquake swarms of aftershocks, reflecting the segmentation characteristics of the Xianshuihe fault zone. The near-field strong motion data have better constraints on the absolute location of the rupture due to the use of more high-frequency information. The rupture process of main shock has a good correspondence with the spatial distribution of aftershocks, i.e., areas with large rupture slip correspond to weak aftershocks, and edges of large slips have strong aftershocks.
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Preface to the special issue of Artificial Intelligence in Seismology
Lihua Fang, Zefeng Li
2023, 36(2): 81-83.   doi: 10.1016/j.eqs.2023.03.003
Abstract Full HTML(314) PDF[5986KB](11)
DiTing: A large-scale Chinese seismic benchmark dataset for artificial intelligence in seismology
Ming Zhao, Zhuowei Xiao, Shi Chen, Lihua Fang
2023, 36(2): 84-94.   doi: 10.1016/j.eqs.2022.01.022
Abstract Full HTML(685) PDF[8090KB](168)
In recent years, artificial intelligence technology has exhibited great potential in seismic signal recognition, setting off a new wave of research. Vast amounts of high-quality labeled data are required to develop and apply artificial intelligence in seismology research. In this study, based on the 2013–2020 seismic cataloging reports of the China Earthquake Networks Center, we constructed an artificial intelligence seismological training dataset (“DiTing”) with the largest known total time length. Data were recorded using broadband and short-period seismometers. The obtained dataset included 2,734,748 three-component waveform traces from 787,010 regional seismic events, the corresponding P- and S-phase arrival time labels, and 641,025 P-wave first-motion polarity labels. All waveforms were sampled at 50 Hz and cut to a time length of 180 s starting from a random number of seconds before the occurrence of an earthquake. Each three-component waveform contained a considerable amount of descriptive information, such as the epicentral distance, back azimuth, and signal-to-noise ratios. The magnitudes of seismic events, epicentral distance, signal-to-noise ratio of P-wave data, and signal-to-noise ratio of S-wave data ranged from 0 to 7.7, 0 to 330 km, –0.05 to 5.31 dB, and –0.05 to 4.73 dB, respectively. The dataset compiled in this study can serve as a high-quality benchmark for machine learning model development and data-driven seismological research on earthquake detection, seismic phase picking, first-motion polarity determination, earthquake magnitude prediction, early warning systems, and strong ground-motion prediction. Such research will further promote the development and application of artificial intelligence in seismology.
USTC-Pickers: a Unified Set of seismic phase pickers Transfer learned for China
Jun Zhu, Zefeng Li, Lihua Fang
2023, 36(2): 95-112.   doi: 10.1016/j.eqs.2023.03.001
Abstract Full HTML(218) PDF[8795KB](65)
Current popular deep learning seismic phase pickers like PhaseNet and EQTransformer suffer from performance drop in China. To mitigate this problem, we build a unified set of customized seismic phase pickers for different levels of use in China. We first train a base picker with the recently released DiTing dataset using the same U-Net architecture as PhaseNet. This base picker significantly outperforms the original PhaseNet and is generally suitable for entire China. Then, using different subsets of the DiTing data, we fine-tune the base picker to better adapt to different regions. In total, we provide 5 pickers for major tectonic blocks in China, 33 pickers for provincial-level administrative regions, and 2 special pickers for the Capital area and the China Seismic Experimental Site. These pickers show improved performance in respective regions which they are customized for. They can be either directly integrated into national or regional seismic network operation or used as base models for further refinement for specific datasets. We anticipate that this picker set will facilitate earthquake monitoring in China.
Benchmark on the accuracy and efficiency of several neural network based phase pickers using datasets from China Seismic Network
Ziye Yu, Weitao Wang, Yini Chen
2023, 36(2): 113-131.   doi: 10.1016/j.eqs.2022.10.001
Abstract Full HTML(256) PDF[8600KB](57)
Seismic phase pickers based on deep neural networks have been extensively used recently, demonstrating their advantages on both performance and efficiency. However, these pickers are trained with and applied to different data. A comprehensive benchmark based on a single dataset is therefore lacking. Here, using the recently released DiTing dataset, we analyzed performances of seven phase pickers with different network structures, the efficiencies are also evaluated using both CPU and GPU devices. Evaluations based on F1-scores reveal that the recurrent neural network (RNN) and EQTransformer exhibit the best performance, likely owing to their large receptive fields. Similar performances are observed among PhaseNet (UNet), UNet++, and the lightweight phase picking network (LPPN). However, the LPPN models are the most efficient. The RNN and EQTransformer have similar speeds, which are slower than those of the LPPN and PhaseNet. UNet++ requires the most computational effort among the pickers. As all of the pickers perform well after being trained with a large-scale dataset, users may choose the one suitable for their applications. For beginners, we provide a tutorial on training and validating the pickers using the DiTing dataset. We also provide two sets of models trained using datasets with both 50 Hz and 100 Hz sampling rates for direct application by end-users. All of our models are open-source and publicly accessible.
Machine learning-based automatic construction of earthquake catalog for reservoir areas in multiple river basins of Guizhou province, China
Longfei Duan, Cuiping Zhao, Xingzhong Du, Lianqing Zhou
2023, 36(2): 132-146.   doi: 10.1016/j.eqs.2023.03.002
Abstract Full HTML(104) PDF[11635KB](27)
Large reservoirs have the risk of reservoir induced seismicity. Accurately detecting and locating microseismic events are crucial when studying reservoir earthquakes. Automatic earthquake monitoring in reservoir areas is one of the effective measures for earthquake disaster prevention and mitigation. In this study, we first applied the automatic location workflow (named LOC-FLOW) to process 14-day continuous waveform data from several reservoir areas in different river basins of Guizhou province. Compared with the manual seismic catalog, the recall rate of seismic event detection using the workflow was 83.9%. Of the detected earthquakes, 88.9% had an onset time difference below 1 s, 81.8% has a deviation in epicenter location within 5 km, and 77.8% had a focal depth difference of less than 5 km, indicating that the workflow has good generalization capacity in reservoir areas. We further applied the workflow to retrospectively process continuous waveform data recorded from 2020 to the first half of 2021 in reservoir areas in multiple river basins of western Guizhou province and identified five times the number of seismic events obtained through manual processing. Compared with manual processing of seismic catalog, the completeness magnitude had decreased from 1.3 to 0.8, and a b-value of 1.25 was calculated for seismicity in western Guizhou province, consistent with the b-values obtained for the reservoir area in previous studies. Our results show that seismicity levels were relatively low around large reservoirs that were impounded over 15 years ago, and there is no significant correlation between the seismicity in these areas and reservoir impoundment. Seismicity patterns were notably different around two large reservoirs that were only impounded about 12 years ago, which may be explained by differences in reservoir storage capacity, the geologic and tectonic settings, hydrogeological characteristics, and active fault the reservoir areas. Prominent seismicity persisted around two large reservoirs that have been impounded for less than 10 years. These events were clustered and had relatively shallow focal depths. The impoundment of the Jiayan Reservoir had not officially begun during this study period, but earthquake location results suggested a high seismicity level in this reservoir area. Therefore, any seismicity in this reservoir area after the official impoundment deserves special attention.
A deep-learning-based approach for seismic surface-wave dispersion inversion (SfNet) with application to the Chinese mainland
Feiyi Wang, Xiaodong Song, Mengkui Li
2023, 36(2): 147-168.   doi: 10.1016/j.eqs.2023.02.007
Abstract Full HTML(104) PDF[14644KB](31)
Surface-wave tomography is an important and widely used method for imaging the crust and upper mantle velocity structure of the Earth. In this study, we proposed a deep learning (DL) method based on convolutional neural network (CNN), named SfNet, to derive the vS model from the Rayleigh wave phase and group velocity dispersion curves. Training a network model usually requires large amount of training datasets, which is labor-intensive and expensive to acquire. Here we relied on synthetics generated automatically from various spline-based vS models instead of directly using the existing vS models of an area to build the training dataset, which enhances the generalization of the DL method. In addition, we used a random sampling strategy of the dispersion periods in the training dataset, which alleviates the problem that the real data used must be sampled strictly according to the periods of training dataset. Tests using synthetic data demonstrate that the proposed method is much faster, and the results for the vS model are more accurate and robust than those of conventional methods. We applied our method to a dataset for the Chinese mainland and obtained a new reference velocity model of the Chinese continent (ChinaVs-DL1.0), which has smaller dispersion misfits than those from the traditional method. The high accuracy and efficiency of our DL approach makes it an important method for vS model inversions from large amounts of surface-wave dispersion data.
Special focus/Rapid Communication
Moment magnitudes of two large Turkish earthquakes on February 6, 2023 from long-period coda
Xinyu Jiang, Xiaodong Song, Tian Li, Kaixin Wu
2023, 36(2): 169-174.   doi: 10.1016/j.eqs.2023.02.008
Abstract Full HTML(676) PDF[6470KB](140)
Two large earthquakes (an earthquake doublet) occurred in south-central Turkey on February 6, 2023, causing massive damages and casualties. The magnitudes and the relative sizes of the two mainshocks are essential information for scientific research and public awareness. There are obvious discrepancies among the results that have been reported so far, which may be revised and updated later. Here we applied a novel and reliable long-period coda moment magnitude method to the two large earthquakes. The moment magnitudes (with one standard error) are 7.95±0.013 and 7.86±0.012, respectively, which are larger than all the previous reports. The first mainshock, which matches the largest recorded earthquakes in the Turkish history, is slightly larger than the second one by 0.11±0.035 in magnitude or by 0.04 to 0.18 at 95% confidence level.
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