General seismic wave and phase detection software driven by deep learning

We developed an automatic seismic wave and phase detection software based on PhaseNet,an efficient and highly generalized deep learning neural network for P-and S-wave phase picking.The software organically combines multiple modules including application terminal interface,docker container,data visualization,SSH protocol data transmission and other auxiliary modules.Characterized by a series of technologically powerful functions,the software is highly convenient for all users.To obtain the P-and S-wave picks,one only needs to prepare threecomponent seismic data as input and customize some parameters in the interface.In particular,the software can automatically identify complex waveforms(i.e.continuous or truncated waves)and support multiple types of input data such as SAC,MSEED,NumPy array,etc.A test on the dataset of the Wenchuan aftershocks shows the generalization ability and detection accuracy of the software.The software is expected to increase the efficiency and subjectivity in the manual processing of large amounts of seismic data,thereby providing convenience to regional network monitoring staffs and researchers in the study of Earth's interior.

TransQuake: A transformer-based deep learning approach for seismic P-wave detection

Recent years,we have witnessed the increasing research interest in developing machine learning,especially deep learning which provides approaches for enhancing the performance of microearthquake detection.While considerable research efforts have been made in this direction,most of the state-of-the-art solutions are based on Convolutional Neural Network(CNN)structure,due to its remarkable capability of modeling local and static features.Indeed,the globally dynamic characteristics contained within time series data(i.e.,seismic waves),which cannot be fully captured by CNN-based models,have been largely ignored in previous studies.In this paper,we propose a novel deep learning approach,TransQuake,for seismic P-wave detection.The approach is based on the most advanced sequential model,namely Transformer.To be specific,TransQuake can exploit the STA/LTA algorithm for adapting the three-component structure of seismic waves as input,and take advantage of the multi-head attention mechanism for conducting explainable model learning.Extensive evaluations of the aftershocks following the 2008 Wenchuan MW 7.9 earthquake clearly demonstrates that TransQuake is able to achieve the best detection performance which excels the results obtained using other baselines.Meanwhile,experimental results also validate the interpretability of the results obtained by TransQuake,such as the attention distribution of seismic waves in different positions,and the analysis of the optimal relationship between coda wave and P-wave for noise identification.

Frequency-Domain Full-Waveform Inversion Based on Tunnel Space Seismic Data

Tunnel seismic detection methods are effective for obtaining the geological structure around the tunnel face,which is critical for safe construction and disaster mitigation in tunnel engineering.However,there is often a lack of accuracy in the acquired geological information and physical properties ahead of the tunnel face in the current tunnel seismic detection methods.Thus,we apply a frequency-domain acoustic full-waveform inversion(FWI)method to obtain high-resolution results for the tunnel structure.We discuss the influence of the frequency group selection strategy and the tunnel observation system settings regarding the inversion results and determine the structural imaging and physical property parameter inversion of abnormal geological bodies ahead of the tunnel face.Based on the conventional strategies of frequency-domain acoustic FWI,we propose a frequency group selection strategy that combines a low-frequency selection covering the vertical wavenumber and a high-frequency selection of antialiasing.This strategy can effectively obtain the spatial structure and physical parameters of the geology ahead of the tunnel face and improve the inversion resolution.In addition,by linearly increasing the side length of the tunnel observation system,we share the influence of the length of the two sides of the observation systems of different tunnels on the inversion results.We found out that the inversion results are the best when the side length is approximately five times the width of the tunnel face,and the influence of increasing the side observation length beyond this range on the inversion results can be ignored.Finally,based on this approach,we invert for the complex multi-stratum model,and an accurate structure and physical property parameters of the complex stratum ahead of the tunnel face are obtained,which verifies the feasibility of the proposed method.

Study on the basement structure in the southeastern North China by blasting seismic

Since the Mesozoic, southeastern North China Craton has experienced intense crustal thinning and lithosphere destruction. Some of the responses of the deep activity in the upper crust crystalline basement have been retained in a series of tectonic evolution. The study of the upper crust velocity structure,especially the properties of the basement interface, is of great significance for studying the tectonic evolution and seismic hazard in the southeastern part of North China. In this study, we selected Pg waves of the blasting seismic data in the southeastern part of North China in recent years, which reflect the west Shandong uplift, offshore sedimentary basins and the Tanlu Fault zone and the Sulu orogenic transition zone, to study the structural and seismological characteristics of basement in North China Craton. The results of this study showed as follows: First, the obvious lag of Pg wave arrival time in Dongying depression and North Jiangsu basin reveals the thick sedimentary, low velocity and unstable basement structure. Second, the advance Pg wave arrival time with high apparent velocity, which reflects the basement structure of the west Shandong uplift, indicates the thin sediments and the shallow basement. Third, combined with many geophysical phenomena, such as electrical structure, density structure and terrestrial heat flow, we hold that the Tanlu tectonic belt and the Sulu orogenic belt have experienced great lithosphere destruction and there is shallow crust and the thinnest lithosphere in the vicinity of the Tanlu fault zone.

Joint Inversion of the 3D P Wave Velocity Structure of the Crust and Upper Mantle under the Southeastern Margin of the Tibetan Plateau Using Regional Earthquake and Teleseismic Data

The special seismic tectonic environment and frequent seismicity in the southeastern margin of the Qinghai-Tibet Plateau show that this area is an ideal location to study the present tectonic movement and background of strong earthquakes in China's Mainland and to predict future strong earthquake risk zones. Studies of the structural environment and physical characteristics of the deep structure in this area are helpful to explore deep dynamic effects and deformation field characteristics, to strengthen our understanding of the roles of anisotropy and tectonic deformation and to study the deep tectonic background of the seismic origin of the block＇s interior. In this paper, the three-dimensional （3D） P-wave velocity structure of the crust and upper mantle under the southeastern margin of the Qinghai-Tibet Plateau is obtained via observational data from 224 permanent seismic stations in the regional digital seismic network of Yunnan and Sichuan Provinces and from 356 mobile China seismic arrays in the southern section of the north-south seismic belt using a joint inversion method of the regional earthquake and teleseismic data. The results indicate that the spatial distribution of the P-wave velocity anomalies in the shallow upper crust is closely related to the surface geological structure, terrain and lithology. Baoxing and Kangding, with their basic volcanic rocks and volcanic clastic rocks, present obvious high-velocity anomalies. The Chengdu Basin shows low-velocity anomalies associated with the Quaternary sediments. The Xichang Mesozoic Basin and the Butuo Basin are characterised by low- velocity anomalies related to very thick sedimentary layers. The upper and middle crust beneath the Chuan-Dian and Songpan-Ganzi Blocks has apparent lateral heterogeneities, including low-velocity zones of different sizes. There is a large range of low-velocity layers in the Songpan-Ganzi Block and the sub-block northwest of Sichuan Province, showing that the middle and lower crust is relatively weak. The Sichuan Basin, which is located in the western margin of the Yangtze platform, shows high-velocity characteristics. The results also reveal that there are continuous low-velocity layer distributions in the middle and lower crust of the Daliangshan Block and that the distribution direction of the low-velocity anomaly is nearly SN, which is consistent with the trend of the Daliangshan fault. The existence of the low-velocity layer in the crust also provides a deep source for the deep dynamic deformation and seismic activity of the Daliangshan Block and its boundary faults. The results of the 3D P-wave velocity structure show that an anomalous distribution of high-density, strong-magnetic and high-wave velocity exists inside the crust in the Panxi region. This is likely related to late Paleozoic mantle plume activity that led to a large number of mafic and ultra-mafic intrusions into the crust. In the crustal doming process, the massive intrusion of mantle-derived material enhanced the mechanical strength of the crustal medium. The P-wave velocity structure also revealed that the upper mantle contains a low-velocity layer at a depth of 80-120 km in the Panxi region. The existence of deep faults in the Panxi region, which provide conditions for transporting mantle thermal material into the crust, is the deep tectonic background for the area＇s strong earthquake activity.

Numerical analysis of the resonance mechanism of the lumped parameter system model for acoustic mine detection

The method of numerical analysis is employed to study the resonance mechanism of the lumped parameter system model for acoustic mine detection. Based on the basic principle of the acoustic resonance technique for mine detection and the characteristics of low-frequency acoustics, the “soil-mine” system could be equivalent to a damping “mass-spring” resonance model with a lumped parameter analysis method. The dynamic simulation software, Adams, is adopted to analyze the lumped parameter system model numerically. The simulated resonance frequency and anti-resonance frequency are 151 Hz and 512 Hz respectively, basically in agreement with the published resonance frequency of 155 Hz and anti-resonance frequency of 513 Hz, which were measured in the experiment. Therefore, the technique of numerical simulation is validated to have the potential for analyzing the acoustic mine detection model quantitatively. The influences of the soil and mine parameters on the resonance characteristics of the soil–mine system could be investigated by changing the parameter setup in a flexible manner.

Seismic Health Monitoring of Foundations Using Artificial Neural Networks

Damage identification plays an important role in structural health monitoring systems. Despite variety in damage identification methods, little attention has been paid to the seismic damage identification of foundations. When shear walls serve as the lateral load resistance system of structures, foundations may subject to the high level of concentrated moment and shear forces. Consequently, they can experience severe damage. Since such damage is often internal and not visible, visual inspections cannot identify the location and the severity of damage. Therefore, a robust method is required for damage localization and quantification of foundations. According to the concept of performance-based seismic design of structures, the seismic behavior of foundations is considered as Force-Controlled. Therefore, for damage identification of foundation, internal forces should be estimated during ground motions. In this study, for real-time seismic damage detection of foundations, a method based on artificial neural networks was proposed. A feed-forward multilayer neural network with one hidden layer was selected to map input samples to output parameters. The lateral displacements of stories were considered as the input parameters of the neural network while moment and shear force demands at critical points of foundations were taken into account as the output parameters. In order to prepare well-distributed data sets for training the neural network, several nonlinear time history analyses were carried out. The proposed method was tested on the foundation of a five-story concrete shear wall building. The obtained results revealed that the proposed method was successfully estimated moment and shear force demands at the critical points of the foundation.

Regional characteristics of the lithospheric structure in the southeast of North China revealed by explosion seismology

Based on the blasting seismic detection data obtained in the southeast of North China in recent years,this paper comprehensively analyzes and studies the crust-mantle lithospheric structure and seismological characteristics of different tectonic regions,such as offshore basins,west Shandong uplift,Tanlu fault zone and Jiangsu-Shandong orogenic belt.The low-velocity Pg waves in Dongying depression and Northern Jiangsu basin reveal the unstable basement structure with extremely thick sediments.The travel time of Pg wave is characterized by relatively low propagation velocity and small crustal thickness of offshore continental margin;the first break time and high apparent velocity of Pg wave in west Shandong uplift indicate that the sedimentary basement is relatively thin.The Pm wave shows the characteristic of dominant wave in the first-order velocity discontinuity of the crust-mantle interface,which reflects the high crustal velocity and stable structure in west Shandong uplift.The Pm and Pl wave are obviously complicated,which can reflect the crust-mantle lithospheric structure of the transitional zone between Tanlu fault zone and Jiangsu-Shandong orogenic belt.The small time difference between Pn and PL waves can be regarded as the highly destructive seismological manifestation of Tanlu fault zone on the crust-lithosphere scale.Based on many geophysical phenomena such as electrical structure,density structure and terrestrial heat flow,it is believed that the lithospheric destruction degree of Tanlu fault zone and Jiangsu-Shandong orogenic belt was high during the destruction of the North China Craton.

Suitable triggering algorithms for detecting strong ground motions using MEMS accelerometers

With the recent development of digital Micro Electro Mechanical System （MEMS） sensors, the cost of monitoring and detecting seismic events in real time can be greatly reduced. Ability of MEMS accelerograph to record a seismic event depends upon the efficiency of triggering algorithm, apart from the sensor＇s sensitivity. There are several classic triggering algorithms developed to detect seismic events, ranging from basic amplitude threshold to more sophisticated pattern recognition. Algorithms based on STA/LTA are reported to be computationally efficient for real time monitoring. In this paper, we analyzed several STA/LTA algorithms to check their efficiency and suitability using data obtained from the Quake Catcher Network （network of MEMS accelerometer stations）. We found that most of the STA/LTA algorithms are suitable for use with MEMS accelerometer data to accurately detect seismic events. However, the efficiency of any particular algorithm is found to be dependent on the parameter set used （i.e., window width of STA, LTA and threshold level）.