Relocation of the MS5.7 Earthquake Sequence in Songyuan, Jilin Province, 2018 and the Analysis of Its Seismogenic Structure

The 2018,Songyuan,Jilin M_S5. 7 earthquake occurred at the intersection of the FuyuZhaodong fault and the Second Songhua River fault. The moment magnitude of this earthquake is M_W5. 3,the centroid depth by the waveform fitting is 12 km,and it is a strike-slip type event. In this paper,with the seismic phase data provided by the China Earthquake Network, the double-difference location method is used to relocate the earthquake sequence,finally the relocation results of 60 earthquakes are obtained. The results show that the aftershock zone is about 4. 3km long and 3. 1km wide,which is distributed in the NE direction. The depth distribution of the seismic sequence is 9km-10 km. 1-2 days after the main shock,the aftershocks were scattered throughout the aftershock zone,and the largest aftershock occurred in the northeastern part of the aftershock zone. After 3-8 days,the aftershocks mainly occurred in the southwestern part of the aftershock zone. The profile distribution of the earthquake sequence shows that the fault plane dips to the southeast with the dip angle of about 75°. Combined with the regional tectonic setting,focal mechanism solution and intensity distribution,we conclude that the concealed fault of the Fuyu-Zhaodong fault is the seismogenic fault of the Songyuan M_S5. 7 earthquake. This paper also relocates the earthquake sequence of the previous magnitude 5. 0 earthquake in 2017. Combined with the results of the focal mechanism solution,we believe that the two earthquakes have the same seismogenic structure,and the earthquake sequence generally develops to the southwest. The historical seismic activity since 2009 shows that after the magnitude 5. 0 earthquake in 2017,the frequency and intensity of earthquakes in the earthquake zone are obviously enhanced,and attention should be paid to the development of seismic activity in the southwest direction of the earthquake zone.     

联合多种高分辨CT征象对肺内孤立结节良恶性鉴别诊断价值

目的:分析肺内孤立结节(SPN)的多种高分辨CT征象对其良、恶性的鉴别诊断价值。方法:对我院从2016年4月到2018年4月间手术病理证实的102例SPN的CT图像加以分析,对比分析联合或单一高分辨CT征象判断恶性病变的阳性预测值。结果:恶性病变中钙化发生率为4.26%,较良性病变中钙化率低。恶性病变中分叶征、血管集束征与毛刺征发生率分别为63.83%、40.43%与70.21%,均较良性病变中发生率高,差异均具有统计学意义(P <0.05)。良、恶性病变高分辨CT征象中,卫星灶、空泡征和胸膜凹陷征发生率差异均无统计学差异(P> 0.05)。联合血管集束征、毛刺征与分叶征中两种或者三种,联合诊断恶性病变的阳性预测值均较单一征象高,差异均有统计学意义(P <0.05)。结论:SPN的高分辨CT征象对其良、恶性的鉴别价值较高,联合多种CT征象较单一征象的价值大。     

FOCAL FAULTS AND STRESS FIELD CHARACTERISTICS OF M7.0 JIUZHAIGOU EARTHQUAKE SEQUENCE IN 2017

On August 8, 2017, Beijing time, an earthquake of M7.0 occurred in Jiuzhaigou County, Aba Prefecture, Sichuan Province, with the epicenter located at 33.20°N 103.82°E. The earthquake caused 25 people dead, 525 people injured, 6 people missing and 170000 people affected. Many houses were damaged to various degrees. Up to October 15, 2017, a total of 7679 aftershocks were recorded, including 2099 earthquakes of M ≥ 1.0. The M7.0 Jiuzhaigou earthquake occurred in the northeastern boundary belt of the Bayan Har block on the Qinghai-Tibet Plateau, where many active faults are developed, including the Tazhong Fault(the eastern segment of the East Kunlun Fault), the Minjiang fault zone, the Xueshan fault zone, the Huya fault zone, the Wenxian fault zone, the Guanggaishan-Daishan Fault, the Bailongjiang Fault, the Longriuba Fault and the Longmenshan Fault. As one of the important passages for the eastward extrusion movement of the Qinghai-Tibet Plateau(Tapponnier et al., 2001), the East Kunlun fault zone has a crucial influence on the tectonic activities of the northeastern boundary belt of Bayan Kala. Meanwhile, the Coulomb stress, fault strain and other research results show that the eastern boundary of the Bayan Har block still has a high risk of strong earthquakes in the future. So the study of the M7.0 Jiuzhaigou earthquake' seismogenic faults and stress fields is of great significance for scientific understanding of the seismogenic environment and geodynamics of the eastern boundary of Bayan Har block. In this paper, the epicenter of the main shock and its aftershocks were relocated by the double-difference relocation method and the spatial distribution of the aftershock sequence was obtained. Then we determined the focal mechanism solutions of 24 aftershocks(M ≥ 3.0)by using the CAP algorithm with the waveform records of China Digital Seismic Network. After that, we applied the sliding fitting algorithm to invert the stress field of the earthquake area based on the previous results of the mechanism solutions. Combining with the previous research results of seismogeology in this area, we discussed the seismogenic fault structure and dynamic characteristics of the M7.0 Jiuzhaigou earthquake. Our research results indicated that:1)The epicenters of the M7.0 Jiuzhaigou earthquake sequence distribute along NW-SE in a stripe pattern with a long axis of about 35km and a short axis of about 8km, and with high inclination and dipping to the southwest, the focal depths are mainly concentrated in the range of 2~25km, gradually deepening from northwest to southeast along the fault, but the dip angle does not change remarkably on the whole fault. 2)The focal mechanism solution of the M7.0 Jiuzhaigou earthquake is:strike 151°, dip 69° and rake 12° for nodal plane Ⅰ, and 245°, 78° and -158° for nodal plane Ⅱ, the main shock type is pure strike-slip and the centroid depth of the earthquake is about 5km. Most of the focal mechanism of the aftershock sequence is strike-slip type, which is consistent with the main shock's focal mechanism solution; 3)In the earthquake source area, the principal compressive stress and the principal tensile stress are both near horizontal, and the principal compressive stress is near east-west direction, while the principal tensile stress is near north-south direction. The Jiuzhaigou earthquake is a strike-slip event that occurs under the horizontal compressive stress.     

APPLICATION OF SEMIAUTOMATIC EXTRACTION OF FLUVIAL TERRACES BASED ON R LANGUAGE-AN EXAMPLE FROM THE YELLOW RIVER TERRACES AT MIJIA SHAN

The generation, abandonment and preservation of terraces formed in active tectonic areas are important to the analysis of the role of the tectonics and climate along the temporal variations, so it appears significant as how to use the effective quantitative methods to extract and accurately depict these terraces. The increasingly convenient acquisition of high-precision topographic data has greatly promoted the advancement of quantitative research in geoscience, making it possible to analyze mid-micro-geomorphic features on a large scale, especially by studying the temporal and spatial evolution of tectonic deformation through accurate capture of micro-geomorphic features. Over the past decade, the rapid development of LiDAR(Light Detection and Ranging)technology has provided unprecedented opportunity to access high-precision topographic data(up to centimeter in vertical and horizontal directions). However, its relatively high cost and relatively complex data processing techniques limit its widespread application in the field of earth sciences. In recent years, with the continuous innovation and advancement of topographic measurement technology, the three-dimensional structure of motion reconstruction technology(Structure from Motion, SfM)has gradually been introduced into the field of digital topographic photogrammetry due to its rapid advantage in providing quick, convenient and cost-effective methods for obtaining high-density geospatial point data. This method thus shows great potential for providing high resolution topographic data with comparable resolution and precision. Therefore, with the acquisition of more and more high-resolution terrain data in recent years, it is an important development trend to explore automated or semi-automated quantitative geomorphological analysis methods. R language, as an excellent programming language, has not been used in the geology and geomorphology, although is widely applied in medicine and meteorology based on its powerful capability of statistician and graphic visualization. In this paper, we focus on the Yellow River multi-terraces formed to the east of the Mijia Shan, which belongs to the Jingtai-Hasi Shan segment of the Haiyuan Fault. With the analysis and visualization of the high-resolution topographic data collected from the SfM in the environment of the R language, we implement the semiautomatic classification and mapping of the Yellow River multi-terraces. The method identifies 20 terraces with different elevation. Our results also imply that the younger terraces have better continuity and elongation, and the older terraces have more deformation, which can be demonstrated from their gradually notable semi-parabolic shape. Besides this, it also suggests the diverse evolution stages of the Yellow River terraces. Our study indicates that R language is expected to become an efficient tool of statistics and visualization of the high-resolution topographic data.     

Information Detection of Seismic Debris Flow by UAV High-resolution Image Based on Transfer Learning

A large number of debris flow disasters (called Seismic debris flows) would occur after an earthquake, which can cause a great amount of damage. UAV low-altitude remote sensing technology has become a means of quickly obtaining disaster information as it has the advantage of convenience and timeliness, but the spectral information of the image is so scarce, making it difficult to accurately detect the information of earthquake debris flow disasters. Based on the above problems, a seismic debris flow detection method based on transfer learning (TL) mechanism is proposed. On the basis of the constructed seismic debris flow disaster database, the features acquired from the training of the convolutional neural network (CNN) are transferred to the disaster information detection of the seismic debris flow. The automatic detection of earthquake debris flow disaster information is then completed, and the results of object-oriented seismic debris flow disaster information detection are compared and analyzed with the detection results supported by transfer learning.     

2017年9月临城震群遗漏地震检测及发震构造初探

以河北测震台网记录的连续波形数据为基础,采用模板匹配滤波方法,检测2017年9月4日—6日河北临城震群遗漏地震。利用波形互相关扫描,检测到19次被测震台网常规分析遗漏的地震,震级范围为M_(L )0.0—1.2,并标定新检测地震事件的P波和S波到时,估计其震中位置及震级。通过地震精定位和最大地震震源机制解,认为此次临城地震序列的主要发震构造应为近EW向活动断层。     

山东乳山地区震群特征及发震背景再研究

2013年10月1日在山东省威海市乳山市发生M3.2级地震,之后发生了一系列震群活动。截至2016年5月,山东台网已经记录到了1万多次余震,其中3级以上地震9次,4级以上地震3次。频繁的地震构造活动引起了乳山市及周边地区强烈震感。为研究乳山震群的发震机理,本文利用山东台网数字化地震波资料和新建的乳山台阵资料,通过双差精定位方法重新确定了震中位置。研究结果表明:余震序列呈现出NW向的条带分布;采用CAP方法（Cut and Paste）反演震群中9次3级以上地震的震源机制解,结果显示几次较大地震的震源深度平均约为5km,与台网编目定位的结果基本相同。从得到的精定位结果并结合震源机制解的结果来看,震群的走向是NW向,倾角是NE向,与最近的乳山断裂有一定距离。由此推断该区域可能是乳山断裂的分支,或者有一条或多条隐伏断裂。     

青藏高原东缘地震活动与居里点深度之间的相关性

本文选取不同的地壳速度分区模型,应用双差定位法对2008—2017年发生在青藏高原东缘的地震进行了重新定位,共得到4921个精确定位结果.重定位后的地震更加集中分布于龙门山断裂带、鲜水河断裂以及四川盆地南缘,震源深度多为5~20 km.根据NGDC-720地磁场模型计算了青藏高原东缘的三分量磁异常及其梯度张量,重定位后的大多数地震位于负磁异常区域以及四川盆地西南缘的强-弱磁异常边界.基于三维分形磁化模型获得了青藏高原东缘的居里点深度,并计算了磁性层的平均地温梯度,进而利用一维稳态热传导方程获得了其地壳温度结构.结果显示青藏高原东缘大多数地震均发生在居里点深度较大、地温梯度较低的区域.大多数M≥2.0地震震源区温度为100~500℃,M≥4.0地震震源区温度多为200~400℃.2008年汶川M_S8.0、2013岷县M_S6.6、2014年鲁甸M_S6.5以及2017九寨沟M_S7.0地震震源区温度均为300℃左右,而2013年芦山M_S7.0地震震源区温度接近约400℃,更多地受控于龙门山断裂带与鲜水河断裂交汇处的局部构造应力场异常.     

门源地区地壳三维体波速度结构及地震重定位研究

本文使用甘肃、青海数字地震台网及中国地震科学探测台阵记录到的门源地区地震的P波和S波到时资料,应用双差层析成像方法联合反演了该地区的地壳三维速度结构和震源位置参数.结合地质构造背景,研究了门源M_S6.4地震孕育发生的深部介质环境及该地区速度结构与地震活动性之间的关系.结果表明：反演之后地震的走时残差均方根显著降低,重定位后的地震在垂直方向上呈现出与断层位置有关的条带状分布.门源地区地壳速度结构存在明显的不均匀性,浅层P波和S波速度结构与地表地质构造及地形特征密切相关.研究区内地震活动性与地壳速度结构具有很强的对应关系,地震主要分布在高速异常区域及其边缘.门源M_S6.4地震震中附近的P波和S波速度结构表现出明显的高速异常,且在震源区下方存在P波低速层,这种特殊的构造条件可能是导致此次地震发生的重要原因.     

2017年四川九寨沟MS7.0地震及余震精定位研究

2017年8月8日21点19分四川省九寨沟县发生M_S7.0级强震,本文应用对相对位置和绝对位置都有较好约束的hypoRelocate精定位方法,对8月15日前M_L大于1.5的854个余震重新定位.重定位使用了6178个Pg走时、3363个Sg走时、158929个地震对的相对走时差以及74个M_L大于3.5的34个地震构造的SH尾波互相关走时.定位结果显示,余震区呈NW向分布,长度约35 km、宽度约12 km.以主震为中心,余震形成西北和东南两个丛集区,其中西北区的余震展布较东南区宽,发震断层近似垂直,与主震的震源机制解吻合.余震深度主要集中在5~15 km,主震深度为16 km.从沿断层走向的垂直剖面可见,主震周围一直延伸到地表的区域,余震相对稀少,出现明显空区,这种现象可能与主震发生时该区域的滑动位移较大,应力得到充分释放有关.另外,我们还发现震后60 h之后余震的深度明显变浅,且西北向的深度明显浅于东南向.通过对比分析流动台站布设前后余震深度的分布特点,我们认为这种时空演化差异主要是由流动台站陆续布设和使用造成的,对余震的主要分布特征影响不大.