Earthquake detection in the Jiangsu region,China using graphics-processing-unit-based Match & Locate and rapid earthquake association and location*

Earthquake detection and location are essential in earthquake studies, which generally consists of two main classes: waveform-based and pick-based methods. To evaluate the ability of two different methods, a graphics-processing-unit-based Match & Locate (GPU-M&L) method and a rapid earthquake association and location (REAL) method are applied to continuous seismic data recorded by 24 digital seismic stations from Jiangsu Seismic Network during 2013 for comparison. GPU-M&L is one of waveform-based methods by waveform cross-correlations while REAL is one of pick-based method to associate arrivals of different seismic phases and locate events through counting the number of P and S picks and travel time residuals. Twenty-six templates are selected from the Jiangsu Seismic Network local catalog by using the GPU-M&L. The number of newly detected and located events is about 2.8 times more than those listed in the local catalog. We both utilize a deep-neural-network-based arrival-time picking method called PhaseNet and a short-term/long-term average (STA/LTA) trigger algorithm for seismic phase detection and picking by applying the REAL. We then refine seismic locations using a least-squares location method (VELEST) and a high-precision relative location method (hypoDD). By applying STA/LTA and PhaseNet, 1006 and 1893 events are associated and located, respectively. The newly detected events are mainly clustered and show steeply dipping fault planes. By analyzing the performance of these methods based on long-term continuous seismic data, the detected catalogs by the GPU-M&L and REAL show that the magnitudes of completeness are 1.4 and 0.8, respectively, which are smaller than 2.6 given by the local catalog. Although REAL provides improvement compared with GPU-M&L, REAL is highly dependent on phase detection and picking which is strongly affected by signal-noise ratio (SNR). Stations at southeast of the study region with low SNR may lead to few detections in the same area.     

基于光谱角匹配预测的高光谱图像无损压缩

在地表信息获取方面,高光谱遥感是对地观测技术的主要方法,观测同时产生了海量高光谱图像的存贮与传输问题.研究发现,高光谱图像具有独特的光谱上下文特征,可从光谱维分析高光谱图像的光谱相关性,并用光谱角来度量相邻像素间的光谱相似性的差异,探测水平或垂直光谱边界,由此提出了基于光谱角匹配预测(SAMP)的无损压缩算法.实验表明,SAMP算法的预测效果好于已有文献中提出的一些优秀算法,且具有低复杂性.     

A Comparison of Match Ozonesonde-Derived and 3D Model Ozone Loss Rates in the Arctic Polar Vortex during the Winters of 1994/95 and 1995/96

Ozone loss rates from ozonesonde data reported in the Match experiments of winters 1994/95 and 1995/96 inside the Arctic polar vortex are compared with simulations of the same winters performed using the SLIMCAT 3D chemistry and transport model. For 1994/95 SLIMCAT reproduces the location and timing of the diagnosed ozone destruction, reaching 10 ppbv/sunlit hour in late January as observed. SLIMCAT underestimates the loss rates observed in February and March by 1–3 ppbv/sunlit hour. By the end of March, SLIMCAT ozone exceeds the observations by 25–35%. In January 1995 the ozonesonde-derived loss rates at levels above 525 K are not chemical in origin but due to poor conservation of air parcels. Correcting temperature biases in the model forcing data significantly improved the agreement between the model and observed ozone at the end of winter 1994/95, increasing ozone destruction in SLIMCAT in February and March. The SLIMCAT simulation of winter 1995/96 does not reproduce the maximum ozone loss rates diagnosed by Match of 13 ppbv/sunlit hour. Comparing the data for the two winters reveals that the SLIMCAT photochemistry is least able to reproduce observed losses at low temperatures or when low temperatures coincide with high solar zenith angles (SZA). When cold (T = 192 K), high SZA (90°)matches are excluded from the 1995/96 analysis, agreement between the diagnoses and SLIMCAT is better with ozone loss rates of up to 6 ppbv/sunlit hour. For the rest of the winter SLIMCAT consistently underestimates the Match rates of ozone loss by 1–3 ppbv/sunlit hour. In March 1996 the monthly mean SLIMCAT ozone is 50% greater than observations at 430–540 K. In both winters, ozone destruction rates peaked more rapidly and declined more slowly in the Match observations than in the SLIMCAT simulations. The differences between the observed and modelled cumulative ozone losses demonstrate that the total ozone destruction by the end of the winter is sensitive to errors in the instantaneous ozone loss rates of 1–3 ppbv/sunlit hour.     

西北区域自动地震速报系统定位结果评估

西北区域自动地震速报系统是中国地震局5大区域速报系统之一.分析了2009年7月1日至2011年8月31日该系统产出的149个天然地震与全国统一地震编目系统定位结果的匹配情况:97.3％的地震发震时刻偏差小于5 s;96％的地震震中偏差分布在5～30 km之间;94％的地震深度偏差不超过5 km;68.5％的地震震级偏差不大于0.3.分析了7个非天然地震的匹配情况.并对未匹配地震及偏差较大地震作了进一步的分析.对自动地震速报系统的整合与完善提出了一些建议.     

2020年和林格尔ML4.5地震微震匹配定位及发震构造探讨

内蒙古和林格尔地处鄂尔多斯块体北缘阴山地震带内,历史上6级以上强震频发.2020年3月30日和林格尔发生ML4.5地震,打破了自2005年以来阴山地震带ML4.0以上地震的长期平静.研究此次地震序列的发震构造对区域应力状态和地震危险性分析有重要作用,然而内蒙古地震台网台站较为稀疏,相对于华北其他地区地震监测能力较低,对...