1951—2018年衢州市椪柑采摘期连阴雨天气变化特征

基于1951—2018年衢州市椪柑采摘期降水量、雨日、日照时数、相对湿度等逐日气象资料,应用统计分析和小波分析方法,分析椪柑采摘期连阴雨天气变化特征及其大气环流背景。结果表明：1951—2018年衢州椪柑采摘期连阴雨日数、次数和强度呈略微增加趋势、滑动3 d无雨次数呈减少趋势;滑动3 d无雨次数存在明显的5 a、7 a和15 a左右的年际和年代际周期变化规律,并且均呈现缩短趋势;椪柑采摘期连阴雨较强年亚欧地区呈两高一低的径向型环流,强冷空气南下活动较频繁、东移缓慢,偏南暖湿气流活跃,致使冷暖空气在中国长江中下游地区长时间交汇,导致连阴雨天气。     

重庆地区重复地震识别及在数字地震台网定位评价中的应用

利用重庆数字地震台网2010年1月至2017年12月的地震波形资料和观测报告,选出5个研究区1 251个M_L≥1.5地震进行波形互相关计算,识别出358对同时被2个地震台站记录且各台波形互相关系数(cc)不小于0.8的重复地震对,涉及342个地震事件,约占地震总数的27%。将筛选出的重复地震对用于定量判断地震目录中震相拾取误差及评估台网定位精度,结果显示:重庆数字地震台网的垂直定位误差约为3 km,水平定位误差约为5 km,Pg、Sg震相拾取误差分别为0.5 s和0.7 s;其中武隆区地震定位和震相拾取精度最高,綦江区最低。     

Automatic onset phase picking for portable seismic array observation

Automatic phase picking is a critical procedure for seismic data processing, especially for a huge amount of seismic data recorded by a large-scale portable seismic array. In this study is presented a new method used for automatic accurate onset phase picking based on the proporty of dense seismic array observations. In our method, the Akaike＇s information criterion （AIC） for the single channel observation and the least-squares cross-correlation for the multi-channel observation are combined together. The tests by the seismic array observation data after triggering with the short-term average/long-term average （STA/LTA） technique show that the phase picking error is less than 0.3 s for local events by using the single channel AIC algorithm. In terms of multi-channel least-squares cross-correlation technique, the clear teleseismic P onset can be detected reliably. Even for the teleseismic records with high noise level, our algorithm is also able to effectually avoid manual misdetections.     

浅地层剖面交互拾取解释技术研究

浅地层剖面探测在海洋学研究与海洋工程调查中使用愈来愈频繁，剖面资料的解释工作变得越来越繁重。因浅地层剖面实际解释工作的需要，结合浅地层剖面资料的特点，研究并实现了剖面反射层位的自动拾取、利用Event号交互拾取特征点、等Ping间隔拾取、求取海底下反射层位厚度等解释功能，这些解释功能的实现加快了剖面解释工作的进度，提高了解释结果的精度，并在实际浅地层剖面资料解释工作中取得了良好的应用效果。     

地震立体层析成像的实现方法及效果分析

立体层析成像是一种新的地震反射波层析成像方法,能为叠前深度偏移提供较为精确的宏观速度模型。本文研究了立体层析成像的实现方法,包括斜率与走时数据的拾取、离散速度模型构建和初始化、射线参数的确定、斜率和走时及射线计算以及反演问题解法等,建立立体层析成像的算法流程。并通过对Marmousi模型试验,对立体层析成像运行所需的主要参数,如初始速度模型、拾取数据量、离散网格尺寸、速度平滑权重等进行测试和分析,总结这些不同参数对立体层析反演结果的影响规律,用以指导生产实践。     

Quick Phase Identification for Dense Seismic Array with Aid from Long Term Phase Records of Co-located Sparse Permanent Stations

The phase identification and travel time picking are critical for seismic tomography, yet it will be challenging when the numbers of stations and earthquakes are huge. We here present a method to quickly obtain P and S travel times of pre-determined earthquakes from mobile dense array with the aid from long term phase records from co-located permanent stations. The records for 1 768 M ≥ 2.0 events from 2011 to 2013 recorded by 350 ChinArray stations deployed in Yunnan Province are processed with an improved AR-AIC method utilizing cumulative envelope and rectilinearity. The reference arrivals are predicted based on phase records from 88 permanent stations with similar spatial coverage, which are further refined with AR-AIC. Totally, 718 573 P picks and 512 035 S picks are obtained from mobile stations, which are 28 and 22 times of those from permanent stations, respectively. By comparing the automatic picks with manual picks from 88 permanent stations, for M ≥ 3.0 events, 81.5% of the P-pick errors are smaller than 0.5 second and 70.5% of S-pick errors are smaller than 1 second. For events with a lower magnitude, 76.5% P-pick errors fall into 0.5 second and 69.5% S-pick errors are smaller than 1 second. Moreover, the Pn and Sn phases are easily discriminated from directly P/S, indicating the necessity of combining traditional auto picking and integrating machine learning method.     

A high accurate automated first-break picking method for seismic records from high-density acquisition in areas with a complex surface

As the application of high-density high-efficiency acquisition technology becomes more and more wide, the areas with complex surface conditions gradually become target exploration areas, and the first-break picking work of massive low signal-to-noise ratio data is a big challenge. The traditional method spends a lot of manpower and time to interactively pick first breaks, a large amount of interactive work affects the accuracy and efficiency of picking. In order to overcome the shortcoming that traditional methods have weak anti-noise to low signal-to-noise ratio primary wave, this paper proposes a high accurate automated first-break picking method for low signal-to-noise ratio primary wave from high-density acquisition in areas with a complex surface. Firstly, this method determines first-break time window using multi-azimuth spatial interpolation technology; then it uses the improved clustering algorithm to initially pick first breaks and then perform multi-angle comprehensive quality evaluation to first breaks according to the following sequence: ‘single trace → spread → single shot → multiple shots’ to identify the abnormal first breaks; finally it determines the optimal path through the constructed evaluation function and using the ant colony algorithm to correct abnormal first breaks. Multi-azimuth time window spatial interpolation technology provides the base for accurately picking first-break time; the clustering algorithm can effectively improve the picking accuracy rate of low signal-to-noise ratio primary waves; the multi-angle comprehensive quality evaluation can accurately and effectively eliminate abnormal first breaks; the ant colony algorithm can effectively improve the correction quality of low signal-to-noise ratio abnormal first breaks. By example analysis and comparing with the commonly used Akaike Information Criterion method, the automated first-break picking theory and technology studied in this paper has high picking accuracy and the ability to stably process low signal-to-noise ratio seismic data, has a significant effect on seismic records from high-density acquisition in areas with a complex surface and can meet the requirements of accuracy and efficiency for massive data near-surface modelling and statics calculation.     

基于波形相似特征的微地震事件初至拾取及全局校正

微地震事件初至拾取是井下微地震监测数据处理的关键步骤之一.初至误差的存在会使微地震震源定位结果产生较大偏差,进而影响后续的压裂裂缝解释.通常初至拾取过程对所有的微地震事件选择相同的特征函数并采用一致的拾取参数进行统一处理,然而当事件的能量、震源机制、传播路径以及背景噪声等存在明显差异时,所得初至拾取结果差别显著.为了提高微地震事件初至拾取标准一致性,本文提出基于波形相似特征的初至拾取及全局校正方法.该方法首先利用互相关函数对每个事件内的各道记录进行时差校正,得到初始初至信息并形成叠加道,再对所有事件的叠加道进行全局互相关得到事件间初至相对校正量,最终初至结果可以通过各个事件的初始初至信息与其相对校正量相加得到.方法将所有微地震事件初至结果作为一个整体处理,从而能够克服常规方法初至拾取标准一致性差的缺陷.实际资料处理结果表明,相比于常规方法,该方法可以有效提高事件初至拾取和定位结果的一致性.     

利用标准时频变换方法在强噪声环境下无偏拾取地震P波、S波到时

地震P波、S波到时是精确分析地震水平位置、深度与速度结构等的重要参数,如何准确拾取P波和S波到时是地震学的一项重要的基础工作.大数据量与强噪声环境给地震到时的自动拾取带来了很大挑战.在频率域中可将信号与噪声分离,但会造成震相的偏移.针对上述问题,本文在STA/LTA、AIC方法的基础上,引入了标准时频变换(Normal...     

压裂微震数据初至拾取的时频谱熵法

在油气田开发过程中,微震监测是获得水力压裂引起裂缝分布的一种较为有效的方法。微震的定位成像与裂缝解释需要利用有效微震信号位置,而微震信号具有低信噪比的特点,传统信号拾取方法无法有效实现较低信噪比条件下初至时刻的准确拾取。本文提出一种基于时频谱熵的初至拾取新方法,该方法首先通过S变换获取含噪信号的时频谱;然后对谱内各个采样点沿频率方向进行分帧操作,并计算每帧频段内的近似负熵值,以最小近似负熵值作为该谱点的负熵值;最后沿时间方向比较各谱点的负熵值,最小值对应的时刻即为初至时刻。本文利用不同信噪比的合成地震数据对该方法进行效果验证,并与长短时窗能量比（STA/LTA）法进行拾取结果对比,结果表明：信噪比在-5 dB时,两种方法拾取效果都很好;信噪比在-10 dB时,时频谱熵法拾取效果更好。时频谱熵法更适合低信噪比情况下的信号初至拾取。