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水上地震反射波法的研究与应用
Water Seismic Wave-Reflection Research and Application
【作者】 王朝令;
【导师】 雷宛;
【作者基本信息】 成都理工大学 , 地球探测与信息技术, 2008, 硕士
【摘要】 本文从地震基本理论出发,分析了水上浅层地震横向和纵向分辨率的决定因素,以及要达到提高分辨率的目的,在采集中需要注意的问题。分析了水上地震地质条件,相对于陆上地震,水上地震干扰少,但干扰能量强,譬如多次波等都是水上地震本身所面临的问题。本文探讨了水上地震采集时所接收到的地震震幅随震源沉放深度变化规律,说明震源必须在2米以上的沉放的深度,才能达到较好的激发效果;同样水上检波器的沉放深度决定着接收到地震波能量,水上检波器必须在1米以上的沉放深度,才能达到较好的接收效果。水上地震地位困难也决定必须采用与陆上不同的定位方法,才能满足工程施工要求,现在水上地震常用的GPS定位,基本上满足了施工要求。由于水上地震本身所具有的特点,在地震资料处理中,必须采用与陆上地震某些不同的方法才能解决水上地震所面临的干扰。本文重点了分析了水上地震所面临的主要干扰多次波的解决方法。简要介绍了常用的消除的多次波的方法,这些方法大多是海上地震所常用的,由于水上地震与海上地震所具有的相似性,对水上地震来说有很强的借鉴价值。对具有表现为多次波严格的周期性特征和横向速度变化不大的资料,适合采用以二维F-K域滤波方法和预测反褶积方法为主以消除多次波。本文详细地介绍了这两种方法的原理和算法,并通过模型对比了采用二维F—K滤波和预测反褶积算法前后的结果,发现经过采用二者相结合的方法可以最大限度地压制多次波干扰。最后,本文通过一个实践项目分析了水上地震勘探的实际应用,从现场施工的数据采集开始,直到最后的数据处理与资料解释等工作过程,结果表明:(1)水上地震勘探的资料采集中,震源沉放深度2米左右和检波器沉放深度1米左右基本可以满足采集要求。(2)对水上地震采集资料具有严格周期性和横向速度变化不大的多次波,采用二维F—K滤波和预测反褶积为主、其它处理手段为辅的处理流程可以比较好地压制多次波干扰。(3)根据不同的工区的需要,灵活运用各种处理方法,不拘泥于理论上的处理流程,在适当的时候,不一定要加反褶积、二维滤波等处理过程。
【Abstract】 From the basic theory of the seismic, the water seismic analysis of horizontal and vertical resolution of the determining factors, and improve the resolution to achieve the objective of the acquisition of the need to pay attention to the problem. Analysis of the water seismic geological conditions, compared to land-based seismic, water seismic has less disturbance, but its has more energy, for example, multiple seismic wave and other are the problems which water seismic face. In this paper, we discuss the amplitude of collected seismic wave change by the depth of seismic focus and that the depth of seismic focus must be two meters below surface of water to achieve better results. Also the amplitude of collected seismic wave change by the depth of water detector, water detector to be one meter below surface of water, to achieve a better take-over. The difficult orientation of water seismic also decided to adopt a different land-based positioning methods in order to meet the requirements of the construction, now commonly used GPS positioning, basically meet the requirements of the construction.Since the water seismic itself with its difficulty, seismic data processing must use some ways which it is differ from land-based seismic to solve the water faced by the seismic disturbance. This article focused on the analysis of the solution of multiple wave which is the main distanbance in water seismic. Gave a briefing on the commonly way of eliminate multiple wave, these methods are mostly used by the seismic at sea, because of the similarity of water seismic and seismic at sea,seismic at sea has worthiness value for water seismic. The data which have a stictly multiple frequency waves, velocity in landscape orientation which has less diversification is suitable for use the way which give prioriy to two-dimensional FK filtering and forecast deconvolution. This paper introduced the principle of these two methods and arithmetic, and through the use of model of comparison F-K filtering and forecast deconvolution arithmetic, cross-reference the results of processing.Finally, through a project of water seismic exploration of the practical applications, from the scene of the data collection started until the final data processing and interpretation of data, the results showed that:(1) In data collection of water seismic, the depth of seismic focus have two meters deep and the depth of water detector have about 1 meter which meet the basic requirements of collection.(2) The data of water seismic collection which is strictly cyclical and velocity in land-orientation has less diversification using two-dimensional F-K filtering and forecast deconvolution-based, supplemented by other means of dealing with the treatment process can be better suppression of multiple wave interference.(3) According to the needs of the work area, flexibility in the use of various ways, not rigidly adhere to the theory, processes. In some time, it’s no necessary to adopt forecast deconvolution or two-dimensional filtering process.
【Key words】 water seismic; repeatedly reflected wave; two-dimensional filtering; forecast deconvolution;
- 【网络出版投稿人】 成都理工大学 【网络出版年期】2008年 09期
- 【分类号】P631.4
- 【被引频次】7
- 【下载频次】365