Data processing of the Kuiyang-ST2000 deep-towed high-resolution multichannel seismic system and application to South China Sea data

The Kuiyang-ST2000 deep-towed high-resolution multichannel seismic system was designed by the First Institute of Oceanography,Ministry of Natural Resources(FIO,MNR).The system is mainly composed of a plasma spark source(source level:216 dB,main frequency:750 Hz,frequency bandwidth:150-1200 Hz)and a towed hydrophone streamer with 48 channels.Because the source and the towed hydrophone streamer are constantly moving according to the towing configuration,the accurate positioning of the towing hydrophone array and the moveout correction of deep-towed multichannel seismic data processing before imaging are challenging.Initially,according to the characteristics of the system and the towing streamer shape in deep water,travel-time positioning method was used to construct the hydrophone streamer shape,and the results were corrected by using the polynomial curve fitting method.Then,a new data-processing workflow for Kuiyang-ST2000 system data was introduced,mainly including float datum setting,residual static correction,phase-based moveout correction,which allows the imaging algorithms of conventional marine seismic data processing to extend to deep-towed seismic data.We successfully applied the Kuiyang-ST2000 system and methodology of data processing to a gas hydrate survey of the Qiongdongnan and Shenhu areas in the South China Sea,and the results show that the profile has very high vertical and lateral resolutions(0.5 m and 8 m,respectively),which can provide full and accurate details of gas hydrate-related and geohazard sedimentary and structural features in the South China Sea.

A novel technique for automatic seismic data processing using both integral and local feature of seismograms

A novel technique for automatic seismic data processing using both integral and local feature of seismograms was presented in this paper. Here, the term integral feature of seismograms refers to feature which may depict the shape of the whole seismograms. However, unlike some previous efforts which completely abandon the DIAL approach, i.e., signal detection, phase identifi- cation, association, and event localization, and seek to use envelope cross-correlation to detect seismic events directly, our technique keeps following the DIAL approach, but in addition to detect signals corresponding to individual seismic phases, it also detects continuous wave-trains and explores their feature for phase-type identification and signal association. More concrete ideas about how to define wave-trains and combine them with various detections, as well as how to measure and utilize their feature in the seismic data processing were expatiated in the paper. This approach has been applied to the routine data processing by us for years, and test results for a 16 days＇ period using data from the Xinjiang seismic station network were presented. The automatic processing results have fairly low false and missed event rate simultaneously, showing that the new technique has good application prospects for improvement of the automatic seismic data processing.

Online Data Processing Methods in Xlet Applications for Seismic Early Warning and Emergency Services on Interactive Digital Television

In this paper, I described the methods that I used for the creation of Xlets, which are Java applets that are developed for the IDTV environment;and the methods for online data retrieval and processing that I utilized in these Xlets. The themes that I chose for the Xlets of the IDTV applications are Earthquake and Tsunami Early Warning;Recent Seismic Activity Report;and Emergency Services. The online data regarding the Recent Seismic Activity Report application are provided by the Kandilli Observatory and Earthquake Research Institute (KOERI) of Bogazici University in Istanbul;while the online data for the Earthquake and Tsunami Early Warning and the Emergency Services applications are provided by the Godoro website which I used for storing (and retrieving by the Xlets) the earthquake and tsunami early warning simulation data, and the DVB network subscriber data (such as name and address information) for utilizing in the Emergency Services (Police, Ambulance and Fire Department) application. I have focused on the methodologies to use digital television as an efficient medium to convey timely and useful information regarding seismic warning data to the public, which forms the main research topic of this paper.

Seismic Data Processing Approaches for the Study of Gas Hydrates in the East China Sea

A comprehensive study of the data profiles, including the 2D seismic data, single channel seismic data, shallow sections, etc., reveals that gas hydrates occur in the East China Sea. A series of special techniques are used in the processing of seismic data, which include enhancing the accuracy of velocity analysis and resolution, estimating the wavelet, suppressing the multiple, preserving the relative amplitude, using the DMO and AVO techniques and some special techniques in dealing with the wave impedance. The existence of gas hydrates is reflected in the subbottom profiles in the appearance of BSRs, amplitude anomalies, velocity anomalies and AVO anomalies, etc. Hence the gas hydrates can be identified and predicted. It is pointed out that the East China Sea is a favorable area of the gas hydrates resource, and the Okinawa Trough is a target area of gas hydrates reservoir.

Research on Seismic Data Processing and Interpretation System Model Based on CSCW

Through analyzing the needs of seismic data processing and interpretation,a system model based on CSCW is designed.Using the technology of CSCW to build the environment of cooperative work allows the field data acquisition to possess the functions of remote real-time guidance by experts and remote real-time processing of the data.The model overcomes the influences and barriers existing in the areas

Seismic Data Processing and Interpretation on the Loess Plateau, Part1: Seismic Data Processing

Branching river channels and the coexistence of valleys, ridges, hiils, and slopes＇as the result of long-term weathering and erosion form the unique loess topography. The Changqing Geophysical Company, working in these complex conditions, has established a suite of technologies for high-fidelity processing and fine interpretation of seismic data. This article introduces the processes involved in the data processing and interpretation and illustrates the results.

Application of multi-scaled morphology in denoising seismic data

In this paper, multi-scaled morphology is introduced into the digital processing domain for land seismic data. First, we describe the basic theory of multi-scaled morphology image decomposition of exploration seismic waves; second, we illustrate how to use multi-scaled morphology for seismic data processing using two real examples. The first example demonstrates suppressing the surface waves in pre-stack seismic records using multi-scaled morphology decomposition and reconstitution and the other example demonstrates filtering different interference waves on the seismic record. Multi-scaled morphology filtering separates signal from noise by the detailed differences of the wave shapes. The successful applications suggest that multi-scaled morphology has a promising application in seismic data processing.

Sedimentary Microfacies and Porosity Modeling of Deep-Water Sandy Debris Flows by Combining Sedimentary Patterns with Seismic Data： An Example from Unit I of Gas Field A, South China Sea

Sandy debris flow deposits are present in Unit I during Miocene of Gas Field A in the Baiyun Depression of the South China Sea. The paucity of well data and the great variability of the sedimentary microfacies make it difficult to identify and predict the distribution patterns of the main gas reservoir, and have seriously hindered further exploration and development of the gas field. Therefore, making full use of the available seismic data is extremely important for predicting the spatial distribution of sedimentary microfacies when constructing three-dimensional reservoir models. A suitable reservoir modeling strategy or workflow controlled by sedimentary microfacies and seismic data has been developed. Five types of seismic attributes were selected to correlate with the sand percentage, and the root mean square （RMS） amplitude performed the best. The relation between the RMS amplitude and the sand percentage was used to construct a reservoir sand distribution map. Three types of main sedimentary microfacies were identified： debris channels, fan lobes, and natural levees. Using constraints from the sedimentary microfacies boundaries, a sedimentary microfacies model was constructed using the sequential indicator and assigned value simulation methods. Finally, reservoir models of physical properties for sandy debris flow deposits controlled by sedimentary microfacies and seismic inversion data were established. Property cutoff values were adopted because the sedimentary microfacies and the reservoir properties from well-logging interpretation are intrinsically different. Selection of appropriate reservoir property cutoffs is a key step in reservoir modeling when using simulation methods based on sedimentary microfacies control. When the abnormal data are truncated and the reservoir properties probability distribution fits a normal distribution, microfacies-controlled reservoir property models are more reliable than those obtained from the sequence Gauss simulation method. The cutoffs for effective porosity of the debris channel, fan lobe, and natural levee facies were 0.2, 0.09, and 0.12, respectively; the corresponding average effective porosities were 0.24, 0.13, and 0.15. The proposed modeling method makes full use of seismic attributes and seismic inversion data, and also makes the property data of single-well depositional microfacies more conformable to a normal distribution with geological significance. Thus, the method allows use of more reliable input data when we construct a model of a sandy debris flow.

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources.Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs.However,manual channel picking is both time consuming and tedious.Moreover,similar to any other process dependent on human intervention,manual channel picking is error prone and inconsistent.To address these issues,automatic channel detection is both necessary and important for efficient and accurate seismic interpretation.Modern systems make use of real-time image processing techniques for different tasks.Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies.In this paper,we propose an innovative automatic channel detection algorithm based on machine learning techniques.The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process.The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches.We provide a field data example to demonstrate the performance of the new algorithm.The training phase gave a maximum accuracy of 84.6%for the classifier and it performed even better in the testing phase,giving a maximum accuracy of 90%.

Application of Fractional Fourier Transform in seismic data denoising

Data processing is a basic and crucial factor in seismic exploration,which can influence the effect of subsequent processing directly. Thus the selection of appropriate method for data processing is one of the most important tasks throughout the work. By simulating,the authors analyze and compare Fractional Fourier Transform( FRFT) and Wigner-Ville distribution( WVD),then summarize the similarities and advantages and disadvantages of the two methods. The results reveal that FRFT is more effective and suitable for application in seismic exploration than WVD.

信息化测震数据管理平台的设计与实现

依托公共安全信息化工程,测震台网数据管理平台在数据资源池环境下,采用分层架构和模块化开发,实现了实时流波形接入和存储、站网基础信息管理、常规数据产品产出、针对特定地震的产品产出、波形数据质量分析等测震台网核心业务的信息化。通过平台的接口和界面,事件波形处理与服务、数据质量分析、震源参数基础数据库建设等测震台网业务取得了长足进步。该平台具有业务集成性、功能实用性、资源共享性和技术先进性等特点,有效提升了测震台网信息化水平,为测震台网现代化发展发挥了积极作用。

应用生成对抗网络的地震数据重建和去噪一体化方法

在实际采集过程中,受地形条件和人为因素的影响,地震数据不仅在空间上会出现采样不足或不规则的情况,而且会混入噪声,不利于后续地震数据的处理和解释。通常将重建与去噪分为两个阶段处理,这样往往会引入额外的误差。为此,文中提出了一种基于条件韦氏生成对抗网络(cWGAN)的地震数据重建去噪一体化方法,该方法研究的重点是在缺失道和噪声的混合干扰下,准确提取地震数据的有效特征。首先,以U-Net模型为基本网络结构来构建生成器模型,分级提取地震数据同相轴特征;在判别器模型中引入条件约束,引导生成器优化梯度方向。其次,建立重建和去噪误差描述模型,该模型设计了一体化损失函数,可以兼顾重建与去噪两方面的处理任务。最后,经过合成数据和实际数据测试,证明文中所提的网络模型恢复的地震数据信噪比更高且具有较强鲁棒性。

应用自适应注意力机制U-net的地震数据高分辨处理

随着油气勘探开发的不断深入,薄储层与岩性油气藏逐渐成为重要的勘探目标,这也对地震资料的分辨率提出了更高的要求。文中提出了一种基于自适应注意力机制的U-net地震数据高分辨处理方法。该方法首先利用U-net结构学习地震数据的特征表示,通过下采样过程的编码器提取地震数据的抽象特征,然后通过上采样的解码器进行特征重建和细化。在上采样的过程中引入了注意力机制,用于自适应地调整网络对不同地震特征的关注程度,网络能够更加有效地捕捉到地震数据更多的细节和特征。Marmousi模型合成地震记录和实际数据实验结果表明,新网络比原U-net误差更小、更稳定,可有效提高预测精度,实现对地震数据的高分辨率处理。

智能物探技术的过去、现在与未来

通过梳理国内外人工智能技术在地球物理勘探(物探)领域中的发展历程、主要研究进展以及发展方向,总结了智能物探的优势和面临的难题,并提出了解决方案。研究结果表明:(1)物探技术在人工智能发展的第2次浪潮中开始与人工智能技术相结合,得益于物探领域数据量的指数级增长、硬件算力的高速发展以及不断出现的新深度学习框架,智能物探技术从早期的机器学习发展为目前的深度学习,在地震资料处理、解释等方面的应用中取得了大量研究成果。(2)目前智能物探技术被广泛应用于标签集的构建、去噪、断裂检测、层位与层序解释、地震相分类和异常体检测、岩性识别与油气藏开发、地震反演成像等方面,大幅提高了工作效率,降低了工作成本,克服了人工交互操作和人工经验的主观性和不可靠性,助力打破传统物探技术瓶颈。(3)智能物探技术的发展面临着缺少公开的标签数据集、缺少解决地球物理领域问题的智能化框架及尚未形成适用于地球物理领域共享的智能化开发平台等难题,可以从解决数据基础、构建智能平台、开展网络架构基础性研究及与应用场景结合等方面着手解决;此外,智能物探技术的发展方向还包含智能地震成像方法研究,储层成像方法研究,油气大数据挖掘、智能风险评估与智能决策以及超算软件装备研发等方面。

松辽盆地西南部砂岩型铀矿地震资料处理技术研究

文章针对松辽盆地西南部砂岩型铀矿地震资料噪声严重,干扰波能量比较强,资料频带窄、主频低、以及地震记录单炮之间、深浅层和远近道之间能量差异比较大的特点,在地震资料处理的叠前去噪、振幅补偿以及提高分辨率等关键环节开展了研究。在去噪环节,采用了叠前多域组合分步去噪方法,针对性地去除面波、线性噪声、强能量干扰以及次生干扰等,增强了目的层有效反射信号强度,提高地震资料信噪比。在振幅补偿中采用地表一致性振幅补偿和几何扩散补偿方法,可以明显消除远近道、浅层与深层之间以及道间能量差异。在提高分辨率环节通过地表一致性反褶积和预测反褶积方法,可消除地震波传播过程中子波变化引起的波形、频谱等特征的差异,压缩子波,缩小子波旁瓣,拓宽有效信号频带,提高地震资料分辨率。最后建立了一套地震资料的处理流程,并建立了相应环节的质量监控措施。通过与前人处理结果对比,此次建立的地震资料处理方法可以明显提高地震资料的品质。

基于多层残差网络的地震提频处理在薄储集层识别中的应用

基于多层残差网络的地震提频处理方法,通过智能化网络将测井高频信息与地震数据相结合,能有效提升纵向分辨率,保持横向连续可追踪,利于薄储集层识别。针对AMH地区常规处理的地震数据仅能识别厚度大于30 m的碳酸盐岩层,无法有效识别厚度较小的薄储集层的问题,提出基于多层残差网络的地震提频处理方法,以井旁地震振幅作为训练数据,测井相对波阻抗作为训练标签,利用深度学习网络多层残差网络开展训练,获取相对波阻抗曲线的预测模型;通过将地震数据作为输入,利用深度网络训练模型计算得到相对波阻抗数据体,进而得到提频后的地震数据体相对应的反射系数体。通过对靶区地质情况的分析认识,对宽频子波进行标定后提取合适的宽频子波,与反射系数体进行褶积,得到提频后的地震数据体;利用提频后的地震数据体开展储集层反演,反演结果纵向具有较高分辨率,与主要目的层能够较好匹配,横向可以进行识别和追踪,利用高分辨地震数据反演结果实现AMH地区的薄储集层识别。结果表明,通过基于多层残差网络的地震提频处理及相应的高分辨模型反演,在AMH地区能够识别厚度大于10 m的薄储集层,较好地解决由于地震分辨率低无法识别薄储集层的问题,有效提高了薄储集层预测的精度,对同类型薄储集层识别具有借鉴意义。

OBN地震数据成像处理基本逻辑与关键方法技术

海洋油气勘探逐渐进入深水深层勘探领域,地下地质构造复杂(横向变速剧烈)、目标油藏复杂(由以构造油气藏为主转向构造与地层岩性油气藏并重),同时还可能伴随海底地形及附近岩性的复杂变化,所有因素促使海洋油气地震勘探技术不断变革。提高海洋油气勘探效益的首要问题是发展尽可能满足高精度地震波成像需求的地震数据采集技术及对应的高精度地震波成像技术。当前,无论海上和陆上油气地震勘探,“两宽一高”地震数据采集技术和全波形反演(FWI)/最小二乘逆时偏移(LS_RTM)为代表的地震波成像技术是标志性的领先技术。海上油气地震勘探中,海底节点(OBN)地震数据采集是目前业界公认的、最有可能真正实现“两宽一高”地震数据采集的技术。与拖缆数据采集相比,OBN数据采集具有宽方位照明、数据信噪比高、无检端鬼波、存在实测的(至少一阶自由表面相关)下行波场、四分量观测等优点。尤其是宽方位照明和存在至少一阶自由表面下行波场的特点,使得OBN数据具备了对中深层复杂构造和近海底介质进行高精度成像的能力。着重讨论了高精度地震波成像对地震数据采集的要求,指出OBN数据采集在海洋油气勘探中的必要性;分析了OBN数据采集的地震波场的特点,据此提出OBN数据地震波成像处理的基本逻辑及相应的关键技术;认为海洋油气勘探中地震波成像处理的特殊问题主要由特征反射层引起,海水面、海底面和地下介质中若干强反射层构成了这些特征反射层,提出了模型驱动波动理论特征反射层相关多次波预测与压制的技术路线,并对比了几种代表性的多次波预测的基础理论;指出对应当前的线性化偏移成像算子叠前数据域与叠前成像域是等价的,据此以成像道集后处理为中心,给出期望成像道集的定义,将弱旁瓣、定量的反射系数作为保真高分辨地震波成像的目标,在两个域中尽可能完美实现地下同一反(绕/散)射点、不同炮检距反(绕/散)射子波的同相位叠加,尽可能好地实现保真高分辨带限反射系数的成像;提出最好把带限反射系数成像推进到宽带波阻抗成像的技术路线;结合OBN数据的特点,给出了OBN数据地震波成像处理的基本技术流程,指出各环节的关键方法技术。最后,针对OBN数据四分量观测的特点,指出是实际观测的多波地震波场中的波现象(主要是P_SV波)与地震波传播及模拟理论不匹配导致了当前多波成像结果达不到预期,建议重点研究实际观测的多波地震波场中的波现象与地震波传播及模拟理论不匹配的物理根源,而不是发展更高端的矢量波成像算法。期望本文的思想观点对OBN地震勘探在海洋油气勘探中的进一步应用产生积极的促进作用。

海洋地球物理数据处理现状及展望

本文详细介绍了青岛海洋地质研究所地球物理数据处理技术的发展历程、技术现状和展望。介绍了目前主流的多波束高精度成像及声学信息提取技术、重磁数据处理及解释技术、浅水高分辨率浅地层剖面/单道地震数据处理技术、低信噪比小道距多道地震成像技术、长排列多道地震数据处理技术、水合物三维地震数据处理技术和地球物理反演与目标体识别技术等关键处理技术的现状,并对今后数据处理技术发展方向进行了展望。这些技术已成功运用于多项海洋基础地质调查、海岸带调查、深海地质调查、油气和水合物资源调查项目中,为海洋地质调查和研究提供了良好的支撑。

2024年1月23日新疆乌什7.1级地震震源参数的初步分析

北京时间2024年1月23日2时9分,新疆阿克苏地区乌什县(41.26°N,78.63°E)发生7.1级地震。中国地震预警网于震后12.2s产出首报预警信息,中国地震台网中心于震后3min发布自动速报结果,震后13min发布正式速报结果,同时联合多家单位启动地震应急产品产出工作,产出震源参数、地震构造、历史地震、余震精定位、震源机制和震源破裂过程等应急产品。结果显示,本次地震是一次发生在青藏高原西缘的逆冲型地震事件,余震展布呈NEE向;主震破裂持续时间约15s,主要能量约在震后8s释放,最大位移量约2.3m,破裂长度约32km;极震区推测烈度达Ⅷ度,区域涉及乌什县、阿合奇县,面积约4614km2;震中附近多个台站峰值加速度值大于该区设防标准,存在造成部分房屋和道路损坏的可能,经对比,与实际震害调查结果相吻合。