地震探测仪器数据传输和压缩理论研究
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摘要
石油资源成为了一个国家的工业血液，关系着国家的国际民生。而现代工业化迅猛发展需要大量石油供给，但不可再生资源需求与资源枯竭已经逐渐成为阻碍世界各国经济进步和社会发展的主要矛盾，因此世界各国对石油地球物理勘探行业的投入越来越大。目前地球物理勘探行业面对的最大的问题是在复杂地质条件下进行勘探，勘探难度随之大幅增加。随着石油地震勘探技术的飞速发展，地震勘探技术将向着高密度、多维化、全波场、高分辨率、超多道等方向发展，这些都要求地球物理探测设备与之相适应。当前石油勘探物理装备最主要特点是地震传感器网络化，其中地震勘探装备中的通信技术就是石油地震勘探仪器网络的关键技术之一，直接决定和制约着整个装备系统的性能。
有线地震数据传输因为线缆成本高昂、施工量巨大、设备维护成本高，因此不适合复杂地形作业。无线地震数据传输不需要传输线缆，可以灵活的部署在各种野外恶劣地形，同时不受道数要求的限制，非常适合大规模复杂地形的石油地震勘探。但是石油地震勘探设备的传统无线数据传输一般采用射频通信技术，没有网络技术支撑，不是真正的无线网络传输，传输节点数目有限，传输控制困难。
无线传感器网络(Wireless Sensor Networks， WSN)是最新传感器网络技术，集传感器、微电子机械、网络通信、无线通信等最新技术于一体的信息网络，是当代信息网络最热门的研究领域之一，广泛应用于公共安全、军事、智能交通、生态环保等更多工业和民用领域。无线传感器网络具有极强的环境适应性、网络生存周期长、布线灵活，维护方便，设备成本投资低等优势，因此可以应用到石油地震勘探仪器网络的无线数据传输中。但是无线传感器网络存在协议相对复杂、嵌入式节点网络资源有限、节点之间有一定故障率等技术方面的问题，这些因素制约着无线传感器网络在地震勘探装备网络中的数据传输。
超光谱成像技术是在多光谱成像技术基础上发展起来的，随着超光谱图像光谱分辨率的提高，超光谱图像数据目前己经成为最重要的遥感数据源。超光谱图像可以获得更多连续地物特性光谱特征和狭窄光谱范围内的隐藏地物特性，因此超光谱图像的遥感技术成为本世纪遥感领域的热门研究方向之一。相对于过去的多光谱图像，高光谱图像不但具有更高的光谱分辨率和空间分辨率，而且具有更宽的波谱范围，提供了更多的地物参考研究数据。
超光谱遥感图像可以视为三维图像数据，具有纹理丰富、波段多、光谱分辨率高的特点，可以更广泛应用在地球物理探测等领域。但是超光谱图像的海量数据和较高数据维数是现有通信系统传输和存储将面临的最大问题。传统超光谱图像编码方法，主要采用静态图像压缩方法。静态图像压缩方法主要是针对单幅超光谱图像数据进行压缩，主要去除单波段的空间冗余，但其没有充分利用超光谱图像谱间相关性，因此，压缩性能有限，信噪比不高。本文针对上述问题进行以下研究：
(1)根据地震勘探仪器的地震数据传输对通信网络要求和WSN传输性能特点，提出来了一种基于WSN全无线地震勘探仪器网络数据传输方案：在子通信区域内使用WSN网络，采用固定分布和随机分布拓扑结构；在交叉站使用无线局域网(WirelessLocal Area Networks，WLAN)保证网络数据传输带宽的需求。使用无线传感器随机部署思想，直接解决恶劣、复杂地形的地震勘探设备布线困难问题，同时利用WSN网络的自组织和自愈特点，保证地震勘探数据传输的可靠性。
(2)为了提高WSN网络的性能，针对无线传感器网络的覆盖、连通和拓扑控制等方面进行研究，从而证明了无线传感器网络节点随机分布的数学模型；将二维无线传感器网络覆盖连通映射为状态突变的渗流理论问题；通过对渗流数学理论分析，建立并证明了无线传感器网络覆盖连通渗流理论模型，确定了无线传感器网络覆盖连通渗流发生的临界节点密度阈值和临界概率的数学表达关系，并通过实验仿真验证了模型的正确性。
(3)对WSN广播算法进行研究，本文分析了泛洪广播算法的广播风暴问题，在覆盖连通模型基础上，改进无线传感器网络的传统概率广播算法。本文利用有向天线，使用有向转发，寻找临界转发概率达到广播渗流，从而保证了WSN覆盖连通性和广播效率。通过算法仿真可得出，本文提出算法的报文可达率和端到端平均时延性能明显优于传统的概率广播算法。本文还通过对WSN路由算法进行研究，分析了AODV路由的广播算法的RREQ泛洪广播信息帧过多导致信道争抢、信号碰撞的问题，提出了使用覆盖连通渗流模型广播的AODV改进路由算法，并通过仿真验证改进路由算法在报文可达性以及平均时延性能，优于现有的AODV算法。进一步证明了提出来的基于覆盖连通渗流模型的有向广播算法性能优势。
(4)本文通过分析超光谱图像的相关特性，比较超光谱图像空间相关和谱间相关的特点，得出谱间相关性大于空间相关性的结论。根据超光谱图像波段的谱间相关系数，把谱间相关系数高的连续波段作为视频图像序列，使用运动估计和预测残差编码方法去除相邻波段之间的谱间相关性；而对于波段谱间相关性较小的波段，使用单独波段预测编码去除波段内自相关性。通过对AVRIS的超光谱图像标准序列进行测试，在一定的bpp条件下，本文算法的PSNR和压缩性能指标高于JPEG2000。
Petroleum is the industrial blood of a country and it is related to the internationalpeople’s livelihood. The rapid development of modern industry is in need of great amountof petroleum provision, but the requirement of nonrenewable resource and resourceexhaustion become into the principal contradiction that hinders the economical and socialdevelopment of countries in the whole world. Up to now, the biggest difficulty forgeophysics industry is to explore in a complicated geological condition which compoundsthe difficulty of exploring. As the boom of techniques of petroleum seismic exploration,seismic exploration technique develops towards to characteristics of high density,multiplicity, full wave field, high-resolution and multi tracks, which all require thecoordination of equipments of geophysics exploration. So far, the major feature ofpetroleum exploration equipments is the internationalization of seismic sensor, and thecommunications technology in seismic exploration equipments is one of the keytechniques of petroleum seismic exploration equipment networks and it directly decidesand constrains the function of the whole equipment system.
Wired seismic data transmission is not suitable for complicated terrain for its highcost of cable, huge amount of work, high cost of equipment sustainment. Wireless seismicdata transmission doesn’t need transmission cable so that it can be deployed in variousfield and abominable terrains, and it is not limited by number of tracks, so it is suitable forpetroleum seismic exploration of large-scale complicated terrain. But the traditionalwireless data transmission of petroleum seismic exploration equipment generally adoptsthe radio-frequency communication technique without the support of networks technique.So it is not the real wireless networks transmission with its transmission node limited andtransmission control difficult.
Wireless sensor networks is a new generation sensor networks technique, which is acomprehensive information process network that combines the sensor techniques, microelectronic mechanical system techniques, modern networks and wireless communicationtechniques. It is one of the hottest research topics in computer information area and iswidely used in public security, military,intelligent transportation and ecological environmental protection and more industrial and civilian-use areas. Wireless sensornetworks has such advantages as strong environmental adaptability, long network lifecircle, flexible cabling and convenient sustainment and low investment of equipment costso that it can be applied to wireless data transmission of petroleum seismic explorationequipment networks. But some technical problems, such as comparatively complicatedexistent protocol, limited embedded-node internet resource and certain error rate betweennodes, constrain the data transmission of wireless sensor networks in seismic explorationequipment networks.
Hyperspectral imaging technology develops based on the multispectral imagingtechnology. As the spectral resolution of hyperspectral image improves, the hyperspectralimage data becomes the most important resource of remote sensing data. Hyperspectralimage can obtain more serial topographic spectral features and hidden topographicfeatures in narrow spectrum. That is why remote sensing technology of hyperspectral image becomes one of the hottest research directions. Comparing to multispectral image,hyperspectral image possesses not only higher spectral resolution and spatial resolutionbut also wider wave spectrum, which provide more reference data for terrain research.
Hyperspectral remote sensing image can be regarded as3-D image data, and hascharacteristics of rich texture, multiband, high spectral resolution so it can be appliedwider to Geophysics exploration and other areas. But the excessive amount data ofhyperspectral image and comparatively high data dimension are the biggest problems tothe current communication systematic transmission and storage. Methods of traditionalhyperspectral image coding, mainly adopting static image compression method, aremostly used in the compression of single hyperspectral image data. It deducts the spatialredundancy of single band but doesn’t completely exploit relativity between thehyperspectral image spectrums, with limited compression function and low signal-to-noiseratio. The above questions are studied in this paper:
(1) According to the data transmission requirement of seismic explorationequipment networks and characteristics of Wireless Sensor Networks, an all-wirelessseismic exploration networks data transmission proposal based on wireless networks isprovided in this paper; WSN networks is used in a sub-area of communication, andfrozen-in distribution and random distribution topological structure are adopted; WLAN(Wireless Local Area Networks) is used in crossed station to guarantee the requirement ofbandwidth of the Networks data transmission. The idea of random deployment of wirelesssensor directly solves the difficulty of arranging cables in bad or complicated terrain. Andat the same time the self-organizing and self-curing characters of WSN networks are madegood use of to ensure the reliability of seismic exploration data transmission.
(2) To improve the functions of WSN networks, I did research in the coverage,connection and topology control of wireless sensor networks and proved therandom-distributed mathematical model of wireless sensor networks node; I mapped thebi-dimensional wireless sensor networks coverage and connection into questions ofpercolation theory of state mutation; by analyzing percolation mathematics theory, I set upand proved wireless sensor networks coverage and connection percolation theory modeland ascertained the mathematic expression relationship between the critical-node densitythreshold and critical probability when percolation of the wireless sensor networkscoverage and connection occurs and also proved the validity of the model throughexperimental simulation.
(3) I did research to WSN broadcasting algorithm and analyzed the problem ofbroadcasting storm of flooding broadcasting algorithm. Based on the coverage andconnection model, I improved the traditional probability broadcasting algorithm ofwireless sensor networks. Directional antenna and directional transmit were used to findout the critical transmit probability to reach broadcasting percolation, which ensured thecoverage and connection ability and broadcasting efficiency of WSN. By usingexperimentalsimulation,it is proved that the improved algorithm is obviously superior tothe traditional probability broadcasting algorithm in message achievable rate and theend-to-end average delay performance. By studying the WSN route algorithm, I analyzedproblems of channel fight and signal crash resulted from the excessiveness of RREQflooding broadcasting information frame in AODV (Ad Hoc Ondemand Distance Vector) route broadcasting algorithm, and proposed improved AODV route algorithm usingcoverage and connection percolation model broadcasting, which is superior to the currentAODV algorithm. And it is further proved the function advantages of directionalbroadcasting algorithm which is proposed based on coverage and connection percolationmodel.
(4) By analyzing the related characteristics of hyperspectral image, I compared thespatial related and spectral related features and came to a conclusion that the spectralrelativity is bigger than the spatial relativity. According to the spectral correlation index ofhyperspectral image band, serial bands which possess high spectral correlation index areused as video image sequence, and motion estimation method and predicted residualcoding method are used to eliminate the spectral relativity between adjacent bands; and tothe bands whose spectral relativity are low, single band predictive coding is used toeliminate internal self-relativity. By testing the standard hyperspectral image sequence andin certain condition of bpp, PSNR and compression function index are higher thanJPEG2000.
有线地震数据传输因为线缆成本高昂、施工量巨大、设备维护成本高，因此不适合复杂地形作业。无线地震数据传输不需要传输线缆，可以灵活的部署在各种野外恶劣地形，同时不受道数要求的限制，非常适合大规模复杂地形的石油地震勘探。但是石油地震勘探设备的传统无线数据传输一般采用射频通信技术，没有网络技术支撑，不是真正的无线网络传输，传输节点数目有限，传输控制困难。
无线传感器网络(Wireless Sensor Networks， WSN)是最新传感器网络技术，集传感器、微电子机械、网络通信、无线通信等最新技术于一体的信息网络，是当代信息网络最热门的研究领域之一，广泛应用于公共安全、军事、智能交通、生态环保等更多工业和民用领域。无线传感器网络具有极强的环境适应性、网络生存周期长、布线灵活，维护方便，设备成本投资低等优势，因此可以应用到石油地震勘探仪器网络的无线数据传输中。但是无线传感器网络存在协议相对复杂、嵌入式节点网络资源有限、节点之间有一定故障率等技术方面的问题，这些因素制约着无线传感器网络在地震勘探装备网络中的数据传输。
超光谱成像技术是在多光谱成像技术基础上发展起来的，随着超光谱图像光谱分辨率的提高，超光谱图像数据目前己经成为最重要的遥感数据源。超光谱图像可以获得更多连续地物特性光谱特征和狭窄光谱范围内的隐藏地物特性，因此超光谱图像的遥感技术成为本世纪遥感领域的热门研究方向之一。相对于过去的多光谱图像，高光谱图像不但具有更高的光谱分辨率和空间分辨率，而且具有更宽的波谱范围，提供了更多的地物参考研究数据。
超光谱遥感图像可以视为三维图像数据，具有纹理丰富、波段多、光谱分辨率高的特点，可以更广泛应用在地球物理探测等领域。但是超光谱图像的海量数据和较高数据维数是现有通信系统传输和存储将面临的最大问题。传统超光谱图像编码方法，主要采用静态图像压缩方法。静态图像压缩方法主要是针对单幅超光谱图像数据进行压缩，主要去除单波段的空间冗余，但其没有充分利用超光谱图像谱间相关性，因此，压缩性能有限，信噪比不高。本文针对上述问题进行以下研究：
(1)根据地震勘探仪器的地震数据传输对通信网络要求和WSN传输性能特点，提出来了一种基于WSN全无线地震勘探仪器网络数据传输方案：在子通信区域内使用WSN网络，采用固定分布和随机分布拓扑结构；在交叉站使用无线局域网(WirelessLocal Area Networks，WLAN)保证网络数据传输带宽的需求。使用无线传感器随机部署思想，直接解决恶劣、复杂地形的地震勘探设备布线困难问题，同时利用WSN网络的自组织和自愈特点，保证地震勘探数据传输的可靠性。
(2)为了提高WSN网络的性能，针对无线传感器网络的覆盖、连通和拓扑控制等方面进行研究，从而证明了无线传感器网络节点随机分布的数学模型；将二维无线传感器网络覆盖连通映射为状态突变的渗流理论问题；通过对渗流数学理论分析，建立并证明了无线传感器网络覆盖连通渗流理论模型，确定了无线传感器网络覆盖连通渗流发生的临界节点密度阈值和临界概率的数学表达关系，并通过实验仿真验证了模型的正确性。
(3)对WSN广播算法进行研究，本文分析了泛洪广播算法的广播风暴问题，在覆盖连通模型基础上，改进无线传感器网络的传统概率广播算法。本文利用有向天线，使用有向转发，寻找临界转发概率达到广播渗流，从而保证了WSN覆盖连通性和广播效率。通过算法仿真可得出，本文提出算法的报文可达率和端到端平均时延性能明显优于传统的概率广播算法。本文还通过对WSN路由算法进行研究，分析了AODV路由的广播算法的RREQ泛洪广播信息帧过多导致信道争抢、信号碰撞的问题，提出了使用覆盖连通渗流模型广播的AODV改进路由算法，并通过仿真验证改进路由算法在报文可达性以及平均时延性能，优于现有的AODV算法。进一步证明了提出来的基于覆盖连通渗流模型的有向广播算法性能优势。
(4)本文通过分析超光谱图像的相关特性，比较超光谱图像空间相关和谱间相关的特点，得出谱间相关性大于空间相关性的结论。根据超光谱图像波段的谱间相关系数，把谱间相关系数高的连续波段作为视频图像序列，使用运动估计和预测残差编码方法去除相邻波段之间的谱间相关性；而对于波段谱间相关性较小的波段，使用单独波段预测编码去除波段内自相关性。通过对AVRIS的超光谱图像标准序列进行测试，在一定的bpp条件下，本文算法的PSNR和压缩性能指标高于JPEG2000。
Petroleum is the industrial blood of a country and it is related to the internationalpeople’s livelihood. The rapid development of modern industry is in need of great amountof petroleum provision, but the requirement of nonrenewable resource and resourceexhaustion become into the principal contradiction that hinders the economical and socialdevelopment of countries in the whole world. Up to now, the biggest difficulty forgeophysics industry is to explore in a complicated geological condition which compoundsthe difficulty of exploring. As the boom of techniques of petroleum seismic exploration,seismic exploration technique develops towards to characteristics of high density,multiplicity, full wave field, high-resolution and multi tracks, which all require thecoordination of equipments of geophysics exploration. So far, the major feature ofpetroleum exploration equipments is the internationalization of seismic sensor, and thecommunications technology in seismic exploration equipments is one of the keytechniques of petroleum seismic exploration equipment networks and it directly decidesand constrains the function of the whole equipment system.
Wired seismic data transmission is not suitable for complicated terrain for its highcost of cable, huge amount of work, high cost of equipment sustainment. Wireless seismicdata transmission doesn’t need transmission cable so that it can be deployed in variousfield and abominable terrains, and it is not limited by number of tracks, so it is suitable forpetroleum seismic exploration of large-scale complicated terrain. But the traditionalwireless data transmission of petroleum seismic exploration equipment generally adoptsthe radio-frequency communication technique without the support of networks technique.So it is not the real wireless networks transmission with its transmission node limited andtransmission control difficult.
Wireless sensor networks is a new generation sensor networks technique, which is acomprehensive information process network that combines the sensor techniques, microelectronic mechanical system techniques, modern networks and wireless communicationtechniques. It is one of the hottest research topics in computer information area and iswidely used in public security, military,intelligent transportation and ecological environmental protection and more industrial and civilian-use areas. Wireless sensornetworks has such advantages as strong environmental adaptability, long network lifecircle, flexible cabling and convenient sustainment and low investment of equipment costso that it can be applied to wireless data transmission of petroleum seismic explorationequipment networks. But some technical problems, such as comparatively complicatedexistent protocol, limited embedded-node internet resource and certain error rate betweennodes, constrain the data transmission of wireless sensor networks in seismic explorationequipment networks.
Hyperspectral imaging technology develops based on the multispectral imagingtechnology. As the spectral resolution of hyperspectral image improves, the hyperspectralimage data becomes the most important resource of remote sensing data. Hyperspectralimage can obtain more serial topographic spectral features and hidden topographicfeatures in narrow spectrum. That is why remote sensing technology of hyperspectral image becomes one of the hottest research directions. Comparing to multispectral image,hyperspectral image possesses not only higher spectral resolution and spatial resolutionbut also wider wave spectrum, which provide more reference data for terrain research.
Hyperspectral remote sensing image can be regarded as3-D image data, and hascharacteristics of rich texture, multiband, high spectral resolution so it can be appliedwider to Geophysics exploration and other areas. But the excessive amount data ofhyperspectral image and comparatively high data dimension are the biggest problems tothe current communication systematic transmission and storage. Methods of traditionalhyperspectral image coding, mainly adopting static image compression method, aremostly used in the compression of single hyperspectral image data. It deducts the spatialredundancy of single band but doesn’t completely exploit relativity between thehyperspectral image spectrums, with limited compression function and low signal-to-noiseratio. The above questions are studied in this paper:
(1) According to the data transmission requirement of seismic explorationequipment networks and characteristics of Wireless Sensor Networks, an all-wirelessseismic exploration networks data transmission proposal based on wireless networks isprovided in this paper; WSN networks is used in a sub-area of communication, andfrozen-in distribution and random distribution topological structure are adopted; WLAN(Wireless Local Area Networks) is used in crossed station to guarantee the requirement ofbandwidth of the Networks data transmission. The idea of random deployment of wirelesssensor directly solves the difficulty of arranging cables in bad or complicated terrain. Andat the same time the self-organizing and self-curing characters of WSN networks are madegood use of to ensure the reliability of seismic exploration data transmission.
(2) To improve the functions of WSN networks, I did research in the coverage,connection and topology control of wireless sensor networks and proved therandom-distributed mathematical model of wireless sensor networks node; I mapped thebi-dimensional wireless sensor networks coverage and connection into questions ofpercolation theory of state mutation; by analyzing percolation mathematics theory, I set upand proved wireless sensor networks coverage and connection percolation theory modeland ascertained the mathematic expression relationship between the critical-node densitythreshold and critical probability when percolation of the wireless sensor networkscoverage and connection occurs and also proved the validity of the model throughexperimental simulation.
(3) I did research to WSN broadcasting algorithm and analyzed the problem ofbroadcasting storm of flooding broadcasting algorithm. Based on the coverage andconnection model, I improved the traditional probability broadcasting algorithm ofwireless sensor networks. Directional antenna and directional transmit were used to findout the critical transmit probability to reach broadcasting percolation, which ensured thecoverage and connection ability and broadcasting efficiency of WSN. By usingexperimentalsimulation,it is proved that the improved algorithm is obviously superior tothe traditional probability broadcasting algorithm in message achievable rate and theend-to-end average delay performance. By studying the WSN route algorithm, I analyzedproblems of channel fight and signal crash resulted from the excessiveness of RREQflooding broadcasting information frame in AODV (Ad Hoc Ondemand Distance Vector) route broadcasting algorithm, and proposed improved AODV route algorithm usingcoverage and connection percolation model broadcasting, which is superior to the currentAODV algorithm. And it is further proved the function advantages of directionalbroadcasting algorithm which is proposed based on coverage and connection percolationmodel.
(4) By analyzing the related characteristics of hyperspectral image, I compared thespatial related and spectral related features and came to a conclusion that the spectralrelativity is bigger than the spatial relativity. According to the spectral correlation index ofhyperspectral image band, serial bands which possess high spectral correlation index areused as video image sequence, and motion estimation method and predicted residualcoding method are used to eliminate the spectral relativity between adjacent bands; and tothe bands whose spectral relativity are low, single band predictive coding is used toeliminate internal self-relativity. By testing the standard hyperspectral image sequence andin certain condition of bpp, PSNR and compression function index are higher thanJPEG2000.
引文
[1]张跃军,王婧,王姿懿,张璐,王小越.2013年国际原油价格分析与趋势预测[J].北京理工大学学报(社会科学版),2013,15(1)：1-4.
[2]范英,姬强.2013年国际原油市场走势分析与价格预测[J].战略与决策研究,2013,28(1)：79-84.
[3]撒利明,董世泰,李向阳.中国石油物探新技术研究及展望[J].2012,47(6)：1014-1024.
[4]程建远,王盼,吴海,江浩.地震勘探仪的发展历程与趋势[J].煤炭科学技术,2013,41(1)：30-36.
[5]杜金虎,熊金良,王喜双,王西文,王学军.世界物探技术现状及中国石油物探技术发展的思考[J].岩性油气藏,2011,23(4)：1-8.
[6]程建远,王寿全,宋国龙.地震勘探技术的新进展与前景展望[J].煤田地质与勘探,2009,37(2)：55-59.
[7]赵波,王赟,芦俊.多分量地震勘探技术新进展及关键问题探讨[J].石油地球物理勘探,2012,47(3)：506-519.
[8]芦俊,王赟,石瑛.利用多波地震与测井数据联合反演预测煤岩的坚固性[J].地球物理学报,2011,54(11)：2967-2972.
[9]叶勇.新一代陆上地震采集装备技术展望[J].勘探地球物理进展,2009,32(6)：391-398.
[10]韩晓泉,穆群英,易碧金.地震勘探仪器的现状及发展趋势[J].物探装备,2008,18(1)：1-6.
[11]吴海超,林君,张林行.地震仪器中应用的网络通讯技术研究[J].地球物理学进展,2012,27(4)：1822-1831.
[12]谢明璞.大规模陆上地震仪器结构设计的关键技术[D].合肥：中国科学技术大学,2009.
[13]马毅超.大规模陆上地震仪器中高速可靠数据传输方法的研究[D]合肥：中国科学技术大学,2011.
[14]易碧金.地震仪器中应用的数据传输技术[J].物探装备,2008,18(6)：354-360.
[15]Savazzi S, Spaqnolini U. Wireless geophone networks for high-density land acquisition:Technologies and future potential [J]. The Leading Edge,2008,27(7):882-886.
[16]杨泓渊,韩立国,林君.无缆遥测地震仪网络同步采样技术[J].仪表技术与传感器,2009,3:15-18.
[17]吴安楚.无线单点检波器高密度地震采集[J].勘探地球物理进展,2009,32(2):101-106.
[18]杨泓渊,韩立国,陈祖斌.无缆遥测地震仪采集站的低功耗设计[J].电测与仪表,2009,46(1):49-53.
[19]李善仓,张克旺.无线传感器网络原理与应用[M].北京:机械工业出版社,2008.
[20]宫鹏.无线传感器网络技术环境应用进展[J].遥感学报,2010,8(4):11-15.
[21] Shahin Farahani. ZigBee Wireless and Transceivers [M]. USA:Newnes,2008.
[22]钱志鸿,王义君.面向物联网的无线传感器网络综述[J]电子与信息学报2013,35(1):215-227.
[23] ZHU Wei,DU Qian, James E.Fowler Multi temporal Hyperspectral ImageCompression [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2011,8(3):416-420.
[24]高文,朱明,田润澜等.一种空间划分与谱间预测相结合的超光谱图像压缩新方法[J].光谱学与光谱分析,2011,31(8):2287-2293.
[25]顾尔维奇著,刘光鼎译.地震勘探[M].北京：地质出版社,1956
[26]刘仲一.地震勘探仪器原理[M].北京:石油工业出版社,1986.
[27]何樵登.地震勘探原理和方法[M].北京:地质出版社,1993.
[28]董敏煜.地震勘探[M].东营：石油大学出版社,2000.
[29]王文良.地震勘探仪器的发展、时代划分及其技术特征[J].石油仪器,2004,18(1):1-8.
[30]易碧金,穆群英,罗福龙.当代地震勘探仪器的应用技术分析[J].地球物理学进展,2004,19(4):837-846.
[31]罗福龙.地震数据采集系统综述和展望[J].勘探技术,2007,(2):41-46.
[32]刘光林,刘泰生,高中录等.地震检波器的发展方向[J].勘探地球物理进展,2003,26(3):178-185.
[33]罗维炳.新一代无线局域网技术在地震数据采集系统中的应用[J].石油仪器,2004,18(1):9-15.
[34]袁子龙,狄帮让,肖忠祥.地震勘探仪器原理[M].北京：石油工业出版社,2006.
[35]罗福龙.地震数据存储技术综述[J].石油仪器,2006,20(3):1-5.
[36]王喜双,谢文导,邓志文.高密度空间采样地震技术发展与展望[J].中国石油勘探,2007,12(1)：49-53.
[37]王辉明,宋志翔,马国庆. MEMS加速度传感器的开发及在地球物理勘探中的应用[J].勘探地球物理进展,2005,28(3):223-226.
[38] A Messaoud M,Boulegroun M Z,Kasmi R,et al. New dimensions in land seismictechnology [J]. Oilfield Review,2005,17(3):42-53.
[39]唐东,林梁政,陈浩.用于地震勘探的新型高精度地震检波器研究[J].振动与冲击,2008,27(2):162-164.
[40]王梅生.数字检波器的应用及效果[J].物探装备,2007,17(4):235-240.
[41]曹务祥.模拟和数字检波器的资料响应特征对比分析[J].勘探地球物理进展,2007,30(2)：96-99.
[42]王辉明,宋志翔,马国庆.MEMS加速度传感器的开发及在地球物理勘探中的应用[J].勘探地球物理进展,2005,28(3):223-226.
[43]王文良. I/O System Four全数字遥测地震仪的性能、特点及与以往地震仪器的对比[J].物探装备,2005,15(4):232-246.
[44]董世泰,高红霞.单点单检波器地震勘探技术[J].石油仪器,2005,19(2)：66-68.
[45]刘振武.中国石油高密度地震技术的实践与未来[J].石油勘探与开发,2009,36(2):129-135.
[46]李征航,黄劲松. GPS测量与数据处理[M].武汉:武汉大学出版社,2005.
[47]吴宁,潘小龙,虞皆侠.高精度GPS同步时钟的研究与实现[J].电力系统自动化,2008,32(10):61-65.
[48]陈联青,贾艳芳,顾欣莉. GPS授时(网络)地震仪[J].物探装备,2006,16(1):1-7.
[49]王松.基于以太网的分布式地震仪网络性能模拟与设计[D].长春:吉林大学,2008.
[50]王铁军,郝会民,李国旗.物探装备技术进展与发展方向[J].中国工程科学,2010,12(5):78-83.
[51]王洪体.数字地震仪网络化与智能化技术研究[D].北京:中国地震局地球物理研究所,2006.
[52]陆其鹄,彭克中,易碧金.我国地球物理仪器的发展[J].地球物理学进展,2007,22(4):1332-1337.
[53]蒋连斌,刘财,王建民.高分辨率地震勘探资料采集技术[M].长春:吉林科学技术出版社,2000.
[54]尹成,吕公河,田继东.三维观测系统属性分析与优化设计[J].石油地球理勘探,2005,40(5):495-509.
[55]马义忠,魏伟,孙伟佳.南阳凹陷高精度三维地震采集观测系统设计[J].石油物探,2008,47(5):498-504.
[56]杨德义,赵镨,王惠.煤矿三维地震勘探技术发展趋势[J].中国煤炭地质,2011,23(6):42-48.
[57]熊翥.高精度三维地震(I):数据采集[J].勘探地球物理展,2009,32(1):1-11.
[58]唐建明,马昭军.宽方位三维三分量地震资料采集观测系统设计-以新场气田三维三分量勘探为例[J].石油物探,2007,46(3):210-318.
[59]段云卿,皮金云,刘兵.三分量小折射表层调查研究[J].石油地球物理勘探,2008,43(6):652-655.
[60]陈金鹰.地震勘探数据采集与传输体系研究[D].成都:成都理工大学,2006.
[61]郭建,刘光鼎.无缆存储式数字地震仪的现状及展望[J].地球物理学进展,2009,24(5):1540-1549.
[62]汪海山.大型地震数据采集记录系统中数据传输的关键技术研究[D].北京:清华大学,2010.
[63]凯洛·霍利斯,当·奇伍德,约翰·尼,费尔南多·赫南迪. FireFly无线地震采集技术在复杂敏感地区的应用[J].物探装备,2009,19(2):71-75.
[64] ION Inc. The application of FireFly in Wamsutter project[R].USA:ION Inc,2008.
[65] Sercel Inc.428XL Technology Manual[R]. Carquefou Cedes France: Sercel Inc,2008.
[66] Sercel Inc.428XL reference training guide[R]. Carquefou Cedes France:Sercel Inc,2006.
[67]王文良.428XL系统的软件功能和地震区域网络系统[J].物探装备,2008,18(59):289-295.
[68]王文良.从428XL的推出看地震数据采集系统的新发展[J].物探装备,2006,16(1):1-15.
[69]夏颖.408UL地震网络的传输同步与传输错处理[J].物探装备,2007,17(1):34-36.
[70]张志勇.428XL无线采集设备的应用.物探装备[J].2009,19(2):137-140.
[71] Sercel Corporation. Unite Cable-free Sismic Acquisition [DB/OL].(2011-05-15)[2013-01-06]. http//www.sercel.com.SERCEL Inc. UNITE brochure[R]. France:SERCEL INC,2008.
[72]陆增援.地震采集中高速传输与实时处理系统的研究[D].合肥:中国科学技术大学,2005.
[73]王兵.高分辨地震勘探仪器设计研究[D].合肥:中国科学技术大学,2008.
[74]曹平.勘探地震数据获取系统设计[D].合肥:中国科学技术大学,2007.
[75]张林行.基于接力式以太网的可控震源地震勘探数据传输技术研究[D].长春:吉林大学,2007.
[76]杨泓渊.复杂山地自定位无缆地震仪的研究与实现[D].长春:吉林大学,2009.
[77]张铭,窦赫蕾,常春藤.OPNET Modeler与网络仿真[M].北京:人民邮电出版社,2007.
[78]崔逊学,赵湛,王成.无线传感器网络领域应用与设计技术[M].北京:国防工业出版社,2009.
[79]孙利民,李建中,陈渝,朱红松.无线传感器网络[M].北京:清华大学出版社,2005.
[80]李晓维.无线传感器网络[M].北京:北京理工大学出版社,2007.
[81]李玉军.无线网络连通性及路由关键技术研究[D].成都:电子科技大学,2010.
[82] Gilbert E.N.Random Plane Network [J]. Journal of Soeiety for Industrial andApplied Mathematics,1961,9(4):533-543.
[83] Philips T.K., Panwar S., A.N. Tantawi. Connectivity properties of a packet radionetwork model, Information Theory [J]. IEEE Transactions on,1989,35(5):1044-1047.
[84] Quintanilla.J, Torquato. S, R.M.Ziff. Efficient measurement of the percolationthreshold for fully penetrable discs [J].Journal of Physics.2000,33:399-407.
[85] Kong.Z.E.M.Yeh, Connectivity and Latency in Large-Scale Wireless Networks withUnreliable Links[C]. Poceedings of IEEE27th Conference on ComputerCommunications, IEEE,2008:11-15.
[86] Z.Kong,E.M Yeh, Analytical Lower Bounds on the Critical Density in ContinuumPercolation in Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks and Workshops[C].2007WiOpt5th International Symposium on, IEEE,2007:1-6.
[87]Santi Paolo, Blough, Douglas M. The critical transmitting range for connectivity in sparse wireless ad hoc networks [J]. Mobile Computing, IEEE Transactions on,2003,2(1):25-39.
[88]P.Gupta, P. R. Kumar, Critical power for asymptotic connectivity, in Decision and Control [C]. Proceedings of the37th IEEE Conference on, IEEE,1998:1106-1110.
[89]O. Dousse, F. Baccelli, P. Thiran. Impact of interferences on connectivity in ad hoc networks [J].IEEE/ACM Trans. Networks,2005,13(3):425-436.
[90]Wan Peng-Jun,Yi,Chih-Wei.Coverage by randomly deployed wireless sensor networks. IEEE Transactions on Information Theory,2006,52(6):2658-2669.
[91]Freed D.Cable-free nodes:The next generation land seismic system [J].The Leading Edge,2008,27(7):878-881.
[92]Chien-Chung Shen, Zhuochuan Huang, Chaiporn, Jaikaeo. Directional Broadcast for Mobile Ad Hoc Networks with Percolation Theory [J]. IEEE Transactions on Mobile Computing,2006,5(4):317-332.
[93]张良培.高光谱遥感[M].北京：测绘出版社,2011.
[94]张兵,高连如.高光谱图像分类与目标探测[M].北京：科学出版社,2011.
[95]冯学智.遥感数字图像处理与应用[M].北京：商务印书馆,2011.
[96]孙显.高分辨率遥感图像理解[M].北京：科学出版社,2011.
[97]万建伟.实用高光谱遥感图像压缩[M].北京：国防工业出版社,2012.
[98]罗建书.高光谱遥感图像数据压缩[M].北京：国防工业出版社,2011.
[99]J.Mielikainen and P. Toivanen.Clustered DPCM for the Lossless Compression of Hyperspectral Images [J]. IEEE Transactions on Geoscience and Remote Sensing,2003,41(12):2943-2946.
[100]E Magli,G. Olmo, and E. Quacchio.Optimized Onboard Lossless and Near Lossless Compression of Hyperspectral Data using CALIC [J].IEEE Geoscience and Remote Sensing Letters,2004,1(1):21-25.
[101]P. Toivanen, Kubasova, and J. Mielikainen. Correlation based Band Ordering Heuristic for Lossless Compression of Hyperspectral Sounder Data [J]. IEEE Geoscience and Remote Sensing Letters,2005,2(1):50-54.
[102]孙蕾,谷德峰,罗建书.最佳递归双向预测的高光谱图像无损压缩.光学精密工程[J].2009,17(11)：2864-2868.
[103]吴铮,何明一,冯燕,贾应彪.基于误差补偿预测树的多光谱遥感图像无损压缩方法[J].遥感学报,2005,9(2)：143-147.
[104]R.N.Hoffman, D.W. Johnson.Application of EOFs to multispectral and imagery: data compression and noise detection for AVIRIS [J]. IEEE Trans.Geoscice. Remote Sensing,1994,32(1):25-34.
[105]D.Tretter, C.A.Bouman. Optimal transforms for multispectral and multilayer image coding [J]. IEEE Trans.Image proceessing,1995,4(3):296-305.
[106]Stephane Mallat. A theory for multiresolution signal decomposition:the wavelet representation [J]. IEEE Transactions on Pattern Recognition and Machine Intelligence.1989,11(7):674-693.
[107]J.M.Shaprio. Embedded image coding using zero tree of wavelet coefficients [J]. IEEE Trans. Signal Processing,1993,41(12):3445-3462.
[108]A.Said, W.A. Pearlman. A new, fast, and efficient image codec based on set partitioning in hierarchical trees [J]. IEEE Trans. Circuits Syst. Video Technology.1996,6(3):243-250.
[109]Swelden W. The lifting schem:a custom-design construction of biorthogonal wavelets [J]. Appl and Comput,1996,3(2):186-200.
[110]Luigi Dragotti P, Poggi Q Ragozini A R P. Compression of multispectral images by three-demensional SPIHT algorithm [J]. IEEE Transactions on Geo science and Remote Sensing,2000,38(1):416-428
[111]J. Starck, D. Donoho, and E. Candes. Astronomical Image Representation by the Curvelet Transform [J]. Astronomy and Astrophysics,2003,398(2):785-800.
[112]M. Do and M. Vetterli.The Contourlet Transform:An Efficient Directional Multi resolution Image Representation [J]. IEEE Transactions on Image Processing,2005,14(12):2091-2106.
[113]A. Dutra, W. Pearlman, and E. Silva. Successive Approximation Wavelet Coding of AVTRIS Hyperspectral Images [J]. IEEE Journal of Selected Topics in Signal Processing,2011,5(3):370-385.
[114]E. LePennec and S. Mallat, Sparse Geometric Image Representations with Bandelets, IEEE Transactions on Image Processing [J].2005,14(4):423-438.
[115]曹平,宋克柱.海洋石油勘探数据采集与记录系统主控软件设计与实现[J].中国科学技术大学学报,2006,36(3)：258-263.
[116]张可立.深水浅层高分辨率拖缆采集数传系统研究与设计[D].合肥：中国科学技术大学,2009.
[117]宋洪治.大型地震传感器网络仿真平台的实现[D].合肥：中国科技大学,2011.
[118]刘乃安,李晓辉.无线局域网(WLAN)原理、技术与应用[M].西安：西安电子科技大学出版社,2004.
[119]谢希仁.计算机网络(第4版)[M].北京：电子工业出版社,2003.
[120]杨孔雨,聂培尧.计算机网络技术原理[M].北京：经济科学出版社,2003
[121]斯托林斯.无线通信与网络(第2版)[M].北京：清华大学出版社,2005.
[122]拉帕波特.无线通信原理与应用(第二版)[M].北京：电子工业出版社,2012.
[123]孙弋.短距离无线通信及组网技术[M].西安：西安电子科技大学出版社,2008.
[124]朱刚,谈振辉,周贤伟.蓝牙技术原理与协议[M].北京：北方交通大学出版社,2002.
[125]蔡绍滨.无线传感器网络的关键技术研究与应用[D].哈尔滨：哈尔滨工业大学,2011.
[126]Savazzi, Stefano, Spagnolin Umberto. Synchronous ultra-wide band wireless sensors networks for oil and gas exploration[C]. IEEE Symposium on Computers and Communications2009, ISCC2009, Sousse, Tunisia:907-912
[127]Savazzi Stefano, Spagnolini Umberto.Wireless geophone networks for high-density land acquisition:Technologies and future potential [J]. Leading Edge,2008,27(7):882-886.
[128]Li, Lei, ZhangBaoxian, Zheng Jun. A study on one-dimensional k-coverage problem in wireless sensor networks [J]. Wireless Communications and Mobile Computing,2013,13(1):1-11.
[129]Zhu Chuan,Zheng Chunlin,Shu Lei, Han Guangjie.A survey on coverage and connectivity issues in wireless sensor networks [J]. Journal of Network and Computer Applications,2012,35(2):619-632.
[130]Li W, Zhang W.Coverage analysis and active scheme of wireless sensor networks [J]. IET Wireless Sensor Systems,2012,2(2):86-91.
[131]Ammari, Habib M.Centralized and clustered k-coverage protocols for wireless sensor networks [J]. IEEE Transactions on Computers,2012,61(1):118-133.
[132]Zhao Ruiqin,Shen Xiaohong,Jiang Zhe, Wang Haiyan.Broadcasting with least redundancy in wireless sensor networks[J]. International Journal of Distributed Sensor Networks,2012,61(1):118-133.
[133]Medagliani Paolo,Leguay Jeremie,Ferrari Gianluigi,Gay Vincent, Lopez-Ramos, Mario. Energy-efficient mobile target detection in Wireless Sensor Networks with random node deployment and partial coverage [J]. Pervasive and Mobile Computing,2012,8(3):429-447.
[134]Yu Genjian,Wen Kunpeng, Feng Huibin.Sensing connectivity coverage and robustness of WSN with random distribution of nodes [J]. International Review on Computers and Software,2012,7(5):2314-2320.
[135]Boadbent SR, Hammersley JM.Percolation processes:I.Crystals and Mazes [J]. Mathematical Proceedings of the Cambridge Philosophical Society,1957,53(3):629-641.
[136]Grimmett G. Percolation[M]. NY:Springer-Verlag,1989.
[137]孔祥言.高等渗流力学[M].合肥：中国科学技术大学出版社,1999.
[138]伍法岳,杨展如.相变与临界现象-模型理论综述[J].物理学进展,1981,1(4)：525-541.
[139]Berger, N. and Biskup, M. Quenched invariance principle for simple ralldom walk on percolation clusters [J].Probab.Theory Related Fields2007,137:83-120.
[140]Braga, G. A., Sanehis, R. and Sehieber, T.A. Critical percolation on a Bethe lattice revisited [J].SIAM Review,2005,47:349-365.
[141]Booth, J Bruck,M Franceschetti. Covering algorithms, continuum percolation and the geometry of wireless networks [J].The Annals of Applied Probability,2003,13(2):722-741.
[142]盛骤,谢式千,潘承毅.概率论与数理统计第四版[M].北京：高等教育出版社,2010.
[143]何书元.随机过程[M].北京：北京大学出版社,2008.
[144]Wang Xueqing, Zhang Shuqin.Research on efficient coverage problem of node in wireless sensor networks[C]. Proceedings of2nd International Symposium on Electronic Commerce and Security, Nanchang,2009,2:532-536.
[145]Wang Xueqing,Sun Fayi,Kong Xiangsong.Research on optimal coverage problem of wireless sensor networks[C]. Proceedings of International Conference on Communications and Mobile Computing,2009,1:548-551.
[146]Zeng Yuanyuan,Sreenan Cormac J,Xiong Naixue,Yang Laurence T, Park Jong Hyuk. Connectivity and coverage maintenance in wireless sensor networks [J]. Journal of Supercomputing,2010,52(1):23-46.
[147]Miles RE. The asymptotic values of certain coverage probabilities [J]. Biometrika,1969,56(3):661-680.
[148]Zhang Honghai and Jennifer C. Hou.Maintaining Sensing Coverage and Connectivity in Large Sensor [J]. Ad Hoc&Sensor Wireless Networks,1(1-2):89-124.
[149]Li Yingshu,Vu Chinh, Ai Chunyu,Chen Guantao, ZhaoYi.Transforming complete coverage algorithms to partial coverage algorithms for wireless sensor networks [J]. IEEE Transactions on Parallel and Distributed Systems,2011,22(4):695-703.
[150]Kini, Ananth V, Singhal, Nikhil, Weber, Steven.Broadcast capacity of a WSN under communication and information coordination [J]. Ad Hoc Networks,2010,8(4):439-455.
[151]Sheu Jang-Ping, Hsu Chih-Shun, Chang, Yen-Jung.Efficient broadcasting protocols for regular wireless sensor networks [J].Wireless Communications and Mobile Computing,2006,6(1):35-48.
[152]Zhang, Qi, Agrawal, Dharma P. Performance evaluation of leveled probabilistic broadcasting in MANETs and wireless sensor networks [J]. Simulation,2005,81(8):533-546.
[153]CaiXuelian Li, Zhu Jiandong, Lina. Performance of routing protocols in very large scale wireless sensor networks [J]. Journal of Networks,2012,7(12):1996-2002.
[154]Li Gang, SunHongmei. Discovering AODV-based multipath routes in wireless ad hoc networks, Security and Communication Networks,2012,5(4):374-383.
[155]王钢,王世刚.基于TMS320DM642DSP的MPEG-4视频编码器优化[J].吉林大学学报(信息科学版),2006,24(6)：590-593.
[156]毕厚杰,王健.新一代视频压缩编码标准-H.264/AVC [M].北京：人民邮电出版社,2009.
[157]ZHAO Fan, LIU Gui-zhong, WANG Xing. An efficient macroblock-based diverse and flexible prediction modes[J]. Signal Processing:Image Communication,2010,25(9):697-708.
[2]范英,姬强.2013年国际原油市场走势分析与价格预测[J].战略与决策研究,2013,28(1)：79-84.
[3]撒利明,董世泰,李向阳.中国石油物探新技术研究及展望[J].2012,47(6)：1014-1024.
[4]程建远,王盼,吴海,江浩.地震勘探仪的发展历程与趋势[J].煤炭科学技术,2013,41(1)：30-36.
[5]杜金虎,熊金良,王喜双,王西文,王学军.世界物探技术现状及中国石油物探技术发展的思考[J].岩性油气藏,2011,23(4)：1-8.
[6]程建远,王寿全,宋国龙.地震勘探技术的新进展与前景展望[J].煤田地质与勘探,2009,37(2)：55-59.
[7]赵波,王赟,芦俊.多分量地震勘探技术新进展及关键问题探讨[J].石油地球物理勘探,2012,47(3)：506-519.
[8]芦俊,王赟,石瑛.利用多波地震与测井数据联合反演预测煤岩的坚固性[J].地球物理学报,2011,54(11)：2967-2972.
[9]叶勇.新一代陆上地震采集装备技术展望[J].勘探地球物理进展,2009,32(6)：391-398.
[10]韩晓泉,穆群英,易碧金.地震勘探仪器的现状及发展趋势[J].物探装备,2008,18(1)：1-6.
[11]吴海超,林君,张林行.地震仪器中应用的网络通讯技术研究[J].地球物理学进展,2012,27(4)：1822-1831.
[12]谢明璞.大规模陆上地震仪器结构设计的关键技术[D].合肥：中国科学技术大学,2009.
[13]马毅超.大规模陆上地震仪器中高速可靠数据传输方法的研究[D]合肥：中国科学技术大学,2011.
[14]易碧金.地震仪器中应用的数据传输技术[J].物探装备,2008,18(6)：354-360.
[15]Savazzi S, Spaqnolini U. Wireless geophone networks for high-density land acquisition:Technologies and future potential [J]. The Leading Edge,2008,27(7):882-886.
[16]杨泓渊,韩立国,林君.无缆遥测地震仪网络同步采样技术[J].仪表技术与传感器,2009,3:15-18.
[17]吴安楚.无线单点检波器高密度地震采集[J].勘探地球物理进展,2009,32(2):101-106.
[18]杨泓渊,韩立国,陈祖斌.无缆遥测地震仪采集站的低功耗设计[J].电测与仪表,2009,46(1):49-53.
[19]李善仓,张克旺.无线传感器网络原理与应用[M].北京:机械工业出版社,2008.
[20]宫鹏.无线传感器网络技术环境应用进展[J].遥感学报,2010,8(4):11-15.
[21] Shahin Farahani. ZigBee Wireless and Transceivers [M]. USA:Newnes,2008.
[22]钱志鸿,王义君.面向物联网的无线传感器网络综述[J]电子与信息学报2013,35(1):215-227.
[23] ZHU Wei,DU Qian, James E.Fowler Multi temporal Hyperspectral ImageCompression [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2011,8(3):416-420.
[24]高文,朱明,田润澜等.一种空间划分与谱间预测相结合的超光谱图像压缩新方法[J].光谱学与光谱分析,2011,31(8):2287-2293.
[25]顾尔维奇著,刘光鼎译.地震勘探[M].北京：地质出版社,1956
[26]刘仲一.地震勘探仪器原理[M].北京:石油工业出版社,1986.
[27]何樵登.地震勘探原理和方法[M].北京:地质出版社,1993.
[28]董敏煜.地震勘探[M].东营：石油大学出版社,2000.
[29]王文良.地震勘探仪器的发展、时代划分及其技术特征[J].石油仪器,2004,18(1):1-8.
[30]易碧金,穆群英,罗福龙.当代地震勘探仪器的应用技术分析[J].地球物理学进展,2004,19(4):837-846.
[31]罗福龙.地震数据采集系统综述和展望[J].勘探技术,2007,(2):41-46.
[32]刘光林,刘泰生,高中录等.地震检波器的发展方向[J].勘探地球物理进展,2003,26(3):178-185.
[33]罗维炳.新一代无线局域网技术在地震数据采集系统中的应用[J].石油仪器,2004,18(1):9-15.
[34]袁子龙,狄帮让,肖忠祥.地震勘探仪器原理[M].北京：石油工业出版社,2006.
[35]罗福龙.地震数据存储技术综述[J].石油仪器,2006,20(3):1-5.
[36]王喜双,谢文导,邓志文.高密度空间采样地震技术发展与展望[J].中国石油勘探,2007,12(1)：49-53.
[37]王辉明,宋志翔,马国庆. MEMS加速度传感器的开发及在地球物理勘探中的应用[J].勘探地球物理进展,2005,28(3):223-226.
[38] A Messaoud M,Boulegroun M Z,Kasmi R,et al. New dimensions in land seismictechnology [J]. Oilfield Review,2005,17(3):42-53.
[39]唐东,林梁政,陈浩.用于地震勘探的新型高精度地震检波器研究[J].振动与冲击,2008,27(2):162-164.
[40]王梅生.数字检波器的应用及效果[J].物探装备,2007,17(4):235-240.
[41]曹务祥.模拟和数字检波器的资料响应特征对比分析[J].勘探地球物理进展,2007,30(2)：96-99.
[42]王辉明,宋志翔,马国庆.MEMS加速度传感器的开发及在地球物理勘探中的应用[J].勘探地球物理进展,2005,28(3):223-226.
[43]王文良. I/O System Four全数字遥测地震仪的性能、特点及与以往地震仪器的对比[J].物探装备,2005,15(4):232-246.
[44]董世泰,高红霞.单点单检波器地震勘探技术[J].石油仪器,2005,19(2)：66-68.
[45]刘振武.中国石油高密度地震技术的实践与未来[J].石油勘探与开发,2009,36(2):129-135.
[46]李征航,黄劲松. GPS测量与数据处理[M].武汉:武汉大学出版社,2005.
[47]吴宁,潘小龙,虞皆侠.高精度GPS同步时钟的研究与实现[J].电力系统自动化,2008,32(10):61-65.
[48]陈联青,贾艳芳,顾欣莉. GPS授时(网络)地震仪[J].物探装备,2006,16(1):1-7.
[49]王松.基于以太网的分布式地震仪网络性能模拟与设计[D].长春:吉林大学,2008.
[50]王铁军,郝会民,李国旗.物探装备技术进展与发展方向[J].中国工程科学,2010,12(5):78-83.
[51]王洪体.数字地震仪网络化与智能化技术研究[D].北京:中国地震局地球物理研究所,2006.
[52]陆其鹄,彭克中,易碧金.我国地球物理仪器的发展[J].地球物理学进展,2007,22(4):1332-1337.
[53]蒋连斌,刘财,王建民.高分辨率地震勘探资料采集技术[M].长春:吉林科学技术出版社,2000.
[54]尹成,吕公河,田继东.三维观测系统属性分析与优化设计[J].石油地球理勘探,2005,40(5):495-509.
[55]马义忠,魏伟,孙伟佳.南阳凹陷高精度三维地震采集观测系统设计[J].石油物探,2008,47(5):498-504.
[56]杨德义,赵镨,王惠.煤矿三维地震勘探技术发展趋势[J].中国煤炭地质,2011,23(6):42-48.
[57]熊翥.高精度三维地震(I):数据采集[J].勘探地球物理展,2009,32(1):1-11.
[58]唐建明,马昭军.宽方位三维三分量地震资料采集观测系统设计-以新场气田三维三分量勘探为例[J].石油物探,2007,46(3):210-318.
[59]段云卿,皮金云,刘兵.三分量小折射表层调查研究[J].石油地球物理勘探,2008,43(6):652-655.
[60]陈金鹰.地震勘探数据采集与传输体系研究[D].成都:成都理工大学,2006.
[61]郭建,刘光鼎.无缆存储式数字地震仪的现状及展望[J].地球物理学进展,2009,24(5):1540-1549.
[62]汪海山.大型地震数据采集记录系统中数据传输的关键技术研究[D].北京:清华大学,2010.
[63]凯洛·霍利斯,当·奇伍德,约翰·尼,费尔南多·赫南迪. FireFly无线地震采集技术在复杂敏感地区的应用[J].物探装备,2009,19(2):71-75.
[64] ION Inc. The application of FireFly in Wamsutter project[R].USA:ION Inc,2008.
[65] Sercel Inc.428XL Technology Manual[R]. Carquefou Cedes France: Sercel Inc,2008.
[66] Sercel Inc.428XL reference training guide[R]. Carquefou Cedes France:Sercel Inc,2006.
[67]王文良.428XL系统的软件功能和地震区域网络系统[J].物探装备,2008,18(59):289-295.
[68]王文良.从428XL的推出看地震数据采集系统的新发展[J].物探装备,2006,16(1):1-15.
[69]夏颖.408UL地震网络的传输同步与传输错处理[J].物探装备,2007,17(1):34-36.
[70]张志勇.428XL无线采集设备的应用.物探装备[J].2009,19(2):137-140.
[71] Sercel Corporation. Unite Cable-free Sismic Acquisition [DB/OL].(2011-05-15)[2013-01-06]. http//www.sercel.com.SERCEL Inc. UNITE brochure[R]. France:SERCEL INC,2008.
[72]陆增援.地震采集中高速传输与实时处理系统的研究[D].合肥:中国科学技术大学,2005.
[73]王兵.高分辨地震勘探仪器设计研究[D].合肥:中国科学技术大学,2008.
[74]曹平.勘探地震数据获取系统设计[D].合肥:中国科学技术大学,2007.
[75]张林行.基于接力式以太网的可控震源地震勘探数据传输技术研究[D].长春:吉林大学,2007.
[76]杨泓渊.复杂山地自定位无缆地震仪的研究与实现[D].长春:吉林大学,2009.
[77]张铭,窦赫蕾,常春藤.OPNET Modeler与网络仿真[M].北京:人民邮电出版社,2007.
[78]崔逊学,赵湛,王成.无线传感器网络领域应用与设计技术[M].北京:国防工业出版社,2009.
[79]孙利民,李建中,陈渝,朱红松.无线传感器网络[M].北京:清华大学出版社,2005.
[80]李晓维.无线传感器网络[M].北京:北京理工大学出版社,2007.
[81]李玉军.无线网络连通性及路由关键技术研究[D].成都:电子科技大学,2010.
[82] Gilbert E.N.Random Plane Network [J]. Journal of Soeiety for Industrial andApplied Mathematics,1961,9(4):533-543.
[83] Philips T.K., Panwar S., A.N. Tantawi. Connectivity properties of a packet radionetwork model, Information Theory [J]. IEEE Transactions on,1989,35(5):1044-1047.
[84] Quintanilla.J, Torquato. S, R.M.Ziff. Efficient measurement of the percolationthreshold for fully penetrable discs [J].Journal of Physics.2000,33:399-407.
[85] Kong.Z.E.M.Yeh, Connectivity and Latency in Large-Scale Wireless Networks withUnreliable Links[C]. Poceedings of IEEE27th Conference on ComputerCommunications, IEEE,2008:11-15.
[86] Z.Kong,E.M Yeh, Analytical Lower Bounds on the Critical Density in ContinuumPercolation in Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks and Workshops[C].2007WiOpt5th International Symposium on, IEEE,2007:1-6.
[87]Santi Paolo, Blough, Douglas M. The critical transmitting range for connectivity in sparse wireless ad hoc networks [J]. Mobile Computing, IEEE Transactions on,2003,2(1):25-39.
[88]P.Gupta, P. R. Kumar, Critical power for asymptotic connectivity, in Decision and Control [C]. Proceedings of the37th IEEE Conference on, IEEE,1998:1106-1110.
[89]O. Dousse, F. Baccelli, P. Thiran. Impact of interferences on connectivity in ad hoc networks [J].IEEE/ACM Trans. Networks,2005,13(3):425-436.
[90]Wan Peng-Jun,Yi,Chih-Wei.Coverage by randomly deployed wireless sensor networks. IEEE Transactions on Information Theory,2006,52(6):2658-2669.
[91]Freed D.Cable-free nodes:The next generation land seismic system [J].The Leading Edge,2008,27(7):878-881.
[92]Chien-Chung Shen, Zhuochuan Huang, Chaiporn, Jaikaeo. Directional Broadcast for Mobile Ad Hoc Networks with Percolation Theory [J]. IEEE Transactions on Mobile Computing,2006,5(4):317-332.
[93]张良培.高光谱遥感[M].北京：测绘出版社,2011.
[94]张兵,高连如.高光谱图像分类与目标探测[M].北京：科学出版社,2011.
[95]冯学智.遥感数字图像处理与应用[M].北京：商务印书馆,2011.
[96]孙显.高分辨率遥感图像理解[M].北京：科学出版社,2011.
[97]万建伟.实用高光谱遥感图像压缩[M].北京：国防工业出版社,2012.
[98]罗建书.高光谱遥感图像数据压缩[M].北京：国防工业出版社,2011.
[99]J.Mielikainen and P. Toivanen.Clustered DPCM for the Lossless Compression of Hyperspectral Images [J]. IEEE Transactions on Geoscience and Remote Sensing,2003,41(12):2943-2946.
[100]E Magli,G. Olmo, and E. Quacchio.Optimized Onboard Lossless and Near Lossless Compression of Hyperspectral Data using CALIC [J].IEEE Geoscience and Remote Sensing Letters,2004,1(1):21-25.
[101]P. Toivanen, Kubasova, and J. Mielikainen. Correlation based Band Ordering Heuristic for Lossless Compression of Hyperspectral Sounder Data [J]. IEEE Geoscience and Remote Sensing Letters,2005,2(1):50-54.
[102]孙蕾,谷德峰,罗建书.最佳递归双向预测的高光谱图像无损压缩.光学精密工程[J].2009,17(11)：2864-2868.
[103]吴铮,何明一,冯燕,贾应彪.基于误差补偿预测树的多光谱遥感图像无损压缩方法[J].遥感学报,2005,9(2)：143-147.
[104]R.N.Hoffman, D.W. Johnson.Application of EOFs to multispectral and imagery: data compression and noise detection for AVIRIS [J]. IEEE Trans.Geoscice. Remote Sensing,1994,32(1):25-34.
[105]D.Tretter, C.A.Bouman. Optimal transforms for multispectral and multilayer image coding [J]. IEEE Trans.Image proceessing,1995,4(3):296-305.
[106]Stephane Mallat. A theory for multiresolution signal decomposition:the wavelet representation [J]. IEEE Transactions on Pattern Recognition and Machine Intelligence.1989,11(7):674-693.
[107]J.M.Shaprio. Embedded image coding using zero tree of wavelet coefficients [J]. IEEE Trans. Signal Processing,1993,41(12):3445-3462.
[108]A.Said, W.A. Pearlman. A new, fast, and efficient image codec based on set partitioning in hierarchical trees [J]. IEEE Trans. Circuits Syst. Video Technology.1996,6(3):243-250.
[109]Swelden W. The lifting schem:a custom-design construction of biorthogonal wavelets [J]. Appl and Comput,1996,3(2):186-200.
[110]Luigi Dragotti P, Poggi Q Ragozini A R P. Compression of multispectral images by three-demensional SPIHT algorithm [J]. IEEE Transactions on Geo science and Remote Sensing,2000,38(1):416-428
[111]J. Starck, D. Donoho, and E. Candes. Astronomical Image Representation by the Curvelet Transform [J]. Astronomy and Astrophysics,2003,398(2):785-800.
[112]M. Do and M. Vetterli.The Contourlet Transform:An Efficient Directional Multi resolution Image Representation [J]. IEEE Transactions on Image Processing,2005,14(12):2091-2106.
[113]A. Dutra, W. Pearlman, and E. Silva. Successive Approximation Wavelet Coding of AVTRIS Hyperspectral Images [J]. IEEE Journal of Selected Topics in Signal Processing,2011,5(3):370-385.
[114]E. LePennec and S. Mallat, Sparse Geometric Image Representations with Bandelets, IEEE Transactions on Image Processing [J].2005,14(4):423-438.
[115]曹平,宋克柱.海洋石油勘探数据采集与记录系统主控软件设计与实现[J].中国科学技术大学学报,2006,36(3)：258-263.
[116]张可立.深水浅层高分辨率拖缆采集数传系统研究与设计[D].合肥：中国科学技术大学,2009.
[117]宋洪治.大型地震传感器网络仿真平台的实现[D].合肥：中国科技大学,2011.
[118]刘乃安,李晓辉.无线局域网(WLAN)原理、技术与应用[M].西安：西安电子科技大学出版社,2004.
[119]谢希仁.计算机网络(第4版)[M].北京：电子工业出版社,2003.
[120]杨孔雨,聂培尧.计算机网络技术原理[M].北京：经济科学出版社,2003
[121]斯托林斯.无线通信与网络(第2版)[M].北京：清华大学出版社,2005.
[122]拉帕波特.无线通信原理与应用(第二版)[M].北京：电子工业出版社,2012.
[123]孙弋.短距离无线通信及组网技术[M].西安：西安电子科技大学出版社,2008.
[124]朱刚,谈振辉,周贤伟.蓝牙技术原理与协议[M].北京：北方交通大学出版社,2002.
[125]蔡绍滨.无线传感器网络的关键技术研究与应用[D].哈尔滨：哈尔滨工业大学,2011.
[126]Savazzi, Stefano, Spagnolin Umberto. Synchronous ultra-wide band wireless sensors networks for oil and gas exploration[C]. IEEE Symposium on Computers and Communications2009, ISCC2009, Sousse, Tunisia:907-912
[127]Savazzi Stefano, Spagnolini Umberto.Wireless geophone networks for high-density land acquisition:Technologies and future potential [J]. Leading Edge,2008,27(7):882-886.
[128]Li, Lei, ZhangBaoxian, Zheng Jun. A study on one-dimensional k-coverage problem in wireless sensor networks [J]. Wireless Communications and Mobile Computing,2013,13(1):1-11.
[129]Zhu Chuan,Zheng Chunlin,Shu Lei, Han Guangjie.A survey on coverage and connectivity issues in wireless sensor networks [J]. Journal of Network and Computer Applications,2012,35(2):619-632.
[130]Li W, Zhang W.Coverage analysis and active scheme of wireless sensor networks [J]. IET Wireless Sensor Systems,2012,2(2):86-91.
[131]Ammari, Habib M.Centralized and clustered k-coverage protocols for wireless sensor networks [J]. IEEE Transactions on Computers,2012,61(1):118-133.
[132]Zhao Ruiqin,Shen Xiaohong,Jiang Zhe, Wang Haiyan.Broadcasting with least redundancy in wireless sensor networks[J]. International Journal of Distributed Sensor Networks,2012,61(1):118-133.
[133]Medagliani Paolo,Leguay Jeremie,Ferrari Gianluigi,Gay Vincent, Lopez-Ramos, Mario. Energy-efficient mobile target detection in Wireless Sensor Networks with random node deployment and partial coverage [J]. Pervasive and Mobile Computing,2012,8(3):429-447.
[134]Yu Genjian,Wen Kunpeng, Feng Huibin.Sensing connectivity coverage and robustness of WSN with random distribution of nodes [J]. International Review on Computers and Software,2012,7(5):2314-2320.
[135]Boadbent SR, Hammersley JM.Percolation processes:I.Crystals and Mazes [J]. Mathematical Proceedings of the Cambridge Philosophical Society,1957,53(3):629-641.
[136]Grimmett G. Percolation[M]. NY:Springer-Verlag,1989.
[137]孔祥言.高等渗流力学[M].合肥：中国科学技术大学出版社,1999.
[138]伍法岳,杨展如.相变与临界现象-模型理论综述[J].物理学进展,1981,1(4)：525-541.
[139]Berger, N. and Biskup, M. Quenched invariance principle for simple ralldom walk on percolation clusters [J].Probab.Theory Related Fields2007,137:83-120.
[140]Braga, G. A., Sanehis, R. and Sehieber, T.A. Critical percolation on a Bethe lattice revisited [J].SIAM Review,2005,47:349-365.
[141]Booth, J Bruck,M Franceschetti. Covering algorithms, continuum percolation and the geometry of wireless networks [J].The Annals of Applied Probability,2003,13(2):722-741.
[142]盛骤,谢式千,潘承毅.概率论与数理统计第四版[M].北京：高等教育出版社,2010.
[143]何书元.随机过程[M].北京：北京大学出版社,2008.
[144]Wang Xueqing, Zhang Shuqin.Research on efficient coverage problem of node in wireless sensor networks[C]. Proceedings of2nd International Symposium on Electronic Commerce and Security, Nanchang,2009,2:532-536.
[145]Wang Xueqing,Sun Fayi,Kong Xiangsong.Research on optimal coverage problem of wireless sensor networks[C]. Proceedings of International Conference on Communications and Mobile Computing,2009,1:548-551.
[146]Zeng Yuanyuan,Sreenan Cormac J,Xiong Naixue,Yang Laurence T, Park Jong Hyuk. Connectivity and coverage maintenance in wireless sensor networks [J]. Journal of Supercomputing,2010,52(1):23-46.
[147]Miles RE. The asymptotic values of certain coverage probabilities [J]. Biometrika,1969,56(3):661-680.
[148]Zhang Honghai and Jennifer C. Hou.Maintaining Sensing Coverage and Connectivity in Large Sensor [J]. Ad Hoc&Sensor Wireless Networks,1(1-2):89-124.
[149]Li Yingshu,Vu Chinh, Ai Chunyu,Chen Guantao, ZhaoYi.Transforming complete coverage algorithms to partial coverage algorithms for wireless sensor networks [J]. IEEE Transactions on Parallel and Distributed Systems,2011,22(4):695-703.
[150]Kini, Ananth V, Singhal, Nikhil, Weber, Steven.Broadcast capacity of a WSN under communication and information coordination [J]. Ad Hoc Networks,2010,8(4):439-455.
[151]Sheu Jang-Ping, Hsu Chih-Shun, Chang, Yen-Jung.Efficient broadcasting protocols for regular wireless sensor networks [J].Wireless Communications and Mobile Computing,2006,6(1):35-48.
[152]Zhang, Qi, Agrawal, Dharma P. Performance evaluation of leveled probabilistic broadcasting in MANETs and wireless sensor networks [J]. Simulation,2005,81(8):533-546.
[153]CaiXuelian Li, Zhu Jiandong, Lina. Performance of routing protocols in very large scale wireless sensor networks [J]. Journal of Networks,2012,7(12):1996-2002.
[154]Li Gang, SunHongmei. Discovering AODV-based multipath routes in wireless ad hoc networks, Security and Communication Networks,2012,5(4):374-383.
[155]王钢,王世刚.基于TMS320DM642DSP的MPEG-4视频编码器优化[J].吉林大学学报(信息科学版),2006,24(6)：590-593.
[156]毕厚杰,王健.新一代视频压缩编码标准-H.264/AVC [M].北京：人民邮电出版社,2009.
[157]ZHAO Fan, LIU Gui-zhong, WANG Xing. An efficient macroblock-based diverse and flexible prediction modes[J]. Signal Processing:Image Communication,2010,25(9):697-708.