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    • 11. 发明申请
    • PROCESSING TIME SERIES DATA EMBEDDED IN HIGH NOISE
    • 处理时间序列数据嵌入高噪声
    • US20100228530A1
    • 2010-09-09
    • US12652405
    • 2010-01-05
    • Henri-Pierre ValeroSandip BoseQiuhua LiuRamachandra ShenoyAbderrhamane Ounadjela
    • Henri-Pierre ValeroSandip BoseQiuhua LiuRamachandra ShenoyAbderrhamane Ounadjela
    • G06F17/10
    • E21B43/26G01V1/288G01V2210/123
    • Automatic detection and accurate time picking of weak events embedded in strong noise such as microseismicity induced by hydraulic fracturing is accomplished by: a noise reduction step to separate out the noise and estimate its spectrum; an events detection and confidence indicator step, in which a new statistical test is applied to detect which time windows contain coherent arrivals across components and sensors in the multicomponent array and to indicate the confidence in this detection; and a time-picking step to accurately estimate the time of onset of the arrivals detected above and measure the time delay across the array using a hybrid beamforming method incorporating the use of higher order statistics. In the context of hydraulic fracturing, this could enhance the coverage and mapping of the fractures while also enabling monitoring from the treatment well itself where there is usually much higher and spatially correlated noise.
    • 自动检测和精确时间采集嵌入强噪声(如水力压裂引起的微震)的弱事件是通过以下方式实现的:通过降噪步骤分离噪声并估计其频谱; 事件检测和置信指标步骤,其中应用新的统计测试以检测哪个时间窗口包含多组件阵列中的组件和传感器之间的相干到达,并指示该检测的置信度; 以及精确估计上述检测到达时间的时间选择步骤,并且使用结合使用更高阶统计量的混合波束成形方法来测量阵列上的时间延迟。 在水力压裂的背景下,这可以增强裂缝的覆盖和映射,同时还能够从处理井本身进行监测,其中通常有更高的空间相关噪声。
    • 16. 发明申请
    • FILTERING ACOUSTIC WAVEFORMS IN DOWNHOLE ENVIRONMENTS
    • 在井下环境中滤波声波
    • US20120147702A1
    • 2012-06-14
    • US13310780
    • 2011-12-04
    • Henri-Pierre ValeroShinichi SunagaTakeshi Endo
    • Henri-Pierre ValeroShinichi SunagaTakeshi Endo
    • G01V1/40
    • G01V1/48G01V1/44G01V2210/23
    • Methods and apparatus to filter acoustic waveforms in downhole environments are described. An example method involves receiving acoustic waveform data representing acoustic signals traversing at least a portion of a borehole adjacent a subterranean formation and performing a direct transform operation on the acoustic waveform data to generate wavelet map data. The wavelet map data comprises a time-frequency representation of the acoustic waveform data. The example method also involves identifying a waveform of interest via the wavelet map data, extracting data associated with the waveform of interest from the wavelet map data, generating filtered wavelet map data based on the extracted data, and performing an inverse transform operation on the filtered wavelet map data to generate filtered acoustic waveform data
    • 描述了在井下环境中滤波声波的方法和装置。 示例性方法包括接收表示穿过邻近地层的钻孔的至少一部分的声信号的声波形数据,并且对声波形数据执行直接变换操作以产生小波图数据。 小波图数据包括声波形数据的时间 - 频率表示。 该示例方法还涉及通过小波变换图数据识别感兴趣的波形,从小波变换数据中提取与感兴趣波形相关联的数据,基于所提取的数据产生滤波后的小波变换数据,并对经滤波的 小波图数据生成滤波后的声波形数据
    • 19. 发明授权
    • Dispersion extraction for acoustic data using time frequency analysis
    • 使用时频分析对声学数据进行色散提取
    • US07649805B2
    • 2010-01-19
    • US11854405
    • 2007-09-12
    • Sandip BoseHenri-Pierre ValeroShuchin Aeron
    • Sandip BoseHenri-Pierre ValeroShuchin Aeron
    • G01V1/00
    • G01V1/48
    • This invention pertains to the extraction of the slowness dispersion characteristics of acoustic waves received by an array of two or more sensors by the application of a continuous wavelet transform on the received array waveforms (data). This produces a time-frequency map of the data for each sensor that facilitates the separation of the propagating components thereon. Two different methods are described to achieve the dispersion extraction by exploiting the time frequency localization of the propagating mode and the continuity of the dispersion curve as a function of frequency. The first method uses some features on the modulus map such as the peak to determine the time locus of the energy of each mode as a function of frequency. The second method uses a new modified Radon transform applied to the coefficients of the time frequency representation of the waveform traces received by the aforementioned sensors. Both methods are appropriate for automated extraction of the dispersion estimates from the data without the need for expert user input or supervision.
    • 本发明涉及通过对接收到的阵列波形(数据)应用连续小波变换来提取由两个或更多个传感器的阵列接收的声波的慢度色散特性。 这产生用于每个传感器的数据的时间 - 频率图,其有助于在其上分离传播部件。 描述了两种不同的方法来通过利用传播模式的时间频率定位和作为频率的函数的色散曲线的连续性来实现色散提取。 第一种方法使用诸如峰值的模态图上的一些特征来确定每种模式的能量的时间轨迹作为频率的函数。 第二种方法使用新的修改的Radon变换,其应用于由上述传感器接收的波形迹线的时间频率表示的系数。 这两种方法适用于从数据中自动提取色散估计值,而无需专家用户输入或监督。