Acquisition Practice of High Signal-to-Noise Ratio in YTB Block of Sichuan Basin

YTB block in Sichuan basin is a favorable area to exploit oil and gas in shallow tight rock. 3D seismic project of this zone has two characteristics. Firstly, it has high requirements for the tolerance rate of the construction process and the acquisition of high signal-to-noise ratio seismic data;Second, there are widely obstacles and noises that lead to difficult acquisition construction organization. In acquisition practice, high signal-to-noise ratio seismic data was obtained by reasonable design of construction scheme, optimization of excitation parameters, improvement of receiving conditions and optimization of obstacle crossing observation system. .

Signal-to-noise ratio application to seismic marker analysis and fracture detection

Seismic data with high signal-to-noise ratios （SNRs） are useful in reservoirexploration. To obtain high SNR seismic data, significant effort is required to achieve noiseattenuation in seismic data processing, which is costly in materials, and human and financialresources. We introduce a method for improving the SNR of seismic data. The SNR iscalculated by using the frequency domain method. Furthermore, we optimize and discussthe critical parameters and calculation procedure. We applied the proposed method on realdata and found that the SNR is high in the seismic marker and low in the fracture zone.Consequently, this can be used to extract detailed information about fracture zones that areinferred bv structural analysis but not observed in conventional seismic data.

Effective DOA Estimation Under Low Signal-to-Noise Ratio Based on Multi-Source Information Meta Fusion

Efficiently performing high-resolution direction of arrival(DOA)estimation under low signal-to-noise ratio(SNR)conditions has always been a challenge task in the literatures.Obvi-ously,in order to address this problem,the key is how to mine or reveal as much DOA related in-formation as possible from the degraded array outputs.However,it is certain that there is no per-fect solution for low SNR DOA estimation designed in the way of winner-takes-all.Therefore,this paper proposes to explore in depth the complementary DOA related information that exists in spa-tial spectrums acquired by different basic DOA estimators.Specifically,these basic spatial spec-trums are employed as the input of multi-source information fusion model.And the multi-source in-formation fusion model is composed of three heterogeneous meta learning machines,namely neural networks(NN),support vector machine(SVM),and random forests(RF).The final meta-spec-trum can be obtained by performing a final decision-making method.Experimental results illus-trate that the proposed information fusion based DOA estimation method can really make full use of the complementary information in the spatial spectrums obtained by different basic DOA estim-ators.Even under low SNR conditions,promising DOA estimation performance can be achieved.

Noises and Signal-to-Noise Ratio of Nanosize EIS and ISFET Biosensors

The results of comparative theoretical analyzes of the behavior of internal low-frequency noises, signal-to-noise ratio and sensitivity to DNA molecules for EIS and ISFET based nanosize biosensors are presented. It is shown that EIS biosensor is more sensitive to the presence of DNA molecules in aqueous solution than ISFET sensor. Internal electrical noises level decreases with the increase of concentration of DNA molecules in aqueous solution. In the frequency range 10−3 - 103 Hz noises level for EIS sensor about in three orders is higher than for ISFET sensor. In the other hand, signal-to-noise ratio for capacitive EIS biosensor is much higher than for ISFET sensor.

Signal-to-noise ratio of Raman signal measured by multichannel detectors

Raman spectroscopy has been widely used to characterize the physical properties of two-dimensional materials(2DMs).The signal-to-noise ratio(SNR or S/N ratio)of Raman signal usually serves as an important indicator to evaluate the instrumental performance rather than Raman intensity itself.Multichannel detectors with outstanding sensitivity,rapid acquisition speed and low noise level have been widely equipped in Raman instruments for the measurement of Raman signal.In this mini-review,we first introduce the recent advances of Raman spectroscopy of 2DMs.Then we take the most commonly used CCD detector and IGA array detector as examples to overview the various noise sources in Raman measurements and analyze their potential influences on SNR of Raman signal in experiments.This overview can contribute to a better understanding on the SNR of Raman signal and the performance of multichannel detector for numerous researchers and instrumental design for industry,as well as offer practical strategies for improving spectral quality in routine measurement.

Local Correlated Noise Improvement of Signal-to-Noise Ratio Gain in an Ensemble of Noisy Neuron

We theoretically investigate the collective response of an ensemble of leaky integrate-and-fire neuron units to a noisy periodic signal by including local spatially correlated noise. By using the linear response theory, we obtained the analytic expression of signal-to-noise ratio (SNR). Numerical simulation results show that the rms amplitude of internal noise can be increased up to?an optimal value where the output SNR reaches a maximum value. Due to the existence of the local spatially correlated noise in the units of the ensemble, the SNR gain of the collective ensemble response can exceed unity and can be optimized when the nearest-neighborhood correlation is negative. This nonlinear collective phenomenon of SNR gain amplification in an ensemble of leaky integrate-and-fire neuron units can be related to the array stochastic resonance (SR) phenomenon. Furthermore, we also show that the SNR gain can also be optimized by tuning the number of neuron units, frequency and?amplitude of the weak periodic signal. The present study illustrates the potential to utilize the local spatially correlation noise and the number of ensemble units for optimizing the collective response of the neuron to inputs, as well as a guidance in the design of information processing devices to weak signal detection.

散射通信系统中SNR自适应预测技术

对流层散射通信的快衰落特性导致接收信号信噪比(Signal-to-Noise Ratio,SNR)不断变化,根据时变SNR进行自适应编码调制,可以使业务速率实时跟随SNR的变化而改变,在保证可靠传输的前提下有效提升通信吞吐量。针对散射通信系统自适应编码调制的需求,在归一化最小均方(Normalization Least Mean Square,NLMS)算法和递归最小二乘(Recursive Least Square,RLS)算法的基础上,提出了改进递归最小二乘(Modified Recursive Least Square,MRLS)SNR预测算法。SNR预测算法可以解决接收端估计的SNR反馈到发送端的延迟问题。经过仿真和外场实验测试,相比NLMS算法和RLS算法,所提出的MRLS算法具有更小的SNR预测误差。采用所提出的MRLS算法进行自适应编码调制流程后,相比NLMS算法和RLS算法,在外场实验中分别可获得约0.4、2 Mb/s的平均业务速率提升,证明了所提出算法的性能优势。

Signal-to-noise ratio comparison of angular signal radiography and phase stepping method

Grating-based x-ray phase contrast imaging has the potential to be applied in future medical applications as it is compatible with both laboratory and synchrotron source. However, information retrieval methods are important because acquisition speed, scanning mode, image quality, and radiation dose depend on them. Phase-stepping(PS) is a widely used method to retrieve information, while angular signal radiography(ASR) is a newly established method. In this manuscript,signal-to-noise ratios(SNRs) of ASR are compared with that of PS. Numerical experiments are performed to validate theoretical results. SNRs comparison shows that for refraction and scattering images ASR has higher SNR than PS method,while for absorption image both methods have same SNR. Therefore, our conclusions would have guideline in future preclinical and clinical applications.

Optimized Deep Learning Model for Effective Spectrum Sensing in Dynamic SNR Scenario

The main components of Cognitive Radio networks are Primary Users(PU)and Secondary Users(SU).The most essential method used in Cognitive networks is Spectrum Sensing,which detects the spectrum band and opportunistically accesses the free white areas for different users.Exploiting the free spaces helps to increase the spectrum efficiency.But the existing spectrum sensing techniques such as energy detectors,cyclo-stationary detectors suffer from various problems such as complexity,non-responsive behaviors under low Signal to Noise Ratio(SNR)and computational overhead,which affects the performance of the sensing accuracy.Many algorithms such as Long-Short Term Memory(LSTM),Convolutional Neural Networks(CNN),and Recurrent Neural Networks(RNN)play an important role in designing intelligent spectrum sensing techniques due to the excellent learning ability of deep learning frameworks,but still require improvisation in terms of sensing accuracy under dynamic environmental conditions.This paper,we propose the novel and hybrid CNN-Cuttle-Fish Optimized Long Short Term Memory(COLSTM),an improved version of LSTM that is well suited for the dynamic changes of environmental SNR with less computational overhead and complexity.The proposed COLSTM based spectrum sensing technique exploits the various statistical features from spectrum data of PU to improve the sensing efficiency.Furthermore,the addition of shuttle-fish optimization in LSTM has reduced the computational overhead and complexity which in turn enhanced the sensing performances.The proposed methodology is validated on spectrum data acquired using RaspberryPi-RTLSDR experimental test-beds.The proposed spectrum sensing technique and the existing classical spectrum sensing techniques are compared.Experimental results show that the proposed scheme has shown the brighter enhancement of performance under different SNR environments.Further,the improvised performance has been achieved at low complexity and low computational overhead when compared with the other existing LSTM networks.

QPSK数字接收机中的SNR估计的新方法

通过对基于判决反馈的信噪比最大似然估计推导过程的分析,得出:判决反馈最大似然估计得到的估计值是有偏估计;利用基带数据的高阶矩特性,可以获得渐近无偏估计。该文提出了两种新的SNR的迭代求解方法。一种是基于NDA(Non-Data-Aided)最大似然估计的梯度迭代求解方法,这种方法与其它迭代方法相比,具有更好的收敛性能。另一种是基于统计参量的迭代方法,它不需要对输入数据进行存储;而且在相同的信噪比估计性能下,与其它迭代运算相比,运算量大大降低,尤其适合于低信噪比下信噪比估计要求高的应用中。最后,文章对比了几种SNR估计子的性能与运算量。

GNSS-R水位监测研究进展与其在我国水利行业应用展望

水位监测在水文学、水利工程、灾害防治等领域都具有非常重要的意义.传统的水位监测方法存在成本高、覆盖范围小等缺点,GNSS不仅具有导航、定位及授时的功能,还可以利用其反射信号获取反射面的特性信息并将其称为全球卫星导航系统反射测量(Global Navigation Satellite System-Reflectometry,GNSS-R).近年来,GNSS-R技术以低成本、全天候、高时空分辨率等优势为水位监测提供了一种新方法.本文对GNSS-R水位监测的研究现状、不同方法的影响因素以及当前应用过程中存在的问题进行了总结归纳提出了下一步的发展趋势,最后对其在我国水利行业的应用进行了展望.

激光选通成像系统MTF与SNR的仿真与实验研究

距离选通技术能有效提高主动成像系统的信噪比,但远距离选通成像系统的调制传递函数(MTF)和信噪比(SNR)与被动成像系统存在较大差异。分析了影响主动成像系统MTF与SNR的主要因素,推导了主动成像系统SNR与照明光强闪烁率的关系。分别使用氙灯和半导体泵浦的固体激光器进行了远距离主动成像实验,测得成像系统空间分辨率与湍流强度和光强闪烁率之间的关系与理论计算吻合。

一种用于LAS-CDMA/AMC系统下行链路的SNR测量方法

基于信道估计值提出了一种适用于单小区情况下LAS-CDMA/AMC系统的SNR测量方法,并通过COSSAP仿真比较了该方法在各种信道环境下与理想SNR测量的系统吞吐量性能.仿真结果表明基于信道估计值的SNR测量方法可以取得近似理想的性能,是一种简单的测量方案.

星载TDI CCD相机MTF和SNR综合响应模拟方法

为了对星载时间延迟积分电荷耦合器件(time-delayed-integration charge coupled device,TDI CCD)相机动态成像过程中各环节的调制传递函数(modulation transfer function,MTF)和信噪比(signal noise ratio,SNR)的退化机制进行研究,以各个环节MTF和SNR的数学模型为节点,以信号传输和转换过程为主线,探讨了各个环节的信号描述及MTF和SNR退化模拟方法;提出以快速傅立叶变换和特定分布随机数生成算法为基础的MTF和SNR综合响应数学模拟流程。上述方法可以对单一环节或几个连续环节的MTF和SNR综合响应进行模拟。利用该方法对TDI CCD相机的沿轨MTF、穿轨MTF和SNR进行了模拟试验。

AWGN信道中非恒包络信号SNR估计算法

本文提出一种利用信号自相关矩阵的奇异值分解（SVD）对加性高斯白噪声（AWGN）信道下非恒包络信号信噪比的盲估计算法,并作了详细理论分析。通过对该算法进行计算仿真,结果表明:该算法性能稳定,对MPSK、MQAM信号在实际信噪比为 l～20dB时,估计误差小于 0.5dB。

基于自适应估计SNR的分组Turbo码译码算法

针对分组Turbo码自适应Chase译码算法中SNR是预先设定值的情况,提出一种新的自适应估计信噪比(SNR)的译码算法。该方案利用了接收码字的统计信息与SNR之间存在的对应关系来调整门限函数,达到控制译码复杂度的目的。仿真结果表明,所提出的译码算法能自适应反映信道情况,降低译码运算复杂度,提高译码处理速度,获得了较好的性能。

基于SNR比较的2次协作的感知无线电频谱检测

在感知无线电系统中多径、阴影和隐藏终端等问题不可避免,协作频谱检测是解决这些问题的有效手段。针对目前协作频谱检测算法的不足,提出一种基于SNR判决的2次协作频谱检测算法,分析了非理想报告信道对性能的影响。数值分析和仿真结果表明,该方法能有效地提高检测概率,减少判决融合决策节点数量,以及减少传给最终融合中心的数据量。

SNR准则下的认知MIMO雷达波形设计

研究了MIMO雷达信号模型下基于最大化信噪比(SNR)准则的最优和稳健波形设计问题。根据对目标冲击响应功率谱密度(PSD)确定和不确定两种条件的分析,推导了最优波形和稳健波形的闭式表达式。仿真结果表明,当目标冲激响应PSD的先验知识准确时,使用最优波形作为发射波形,在接收端获得在给定条件下最优的SNR;当目标冲击响应PSD的先验知识不准确时,使用稳健波形作为发射波形,能够降低目标冲激响应PSD的不确定性对输出端信噪比(SNR)的负面影响。

迭代均衡系统中的一种SNR估计方法

该文首先分析了最大后敛概率(Maximum A posterior Probability,MAP)迭代均衡及其简化算法Max-Log- MAP(MLM)、缩放软信息的Max-Log-MAP算法(Scaled Max-Log-MAP,SMLM)对SNR估计误差的敏感性问题,然后利用SMLM算法对SNR估计误差不敏感的特性,提出了一种基于SMLM的软输出信息估计信道SNR的方法。最后,利用该算法,还提出了一种新的迭代均衡算法,并研究了其性能,结果表明该算法不仅不需要任何导引符号开销,还能获得非常接近具有理想的SNR的MAP算法的误码性能。

基于SNR比较的协作频谱感知研究

频谱感知是认知无线电中通过发现可用频率资源提高频谱利用率的关键技术。协作频谱感知能一定程度上消除多径和阴影的影响,为了进一步提高频谱检测性能,提出了一种基于SNR比较的协作频谱感知算法。在此算法中,各个认知用户将各自的本地判决结果和自身的SNR值发送到数据融合中心,通过SNR比较,对各CR用户进行筛选,选出符合算法要求的CR用户参加协作检测,进行数据融合。计算机仿真表明,该算法能有效提高检测性能,并减少判决融合的节点数量。