Readout Techniques for Drift and Low Frequency Noise Rejection in Infrared Arrays

Three different methods are presented to subtract thermal drifts and low-frequency noise from the signal of infrared array. The first is dead pixels with open Indium bumps, the second is reference output as implemented on the Hawaii2 multiplexer, and the third is dark pixels to emulate reference cells having a capacity connected to the gate of the unit cell field-effect transistor (FET). The third method is the most effective and yields a reduction in readout noise from15.4–9.4 erms. A novel method will be described to extend this readout technique to the Aladdin 1 K × 1 K InSb array. This revised version was published online in July 2006 with corrections to the Cover Date.     

Use of Noise to Augment Training Data: A Neural Network Method of Mineral–Potential Mapping in Regions of Limited Known Deposit Examples

One of the main factors that affects the performance of MLP neural networks trained using the backpropagation algorithm in mineral-potential mapping isthe paucity of deposit relative to barren training patterns. To overcome this problem, random noise is added to the original training patterns in order to create additional synthetic deposit training data. Experiments on the effect of the number of deposits available for training in the Kalgoorlie Terrane orogenic gold province show that both the classification performance of a trained network and the quality of the resultant prospectivity map increasesignificantly with increased numbers of deposit patterns. Experiments are conducted to determine the optimum amount of noise using both uniform and normally distributed random noise. Through the addition of noise to the original deposit training data, the number of deposit training patterns is increased from approximately 50 to 1000. The percentage of correct classifications significantly improves for the independent test set as well as for deposit patterns in the test set. For example, using ±40% uniform random noise, the test-set classification performance increases from 67.9% and 68.0% to 72.8% and 77.1% (for test-set overall and test-set deposit patterns, respectively). Indices for the quality of the resultant prospectivity map, (i.e. D/A, D × (D/A), where D is the percentage of deposits and A is the percentage of the total area for the highest prospectivity map-class, and area under an ROC curve) also increase from 8.2, 105, 0.79 to 17.9, 226, 0.87, respectively. Increasing the size of the training-stop data set results in a further increase in classification performance to 73.5%, 77.4%, 14.7, 296, 0.87 for test-set overall and test-set deposit patterns, D/A, D × (D/A), and area under the ROC curve, respectively.     

Noise reduction and detection of weak, coherent signals through phase-weighted stacks

We present a new tool for efficient incoherent noise reduction for array data employing complex trace analysis. An amplitude-unbiased coherency measure is designed based on the instantaneous phase, which is used to weight the samples of an ordinary, linear stack. The result is called the phase-weighted stack (PWS) and is cleaned from incoherent noise. PWS thus permits detection of weak but coherent arrivals. The method presented can easily be extended to phase-weighted cross-correlations or be applied in the τ p domain. We illustrate and discuss the advantages and disadvantages of PWS in comparison with other coherency measures and present examples. We further show that our non-linear stacking technique enables us to detect a weak lower-mantle P -to- S conversion from a depth of approximately 840 km on array data. Hints of an 840 km discontinuity have been reported; however, such a discontinuity is not yet established due to the lack of further evidence.     

基于A- CGAN的深反射地震数据随机噪声压制方法研究

基于深度学习的地震数据噪声压制方法是当前地震数据去噪处理的重要方向。深度学习方法突破了传统滤波处理的局限,在对常规地震数据的噪声压制中表现出效率高、信噪分离效果好的特点。但针对深部弱有效反射数据,当前的深度学习方法特征提取能力有限,难以取得较好的去噪效果。笔者等结合深反射地震数据特点,针对当前深度学习噪声压制方法在特征提取及对数据集依赖上的局限,提出了基于注意力循环生成对抗网络(Attention Cycle- Consistent Generative Adversarial Networks,A- CGAN)的深反射地震数据随机噪声压制方法。借助循环一致生成对抗网络(Cycle- Consistent Generative Adversarial Networks,Cycle- GAN)的域映射思想,降低对数据集的要求。为了构建适用于深反射地震数据的去噪网络,从3个方面对Cycle- GAN进行改进：在Cycle- GAN的生成器（去噪器）中加入残差结构和注意力机制,用于加深网络深度和提高其特征提取能力;在Cycle- GAN的鉴别器中使用块判决,提高鉴别精度和准确度;在损失函数部分加入感知一致性损失函数,提升网络模型恢复纹理细节信息的能力。通过合成地震数据和实际深反射地震数据测试,验证了优化算法的有效性,体现了良好的应用价值。     

GM4型磁通门磁力仪性能检验方法

目前,磁通门磁力仪广泛应用于观测地球磁场相对变化的仪器。相对于应用,仪器的性能测试不为人们所熟悉。本文针对GM4型磁通门磁力仪的噪声特性,频率特性,线性度,温度特性等关键性能指标的测试依据、测试方法及流程给予详细的阐述。     

利用背景噪声反演鄂尔多斯块体及其南缘地区地壳速度结构

文中利用分布在鄂尔多斯块体及其南部周缘地区的53 个宽频带地震固定台站的连续波形记录,采用双台互相关计算 方法由背景噪声提取瑞利波格林函数,经时频分析获得相速度和群速度频散曲线,并分别计算了汾渭地堑、秦岭北缘、鄂 尔多斯块体内部和六盘山地区4 个不同构造区的平均频散曲线,进而反演了各构造区的地壳上地幔一维横波速度结构。结 果显示：地壳厚度在汾渭地堑为34 km,在秦岭北缘地区和鄂尔多斯块体均为40 km,在六盘山地区最厚,达49~50 km;相 应的上地幔顶部横波速度分别为4.20,4.2,4.30 和4.15 km/s;地壳内结构浅部特征差异最大,在地壳中部六盘山地区的速 度较低,下部地壳不同地区的波速较一致。     

MT法中利用阻抗相位资料对畸变视电阻率曲线的校正

从理论上论证了在一定条件下大地电磁响应中的相位资料将不受随机电磁噪声的影响，介绍了由相位资料递推计算视电阻率的方法，通过与远参考资料的比较，证明利用阻抗相位资料可分段恢复校正畸变的实测视电阻率数据。     

风廓线雷达估算大气返回信号功率方法研究

基于风廓线雷达大气返回信号功率谱中噪声电平的估算方法,统计分析了北京延庆对流层风廓线雷达（CFL-08）2006年10～12月的探测数据。对该频段风廓线雷达环境噪声的空间和时间变化进行了分析,观测期间环境噪声在5km以下随高度递减,10月的平均环境噪声大于11月、12月的平均分布。给出目前风廓线雷达用信噪比估算大气返回信号功率的两种方法,并对两种方法进行了环境噪声的剔除,经过修正后的大气返回信号功率输出结果趋于一致。