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.     

Implications of low-pass filtering on power spectra and autocorrelation functions of turbulent velocity signals

Filtering either through the electronics of an instrument or through digital procedure is performed routinely on geophysical data. When velocity fluctuations are measured in turbulent flows using electromagnetic current meters (ECMs), a builtin lowpass Butterworth filter of order n usually attenuates fluctuations at high frequencies. However, the effects of this filter may not be acknowledged in turbulence studies, thus impeding comparisons between data collected with different ECMs. This paper explores the implications of the filters on the characteristics of velocity signals, mainly on variance, power spectra, and correlation analyses. Variance losses resulting from filtering can be important but will vary with the order n of the Butterworth filter, decreasing as n increases. Knowing the filter response, it is possible to reconstruct the original signal spectrum to evaluate the effect of filtering on variance and to allow comparisons between data collected with different instruments. The autocorrelation function also is affected by filtering which increases the value of the coefficients in the first lags, resulting in an overestimation of the integral length scale of coherent structures. These important effects add to those related to size and shape differences in ECM sensors and must be taken into account in comparative studies.     

Radiomagnetotelluric mapping, groundwater numerical modelling and 18-Oxygen isotopic data as combined tools to determine the hydrogeological system of a landslide prone area

Three methods were combined to determine the groundwater recharge and transfer processes of a landslide prone area. First, the radiomagnetotelluric method was used to investigate the distribution of electrical resistivity (ρ) of the subsurface and build a three-dimensional model of permeability (k), through an experimental relation between ρ and k. Second, this structural model of permeability and additional climatologic data were used to fix boundary and recharge conditions to perform a three-dimensional and transient numerical simulation of the groundwater flow. Finally 18-Oxygen time series observed at the main springs were used to validate the model. This association of methods led to an improved characterization of the groundwater flow system at local scale and a better understanding of the role of this system on the landslide phenomenon. This structured approach is thought to be useful to design specific remediation strategies to drain the unstable mass.     

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.     

The electrical structure of the Slave craton

The Slave craton in northwestern Canada, a relatively small Archean craton (600×400 km), is ideal as a natural laboratory for investigating the formation and evolution of Mesoarchean and Neoarchean sub-continental lithospheric mantle (SCLM). Excellent outcrop and the discovery of economic diamondiferous kimberlite pipes in the centre of the craton during the early 1990s have led to an unparalleled amount of geoscientific information becoming available.

Over the last 5 years deep-probing electromagnetic surveys were conducted on the Slave, using the natural-source magnetotelluric (MT) technique, as part of a variety of programs to study the craton and determine its regional-scale electrical structure. Two of the four types of surveys involved novel MT data acquisition; one through frozen lakes along ice roads during winter, and the second using ocean-bottom MT instrumentation deployed from float planes.

The primary initial objective of the MT surveys was to determine the geometry of the topography of the lithosphere–asthenosphere boundary (LAB) across the Slave craton. However, the MT responses revealed, completely serendipitously, a remarkable anomaly in electrical conductivity in the SCLM of the central Slave craton. This Central Slave Mantle Conductor (CSMC) anomaly is modelled as a localized region of low resistivity (10–15 Ω m) beginning at depths of 80–120 km and striking NE–SW. Where precisely located, it is spatially coincident with the Eocene-aged kimberlite field in the central part of the craton (the so-called “Corridor of Hope”), and also with a geochemically defined ultra-depleted harzburgitic layer interpreted as oceanic or arc-related lithosphere emplaced during early tectonism. The CSMC lies wholly within the NE–SW striking central zone defined by Grütter et al. [Grütter, H.S., Apter, D.B., Kong, J., 1999. Crust–mantle coupling; evidence from mantle-derived xenocrystic garnets. Contributed paper at: The 7th International Kimberlite Conference Proceeding, J.B. Dawson Volume, 1, 307–313] on the basis of garnet geochemistry (G10 vs. G9) populations.

Deep-probing MT data from the lake bottom instruments infer that the conductor has a total depth-integrated conductivity (conductance) of the order of 2000 Siemens, which, given an internal resistivity of 10–15 Ω m, implies a thickness of 20–30 km. Below the CSMC the electrical resistivity of the lithosphere increases by a factor of 3–5 to values of around 50 Ω m. This change occurs at depths consistent with the graphite–diamond transition, which is taken as consistent with a carbon interpretation for the CSMC.

Preliminary three-dimensional MT modelling supports the NE–SW striking geometry for the conductor, and also suggests a NW dip. This geometry is taken as implying that the tectonic processes that emplaced this geophysical–geochemical body are likely related to the subduction of a craton of unknown provenance from the SE (present-day coordinates) during 2630–2620 Ma. It suggests that the lithospheric stacking model of Helmstaedt and Schulze [Helmstaedt, H.H., Schulze, D.J., 1989. Southern African kimberlites and their mantle sample: implications for Archean tectonics and lithosphere evolution. In Ross, J. (Ed.), Kimberlites and Related Rocks, Vol. 1: Their Composition, Occurrence, Origin, and Emplacement. Geological Society of Australia Special Publication, vol. 14, 358–368] is likely correct for the formation of the Slave's current SCLM.     

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.     

岩矿石吸收系数的算和测定方法

岩矿石吸收系数是钻孔电磁波法解决地质问题的重要的物性参数。岩矿石有无吸收系数的差异，是开展钻孔电磁波法能否奏效的前提．了解岩矿石的吸收系数可对钻孔电磁波法的透距和分辨力作出估计，并可为钻孔电磁波的反演解释提供重要的依据．本文不但给出了岩矿石吸收系数的计算方法，也给出了室内岩矿石标本吸收系数的测定方法和野外实测岩矿石吸收系数的方法。     

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。