Stacking angle‐domain common‐image gathers for normalization of illumination

Unequal illumination of the subsurface highly impacts the quality of seismic imaging. Different image points receive different folds of reflection‐angle illumination, which can be caused by irregular acquisition or by wave propagation in complex media. Illumination problems can deteriorate amplitudes in migrated images. To address this problem, we present a method of stacking angle‐domain common‐image gathers, in which we use local similarity with soft thresholding to determine the folds of local illumination. Normalization by local similarity regularizes local illumination of reflection angles for each image point of the subsurface model. This approach compensates for irregular illumination by selective stacking in the image space, regardless of the cause of acquisition or propagation irregularities. Additional migration is not required because the methodology is implemented in the reflection angle domain after migration. We use two synthetic examples to demonstrate that our method can normalize migration amplitudes and effectively suppress migration artefacts.     

Evaluating the performance of a new classifier – the GP-OAD: A comparison with existing methods for classifying rock type and mineralogy from hyperspectral imagery

In this study, we compare three commonly used methods for hyperspectral image classification, namely Support Vector Machines (SVMs), Gaussian Processes (GPs) and the Spectral Angle Mapper (SAM). We assess their performance in combination with different kernels (i.e. which use distance-based and angle-based metrics). The assessment is done in two experiments, under ideal conditions in the laboratory and, separately, in the field (an operational open pit mine) using natural light. For both experiments independent training and test sets are used. Results show that GPs generally outperform the SVMs, irrespective of the kernel used. Furthermore, angle-based methods, including the Spectral Angle Mapper, outperform GPs and SVMs when using distance-based (i.e. stationary) kernels in the field experiment. A new GP method using an angle-based (i.e. a non-stationary) kernel – the Observation Angle Dependent (OAD) covariance function – outperforms SAM and SVMs in both experiments using only a small number of training spectra. These findings show that distance-based kernels are more affected by changes in illumination between the training and test set than are angular-based methods/kernels. Taken together, this study shows that independent training data can be used for classification of hyperspectral data in the field such as in open pit mines, by using Bayesian machine-learning methods and non-stationary kernels such as GPs and the OAD kernel. This provides a necessary component for automated classifications, such as autonomous mining where many images have to be classified without user interaction.     

利用偏移进行视反射率估计的初步研究

视反射率估计是地震数据处理解释中的一项重要内容,通常采用反演的方法得到.本文以地震偏移和地震线性反演理论相结合为基础,并利用保幅单程波传播算子和保幅波动方程叠前偏移算法以及成像空间中的角度域波动方程偏移成像和照明补偿等方法技术,提出了一种利用单程波波动方程偏移进行地下反射面视反射率估计方法,并进行了理论模型的数值试验.这种估计方法得到的视反射率估计是一种近法向入射的小角度反射率.