[1] Velocity spectra from moderate-sized earthquakes were used to investigate the P wave attenuation structure in central Mexico. In particular, we included regional events with magnitudes in the range of 4.5 to 6.1 recorded from 2005 to 2007 on the Middle American Subduction Experiment (MASE) array, which consists of 100 broadband sensors across central Mexico from Acapulco to Tempoal, near the Gulf of Mexico. By assuming a Brune-type source, a frequency-independent t* value was obtained for each seismogram in the frequency band 1 to 30 Hz. These measurements were then inverted for twodimensional spatial variations in Q p in the cross section along the MASE array, perpendicular to the trench. The model has uniform 20 km vertical grid spacing down to a depth of 200 km and 50 km or 100 km horizontal grid spacing depending on ray coverage. The inversion results show low attenuation in the subducting slab and high attenuation in the mantle wedge and the crust below and to the north of the volcanic belt. The focused high-attenuation zone (Q p < 200) in the mantle wedge lies away from the top of the slab, between depths of 80 km and 120 km beneath the volcanic belt, and is likely to be related to relatively high temperature, fluids, and partial melts produced in subduction process. The high-attenuation region in the lower crust correlates with the low-resistivity and low-velocity region and could be caused by partial melts and fluids from dehydration and magmatic processes.

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Abstract:Feature-based matching methods have been widely used in remote sensing image matching given their capability to achieve excellent performance despite image geometric and radiometric distortions. However, most of the feature-based methods are unreliable for complex background variations, because the gradient or other image grayscale information used to construct the feature descriptor is sensitive to image background variations. Recently, deep learning-based methods have been proven suitable for high-level feature representation and comparison in image matching. Inspired by the progresses made in deep learning, a new technical framework for remote sensing image matching based on the Siamese convolutional neural network is presented in this paper. First, a Siamese-type network architecture is designed to simultaneously learn the features and the corresponding similarity metric from labeled training examples of matching and non-matching true-color patch pairs. In the proposed network, two streams of convolutional and pooling layers sharing identical weights are arranged without the manually designed features. The number of convolutional layers is determined based on the factors that affect image matching. The sigmoid function is employed to compute the matching and non-matching probabilities in the output layer. Second, a gridding sub-pixel Harris algorithm is used to obtain the accurate localization of candidate matches. Third, a Gaussian pyramid coupling quadtree is adopted to gradually narrow down the searching space of the candidate matches, and multiscale patches are compared synchronously. Subsequently, a similarity measure based on the output of the sigmoid is adopted to find the initial matches. Finally, the random sample consensus algorithm and the whole-to-local quadratic polynomial constraints are used to remove false matches. In the experiments, different types of satellite datasets, such as ZY3, GF1, IKONOS, and Google Earth images, with complex background variations are used to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed method, which can significantly improve the matching performance of multi-temporal remote sensing images with complex background variations, is better than the state-of-the-art matching methods. In our experiments, the proposed method obtained a large number of evenly distributed matches (at least 10 times more than other methods) and achieved a high accuracy (less than 1 pixel in terms of root mean square error).

The Sierpiński triangle (ST) is a well-known fractal structure. Synthesis of stable molecular STs with robust covalent linkages is attractive but challenging. Here, we demonstrate the formation of a series of high-quality covalent STs via the on-surface dehydration reaction of 1,3-benzenediboronic acid with the presence of water as an equilibrium regulator at ambient atmosphere. Extended molecular fractals up to third generation are obtained, as disclosed by scanning tunnel microscope. The covalent STs show intriguing bright and dark contrasts irrespective of the fractal generations, which is related with epitaxial relationship of fractal structure to the underlying graphite lattice, as supported by theoretical simulations.