Geology: North America's broken heart
On a bright October Saturday, the trees have reached their full autumn blaze at Interstate State Park on the border between Minnesota and Wisconsin in the US heartland. Crowds of people gawking at leaves thread their way along paths that wind through bulwarks of dark basalt, leading to views of the St Croix River. Along one of the walkways, a photographer directs a young couple in coordinating grey shirts to lean against the rocks as she snaps a romantic portrait. If the two sweethearts are looking to commemorate their ever lasting love, they should have picked a different backdrop. The fractured basalt that frames their faces is part of a great gash that opened up in the middle of North America and nearly split the continent 1.1 billion years ago — hardly a symbol of a happy union. The volcanic rocks are remnants of what is called the Midcontinent Rift, and it is an enormous geological puzzle. Rifts are wounds in Earth’s outer layer that can grow to eventually form new oceans. That is how the A billion years ago, a huge rift nearly cleaved North America down the middle. And then it failed. Researchers may be getting close to finding out why.
The North American Midcontinent Rift beneath Lake Superior from Glimpce seismic reflection profiling
The Midcontinent rift system is a 1.1-b.y.-old structure extending from Kansas, through the Lake Superior region, and into southern Michigan. The rift is filled with thick sequences of basaltic volcanic rocks and clastic sediments. For most of its extent it is buried beneath Paleozoic rocks but can be traced by its strong gravity and magnetic anomalies. The rocks of the rift system are exposed only in the Lake Superior region and comprise the Keweenawan Supergroup. Much of the geology of the Keweenawan is beneath Lake Superior and has only been inferred from potential field studies and seismic refraction studies and extrapolation from on-shore geology. Seismic reflection surveys by the Great Lakes International Multidisciplinary Program on Crustal Evolution in 1986 imaged much of the deep structure of the rift beneath the lake in detail. The reflection profiles across the rift reveal a deep, asymmetrical central graben whose existence and magnitude was not previously documented. They show that, in addition to crustal sagging documented by previous investigations, normal faulting played a major role in subsidence of the axial region of the rift. A sequence of volcanic and sedimentary rocks, in places greater than 30 km thick, fills the graben. Thinner volcanic and sedimentary units lie on broad flanks of the rift outside of the graben. Near the axis, the prerift crust is thinned to about one fourth of its original thickness, apparently by a combination of low-angle extensional faulting and ductile stretching or distributed shear. The sense of asymmetry of the central graben changes along the trend of the rift, documenting the segmented nature of the structure and suggesting the existence of accommodation zones between the segments. The location of the accommodation zones is inferred from abrupt disruptions in the Bouguer gravity signature of the rift. Uplift of the central graben occurred when the original graben-bounding normal faults were reactivated as high-angle reverse faults with throws of 5 km or more in places. The Midcontinent rift has some striking similarities to some younger passive continental margins. We propose that it preserves a record of nearly complete continental separation which, had it not been arrested, would have created a Middle Proterozoic ocean basin.
Multivariate Image Texture by Multivariate Variogram for Multispectral Image Classification
Traditional image texture measure usually allows a texture description of a single band of the spectrum, characterizing the spatial variability of gray-level values within the singleband image. A problem with the approach while applied to multispectral images is that it only uses the texture information from selected bands. In this paper, we propose a new multivariate texture measure based on the multivariate variogram. The multivariate texture is computed from all bands of a multispectral image, which characterizes the multivariate spatial autocorrelation among those bands. In order to evaluate the performance of the proposed texture measure, the derived multivariate texture image is combined with the spectral data in image classification. The result is compared to classifications using spectral data alone and plus traditional texture images. A machine learning classifier based on Support Vector Machines (SVMs) is used for image classification. The experimental results demonstrate that the inclusion of multivariate texture information in multispectral image classification significantly improves the overall accuracy, with 5 to 13.5 percent of improvement, compared to the classification with spectral information alone. The results also show that when incorporated in image classification as an additional band, the multivariate texture results in high overall accuracy, which is comparable with or higher than the best results from the existing single-band and two-band texture measures, such as the variogram, cross variogram and Gray-Level Co-occurrence Matrix (GLCM) based texture. Overall, the multivariate texture provides the useful spatial information for land-cover classification, which is different from the traditional single band texture. Moreover, it avoids the band selection procedure which is prerequisite to traditional texture computation and would help to achieve high accuracy in the most classification tasks.