A workflow is presented for the automatic detection and accurate time picking of weak events, such as microseismicity induced by hydraulic fracturing, in data collected by an array of multi-component acoustic sensors. These events are embedded in strong noise that could be spatially and temporally correlated. The proposed workflow comprises two steps. In the first, a new statistical test is applied to detect the time windows that contain coherent arrivals across components and sensors in the multicomponent array and to indicate the confidence in this detection. The second is a time-picking step to accurately estimate the onset time of the arrivals detected above and measure the time delay across the array using a novel hybrid beamforming method incorporating the use of higher order statistics. Such an approach is designed to significantly improve the number of events that are detected as well as the level of noise that can be handled. In the context of hydraulic fracturing, this could enhanced the coverage and mapping of the fractures. Examples on real field data are presented indicating the effectiveness of this workflow.

Context plays an important role when performing classification, and in this paper we examine context from two perspectives. First, the classification of items within a single task is placed within the context of distinct concurrent or previous classification tasks (multiple distinct data collections). This is referred to as multi-task learning (MTL), and is implemented here in a statistical manner, using a simplified form of the Dirichlet process. In addition, when performing many classification tasks one has simultaneous access to all unlabeled data that must be classified, and therefore there is an opportunity to place the classification of any one feature vector within the context of all unlabeled feature vectors; this is referred to as semi-supervised learning. In this paper we integrate MTL and semi-supervised learning into a single framework, thereby exploiting two forms of contextual information. Example results are presented on a "toy" example, to demonstrate the concept, and the algorithm is also applied to three real data sets.

Semi supervised learning and active learning are considered for unexploded ordnance (UXO) detection. Semi supervised learning algorithms are designed using both labeled and unlabeled data, where here labeled data correspond to sensor signatures for which the identity of the buried item (UXO/non-UXO) is known; for unlabeled data, one only has access to the corresponding sensor data. Active learning is used to define which unlabeled signatures would be most informative to improve the classifier design if the associated label could be acquired (where for UXO sensing, the label is acquired by excavation). A graph-based semi supervised algorithm is applied, which employs the idea of a random Markov walk on a graph, thereby exploiting knowledge of the data manifold (where the manifold is defined by both the labeled and unlabeled data). The algorithm is used to infer labels for the unlabeled data, providing a probability that a given unlabeled signature corresponds to a buried UXO. An efficient active-learning procedure is developed for this algorithm, based on a mutual information measure. In this manner, one initially performs excavation with the purpose of acquiring labels to improve the classifier, and once this active-learning phase is completed, the resulting semi supervised classifier is then applied to the remaining unlabeled signatures to quantify the probability that each such item is a UXO. Example classification results are presented for an actual UXO site, based on electromagnetic induction and magnetometer data. Performance is assessed in comparison to other semi supervised approaches, as well as to supervised algorithms.

We present an algorithm for learning parametric classifiers on a partially labeled data manifold, based on a graph representation of the manifold. The unlabeled data are utilized by basing classifier learning on neighborhoods, formed via Markov random walks. The proposed algorithm yields superior performance on three benchmark data sets and the margin of improvements over existing semi-supervised algorithms is significant.

A semi-supervised multitask learning (MTL) framework is presented, in which M parameterized semi-supervised classifiers, each associated with one of M par- tially labeled data manifolds, are learned jointly under the constraint of a soft- sharing prior imposed over the parameters of the classifiers. The unlabeled data are utilized by basing classifier learning on neighborhoods, induced by a Markov random walk over a graph representation of each manifold. Experimental results on real data sets demonstrate that semi-supervised MTL yields significant im- provements in generalization performance over either semi-supervised single-task learning (STL) or supervised MTL.