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Under existing detection pipelines, seismic event hypotheses are formed from a parametric description of the waveform data obtained from a single pass over the incoming data stream. The full potential of signal processing algorithms is not being exploited due to simplistic assumptions made about the background against which signals are being detected. A vast improvement in the available computational resources allows the possibility of more sensitive and more robust context-based detection pipelines which glean progressively more information from multiple passes over the data. In the first year of this two year contract we designed and implemented several extensions to an existing prototype detection framework to demonstrate the feasibility of improving performance from a systematic reprocessing of the raw data: signal cancellation for stripping the incoming data stream of repeating and irrelevant signals, adaptive beam forming and matched field processing for suppressing background signals and aftershock sequences, and the testing of event hypotheses by evaluating detection probabilities for both detecting and non-detecting stations, followed by optimized beam forming. In this second year of the contract, we have evaluated and enhanced significantly the signal cancellation procedures, revised the detection framework architecture in order that the procedure can be distributed and scaled efficiently, and developed a procedure for optimal detection and location of aftershocks from a target source region as a component of an iterative pipeline. This last procedure exploits significantly the probability of detection work performed in the first year of the contract.