Microseismic search engine

Abstract

Similar to a web search engine, we develop a microseismic search engine that can help to estimate event location, magnitude, and focal mechanism all together in less than a second. The method includes the calculation of all possible microseismic events over a 3D grid with a known velocity model. We then index and rank all of the seismic waveforms following the characteristics of phase and amplitude information and create a database by applying multiple randomized K-dimensional tree method. When a microseismic event occurs, approximate best matches to the entry waveform shall be found immediately by comparing the input data with the characteristic features in the database. The method does not just return one solution, but a series of solutions just like the web search engine. Thus, the solution space delineates the resolution and uncertainty of the results. Also similar to a web search engine, it does not require any input parameter or processing experience, thus the solutions are the same for any user. We demonstrate the method with both synthetic and real data. It shows a great potential for routinely monitoring microseismic events in real time during hydraulic fracturing operation.

New distributed-topsis approach for multi-criteria decision-making problems in a big data context

“…The MapReduce model was proposed by two engineers at Google, who observed that many massively parallel processing operations, implemented for the needs of their search engine, followed an identical parallelization strategy. From these observations was born the MapReduce programming model, first described in 2004 [16]. The Map inspires his processing abstraction and Reduce primitives present in many functional languages like Lisp.…”

“…In this paper, we propose a parallel implementation to address this problem. A proven effective parallel computing paradigm is MapReduce [16]. It is a massively parallel programming model suitable for processing very large amounts of data.…”