Mircoseismic technology is becoming more and more important in industry in the development of unconventional oil and gas resources. Due to the physical and operational environment , the signal-to-noise ratio (SNR) of microseismic signals is usually low and it is hard to avoid missing relevant data. However, the signal fidelity is really significant for the subsequent processing of source location and determining source characteristic of microseismic events. It is always challenging to guarantee the accuracy of source location and fracture prediction. Recovering the missed signal and suppressing the noises are an essential step in microseismic signal processing.
However, the compressed sensing theory breaks through the limitation of the traditional Nyquist sampling theorem. The main idea is to recover the complete data set accurately by sampling a number of samples when the signal is sparse itsel for in the certain transformation domain. The microseismic signal is sparse itself which exactly satisfies this requirement. This method utilizes variable space to describe the signal, sampling compressed data which contains complete original information, when using original data, it can be recovered by an optimization problem from compressed data, recovering high resolution signal from low resolution observation, realizing sparse transform and reconstruction of microseismic signal, meanwhile, valid information can also be recovered.

Funding: China Scholarship Council

Advisors: Associate Professor Lutz Gross, Dr Sebastian Hoerning

Project members


PhD Candidate