Denoising Method Research Of Surface Micro-seismic Data

Compared to the borehole, surface seismic monitoring is a new and more widelytechnology. It can monitor the reservoir fracturing fractures and reservoir drive, and it has thevital significance in the exploration and development of oil and gas field. One of its keytechnologys is to improve the signal-to-noise ratio of the raw data, and extract microseismicevents effectively. The paper mainly studied the principle of the monitoring, data analysis, andnoise suppression method, at last we use models and practical data to verify the denoisingmethods we studied, it successfully suppress the noise and improve the S/N ratio of the rawmonitoring data.First this paper introduces the principle of surface micro seismic monitoring and thepresent situation. And then analyzed the actual monitoring data in detail, including thecharacteristics of an effective and noise type, through the comparative analysis of signal andnoise, design a set of suitable for noise suppression of surface micro seismic datapreprocessing and the subsequent processing plan.According to monitoring data of industrial interference, drilling interference,presented linear adjustable denoising spectrum method based on the cosine functionapproximation. Cosine approximation method solved the notch and frequency domainsuppression conventional methods of denoising that are not thorough and damage theeffective signals of defects, the simulation of the single frequency noise and minus ideasachieve a real sense of denoising; And linear frequency modulation analysis solved theproblem of the single frequency target frequency accuracy, through the use of frequencyscanning, only54times can reach0.0001frequency precision, greatly improve thecomputational efficiency and precision of the simulation of single frequency.According to monitoring data of the random noise, singular value decomposition method of denoising is studied. For real effective event in phase axis is irregular, the static correctionis difficult situation, first of all in time domain using automatic scanning and moveoutcorrection method, effect of lag correction method better than the automatic scanning method,but both methods prior to calibration events; And frequency domain denoising was proposed,by building the Hankel matrix, to solve the problem of frequency domain matrix rank, anddon’t need a correction of events.According to the actual monitoring data characteristic, from the perspective of aneffective denoising transform method is proposed, the microseismic events as the targeteffectively, in the pdomain identify, analysis and extraction. For classic ptransformation existence has low computing efficiency and low resolution, high resolutionptransform is put forward; In order to more appropriate pideas, put forward aneffective moveout correction method, and use the adjacent events to constraint.Finally, this paper use models and practical data to verify the denosing methods, thestudies on the ground the microseismic denoising method can effectively suppress the actualmonitoring noises, improved signal-to-noise ratio of data and event recognition.