AMBIENT NOISE SEISMIC TOMOGRAPHY

Abstract

Ambient noise can be used to generate tomographic images that give us an idea of various geological features prevalent in the subsurface. Ambient noise is used to generate shallow depth images. Such images can be used to infer about the presence of groundwater aquifers, or hydrocarbon traps, formed by faults and fractures. Faults hold lots of academic and economic significance. They can also act as structural traps for accumulation of hydrocarbons or can behave as migration pathways aiding the escape of hydrocarbons. Ambient noise can be low frequency or high frequency. Low frequency noise are usually generated by ocean waves, and penetrate to depths of about 10 km. High frequency noise are generated by various cultural noise, etc. Here, I will be using high frequency noise that will be used as diffused wave fields. Such wave fields can be used to generate Greens function by cross correlating them with each other. The Green function can be used to give us an idea about the velocity structure of the region. In order to achieve the above mentioned objective, various methods are prevalent. In my thesis, I will be working on the method given by G. D. Bensen et al., 2007. I used the concepts mentioned in the paper to generate MATLAB codes to generate cross correlated data and subsequent Green function. I have used noise data from five stations spread around Garhwal region. The data used is uploaded on an hourly basis, for a period of twenty-four hours. The date of the recording of the data is 30th December, 2007. The station names from where the data is recorded are: RAJGA, KHURM, AYARC, SRIKO, and CNTTE. After obtaining the Green function from these stations, I applied FTAN analysis using python package to obtain the dispersion curves.