Compressive sensing can recover information from a limited number of measurements. Just like seismic inversions, once a small number of observations are gathered, we re-formate this ill-conditioned inverse problem into an optimization problem, adding additional regulations. As the data we are facing increases rapidly. Nowadays, our research becomes a very promising answer to the data delude by controlling the size of the information when we sense it. Also the use of low dimensional data acquisition equipment reduces not only the investment on the tools but also time and energy on acquisition and processing. Applications have been realized in infrared cameras, biological microscopy and medical imaging. It also has high potential in geophysical exploration. As there are high and urgent demands on seismic imaging with high quality, an important problem is the design of seismic survey. My research may help to design more efficient surveys. Another scenario of this application is well logging. Terabytes of data are acquired and transmitted and later processed remotely every day for a single inference - what kind of layer it is. If the inference can be efficiently made during the acquisition, huge amount of redundant of data can be avoided.
