The Study Of Deep Target Forward Modeling And Application

With the development of exploration activities, the hydrocarbon resources of mid-deep layer system have been explored, and have become the emphases of the exploration gradually at present. To study the deep seismic exploring, firstly, the rules of the seismic wave propagation and attenuation in the subsurface must be studied. Then, the rules are used to study how to heighten the energy of the seismic wave propagation in the subsurface more effectively.Firstly, in the work, viscoelastic wave equation is analyzed and the viscoelastic wave equation is forward modeled by finite difference method. Then, absorption and attenuation of seismic wave in viscoelastic medium is analyzed from the theories through viscoelastic wave equation.Various of models are built up and their wave fields are analysed. Form numerical simulation of the shallow-mid-deep's model, we can find that energy decreases and resolution drops gradually with the depth increases. From the numerical simulation of the elastic-viscoelastic's model, we can find the viscoelastic back wave's amplitude is more obviously attenuated than elastic back wave's. And, the viscoelastic back wave's shape and phase are distorted seriously. Also, viscoelastic back wave's dominant frequency moves to low frequency and valid frequency band becomes narrow. From the numerical simulation of complex model, we can find low frequency signals have more powerful capability of breakthrough high velocity shielding layers. So, we can utilize low frequency to decline the diffusion and shielding of the high velocity basaltic shielding layers, in order to raise deep layer's signal-to-noise ratio.In addition, the unite nonlinearity method of genetic algorithm and simulated annealing algorithm is used to inverse the parameters of viscoelastic medium and obtain quality factor. Then, compensation of seismic wave absorption and attenuation can be obtained by inverse Q filtering. The results of theoretical model and factual data indicate that the energy and resolution of the seismic data can be improved by inverse Q filtering.