Simultaneous estimation of two slopes from seismic data, applied to signal/noise separation

I present an efficient new approach to simultaneously estimate two slopes from seismic data. I employ a Newton iteration to overcome the problem’s nonlinearity. In spite of my method’s theoretical inability to handle aliased data, it robustly estimates two independent slopes in many circumstances. I apply my method to the problem of signal/noise separation on synthetic and real data examples. The estimated slopes provide approximate inverse signal and noise covariance operators good enough to obtain an excellent separation, with only a limited amount of prior information required.