An improved weighting Euler deconvolution for noisy data

Routine Euler deconvolution estimates the source location from potential data without requiring additional constraint information, making it used widely in geophysical applications. Since derivative operators are employed in the Euler deconvolutions, they are somewhat sensitive to noise in the data. The Euler deconvolution is typically solved as an over-determined linear system of equations to handle noisy data. This approach however does not account noise that is also present in the forward operator. An improved weighted Euler deconvolution is proposed to reduce the noise contained in the forward operator matrix. Model tests and a field application demonstrate that the proposed method produces accurate results and is stable for noisy data. By using small window sizes, the proposed method avoids the potential horizontal smearing of the source location and suggests that a large window size is not necessary for the anomaly induced from deep sources.