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We have shown that non-stationary PEF's provide an effective means for
the electroseismic signal/noise separation. We demonstrate that the
physics of the problem can be used to develop models for PEF
estimation in this case where the signal may be so much weaker than
the noise that alterations of the data cannot produce suitable
models. The generality of this approach is important in
electroseismic signal processing, as it enables us to attenuate the
strong coherent noise and enhance previously obscured signal, without
any *a priori* knowledge of the signal. These results are far
superior to those obtained with f-k filtering, stationary PEF's
Haines and Guitton (2002), or other means. The real data example
shown here is encouraging, and indicates that the development of an
effective, fully general, algorithm for processing electroseismic data
is an attainable goal. The next major step toward a generally
applicable algorithm is the development of a better model for noise
PEF estimation. One likely option is horizontal geophone data, as it
should closely resemble the coseismic noise. The surprisingly poor
quality of the horizontal data collected as part of this project makes
it unsuitable for such a use, but is likely due to the poor condition
of the geophones. We plan to collect additional data using better
quality geophones, and hope to find that these data can be used for
noise PEF estimation.
Assuming that horizontal geophone data can effectively be used for PEF
estimation, we suggest the following processing scheme for
electroseismic data:

- 1.
- Pre-processing (60 Hz removal, frequency filtering).
- 2.
- Develop rough velocity model using coseismic and/or geophone
data.
- 3.
- Estimate signal PEF on dipole amplitude pattern, using velocity
model.
- 4.
- Estimate noise PEF on horizontal geophone data.
- 5.
- Use these non-stationary PEF's in the signal/noise separation
outlined here.

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** Up:** Haines and Guitton: Electroseismics
** Previous:** Real Electroseismic data
Stanford Exploration Project

7/8/2003