watex.methods.em.MT.remove_ss_emap#

MT.remove_ss_emap(fltr='ama', out=False, smooth=True, drop_outliers=True, rotate=0.0, update_z=True)[source]#

Filter Z to remove the static schift using the EMAP moving average filters.

Three available filters:

  • ‘ama’: Adaptative moving average

  • ‘tma’: Trimming moving-average

  • ‘flma’: Fixed-length dipole moving average

Could export new Edi if the keyword argument export is set to True

Parameters

  • fltr (str , default='ama') –

    Type of filter to apply. Default is Adaptative moving-average of Torres-verdin [1]. Can be [‘ama’|’tma’|’flma’]

    The AMA filter estimates static-corrected apparent resistivities at a single reference frequency by calculating a profile of average impedances along the length of the line. Sounding curves are then shifted so that they intersect the averaged profile.

  • out (bool , default =False,) – Output new filtered EDI. Otherwise return Z collections objects of corrected Tensors.

  • smooth (bool, default=False,) – Smooth the tensor data along the frequencies.

  • versionadded (..) –

  • drop_outliers (bool, default=True) – Suppress outliers in the data when smoothing data along the frequencies axis. Note that drop_outliers does not take effect if smooth is False.

  • versionadded – Polish the tensor data along the frequency axis remove noises and deal with the static shift effect when interferences noises are strong enough.

Returns

self

Return type

watex.methods.ZC for methods chaining.

References

1

Torres-Verdin and Bostick, 1992, Principles of spatial surface electric field filtering in magnetotellurics: electromagnetic array profiling(EMAP), Geophysics, v57, p603-622.https://doi.org/10.1190/1.2400625

See also

remove_static_shift

Remove static shift using the spatial filter median and write a new edifile.

Examples

>>> import watex
>>> from watex.methods import ZC
>>> edi_sample = watex.fetch_data ('edis', samples =17 , return_data =True )
>>> zo = ZC ().fit(edi_sample)
>>> zo.ediObjs_[0].Z.z[:, 0, 1][:7]
array([10002.46 +9747.34j , 11679.44 +8714.329j, 15896.45 +3186.737j,
       21763.01 -4539.405j, 28209.36 -8494.808j, 19538.68 -2400.844j,
        8908.448+5251.157j])
>>> zc = zo.remove_ss_emap()
>>> zc[0].z[:, 0, 1] [:7]
array([12120.08320804+6939.9874753j , 13030.91462606+6522.58481295j,
       15432.0206124 +4970.42806287j, 21899.60942244+3826.47476912j,
       29109.17100085+4537.17072741j, 19252.07839732+4108.71578943j,
        9473.20464326+4146.50327315j])