invert4geom.regional

Module Contents

Functions

regional_dc_shift(grav_df[, dc_shift, grav_grid, ...])	separate the regional field by applying a constant shift (DC-shift) to the gravity
regional_filter(filter_width, grav_grid, grav_df[, ...])	separate the regional field with a low-pass filter
regional_trend(trend, grav_grid, grav_df[, ...])	separate the regional field with a trend
regional_eq_sources(source_depth, grav_df, input_grav_name)	separate the regional field by estimating deep equivalent sources
regional_constraints(constraint_points, grav_grid, ...)	separate the regional field by sampling and regridding at the constraint points

regional_dc_shift(grav_df, dc_shift=None, grav_grid=None, constraint_points=None, coord_names=('easting', 'northing'), regional_col_name='reg')

separate the regional field by applying a constant shift (DC-shift) to the gravity data. If constraint points of the layer of interested are supplied, the DC shift will minimize the residual misfit at these constraint points.

Parameters:

• grav_df (pd.DataFrame) – gravity data with columns defined by coord_names and input_grav_name.

• dc_shift (float) – shift to apply to the data

• grav_grid (xr.DataArray) – gridded gravity misfit data

• constraint_points (pd.DataFrame) – a dataframe of constraint points with columns X and Y columns defined by the coord_names parameter.

• coord_names (tuple) – names of the X and Y column names in constraint points dataframe

• regional_col_name (str) – name for the new column in grav_df for the regional field.

Returns:

grav_df with new regional column

Return type:

pd.DataFrame

regional_filter(filter_width, grav_grid, grav_df, regional_col_name='reg')

separate the regional field with a low-pass filter

Parameters:

• filter_width (float)

• grav_grid (xarray.DataArray)

• grav_df (pandas.DataFrame)

• regional_col_name (str)

Return type:

pandas.DataFrame

regional_trend(trend, grav_grid, grav_df, fill_method='verde', regional_col_name='reg')

separate the regional field with a trend

Parameters:

• trend (int)

• grav_grid (xarray.DataArray)

• grav_df (pandas.DataFrame)

• fill_method (str)

• regional_col_name (str)

Return type:

pandas.DataFrame

regional_eq_sources(source_depth, grav_df, input_grav_name, eq_damping=None, block_size=None, depth_type='relative', input_coord_names=('easting', 'northing'), regional_col_name='reg')

separate the regional field by estimating deep equivalent sources

eq_damping : float: smoothness to impose on estimated coefficients block_size : float: block reduce the data to speed up depth_type : str: constant depths, not relative to observation heights

Parameters:

• source_depth (float)

• grav_df (pandas.DataFrame)

• input_grav_name (str)

• eq_damping (float | None)

• block_size (float | None)

• depth_type (str)

• input_coord_names (tuple[str, str])

• regional_col_name (str)

Return type:

pandas.DataFrame

regional_constraints(constraint_points, grav_grid, grav_df, region, spacing, tension_factor=1, registration='g', constraint_block_size=None, grid_method='pygmt', dampings=None, delayed=False, constraint_weights_col=None, eqs_gridding_trials=10, eqs_gridding_damping_lims=(0.1, 100), eqs_gridding_depth_lims=(1000.0, 100000.0), force_coords=None, regional_col_name='reg')

separate the regional field by sampling and regridding at the constraint points

Parameters:

• constraint_points (pandas.DataFrame)

• grav_grid (xarray.DataArray)

• grav_df (pandas.DataFrame)

• region (tuple[float, float, float, float])

• spacing (float)

• tension_factor (float)

• registration (str)

• constraint_block_size (float | None)

• grid_method (str)

• dampings (Any | None)

• delayed (bool)

• constraint_weights_col (str | None)

• eqs_gridding_trials (int)

• eqs_gridding_damping_lims (tuple[float, float])

• eqs_gridding_depth_lims (tuple[float, float])

• force_coords (tuple[pandas.Series | nptyping.NDArray, pandas.Series | nptyping.NDArray] | None)

• regional_col_name (str)

Return type:

pandas.DataFrame