Aeromagnetic Compensation

The following are key functions related to aeromagnetic compensation.

Tolles-Lawson

MagNav.create_TL_A — Function

create_TL_A(Bx, By, Bz;
            Bt       = sqrt.(Bx.^2+By.^2+Bz.^2),
            terms    = [:permanent,:induced,:eddy],
            Bt_scale = 50000,
            return_B = false)

Create Tolles-Lawson A matrix using vector magnetometer measurements. Optionally returns the magnitude & derivatives of total field.

Arguments:

Bx,By,Bz: vector magnetometer measurements [nT]
Bt: (optional) magnitude of vector magnetometer measurements or scalar magnetometer measurements for modified Tolles-Lawson [nT]
terms: (optional) Tolles-Lawson terms to use {:permanent,:induced,:eddy,:bias}
Bt_scale: (optional) scaling factor for induced & eddy current terms [nT]
return_B: (optional) if true, also return Bt & B_dot

Returns:

A: Tolles-Lawson A matrix
Bt: if return_B = true, magnitude of total field measurements [nT]
B_dot: if return_B = true, finite differences of total field vector [nT]

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create_TL_A(flux::MagV, ind = trues(length(flux.x));
            Bt       = sqrt.(flux.x.^2+flux.y.^2+flux.z.^2)[ind],
            terms    = [:permanent,:induced,:eddy],
            Bt_scale = 50000,
            return_B = false)

Create Tolles-Lawson A matrix using vector magnetometer measurements. Optionally returns the magnitude & derivatives of total field.

Arguments:

flux: MagV vector magnetometer measurement struct
ind: (optional) selected data indices
Bt: (optional) magnitude of vector magnetometer measurements or scalar magnetometer measurements for modified Tolles-Lawson [nT]
terms: (optional) Tolles-Lawson terms to use {:permanent,:induced,:eddy,:bias}
Bt_scale: (optional) scaling factor for induced & eddy current terms [nT]
return_B: (optional) if true, also return Bt & B_dot

Returns:

A: Tolles-Lawson A matrix
Bt: if return_B = true, magnitude of total field measurements [nT]
B_dot: if return_B = true, finite differences of total field vector [nT]

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MagNav.create_TL_coef — Function

create_TL_coef(Bx, By, Bz, B;
               Bt         = sqrt.(Bx.^2+By.^2+Bz.^2),
               λ          = 0,
               terms      = [:permanent,:induced,:eddy],
               pass1      = 0.1,
               pass2      = 0.9,
               fs         = 10.0,
               pole::Int  = 4,
               trim::Int  = 20,
               Bt_scale   = 50000,
               return_var = false)

Create Tolles-Lawson coefficients using vector & scalar magnetometer measurements with a bandpass, low-pass, or high-pass filter.

Arguments:

Bx,By,Bz: vector magnetometer measurements [nT]
B: scalar magnetometer measurements [nT]
Bt: (optional) magnitude of vector magnetometer measurements or scalar magnetometer measurements for modified Tolles-Lawson [nT]
λ: (optional) ridge parameter
terms: (optional) Tolles-Lawson terms to use {:permanent,:induced,:eddy,:bias}
pass1: (optional) first passband frequency [Hz]
pass2: (optional) second passband frequency [Hz]
fs: (optional) sampling frequency [Hz]
pole: (optional) number of poles for Butterworth filter
trim: (optional) number of elements to trim after filtering
Bt_scale: (optional) scaling factor for induced & eddy current terms [nT]
return_var: (optional) if true, also return B_var

Returns:

coef: Tolles-Lawson coefficients
B_var: if return_var = true, fit error variance

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create_TL_coef(flux::MagV, B, ind = trues(length(flux.x));
               Bt         = sqrt.(flux.x.^2+flux.y.^2+flux.z.^2)[ind],
               λ          = 0,
               terms      = [:permanent,:induced,:eddy],
               pass1      = 0.1,
               pass2      = 0.9,
               fs         = 10.0,
               pole::Int  = 4,
               trim::Int  = 20,
               Bt_scale   = 50000,
               return_var = false)

Create Tolles-Lawson coefficients using vector & scalar magnetometer measurements with a bandpass, low-pass, or high-pass filter.

Arguments:

flux: MagV vector magnetometer measurement struct
B: scalar magnetometer measurements [nT]
ind: (optional) selected data indices
Bt: (optional) magnitude of vector magnetometer measurements or scalar magnetometer measurements for modified Tolles-Lawson [nT]
λ: (optional) ridge parameter
terms: (optional) Tolles-Lawson terms to use {:permanent,:induced,:eddy,:bias}
pass1: (optional) first passband frequency [Hz]
pass2: (optional) second passband frequency [Hz]
fs: (optional) sampling frequency [Hz]
pole: (optional) number of poles for Butterworth filter
trim: (optional) number of elements to trim after filtering
Bt_scale: (optional) scaling factor for induced & eddy current terms [nT]
return_var: (optional) if true, also return B_var

Returns:

coef: Tolles-Lawson coefficients
B_var: if return_var = true, fit error variance

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Model Training/Fitting

MagNav.comp_train — Function

comp_train(comp_params::CompParams, xyz::XYZ, ind,
           mapS::Union{MapS,MapSd,MapS3D} = mapS_null;
           temp_params::TempParams        = TempParams(),
           xyz_test::XYZ                  = xyz,
           ind_test                       = BitVector(),
           silent::Bool                   = false)

Train an aeromagnetic compensation model.

Arguments:

comp_params: CompParams aeromagnetic compensation parameters struct, either:
- NNCompParams: neural network-based aeromagnetic compensation parameters struct
- LinCompParams: linear aeromagnetic compensation parameters struct
xyz: XYZ flight data struct
ind: selected data indices
mapS: (optional) MapS, MapSd, or MapS3D scalar magnetic anomaly map struct, only used for y_type = :b, :c
temp_params: (optional) TempParams temporary (WIP) parameters struct
xyz_test: (optional) XYZ held-out test data struct
ind_test: (optional) indices for test data struct
silent: (optional) if true, no print outs

Returns:

comp_params: CompParams aeromagnetic compensation parameters struct
y: length-N target vector
y_hat: length-N prediction vector
err: length-N compensation error
features: length-Nf feature vector (including components of TL A, etc.)

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comp_train(comp_params::CompParams, xyz_vec::Vector, ind_vec::Vector,
           mapS::Union{MapS,MapSd,MapS3D} = mapS_null;
           temp_params::TempParams        = TempParams(),
           xyz_test::XYZ                  = xyz_vec[1],
           ind_test                       = BitVector(),
           silent::Bool                   = false)

Train an aeromagnetic compensation model.

Arguments:

comp_params: CompParams aeromagnetic compensation parameters struct, either:
- NNCompParams: neural network-based aeromagnetic compensation parameters struct
- LinCompParams: linear aeromagnetic compensation parameters struct
xyz_vec: vector of XYZ flight data structs
ind_vec: vector of selected data indices
mapS: (optional) MapS, MapSd, or MapS3D scalar magnetic anomaly map struct, only used for y_type = :b, :c
temp_params: (optional) TempParams temporary (WIP) parameters struct
xyz_test: (optional) XYZ held-out test data struct
ind_test: (optional) indices for test data struct
silent: (optional) if true, no print outs

Returns:

comp_params: CompParams aeromagnetic compensation parameters struct
y: length-N target vector
y_hat: length-N prediction vector
err: length-N compensation error
features: length-Nf feature vector (including components of TL A, etc.)

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comp_train(comp_params::CompParams, lines,
           df_line::DataFrame, df_flight::DataFrame, df_map::DataFrame;
           temp_params::TempParams = TempParams(),
           silent::Bool            = false)

Train an aeromagnetic compensation model.

Arguments:

comp_params: CompParams aeromagnetic compensation parameters struct, either:
- NNCompParams: neural network-based aeromagnetic compensation parameters struct
- LinCompParams: linear aeromagnetic compensation parameters struct
lines: selected line number(s)
df_line: lookup table (DataFrame) of lines

Field	Type	Description
`flight`	`Symbol`	flight name (e.g., `:Flt1001`)
`line`	`Real`	line number, i.e., segments within `flight`
`t_start`	`Real`	start time of `line` to use [s]
`t_end`	`Real`	end time of `line` to use [s]
`map_name`	`Symbol`	(optional) name of magnetic anomaly map relevant to `line`

df_flight: lookup table (DataFrame) of flight data files

Field	Type	Description
`flight`	`Symbol`	flight name (e.g., `:Flt1001`)
`xyz_type`	`Symbol`	subtype of `XYZ` to use for flight data {`:XYZ0`,`:XYZ1`,`:XYZ20`,`:XYZ21`}
`xyz_set`	`Real`	flight dataset number (used to prevent improper mixing of datasets, such as different magnetometer locations)
`xyz_file`	`String`	path/name of flight data CSV, HDF5, or MAT file (`.csv`, `.h5`, or `.mat` extension required)

df_map: lookup table (DataFrame) of map data files

Field	Type	Description
`map_name`	`Symbol`	name of magnetic anomaly map
`map_file`	`String`	path/name of map data HDF5 or MAT file (`.h5` or `.mat` extension required)

temp_params: (optional) TempParams temporary (WIP) parameters struct
silent: (optional) if true, no print outs

Returns:

comp_params: CompParams aeromagnetic compensation parameters struct
y: length-N target vector
y_hat: length-N prediction vector
err: length-N mean-corrected (per line) compensation error
features: length-Nf feature vector (including components of TL A, etc.)

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Model Testing

MagNav.comp_test — Function

comp_test(comp_params::CompParams, xyz::XYZ, ind,
          mapS::Union{MapS,MapSd,MapS3D} = mapS_null;
          temp_params::TempParams        = TempParams(),
          silent::Bool                   = false)

Evaluate performance of an aeromagnetic compensation model.

Arguments:

comp_params: CompParams aeromagnetic compensation parameters struct, either:
- NNCompParams: neural network-based aeromagnetic compensation parameters struct
- LinCompParams: linear aeromagnetic compensation parameters struct
xyz: XYZ flight data struct
ind: selected data indices
mapS: (optional) MapS, MapSd, or MapS3D scalar magnetic anomaly map struct, only used for y_type = :b, :c
temp_params: (optional) TempParams temporary (WIP) parameters struct
silent: (optional) if true, no print outs

Returns:

y: length-N target vector
y_hat: length-N prediction vector
err: length-N compensation error
features: length-Nf feature vector (including components of TL A, etc.)

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comp_test(comp_params::CompParams, lines,
          df_line::DataFrame, df_flight::DataFrame, df_map::DataFrame;
          temp_params::TempParams = TempParams(),
          silent::Bool            = false)

Evaluate performance of an aeromagnetic compensation model.

Arguments:

comp_params: CompParams aeromagnetic compensation parameters struct, either:
- NNCompParams: neural network-based aeromagnetic compensation parameters struct
- LinCompParams: linear aeromagnetic compensation parameters struct
lines: selected line number(s)
df_line: lookup table (DataFrame) of lines

Field	Type	Description
`flight`	`Symbol`	flight name (e.g., `:Flt1001`)
`line`	`Real`	line number, i.e., segments within `flight`
`t_start`	`Real`	start time of `line` to use [s]
`t_end`	`Real`	end time of `line` to use [s]
`map_name`	`Symbol`	(optional) name of magnetic anomaly map relevant to `line`

df_flight: lookup table (DataFrame) of flight data files

Field	Type	Description
`flight`	`Symbol`	flight name (e.g., `:Flt1001`)
`xyz_type`	`Symbol`	subtype of `XYZ` to use for flight data {`:XYZ0`,`:XYZ1`,`:XYZ20`,`:XYZ21`}
`xyz_set`	`Real`	flight dataset number (used to prevent improper mixing of datasets, such as different magnetometer locations)
`xyz_file`	`String`	path/name of flight data CSV, HDF5, or MAT file (`.csv`, `.h5`, or `.mat` extension required)

df_map: lookup table (DataFrame) of map data files

Field	Type	Description
`map_name`	`Symbol`	name of magnetic anomaly map
`map_file`	`String`	path/name of map data HDF5 or MAT file (`.h5` or `.mat` extension required)

temp_params: (optional) TempParams temporary (WIP) parameters struct
silent: (optional) if true, no print outs

Returns:

y: length-N target vector
y_hat: length-N prediction vector
err: length-N mean-corrected (per line) compensation error
features: length-Nf feature vector (including components of TL A, etc.)

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