MicEMD.fdem package¶

Submodules¶

MicEMD.fdem.collection module¶

The Collection class, conclude the parameters about collecting in underground detection system

Class: - Collection: the class conclude the parameters about collecting in FDEM

class MicEMD.fdem.collection.Collection(spacing, height, SNR, x_min, x_max, y_min, y_max, collection_direction)¶

Bases: object

spacing: float: the spacing of collection in x and y axis
height: float: The height of the detector above the ground
SNR: float: the SNR of the environment
x_min: float: the min value of the x axis collection
x_max: float: the max value of the x axis collection
y_min: float: the min value of the y axis collection
y_max: float: the max value of the y axis collection
collection_direction: str: the direction of the collection magnetic field

receiver_location:: return the sample location of the receiver
source_locations:: return the location of the transmitter

property receiver_location¶

define and get the sample location of the receiver

res: 2-D-ndarry: the location of the receiver, conclude the location of xyz axis every line is a location [x, y, z], the number of row represent the number of survey point

property source_locations¶

define and get the sample location of the transmitter

res: 2-D-ndarry: the location of the receiver, conclude the location of xyz axis every line is a location [x, y, z], the number of row represent the number of survey point

MicEMD.fdem.detector module¶

The detector class, represent the detector in FDEM

Class: - Detector: the detector class in FDEM

class MicEMD.fdem.detector.Detector(radius, current, frequency, pitch, roll)¶

Bases: object

the detector of the FDEM

radius: float: the radius of the detector
current: float: the current of the detector
frequency: float: the frequency of the detector
pitch: float: the pitch angle of the detector
roll: float: the roll angle of the detector

Methods: mag_moment:

return the magnetic moment value of transmitter coil

property mag_moment¶

Calculate magnetic moment value of transmitter coil according to detector parameters.

TYPEfloat: The value of magnetic moment of z axis.

MicEMD.fdem.inversion module¶

The inversion class in FDEM

Class: - Inversion: the implement class of the BaseFDEMInversion

Methods: - inverse: the interface of the inverse in FDEM

class MicEMD.fdem.inversion.Inversion(data, method, inv_para)¶

Bases: MicEMD.fdem.inversion.BaseFDEMInversion

inverse the properties of the target

based on the optimization algorithms to solve the inversion problem

data: tuple: the data conclude the observed data, target class and detector class
method: str: the name of the optimization
inv_para: dict: the parameters setting of the optimization

true_properties:: Returns the true properties of the target
inv_objective_function:: Returns the calculated objective_function value in x position
inv_objectfun_gradient:: Returns the calculated gradient value in x position
inv_residual_vector:: Returns the residual vector.
inv_get_preicted_data:: Returns Predicted secondary fields calculated by the dipole model at all receiver_loc.
inv_forward_calculation:: Returns predicted secondary fields according the linear magnetic dipole model at receiver_loc.
inv_residual_vector_grad:: Returns the gradient of all the residual vectors.
inv_forward_grad:: Returns the gradient of inv_forward_calculation()
polar_tensor_to_properties:: ransform the polar tensor to properties transform M11, M22, M33, M12, M13, M23 to polarizability and pitch and roll angle and return
find_xyz_polarizability_index:: make the order of eigenvalue correspond to the polarizability order
run:: run the process of the inversion and return the estimate values
error:: Returns the error between true value and estimate value

property error¶

calculate the error of the true value and estimate value

error: ndarray: return the error

find_xyz_polarizability_index(polarizability)¶

make the order of eigenvalue correspond to the polarizability order

polarizabilityTYPE: DESCRIPTION.

None.

inv_forward_calculation(detector, receiver_loc, x)¶

Forward calculation in inversion process. It generates predicted secondary fields according the linear magnetic dipole model at receiver_loc.

detector : class Detector receiver_loc : numpy.array, size=3

A receiver location.

xnumpy.array, size=9: See inv_objective_function x.

Bnumpy.mat, shape=(3*1): Predicted secondary field.

Wan Y, Wang Z, Wang P, et al. A Comparative Study of Inversion Optimization Algorithms for Underground Metal Target Detection[J]. IEEE Access, 2020, 8: 126401-126413.

inv_forward_grad(detector, receiver_loc, x)¶

Use the difference method to calculate the gradient of inv_forward_calculation().

detector : class Detector receiver_loc : numpy.array, size=3

A receiver location.

xnumpy.array, size=9: See inv_objective_function x.

grad : numpy.mat, shape=(3*9)

inv_get_preicted_data(detector, receiver_locations, x)¶

It generates predicted secondary fields at all receiver locations.

detector : class Detector receiver_locations : numpy.ndarray, shape=(N*3)

See inv_objective_function receiver_locations.

xnumpy.array, size=9: See inv_objective_function x.

predicted_mag_datanumpy.ndarray, shape=(N*3): Predicted secondary fields.

inv_objectfun_gradient(detector, receiver_locations, true_mag_data, x)¶

The gradient of the objective function with respect to x.

detector : class Detector receiver_locations : numpy.ndarray, shape=(N*3)

See inv_objective_function receiver_locations.

true_mag_datanumpy.ndarray, shape=(N*3): See inv_objective_function true_mag_data.
xnumpy.array, size=9: See inv_objective_function x.

gradnumpy.array, size=9: The partial derivative of the objective function with respect to nine parameters.

inv_objective_function(detector, receiver_locations, true_mag_data, x)¶

Objective function.

detector : class Detector receiver_locations : numpy.ndarray, shape=(N*3), N=len(receiver_locations)

All acquisition locations of the detector. Each row represents an acquisition location and the three columns represent x, y and z axis locations of an acquisition location.

true_mag_datanumpy.ndarray, shape=(N*3): All secondary fields of acquisition locations (x, y and z directions).
xnumpy.array, size=9: target’s parameters in inversion process, including position x, y, z, polarizability M11, M22, M33, M12, M13, M23.

objective_fun_value : float

inv_residual_vector(detector, receiver_locations, true_mag_data, x)¶

Construct the residual vector.

detector : class Detector receiver_locations : numpy.ndarray, shape=(N*3)

See inv_objective_function receiver_locations.

true_mag_datanumpy.ndarray, shape=(N*3): See inv_objective_function true_mag_data.
xnumpy.array, size=9: See inv_objective_function x.

residualnumpy.mat, shape=(N*3,1): Residual vector.

inv_residual_vector_grad(detector, receiver_locations, x)¶

Calculate the gradient of all the residual vectors.

detector : class Detector receiver_locations : numpy.ndarray, shape=(N * 3)

See inv_objective_function receiver_locations.

xnumpy.array, size=9: See inv_objective_function x.

gradnumpy.mat, shape=(N*3,9): Jacobian matrix of the residual vector.

polar_tensor_to_properties(polar_tensor_vector)¶

transform the polar tensor to properties transform M11, M22, M33, M12, M13, M23 to polarizability and pitch and roll angle

polar_tensor_vectornumpy.array, size=6: M11, M22, M33, M12, M13, M23.

None.

run()¶

run the process of the inversion

res: ndarray: return the estimate properties[x, y, z, bx, by, bz, pitch, roll]

property true_properties¶

return the true properties

res: ndarry: return the true properties, conclude position, polarizability, pitch and roll angle of the target, amount to 8 inverse parameters

MicEMD.fdem.inversion.inverse(data, method, inv_para, *args, **kwargs)¶

the interface is used to handle the process of inversion

data: tuple: the data conclude the observed data, target class and detector class
method: str: the name of the optimization
inv_para: dict: the parameters setting of the optimization

res: dict: res conclude the predicted value, true value and the error value

MicEMD.fdem.model module¶

The model class, represent the model in FDEM

Class: - Model: the implement class of the BaseFDEMModel

class MicEMD.fdem.model.Model(Survey)¶

Bases: MicEMD.fdem.model.BaseFDEMModel

the model class we simulate the FDEM response based on Simpeg in MicEMD

Survey: class: the Survey class

dpred:: Returns the observed data
mag_data_add_noise:: add the noise for the mag_data and return
add_wgn:: add the noise for the data

add_wgn(data, snr)¶

add the noise for the data

dataTYPE: DESCRIPTION.
snrTYPE: DESCRIPTION.

res: ndarry

dpred()¶

getIndicesCylinder(center, radius, height, oritation, ccMesh)¶

Create the mesh indices of a custom cylinder

centerTYPE: DESCRIPTION.
radiusTYPE: DESCRIPTION.
heightTYPE: DESCRIPTION.
oritationTYPE: DESCRIPTION.
ccMeshTYPE: DESCRIPTION.

None.

https://zhuanlan.zhihu.com/p/24760577

mag_data_add_noise(mag_data, snr)¶

add the noise for the mag_data

mag_dataTYPE: DESCRIPTION.
snrTYPE: DESCRIPTION.

res: ndarry

MicEMD.fdem.results module¶

MicEMD.fdem.simulation module¶

The simulation in TDEM

Class: - Simulation: the implement class of the BaseFDEMSimulation

Methods: - simulate: the interface of the simulation in FDEM

class MicEMD.fdem.simulation.Simulation(model)¶

Bases: MicEMD.fdem.simulation.BaseSimulation

simulate the electromagnetic response

model: class: the model class which call the pred method to generate the observed data

pred: ndarry: Returns the observed data

pred()¶

MicEMD.fdem.simulation.simulate(target, detector, collection, model='simpeg', *args, **kwargs)¶

the simulate interface is used to handle organization and dispatch of directives of the simulation

target: class: the target class
detector: class: the detector class
collection: class: the collection class
model: class: the model class

result: ndarry: the observed data conclude the position and the magnetic field intensity

MicEMD.fdem.source module¶

The Source class, represent the source in FDEM

Class: - Source: implement class of the BaseTDEMSource in FDEM

class MicEMD.fdem.source.Source(Target, Detector, Collection, *args)¶

Bases: MicEMD.fdem.source.BaseFDEMSource

property source_list¶

return the source list conclude the information of transmitter and receiver

res: list: res conclude all the MagDipole class in simpeg

MicEMD.fdem.survey module¶

The survey class, represent the survey in FDEM

Class: - Survey: the implement class of the BaseFDEMSurvey

class MicEMD.fdem.survey.Survey(Source)¶

Bases: MicEMD.fdem.survey.BaseFDEMSurvey

property survey¶

MicEMD.fdem.target module¶

The target class, represent the target in underground detection

Class: - Target: the target class in FDEM

class MicEMD.fdem.target.Target(conductivity, permeability, radius, pitch, roll, length, position_x, position_y, position_z, **kwargs)¶

Bases: object

Refer in particular to cylinder targets.

conductivity: float: the conductivity of the target
permeability: float: the permeability of the target
radius: float: The radius of the base of a cylinder
pitch: float: the pitch angle of the cylinder
roll: float: the roll angle of the cylinder
length: float: the length of the target
position: list: the position of the target, [x, y, z]

get_principal_axis_polarizability: return the principal axis polarizabilities of the target

get_principal_axis_polarizability(frequency)¶

Calculate the principal axis polarizabilities of the target according to the target’s parameters and the frequency of primary field.

frequencyfloat: Frequency of the primary field.

polarizability : numpy.array, size=3

Wan Y, Wang Z, Wang P, et al. A Comparative Study of Inversion Optimization Algorithms for Underground Metal Target Detection[J]. IEEE Access, 2020, 8: 126401-126413. equation (9)(10).

MicEMD.fdem package¶

Submodules¶

MicEMD.fdem.collection module¶

MicEMD.fdem.detector module¶

MicEMD.fdem.inversion module¶

MicEMD.fdem.model module¶

MicEMD.fdem.results module¶

MicEMD.fdem.simulation module¶

MicEMD.fdem.source module¶

MicEMD.fdem.survey module¶

MicEMD.fdem.target module¶

Module contents¶