filename4 = Path(DATA_PATH, 'ynyx_bgar.grd') gd4, ff = GDALGrid.getFileGeoDict(filename4) grid4 = GDALGrid.load(filename4, gd4) from geoist.pfm import pftrans from geoist.vis import giplt x, y, elev = Grid2Xyz(grid1) x, y, fga = Grid2Xyz(grid2) x, y, bgas = Grid2Xyz(grid3) x, y, bgar = Grid2Xyz(grid4) shape = (grid3.getGeoDict().nx, grid3.getGeoDict().ny) height = 1000 # How much higher to go bgas_contf = pftrans.upcontinue(x, y, bgas, shape, height) bgas_dx = pftrans.derivx(x, y, bgas_contf, shape) bgas_dy = pftrans.derivy(x, y, bgas_contf, shape) bgas_dz = pftrans.derivz(x, y, bgas_contf, shape) bgas_tga = pftrans.tga(x, y, bgas_contf, shape) import numpy as np from geoist.others.geodict import GeoDict xmin = np.min(x) xmax = np.max(x) ymin = np.min(y) ymax = np.max(y) nx = len(set(x)) ny = len(set(y)) dx = (xmax - xmin) / (nx - 1) dy = (ymax - ymin) / (ny - 1)
"""
import matplotlib.pyplot as plt
from geoist import gridder
from geoist.inversion import geometry
from geoist.pfm import prism, pftrans, giutils
from geoist.vis import giplt
model = [geometry.Prism(-1000, 1000, -1000, 1000, 0, 2000, {'density': 100})]
area = (-5000, 5000, -5000, 5000)
shape = (51, 51)
z0 = -500
xp, yp, zp = gridder.regular(area, shape, z=z0)
gz = giutils.contaminate(prism.gz(xp, yp, zp, model), 0.001)
# Need to convert gz to SI units so that the result can be converted to Eotvos
gxz = giutils.si2eotvos(pftrans.derivx(xp, yp, giutils.mgal2si(gz), shape))
gyz = giutils.si2eotvos(pftrans.derivy(xp, yp, giutils.mgal2si(gz), shape))
gzz = giutils.si2eotvos(pftrans.derivz(xp, yp, giutils.mgal2si(gz), shape))
gxz_true = prism.gxz(xp, yp, zp, model)
gyz_true = prism.gyz(xp, yp, zp, model)
gzz_true = prism.gzz(xp, yp, zp, model)
plt.figure()
plt.title("Original gravity anomaly")
plt.axis('scaled')
giplt.contourf(xp, yp, gz, shape, 15)
plt.colorbar(shrink=0.7)
giplt.m2km()
plt.figure(figsize=(14, 10))
radius=1000,
props={'magnetization': giutils.ang2vec(1, inc, dec)})
]
print("Centers of the model spheres:")
print(model[0].center)
print(model[1].center)
# Generate some magnetic data from the model
shape = (100, 100)
area = [-5000, 5000, -5000, 5000]
x, y, z = gridder.regular(area, shape, z=-150)
data = sphere.tf(x, y, z, model, inc, dec)
# We also need the derivatives of our data
xderiv = pftrans.derivx(x, y, data, shape)
yderiv = pftrans.derivy(x, y, data, shape)
zderiv = pftrans.derivz(x, y, data, shape)
# Now we can run our Euler deconv solver on a moving window over the data.
# Each window will produce an estimated point for the source.
# We use a structural index of 3 to indicate that we think the sources are
# spheres.
# Run the Euler deconvolution on moving windows to produce a set of solutions
# by running the solver on 10 x 10 windows of size 1000 x 1000 m
solver = euler.EulerDeconvMW(x,
y,
z,
data,
xderiv,