def calibrate_vp(self):
method = 'L-BFGS-B'
options = dict()
vp0 = (self.km0, self.gm0, self.alpha0, self.kfl0)
vp_bounds = ((self.km_min, self.km_max), (self.gm_min, self.gm_max), (self.alpha_min, self.alpha_max), (self.kfl_min, self.kfl_max))
vp_par = minimize(self.vp_residual, vp0, method=method, bounds=vp_bounds, options=options)
self.Km = vp_par.x[0]
self.Gm = vp_par.x[1]
self.alpha = vp_par.x[2]
self.Kfl = vp_par.x[3]
if self.vp_std is None:
self.vp_std = np.std(self.vp - RP.Vp(self.Km, self.Gm, self.phi, self.alpha, self.Kfl, self.rho), ddof=0)
def sample(self, n=1):
phi_std = self.phi_std*self.phi_std_factor
phi = stats.norm.rvs(loc=self.phi_mean, scale=phi_std, size=n)
rho_mean = RP.rho(phi, self.rho_gr, self.rho_fl)
rho_std = self.rho_std*self.rho_std_factor
rho = stats.norm.rvs(loc=rho_mean, scale=rho_std, size=n)
vp_mean = RP.Vp(self.Km, self.Gm, phi, self.alpha, self.Kfl, rho)
vp_std = self.vp_std*self.vp_std_factor
vp = stats.norm.rvs(loc=vp_mean, scale=vp_std, size=n)
if self.otherspm is not None:
others = self.otherspm.sample(n)
return np.vstack([phi, rho, vp, others.T]).T
else:
return np.vstack([phi, rho, vp]).T
def prob(self, x):
phi = x[:, 0]
rho = x[:, 1]
vp = x[:, 2]
phi_std = self.phi_std*self.phi_std_factor
p_phi = stats.norm.pdf(phi, loc=self.phi_mean, scale=phi_std)
rho_mean = RP.rho(phi, self.rho_gr, self.rho_fl)
rho_std = self.rho_std*self.rho_std_factor
p_rho = stats.norm.pdf(rho, loc=rho_mean, scale=rho_std)
vp_mean = RP.Vp(self.Km, self.Gm, phi, self.alpha, self.Kfl, rho)
vp_std = self.vp_std*self.vp_std_factor
p_vp = stats.norm.pdf(vp, loc=vp_mean, scale=vp_std)
p_others = 1.0
if self.otherspm is not None:
p_others = self.otherspm.prob(x[:, 3:])
return p_phi*p_rho*p_vp*p_others
def vp_residual(self, x):
return np.mean((self.vp - RP.Vp(x[0], x[1], self.phi, x[2], x[3], self.rho))**2)