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import streamlit as st
import bruges as b
from einops import repeat
import numpy as np
from bruges.reflection.reflection import zoeppritz_rpp as zrpp
import pandas as pd
import altair as alt
def vs_from_poisson(vp, poisson):
'''compute pressure velocity from
shear velocity and poissons ratio'''
return np.sqrt((vp**2 - 2*poisson*vp**2)/(2 - 2*poisson))
def gardners(vp):
'''compute density via gardners relation'''
return 1000*0.31*np.power(vp, 0.25)/1.33
def cartesian_product(*arrays):
'''return cartesion product of several arrays'''
ndim = len(arrays)
return (np.stack(np.meshgrid(*arrays), axis=-1)
.reshape(-1, ndim))
def loop_zrpp(vp1,vs1,rho1,vp2,vs2,rho2,theta1):
'''compute reflectivity for many values of
input parameters.
"1" denotes layer one, "2" denotes layer two.
outside this function, I tend to use 0-indexing though.'''
refl_loop = np.empty(len(vp1), dtype=complex)
for i in range(vp1.shape[0]):
refl_loop[i] = zrpp(vp1=vp1[i], vs1=vs1[i], rho1=rho1[i],
vp2=vp2[i], vs2=vs2[i], rho2=rho2[i],
theta1=theta1[i])
return refl_loop
def wb_ava_fig(vwater=1520., rwater = 1025., poissons=0.49):
'''
AVO: Amplitude Versus Offset
AVA: Amplitude Versus Angle
compute AVA at the WB for a range of values,
then plot using altair
'''
### Set up some variables
VP1 = np.arange(1530,1820,50)
THE = np.arange(0.0, 88., 1.)
POI = np.array([poissons])
### Set variables that depend on those variables
params = cartesian_product(VP1, THE, POI)
VP1, THE, POI = [a.ravel() for a in np.hsplit(params, 3)]
VP0 = np.full(VP1.shape, vwater)
# V-RMS for ~200 m water depth
VS0 = np.full(VP1.shape, 0.)
RH0 = np.full(VP1.shape, rwater)
# 1025 kg/m^3 per Inversion of the physical properties of seafloor surface, South China Sea, Zhou et al 2021
VP1 = VP1
VS1 = vs_from_poisson(VP1,POI)
RH1 = gardners(VP1)
### Shove into a dict to pass to zrpp
params = {"vp1": VP0, "vs1": VS0, "rho1": RH0,
"vp2": VP1, "vs2": VS1, "rho2": RH1,
"theta1":THE}
# Compute zoeppritz equation
r = loop_zrpp(**params)
# Put the results into a DataFrame
df = pd.DataFrame({"Vp sub-WB": VP1, "Poisson_s ratio": POI, "Vs sub-WB": VS1,
"Angle": THE, "Amplitude": np.real(r)})
# Select only points pre-critical angle
df["Ang_Crit"] = np.degrees(np.arcsin(1500 / df["Vp sub-WB"].values))
df = df[df["Angle"] < df["Ang_Crit"]]
# Create the altair figure
highlight = alt.selection(type='single', on='mouseover',
fields=["Vp sub-WB:Q"], nearest=True)
base = alt.Chart(df).encode(
x="Angle",
y="Amplitude",
color="Vp sub-WB:Q",
tooltip=["Vp sub-WB", "Vs sub-WB", "Angle", "Amplitude"]
)
points = base.mark_circle().encode(
opacity=alt.value(0)
).add_selection(
highlight
).properties(
width=600,
height=450
)
lines = base.mark_line().encode(
size=alt.condition(~highlight, alt.value(3), alt.value(7))
)
return points + lines
vwater = st.slider("Select a value for water velocity", min_value=1495, max_value=1545, value=1520)
rwater = st.slider("Select a value for density of water", min_value=1005, max_value=1045, value=1025)
poissons = st.slider("Select a value for Poisson's ratio", min_value=0.4, max_value=0.5, value=0.48)
# st.write("Poisson's ratio is:", p)
st.altair_chart(wb_ava_fig(vwater, rwater, poissons))
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