single dipping layer

app.py

@@ -1,114 +1,44 @@
 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
 
+MAXDIP = 60.
+XMAX = 30
+YMAX = XMAX*np.tan(np.deg2rad(MAXDIP))
 
-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):
+def fig_1(dip=26.6):
     '''
     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
+    X = np.array([0,XMAX])
+    dipping_reflector = np.array([0,np.abs(X[0]-X[1])*np.tan(np.deg2rad(dip))])
+
+    df = pd.DataFrame({"x":X,
+                       "z":dipping_reflector
+                       }
+                       )
+
+    lines = alt.Chart(df).mark_line().encode(
+        alt.X(field="x", type="quantitative", axis = alt.Axis(orient="top"), 
+                                                              scale=alt.Scale(domain=[0,XMAX])),
+        alt.Y(field="z", type="quantitative", scale=alt.Scale(domain=[0,YMAX],
+                                                              reverse=True))
     ).properties(
-        width=600,
-        height=450
-    )
-
-    lines = base.mark_line().encode(
-        size=alt.condition(~highlight, alt.value(3), alt.value(7))
+        width=YMAX*10,
+        height=XMAX*10
     )
     
-    return points + lines
+    return 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)
+refector_dip = st.slider("Set reflector dip in degrees", min_value=0.1, max_value=MAXDIP, value=26.6)
 # st.write("Poisson's ratio is:", p)
 
 
-st.altair_chart(wb_ava_fig(vwater, rwater, poissons))
+st.altair_chart(fig_1(refector_dip))