diff --git a/notebooks/DC_schumberger_Inv.ipynb b/notebooks/DC_schumberger_Inv.ipynb
index 380a7996..35671ebd 100644
--- a/notebooks/DC_schumberger_Inv.ipynb
+++ b/notebooks/DC_schumberger_Inv.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:4229cd58b6b60201a30952dbf77a9ee8094f47ebfbad5229647a4b44c623620f"
+  "signature": "sha256:217ff4129b31cd5fb26fce3dc7aef27cf513d55e30cd6c91c3716bcc65a22097"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -160,7 +160,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "fig, ax = plt.subplots(1,1, figsize = (7, 7))\n",
+      "fig, ax = plt.subplots(1,1, figsize = (7.5, 7))\n",
       "Utils1D.plotLayer(np.log(sigma), mesh.vectorCCz, 'linear', showlayers=True, ax = ax)\n",
       "ax.invert_xaxis()\n",
       "ax.set_ylim(-500, 0)\n",
@@ -176,13 +176,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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zPP3Xf/3XjSFAkvb390O/7k2nE/rNY7C1taWvX78GRwjan+6vunrtwKD2Xq4GAQAAonb9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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x58414d0>"
+        "<matplotlib.figure.Figure at 0x6591a50>"
        ]
       }
      ],
-     "prompt_number": 45
+     "prompt_number": 46
     },
     {
      "cell_type": "code",
@@ -722,7 +722,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "fig, ax = plt.subplots(1,1, figsize = (7, 7))\n",
+      "fig, ax = plt.subplots(1,1, figsize = (7.5, 7))\n",
       "ax.plot(1., 1., 'k', lw = 2)\n",
       "ax.plot(1., 1., 'r', lw = 2)\n",
       "ax.legend(('True', 'Predicted'), loc = 3, fontsize = 16)\n",
@@ -740,13 +740,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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l27JXGqjr6+ttrzgvOzk5kWmabueib775RpZl6Re/+IXW19cViUSaArdarbqd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\njbp9U+CNHzx+/PjxVVeiVz6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u5QAAeMnQR6FbltWyP9tRH9aFQkGrq6st6zln0/WcYA4Gg13L29mquxF8cXFR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\nAAcAwIP+P5bBBbF9N08zAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0xc678450>"
+        "<matplotlib.figure.Figure at 0x51b9310>"
        ]
       }
      ],
-     "prompt_number": 41
+     "prompt_number": 47
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
     }
    ],
    "metadata": {}
diff --git a/notebooks/obspred_dc1d_mod.png b/notebooks/obspred_dc1d_mod.png
index f98f1aae..bd9f7cb8 100644
Binary files a/notebooks/obspred_dc1d_mod.png and b/notebooks/obspred_dc1d_mod.png differ