diff --git a/SimPEG/forward/DCProblem.py b/SimPEG/forward/DCProblem.py
index 48f25f12..a2ab3967 100644
--- a/SimPEG/forward/DCProblem.py
+++ b/SimPEG/forward/DCProblem.py
@@ -1,5 +1,5 @@
 from SimPEG.mesh import TensorMesh
-from SimPEG.forward import Problem, SyntheticProblem
+from SimPEG.forward import Problem, SyntheticProblem, ModelTransforms
 from SimPEG.tests import checkDerivative
 from SimPEG.utils import ModelBuilder, sdiag, mkvc
 from SimPEG import Solver
@@ -7,7 +7,7 @@ import numpy as np
 import scipy.sparse as sp
 import scipy.sparse.linalg as linalg
 
-class DCProblem(Problem):
+class DCProblem(Problem, ModelTransforms.LogModel):
     """
         **DCProblem**
 
diff --git a/SimPEG/forward/ModelTransforms.py b/SimPEG/forward/ModelTransforms.py
new file mode 100644
index 00000000..8b7dc65a
--- /dev/null
+++ b/SimPEG/forward/ModelTransforms.py
@@ -0,0 +1,49 @@
+import numpy as np
+
+
+class LogModel(object):
+    """docstring for LogModel"""
+    def modelTransform(self, m):
+        """
+            :param numpy.array m: model
+            :rtype: numpy.array
+            :return: transformed model
+
+            The modelTransform changes the model into the physical property.
+
+            A common example of this is to invert for electrical conductivity
+            in log space. In this case, your model will be log(sigma) and to
+            get back to sigma, you can take the exponential:
+
+            .. math::
+
+                m = \log{\sigma}
+
+                \exp{m} = \exp{\log{\sigma}} = \sigma
+        """
+        return np.exp(mkvc(m))
+
+    def modelTransformDeriv(self, m):
+        """
+            :param numpy.array m: model
+            :rtype: scipy.csr_matrix
+            :return: derivative of transformed model
+
+            The modelTransform changes the model into the physical property.
+            The modelTransformDeriv provides the derivative of the modelTransform.
+
+            If the model transform is:
+
+            .. math::
+
+                m = \log{\sigma}
+
+                \exp{m} = \exp{\log{\sigma}} = \sigma
+
+            Then the derivative is:
+
+            .. math::
+
+                \\frac{\partial \exp{m}}{\partial m} = \\text{sdiag}(\exp{m})
+        """
+        return sdiag(np.exp(mkvc(m)))
diff --git a/SimPEG/forward/Problem.py b/SimPEG/forward/Problem.py
index 32dad675..679e8686 100644
--- a/SimPEG/forward/Problem.py
+++ b/SimPEG/forward/Problem.py
@@ -175,13 +175,8 @@ class Problem(object):
             in log space. In this case, your model will be log(sigma) and to
             get back to sigma, you can take the exponential:
 
-            .. math::
-
-                m = \log{\sigma}
-
-                \exp{m} = \exp{\log{\sigma}} = \sigma
         """
-        return np.exp(mkvc(m))
+        return m
 
     def modelTransformDeriv(self, m):
         """
@@ -191,22 +186,8 @@ class Problem(object):
 
             The modelTransform changes the model into the physical property.
             The modelTransformDeriv provides the derivative of the modelTransform.
-
-            If the model transform is:
-
-            .. math::
-
-                m = \log{\sigma}
-
-                \exp{m} = \exp{\log{\sigma}} = \sigma
-
-            Then the derivative is:
-
-            .. math::
-
-                \\frac{\partial \exp{m}}{\partial m} = \\text{sdiag}(\exp{m})
         """
-        return sdiag(np.exp(mkvc(m)))
+        return sp.eye(m.size)
 
 
 
@@ -239,3 +220,6 @@ class SyntheticProblem(object):
         eps = np.linalg.norm(mkvc(dobs),2)*1e-5
         Wd = 1/(abs(dobs)*std+eps)
         return dobs, Wd
+
+
+
diff --git a/SimPEG/forward/__init__.py b/SimPEG/forward/__init__.py
index 5e326a59..5f9dafaf 100644
--- a/SimPEG/forward/__init__.py
+++ b/SimPEG/forward/__init__.py
@@ -1,2 +1,3 @@
 from Problem import *
 from DCProblem import DCProblem
+import ModelTransforms