diff --git a/notebooks/12_gravity-inversion-with-constructor.ipynb b/notebooks/12_gravity-inversion-with-constructor.ipynb
index 2eb5991..4f2c16a 100644
--- a/notebooks/12_gravity-inversion-with-constructor.ipynb
+++ b/notebooks/12_gravity-inversion-with-constructor.ipynb
@@ -273,7 +273,7 @@
     {
      "data": {
       "text/plain": [
-       "(<matplotlib.collections.QuadMesh at 0x7f1379fd92b0>,)"
+       "(<matplotlib.collections.QuadMesh at 0x7f0775c20ad0>,)"
       ]
      },
      "execution_count": 8,
@@ -436,7 +436,13 @@
    "outputs": [],
    "source": [
     "depth_weights = depth_weighting(mesh, 0) ** 2\n",
-    "smallness = ii.Smallness(mesh=mesh, cell_weights=depth_weights)"
+    "model_norm = ii.create_tikhonov_regularization(\n",
+    "    mesh,\n",
+    "    cell_weights=depth_weights,\n",
+    "    alpha_x=1e3,\n",
+    "    alpha_y=1e3,\n",
+    "    alpha_z=1e3,\n",
+    ")"
    ]
   },
   {
@@ -466,7 +472,7 @@
     "\n",
     "inversion = ii.create_l2_inversion(\n",
     "    data_misfit,\n",
-    "    smallness,\n",
+    "    model_norm,\n",
     "    starting_beta=1e6,\n",
     "    initial_model=initial_model,\n",
     "    minimizer=ii.conjugate_gradient,\n",
@@ -492,7 +498,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8dafaca515cd4357a87b499ddcbbe63b",
+       "model_id": "dba01a727b894119a4d239bd1cbaff6a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -546,10 +552,13 @@
        "┃<span style=\"font-weight: bold\"> Iteration </span>┃<span style=\"font-weight: bold\"> β        </span>┃<span style=\"font-weight: bold\"> φ_d      </span>┃<span style=\"font-weight: bold\"> φ_m      </span>┃<span style=\"font-weight: bold\"> β φ_m    </span>┃<span style=\"font-weight: bold\"> φ        </span>┃<span style=\"font-weight: bold\"> χ        </span>┃\n",
        "┡━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
        "│ 0         │ 1.00e+06 │ 1.67e+06 │ 0.00e+00 │ 0.00e+00 │ 1.67e+06 │ 1.74e+03 │\n",
-       "│ 1         │ 1.00e+06 │ 6.64e+03 │ 5.78e-02 │ 5.78e+04 │ 6.45e+04 │ 6.91e+00 │\n",
-       "│ 2         │ 5.00e+05 │ 2.55e+03 │ 6.34e-02 │ 3.17e+04 │ 3.42e+04 │ 2.65e+00 │\n",
-       "│ 3         │ 2.50e+05 │ 1.13e+03 │ 6.72e-02 │ 1.68e+04 │ 1.79e+04 │ 1.18e+00 │\n",
-       "│ 4         │ 1.25e+05 │ 5.80e+02 │ 7.03e-02 │ 8.79e+03 │ 9.37e+03 │ 6.03e-01 │\n",
+       "│ 1         │ 1.00e+06 │ 5.03e+04 │ 1.20e-01 │ 1.20e+05 │ 1.70e+05 │ 5.23e+01 │\n",
+       "│ 2         │ 5.00e+05 │ 2.30e+04 │ 1.57e-01 │ 7.87e+04 │ 1.02e+05 │ 2.39e+01 │\n",
+       "│ 3         │ 2.50e+05 │ 1.00e+04 │ 1.93e-01 │ 4.83e+04 │ 5.83e+04 │ 1.04e+01 │\n",
+       "│ 4         │ 1.25e+05 │ 4.41e+03 │ 2.24e-01 │ 2.80e+04 │ 3.24e+04 │ 4.58e+00 │\n",
+       "│ 5         │ 6.25e+04 │ 2.13e+03 │ 2.49e-01 │ 1.56e+04 │ 1.77e+04 │ 2.22e+00 │\n",
+       "│ 6         │ 3.12e+04 │ 1.28e+03 │ 2.67e-01 │ 8.36e+03 │ 9.64e+03 │ 1.33e+00 │\n",
+       "│ 7         │ 1.56e+04 │ 9.61e+02 │ 2.81e-01 │ 4.40e+03 │ 5.36e+03 │ 1.00e+00 │\n",
        "└───────────┴──────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n",
        "</pre>\n"
       ],
@@ -558,10 +567,13 @@
        "┃\u001b[1m \u001b[0m\u001b[1mIteration\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mβ       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mφ_d     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mφ_m     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mβ φ_m   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mφ       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mχ       \u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
        "│ 0         │ 1.00e+06 │ 1.67e+06 │ 0.00e+00 │ 0.00e+00 │ 1.67e+06 │ 1.74e+03 │\n",
-       "│ 1         │ 1.00e+06 │ 6.64e+03 │ 5.78e-02 │ 5.78e+04 │ 6.45e+04 │ 6.91e+00 │\n",
-       "│ 2         │ 5.00e+05 │ 2.55e+03 │ 6.34e-02 │ 3.17e+04 │ 3.42e+04 │ 2.65e+00 │\n",
-       "│ 3         │ 2.50e+05 │ 1.13e+03 │ 6.72e-02 │ 1.68e+04 │ 1.79e+04 │ 1.18e+00 │\n",
-       "│ 4         │ 1.25e+05 │ 5.80e+02 │ 7.03e-02 │ 8.79e+03 │ 9.37e+03 │ 6.03e-01 │\n",
+       "│ 1         │ 1.00e+06 │ 5.03e+04 │ 1.20e-01 │ 1.20e+05 │ 1.70e+05 │ 5.23e+01 │\n",
+       "│ 2         │ 5.00e+05 │ 2.30e+04 │ 1.57e-01 │ 7.87e+04 │ 1.02e+05 │ 2.39e+01 │\n",
+       "│ 3         │ 2.50e+05 │ 1.00e+04 │ 1.93e-01 │ 4.83e+04 │ 5.83e+04 │ 1.04e+01 │\n",
+       "│ 4         │ 1.25e+05 │ 4.41e+03 │ 2.24e-01 │ 2.80e+04 │ 3.24e+04 │ 4.58e+00 │\n",
+       "│ 5         │ 6.25e+04 │ 2.13e+03 │ 2.49e-01 │ 1.56e+04 │ 1.77e+04 │ 2.22e+00 │\n",
+       "│ 6         │ 3.12e+04 │ 1.28e+03 │ 2.67e-01 │ 8.36e+03 │ 9.64e+03 │ 1.33e+00 │\n",
+       "│ 7         │ 1.56e+04 │ 9.61e+02 │ 2.81e-01 │ 4.40e+03 │ 5.36e+03 │ 1.00e+00 │\n",
        "└───────────┴──────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n"
       ]
      },
@@ -577,6 +589,28 @@
   {
    "cell_type": "code",
    "execution_count": 17,
+   "id": "967d9134",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.08633338056858217\n",
+      "0.0776953068325558\n",
+      "0.07178035072308296\n",
+      "0.04563932787612317\n"
+     ]
+    }
+   ],
+   "source": [
+    "for term in model_norm:\n",
+    "    print(term(model))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
    "id": "4cd79386-3d2b-4e27-b7d3-6704d28c1a87",
    "metadata": {
     "execution": {
@@ -593,18 +627,18 @@
       "text/html": [
        "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">[x] ChiTarget</span>\n",
        "├── Condition: chi &lt; chi_target\n",
-       "├── chi        = 6.03e-01\n",
+       "├── chi        = 1.00e+00\n",
        "└── chi_target = 1.00e+00\n",
        "</pre>\n"
       ],
       "text/plain": [
        "\u001b[1;32m[x] ChiTarget\u001b[0m\n",
        "├── Condition: chi < chi_target\n",
-       "├── chi        = 6.03e-01\n",
+       "├── chi        = 1.00e+00\n",
        "└── chi_target = 1.00e+00\n"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -615,7 +649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "44ef609b-fe1a-4f1d-bc11-6dcf60b156ec",
    "metadata": {
     "execution": {
@@ -678,62 +712,95 @@
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1000000.0</td>\n",
-       "      <td>6.637210e+03</td>\n",
-       "      <td>0.057847</td>\n",
-       "      <td>57846.559635</td>\n",
-       "      <td>6.448377e+04</td>\n",
-       "      <td>6.906566</td>\n",
+       "      <td>5.028302e+04</td>\n",
+       "      <td>0.119606</td>\n",
+       "      <td>119605.534220</td>\n",
+       "      <td>1.698886e+05</td>\n",
+       "      <td>52.323642</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>500000.0</td>\n",
-       "      <td>2.547458e+03</td>\n",
-       "      <td>0.063398</td>\n",
-       "      <td>31698.978802</td>\n",
-       "      <td>3.424644e+04</td>\n",
-       "      <td>2.650841</td>\n",
+       "      <td>2.299463e+04</td>\n",
+       "      <td>0.157437</td>\n",
+       "      <td>78718.423465</td>\n",
+       "      <td>1.017131e+05</td>\n",
+       "      <td>23.927817</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>250000.0</td>\n",
-       "      <td>1.131466e+03</td>\n",
-       "      <td>0.067249</td>\n",
-       "      <td>16812.309635</td>\n",
-       "      <td>1.794378e+04</td>\n",
-       "      <td>1.177384</td>\n",
+       "      <td>1.003895e+04</td>\n",
+       "      <td>0.193140</td>\n",
+       "      <td>48284.884854</td>\n",
+       "      <td>5.832384e+04</td>\n",
+       "      <td>10.446362</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>125000.0</td>\n",
-       "      <td>5.797528e+02</td>\n",
-       "      <td>0.070289</td>\n",
-       "      <td>8786.136143</td>\n",
-       "      <td>9.365889e+03</td>\n",
-       "      <td>0.603281</td>\n",
+       "      <td>4.405399e+03</td>\n",
+       "      <td>0.224059</td>\n",
+       "      <td>28007.433316</td>\n",
+       "      <td>3.241283e+04</td>\n",
+       "      <td>4.584182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>62500.0</td>\n",
+       "      <td>2.132387e+03</td>\n",
+       "      <td>0.248899</td>\n",
+       "      <td>15556.175973</td>\n",
+       "      <td>1.768856e+04</td>\n",
+       "      <td>2.218925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>31250.0</td>\n",
+       "      <td>1.279920e+03</td>\n",
+       "      <td>0.267472</td>\n",
+       "      <td>8358.499284</td>\n",
+       "      <td>9.638419e+03</td>\n",
+       "      <td>1.331862</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>15625.0</td>\n",
+       "      <td>9.608719e+02</td>\n",
+       "      <td>0.281448</td>\n",
+       "      <td>4397.630719</td>\n",
+       "      <td>5.358503e+03</td>\n",
+       "      <td>0.999867</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "           beta         phi_d     phi_m  beta * phi_m           phi  \\\n",
-       "iter                                                                  \n",
-       "0     1000000.0  1.667546e+06  0.000000      0.000000  1.667546e+06   \n",
-       "1     1000000.0  6.637210e+03  0.057847  57846.559635  6.448377e+04   \n",
-       "2      500000.0  2.547458e+03  0.063398  31698.978802  3.424644e+04   \n",
-       "3      250000.0  1.131466e+03  0.067249  16812.309635  1.794378e+04   \n",
-       "4      125000.0  5.797528e+02  0.070289   8786.136143  9.365889e+03   \n",
+       "           beta         phi_d     phi_m   beta * phi_m           phi  \\\n",
+       "iter                                                                   \n",
+       "0     1000000.0  1.667546e+06  0.000000       0.000000  1.667546e+06   \n",
+       "1     1000000.0  5.028302e+04  0.119606  119605.534220  1.698886e+05   \n",
+       "2      500000.0  2.299463e+04  0.157437   78718.423465  1.017131e+05   \n",
+       "3      250000.0  1.003895e+04  0.193140   48284.884854  5.832384e+04   \n",
+       "4      125000.0  4.405399e+03  0.224059   28007.433316  3.241283e+04   \n",
+       "5       62500.0  2.132387e+03  0.248899   15556.175973  1.768856e+04   \n",
+       "6       31250.0  1.279920e+03  0.267472    8358.499284  9.638419e+03   \n",
+       "7       15625.0  9.608719e+02  0.281448    4397.630719  5.358503e+03   \n",
        "\n",
        "              chi  \n",
        "iter               \n",
        "0     1735.219501  \n",
-       "1        6.906566  \n",
-       "2        2.650841  \n",
-       "3        1.177384  \n",
-       "4        0.603281  "
+       "1       52.323642  \n",
+       "2       23.927817  \n",
+       "3       10.446362  \n",
+       "4        4.584182  \n",
+       "5        2.218925  \n",
+       "6        1.331862  \n",
+       "7        0.999867  "
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -745,7 +812,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "9feec0e0-ce9b-43cf-b7e5-bc4f77ae879f",
    "metadata": {
     "execution": {
@@ -759,7 +826,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 600x800 with 3 Axes>"
       ]
@@ -786,7 +853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "70b2b5ea-d6dd-4cf3-855c-9e8fd600c7cd",
    "metadata": {
     "execution": {
@@ -801,16 +868,16 @@
     {
      "data": {
       "text/plain": [
-       "(<matplotlib.collections.QuadMesh at 0x7f136276b390>,)"
+       "(<matplotlib.collections.QuadMesh at 0x7f075e46e850>,)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -826,9 +893,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:inversion_ideas]",
+   "display_name": "inversion_ideas",
    "language": "python",
-   "name": "conda-env-inversion_ideas-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/inversion_ideas/__init__.py b/src/inversion_ideas/__init__.py
index 29dee61..f2c2269 100644
--- a/src/inversion_ideas/__init__.py
+++ b/src/inversion_ideas/__init__.py
@@ -16,7 +16,11 @@
 from .inversion_log import InversionLog, InversionLogRich
 from .minimize import GaussNewtonConjugateGradient, conjugate_gradient
 from .preconditioners import JacobiPreconditioner, get_jacobi_preconditioner
-from .recipes import create_l2_inversion, create_sparse_inversion
+from .recipes import (
+    create_l2_inversion,
+    create_sparse_inversion,
+    create_tikhonov_regularization,
+)
 from .regularization import Flatness, Smallness, SparseSmallness, TikhonovZero
 from .simulations import wrap_simulation
 
@@ -44,6 +48,7 @@
     "conjugate_gradient",
     "create_l2_inversion",
     "create_sparse_inversion",
+    "create_tikhonov_regularization",
     "get_jacobi_preconditioner",
     "typing",
     "utils",
diff --git a/src/inversion_ideas/recipes.py b/src/inversion_ideas/recipes.py
index 6d754d0..73fc6cc 100644
--- a/src/inversion_ideas/recipes.py
+++ b/src/inversion_ideas/recipes.py
@@ -1,16 +1,20 @@
 """
-Recipe functions to easily build commonly used inversions.
+Recipe functions to easily build commonly used inversions and objective functions.
 """
 
 from collections.abc import Callable
 
-from .base import Minimizer, Objective
+import numpy as np
+import numpy.typing as npt
+
+from .base import Combo, Minimizer, Objective
 from .conditions import ChiTarget, ObjectiveChanged
 from .data_misfit import DataMisfit
 from .directives import Irls, MultiplierCooler
 from .inversion import Inversion
 from .inversion_log import Column
 from .preconditioners import JacobiPreconditioner
+from .regularization import Flatness, Smallness
 from .typing import Model, Preconditioner
 
 
@@ -256,3 +260,82 @@ def create_sparse_inversion(
             ),
         )
     return inversion
+
+
+def create_tikhonov_regularization(
+    mesh,
+    *,
+    active_cells: npt.NDArray[np.bool] | None = None,
+    cell_weights: npt.NDArray | dict[str, npt.NDArray] | None = None,
+    reference_model: Model | None = None,
+    alpha_s: float | None = None,
+    alpha_x: float | None = None,
+    alpha_y: float | None = None,
+    alpha_z: float | None = None,
+    reference_model_in_flatness: bool = False,
+) -> Combo:
+    """
+    Create a linear combination of Tikhonov (L2) regularization terms.
+
+    Define a :class:`inversion_ideas.base.Combo` with L2 smallness and flatness
+    regularization terms.
+
+    Parameters
+    ----------
+    mesh : discretize.base.BaseMesh
+        Mesh to use in the regularization.
+    active_cells : (n_params) array or None, optional
+        Array full of bools that indicate the active cells in the mesh.
+    cell_weights : (n_params) array or dict of (n_params) arrays or None, optional
+        Array with cell weights.
+        For multiple cell weights, pass a dictionary where keys are strings and values
+        are the different weights arrays.
+        If None, no cell weights are going to be used.
+    reference_model : (n_params) array or None, optional
+        Array with values for the reference model.
+    alpha_s : float or None, optional
+        Multiplier for the smallness term.
+    alpha_x, alpha_y, alpha_z : float or None, optional
+        Multipliers for the flatness terms.
+
+    Returns
+    -------
+    inversion_ideas.base.Combo
+        Combo of L2 regularization terms.
+
+    Notes
+    -----
+    TODO
+    """
+    # TODO: raise errors:
+    #   if dims == 2 and alpha_z is passed
+    #   if dims == 1 and alpha_y or alpha_z are passed
+    smallness = Smallness(
+        mesh,
+        active_cells=active_cells,
+        cell_weights=cell_weights,
+        reference_model=reference_model,
+    )
+    if alpha_s is not None:
+        smallness = alpha_s * smallness
+
+    kwargs = {
+        "active_cells": active_cells,
+        "cell_weights": cell_weights,
+    }
+    if reference_model_in_flatness:
+        kwargs["reference_model"] = reference_model
+
+    flatness_x = Flatness(mesh, **kwargs, direction="x")
+    if alpha_x is not None:
+        flatness_x = alpha_x * flatness_x
+
+    flatness_y = Flatness(mesh, **kwargs, direction="y")
+    if alpha_y is not None:
+        flatness_y = alpha_y * flatness_y
+
+    flatness_z = Flatness(mesh, **kwargs, direction="z")
+    if alpha_z is not None:
+        flatness_z = alpha_z * flatness_z
+
+    return (smallness + flatness_x + flatness_y + flatness_z).flatten()
diff --git a/src/inversion_ideas/regularization/_mesh_based.py b/src/inversion_ideas/regularization/_mesh_based.py
index 86c8b72..c3519f3 100644
--- a/src/inversion_ideas/regularization/_mesh_based.py
+++ b/src/inversion_ideas/regularization/_mesh_based.py
@@ -119,7 +119,7 @@ def __init__(
         *,
         active_cells: npt.NDArray[np.bool] | None = None,
         cell_weights: npt.NDArray | dict[str, npt.NDArray] | None = None,
-        reference_model=None,
+        reference_model: Model | None = None,
     ):
         self.mesh = mesh
         self.active_cells = (
@@ -341,7 +341,7 @@ def __init__(
         *,
         active_cells: npt.NDArray[np.bool] | None = None,
         cell_weights: npt.NDArray | dict[str, npt.NDArray] | None = None,
-        reference_model=None,
+        reference_model: Model | None = None,
     ):
         self.mesh = mesh
         self.direction = direction
@@ -558,7 +558,7 @@ def __init__(
         norm: float,
         active_cells: npt.NDArray | None = None,
         cell_weights: npt.NDArray | dict[str, npt.NDArray] | None = None,
-        reference_model=None,
+        reference_model: Model | None = None,
         threshold: float = 1e-8,
         cooling_factor=1.25,
         model_previous: Model | None = None,