russell2019_ANN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "russell2019_ANN.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyPdhewwMme0oIEXAs+IuLjt",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yohanesnuwara/computational-geophysics/blob/master/russell2019_ANN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yBJRtq6TLwDJ"
      },
      "source": [
        "## Reproducing \"Machine Learning and Geophysical Inversion: A Numerical Study\" paper by Russell (2019) in Python\n",
        "\n",
        "Yohanes Nuwara"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hrQ0syLO4R3z"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.layers import Dense\n",
        "from keras.models import Sequential\n",
        "from keras.callbacks import ModelCheckpoint, LambdaCallback"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "OW8rtcm1UvaK",
        "outputId": "6fb90980-940c-403e-8410-2b715be77562"
      },
      "source": [
        "# Make wavelet\n",
        "def Ricker(f, t):\n",
        "  pift = np.pi * f * t\n",
        "  wav = (1 - 2 * pift ** 2) * np.exp(-pift ** 2)\n",
        "  return wav\n",
        "\n",
        "t = np.linspace(-0.2,0.2,100)\n",
        "w = Ricker(20, t)\n",
        "\n",
        "# Make wedge model\n",
        "length, depth = 40, 100\n",
        "model = 1 + np.tri(depth, length, -depth//3)\n",
        "model[:depth//3,:] = 0\n",
        "rocks = np.array([[2700, 2750], [2400, 2450], [2800, 3000]])\n",
        "earth = np.take(rocks, model.astype(int), axis=0)\n",
        "imp = np.apply_along_axis(np.product, -1, earth)\n",
        "rc =  (imp[1:,:] - imp[:-1,:]) / (imp[1:,:] + imp[:-1,:])\n",
        "synth = np.apply_along_axis(lambda t: np.convolve(t, w, mode='same'), axis=0, arr=rc)\n",
        "\n",
        "plt.imshow(synth, cmap=\"cubehelix\", aspect=0.2, interpolation='bicubic')\n",
        "plt.title(\"Wedge Model\", size=20)\n",
        "plt.xlabel(\"x\", size=15); plt.ylabel(\"z\", size=15)\n",
        "plt.show()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OUGrCrSwu2Cs",
        "outputId": "c47fb96a-e33d-4027-830a-bdd09ce72857"
      },
      "source": [
        "# Create a Neural Network \n",
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(2, activation='sigmoid', use_bias=False, input_shape=(2,)))\n",
        "model.add(Dense(1, activation='linear', use_bias=False))\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_2 (Dense)              (None, 2)                 4         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 2         \n",
            "=================================================================\n",
            "Total params: 6\n",
            "Trainable params: 6\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZ3k6xslMiL_"
      },
      "source": [
        "# Create a callback that records weight of every epoch\n",
        "weights_dict = {}\n",
        "checkpoint = LambdaCallback \\\n",
        "( on_epoch_end=lambda epoch, logs: weights_dict.update({epoch:model.get_weights()}))"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OHmiVWACMlSX",
        "outputId": "0bc5fcf9-335d-4180-bc0f-3faa1b964ff8"
      },
      "source": [
        "# Synthetic data: seismic trace\n",
        "s = np.array([-0.1,  0.3, -0.3,  0.1])\n",
        "x = np.ones(len(s))\n",
        "X = np.vstack((x, s)).T\n",
        "\n",
        "X"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1. , -0.1],\n",
              "       [ 1. ,  0.3],\n",
              "       [ 1. , -0.3],\n",
              "       [ 1. ,  0.1]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UQgfkErfNi1l",
        "outputId": "123e3ef5-3a9c-4607-9dd4-0cf7c3f083cb"
      },
      "source": [
        "# Synthetic data: Reflectivity\n",
        "y = np.array([0, 0.1, -0.1, 0]).T.reshape(-1,1)\n",
        "\n",
        "y"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0. ],\n",
              "       [ 0.1],\n",
              "       [-0.1],\n",
              "       [ 0. ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "euGQoV5ovB2a",
        "outputId": "6224ea77-7ea3-4a07-a3ec-762b1b4f0005"
      },
      "source": [
        "# Training with the model\n",
        "callback = model.fit(X, y, epochs=5000, callbacks=[checkpoint])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "Epoch 2501/5000\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 5.1083e-04\n",
            "Epoch 2502/5000\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 5.1082e-04\n",
            "Epoch 2503/5000\n",
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            "1/1 [==============================] - 0s 12ms/step - loss: 5.0890e-04\n",
            "Epoch 4961/5000\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 5.0890e-04\n",
            "Epoch 4962/5000\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 5.0890e-04\n",
            "Epoch 4963/5000\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 5.0890e-04\n",
            "Epoch 4964/5000\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 5.0890e-04\n",
            "Epoch 4965/5000\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 5.0890e-04\n",
            "Epoch 4966/5000\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 5.0890e-04\n",
            "Epoch 4967/5000\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 5.0890e-04\n",
            "Epoch 4968/5000\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 5.0890e-04\n",
            "Epoch 4969/5000\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 5.0890e-04\n",
            "Epoch 4970/5000\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 5.0890e-04\n",
            "Epoch 4971/5000\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 5.0890e-04\n",
            "Epoch 4972/5000\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 5.0890e-04\n",
            "Epoch 4973/5000\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 5.0890e-04\n",
            "Epoch 4974/5000\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 5.0889e-04\n",
            "Epoch 4975/5000\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 5.0890e-04\n",
            "Epoch 4976/5000\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 5.0890e-04\n",
            "Epoch 4977/5000\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 5.0889e-04\n",
            "Epoch 4978/5000\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 5.0890e-04\n",
            "Epoch 4979/5000\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 5.0889e-04\n",
            "Epoch 4980/5000\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 5.0889e-04\n",
            "Epoch 4981/5000\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 5.0889e-04\n",
            "Epoch 4982/5000\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 5.0889e-04\n",
            "Epoch 4983/5000\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 5.0889e-04\n",
            "Epoch 4984/5000\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 5.0889e-04\n",
            "Epoch 4985/5000\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 5.0889e-04\n",
            "Epoch 4986/5000\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 5.0889e-04\n",
            "Epoch 4987/5000\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 5.0889e-04\n",
            "Epoch 4988/5000\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 5.0889e-04\n",
            "Epoch 4989/5000\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 5.0889e-04\n",
            "Epoch 4990/5000\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 5.0889e-04\n",
            "Epoch 4991/5000\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 5.0889e-04\n",
            "Epoch 4992/5000\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 5.0889e-04\n",
            "Epoch 4993/5000\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 5.0889e-04\n",
            "Epoch 4994/5000\n",
            "1/1 [==============================] - 0s 24ms/step - loss: 5.0889e-04\n",
            "Epoch 4995/5000\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 5.0889e-04\n",
            "Epoch 4996/5000\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 5.0889e-04\n",
            "Epoch 4997/5000\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 5.0889e-04\n",
            "Epoch 4998/5000\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 5.0889e-04\n",
            "Epoch 4999/5000\n",
            "1/1 [==============================] - 0s 4ms/step - loss: 5.0889e-04\n",
            "Epoch 5000/5000\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 5.0889e-04\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "209uvdbjv5J2",
        "outputId": "84264eec-d74e-470f-9514-df2d8a93fea3"
      },
      "source": [
        "# Compare true vs. predicted reflectivity\n",
        "print(\"True reflectivity:\")\n",
        "print(y)\n",
        "\n",
        "print(\"\\nPredicted reflectivity:\")\n",
        "print(model.predict(X))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "True reflectivity:\n",
            "[[ 0. ]\n",
            " [ 0.1]\n",
            " [-0.1]\n",
            " [ 0. ]]\n",
            "\n",
            "Predicted reflectivity:\n",
            "[[-0.02869523]\n",
            " [ 0.08825724]\n",
            " [-0.09110817]\n",
            " [ 0.03154659]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "4YlO6jFIv9kc",
        "outputId": "1e71c322-4fe6-4323-a132-d540d8ebec81"
      },
      "source": [
        "# Plot loss history\n",
        "plt.plot(callback.history['loss'])\n",
        "plt.xlim(0,1000)\n",
        "plt.title(\"Training Loss\", size=20)\n",
        "plt.xlabel(\"Epoch\", size=15)\n",
        "plt.ylabel(\"Loss\", size=15)\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i8T3CK6ewimy",
        "outputId": "6bd8e88e-c6a7-4fd5-ed72-3c361a363bc0"
      },
      "source": [
        "# Print weights of layer 1\n",
        "model.layers[0].get_weights()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[-1.0630618 , -0.45149684],\n",
              "        [-1.4469109 ,  1.4399436 ]], dtype=float32)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DjFduB5Lwyl4",
        "outputId": "994f9359-6b66-439d-fd8b-1cedceddbf20"
      },
      "source": [
        "# Print weights of layer 2\n",
        "model.layers[1].get_weights()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[-0.59749764],\n",
              "        [ 0.39891523]], dtype=float32)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2IcjrqYhwCAq"
      },
      "source": [
        "# Retrieve weights\n",
        "w1, w2 = [], []\n",
        "for epoch,weights in weights_dict.items():\n",
        "  # Weights of first layer\n",
        "  w1.append(weights[0])\n",
        "  # Weights of second layer\n",
        "  w2.append(weights[1])\n",
        "\n",
        "w1, w2 = np.array(w1), np.array(w2)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "qbASM-XX2wIV",
        "outputId": "1641b2e6-1f1f-4dcd-81fc-5f25931bef46"
      },
      "source": [
        "# Plot weights history\n",
        "plt.figure(figsize=(9,4))\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "for i in range(2):\n",
        "  for j in range(2):\n",
        "    plt.plot(w1[:,i,j], label=\"w1_{},{}\".format(i,j))\n",
        "plt.xlim(0,5000)\n",
        "plt.title(\"Evolution of 1st Layer Weights\", size=17, pad=10)\n",
        "plt.xlabel(\"Epoch\", size=15); plt.ylabel(\"$w_1$\", size=15)\n",
        "plt.legend(loc='right')\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "for i in range(2):\n",
        "  plt.plot(w2[:,i,0], label=\"w1_{},0\".format(i))\n",
        "plt.xlim(0,5000)\n",
        "plt.title(\"Evolution of 2nd Layer Weights\", size=17, pad=10)\n",
        "plt.xlabel(\"Epoch\", size=15); plt.ylabel(\"$w_2$\", size=15)\n",
        "plt.legend(loc='right')\n",
        "\n",
        "plt.tight_layout(1.1)\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}