{
"cells": [
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"source": [
"# TP4 : Sismique réfraction\n",
"\n",
"### Instructions pour le devoir\n",
"\n",
"Ce laboratoire porte sur la méthode de la sismique réfraction. Il comporte une section théorique et une section expérimentale où vous aurez à traiter des données acquises sur le terrain.\n",
" \n",
"- Remplissez vos réponses dans les cellules indiquées. Les réponses peuvent prendre la forme d'un segment de code ou d'une réponse textuelle, ou les deux.\n",
"- Bien commenter les cellules de code afin de décrire la démarche.\n",
"- Assurez-vous que chaque cellule s'exécute et donne la réponse désirée avant de remettre votre notebook. Pour ce faire, il est recommandé d'exécuter Kernel -> Restart and Run All.\n",
"- Vous devez remettre les fichiers du jupyter notebook complété (.ipynb), ainsi que sa version pdf (File->Download as -> pdf).\n",
"- Ce travail doit être fait de façon individuelle, chaque personne doit remettre sa propre copie.\n",
"\n",
"### Installation des librairies nécessaires\n",
"\n",
"Il se peut que votre environnement n'ai pas encore les librairies nécessaires pour ce TP. Dans ce cas, faites rouler la cellule suivante."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [
{
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]
}
],
"source": [
"!pip install obspy\n",
"!pip install ipympl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import obspy \n",
"from FonctionsTP4 import seismic_wiggle, picker\n",
"import numpy as np\n",
"#%matplotlib widget\n",
"%matplotlib notebook"
]
},
{
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"
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Partie 1: Section théorique\n",
"\n",
"### Q1 Temps de parcours (1 points)\n",
"Selon un milieu à deux couches montré à la figure suivante\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__A)__ Déterminez le temps de parcours $t_{air}$ pour l’onde qui voyage dans l’air en fonction de la distance d et la vitesse dans l’air ($v_{air}$ = 340.29 m/s)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Réponse:\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__B)__ Déterminez le temps de parcours de l’onde directe en fonction de la distance d et de la vitesse V1."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Réponse:\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__C)__ Déterminez l’angle critique $i_c$ en fonction des vitesses V1 et V2. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Réponse:\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__D)__ Déterminez le temps de parcours de la réflexion P-P en fonction de la vitesse V1, de l’épaisseur h et de la distance d."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Réponse:\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__E)__ Déterminez le temps de parcours de l’onde réfractée P en fonction de V1, de l’épaisseur h et de la distance d."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Réponse:\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__F)__ Déterminez la distance critique de l’onde P, c’est-à-dire la distance la plus courte à laquelle l’onde réfractée peut atteindre la surface, en fonction de V1, V2, de l’épaisseur h et de la distance d."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Réponse:\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__G)__\tDéterminez le temps d’intercepte de l’onde P réfractée en fonction de V1, V2, de l’épaisseur h et de la distance d."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Réponse:\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__H)__\tDéterminez la distance de croisement, c’est-à-dire la distance où le temps de parcours de l’onde directe est égal au temps de parcours de l’onde réfractée, en fonction de V1, V2, de l’épaisseur h et de la distance d."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Réponse:\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Q2 Milieu à N couches (1 points)\n",
"\n",
"Pour un milieu tabulaire à N couches avec V_1\n",
"Réponse:\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Q3 Milieu à N couches (2 points)\n",
"Le tableau suivant donne les temps des premières arrivées mesurées lors d’un levé de sismique réfraction réalisé pour déterminer l’épaisseur et l’attitude du mort-terrain. Les tirs A et B sont les tirs direct et inverse réalisés à 15 m des extrémités du dispositif de géophones. La surface du sol est plane. \n",
"\n",
"\\begin{array}{|c|c|c|}\n",
"\\hline \\textrm{Position du géophone (m)} & \\textrm{Temps (ms) tir direct} & \\textrm{Temps (ms) tir inverse} \\\\\\hline\n",
"0 \\textrm{ (tir A)}&\t0\t& - \\\\\\hline\n",
"15\t& 10\t& 85 \\\\\\hline\n",
"30\t& 20\t& 80 \\\\\\hline\n",
"45\t& 30\t& 74 \\\\\\hline\n",
"60\t& 40\t& 69 \\\\\\hline\n",
"75\t& 50\t& 63 \\\\\\hline\n",
"90\t& 60\t& 57 \\\\\\hline\n",
"105\t& 65\t& 51 \\\\\\hline\n",
"120\t& 69\t& 46 \\\\\\hline\n",
"135\t& 73\t& 40 \\\\\\hline\n",
"150\t& 77\t& 30 \\\\\\hline\n",
"165\t& 81\t& 20 \\\\\\hline\n",
"180\t& 85\t& 10 \\\\\\hline\n",
"195 \\textrm{ (tir B)}& - & 0 \\\\\\hline\n",
"\\end{array}\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__A)__\tTracez les dromochroniques."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Répondre ici"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__B)__\tTrouvez les vitesses de la première et de la deuxième couche, le pendage de la première, et les profondeurs au réfracteur à la verticale des points de tir A et B."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Réponse:\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Partie 2: Section expérimentale\n",
"\n",
"\n",
"## Introduction (Mise en contexte)\n",
"\n",
"Votre firme XYZ inc. a été chargée d’évaluer les volumes de sol contaminé aux HAP sur un site\n",
"d’une ancienne usine d’imprégnation à la créosote de traverses de chemin de fer, qui est en\n",
"activité depuis plus d’une centaine d’années. Les contaminants organiques contiennent deux\n",
"phases : une plus lourde que l’eau qui s’est écoulée jusqu’au socle, et une plus légère que l’eau\n",
"qui flotte sur la nappe. La géologie au site d’études consiste en des alluvions plutôt homogènes\n",
"reposant sur un socle de calcaire. Afin de guider votre compréhension du transport des\n",
"contaminants, vous avez décidé de procéder à un levé de sismique réfraction. En particulier, la\n",
"position de la nappe ainsi que la profondeur au roc doivent être déterminées afin de bien\n",
"comprendre la migration des deux phases de HAP.\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Étape 1: Formulation du problème (0,25 points)\n",
"\n",
"Quels sont les objectifs du levé géophysique ?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Réponse:\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Étape 2: Choix des propriétés géophysiques (0,25 points)\n",
"\n",
"Justifiez le choix d'une méthode sensible aux changements de vitesse sismique afin d'atteindre les objectifs du levé."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Réponse:\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Étape 3: Choix de la méthode géophysique (0,25 points)\n",
"\n",
"Justifiez votre choix d'un levé en sismique réfraction. Selon ce que vous connaissez de la géologie, y-a-t-il des limitations connues de la méthode qui pourraient affecter vos résultats ?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Réponse:\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Étape 4: Acquisition des données (1 points)\n",
"\n",
"Vous devez simuler l'acquisition de vos propres données sur le terrain. Remplissez la feuille de terrain fournie en annexe."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Étape 5 Traitement des données (3,5 points)\n",
"\n",
"Dans cette section, vous aurez à traiter et interpréter les données de sismique réfraction acquises lors de la campagne de caractérisation du site contaminé aux HAP.\n",
"\n",
"Chacun des tirs se trouve dans un fichier au format .sg2. Dans ce levé, les 48 géophones étaient positionnés à 2m d'intervalle. La position des tirs est affichée dans le tableau suivant.\n",
"\n",
"
\n",
"\n",
"| Tirs | 1026 | 1030 | 1034 | 1037 | 1041 |\n",
"| :--: | :--: | :--: | :--: | :--: | :--: |\n",
"| __Positions (m)__ | -1 | 25 | 47 | 65 | 89 |\n",
"\n",
"
\n",
"\n",
"\n",
"\n",
"Les positions des tirs et des receveurs définies par les deux variables suivantes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ShotPos = np.array([-1, 25, 47, 65, 89])\n",
"RecPos = np.arange(0, 96, 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__A)__ Faites le pointé des premières arrivées pour tous les tirs. Pour vous aider, nous vous fournissons le script de la cellule suivante. \n",
"\n",
"1. À la première ligne, nous utilisons la fonction `obspy.read` pour lire le fichier `.sg2`. Ceci ressort un objet obspy appelé `Trace`, qui contient plusieurs informations pour chaque trace, dont les données.\n",
"2. Le temps d'échantillonnage est ensuite extrait du fichier.\n",
"3. Nous extrayons les données pour en faire un `np.array`.\n",
"4. Nous créons une figure montrant les données avec la fonction `seismic_wiggle`. Vous devriez jouer avec les paramètres de cette fonction pour mieux identifier les premières arrivées. Voir le fichier `FonctionTP4.py` pour la documentation.\n",
"5. La fonction `picker` sert à ajouter la capacité à la figure d'identifier les premières arrivées. Un clic gauche sur une trace permet d'identifier l'arrivée, un clic droit sur un temps identifié le supprime, appuyez sur la touche `f` et les premières arrivées seront sauvegardées dans le fichier indiqué par l'argument `filename`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"rec1026 = obspy.read('rec1026.sg2')\n",
"dt = rec1026[0].stats.delta\n",
"data1026 = np.stack([t.data for t in rec1026], axis=1)\n",
"ax = seismic_wiggle(data1026, dt=dt, offsets=RecPos, normalize=\"trace\", scale=6, clip=2, )\n",
"picks1026 = picker(ax, RecPos, filename=\"rec1026.npy\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Les pointés peuvent ensuite être obtenus grâce à la variable `picks1026`. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(picks1026.xs) #position des traces\n",
"print(picks1026.ys) #temps d'arrivée"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Refaites ce processus pour chaque tir."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Réponse Tir 1030"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Réponse Tir 1034"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Réponse Tir 1037"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Réponse Tir 1041"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__B)__ Produire une figure montrant les dromochroniques de tous les points de tir, soit un graphique avec la position de la trace en x et le temps d'arrivées en y. Dans la légende, identifiez chacun des tirs. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Répondre ici"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__C)__ Produire une figure montrant les temps d’arrivée des 5 points de tir en fonction de la distance de la source (distance du géophone – distance du tir)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Répondre ici"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__D)__ Justifiez le traitement en couches planes horizontales à partir de cette figure."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Réponse :\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__E)__ On vous demande de faire le traitement du levé selon un modèle de n couches horizontales. Pour cela, \n",
" regroupez tous les temps d’arrivée de tous les tirs dans un même vecteur, et faites votre traitement comme s’il n’y avait qu’un seul tir. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__E i)__ Créez deux vecteurs: un vecteur contenant les valeurs absolues de la distance à la source de tous les pointés que vous nommerez `xall`et un vecteur contenant tous les pointés (unité secondes), que vous nommerez `tall`. Vous pouvez utiliser la fonction `np.hstack`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Répondre ici"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La fonction suivante permet de séparer en plusieurs segments les pointés selon leur distance de croisement, et retourne la vitesse apparente de chaque segment, ainsi que le temps d'intercepte en faisant une régression linéaire. Cette fonction affiche aussi le graphique des temps d'arrivées et des régressions linéaires de chaque segment."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def vitesses(x, t, xi):\n",
" \"\"\"\n",
" Sépare les pointés en segment, et retourne la vitesse et le temps d'intercepte.\n",
" Affiche une figure des temps d'arrivées et des segments identifiés\n",
" \n",
" :param x: vecteur de position des pointé (m)\n",
" :param t: vecteur des temps des pointés (s)\n",
" :param xi: liste contenant les distances de croisement désirées\n",
" \n",
" :return v: liste contenant la vitesse de chaque segement (inverse de la pente)\n",
" :return to: temps d'intercepte de chaque segment.\n",
" \"\"\"\n",
" xi = [0] + xi + [x.max()]\n",
" v = []\n",
" t0 = []\n",
" fig = plt.figure(figsize=(8, 5))\n",
" ax = fig.add_subplot(111)\n",
" for ii in range(len(xi)-1):\n",
" thisx = x[(xi[ii] < x) & (x <= xi[ii+1])]\n",
" thist = t[(xi[ii] < x) & (x <= xi[ii+1])]\n",
" fit = np.polyfit(thisx, thist, 1)\n",
" v.append(1.0/fit[0])\n",
" t0.append(fit[1])\n",
" line, = ax.plot(thisx, thist, \".\")\n",
" ax.plot([xi[ii], xi[ii+1]], [fit[0]*xi[ii] + fit[1], fit[0]*xi[ii+1] + fit[1]], line.get_color())\n",
" plt.show()\n",
" return v, t0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__E ii)__ Écrire une fonction permettant de calculer l'épaisseur de chaque couche selon la méthode de distance de croisement. Votre fonction doit prendre en entrée une liste des vitesses de chaque couche ainsi que le temps d'intercepte. Les distances de croisement devraient être calculées grâce aux vitesses et au temps d'intercepte. Votre fonction devrait retourner les distances de croisement et les épaisseurs de chaque couche. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Répondre ici\n",
"def ncouche_xi(v, t0):\n",
" h = []\n",
" xi = []\n",
" #Complétez\n",
" return xi, h"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__E iii)__ Écrire une autre fonction qui estime l'épaisseur de chaque couche, cette fois, avec la méthode des temps d'intercepte."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Répondre ici\n",
"def ncouche_t0(v, t0):\n",
" h = []\n",
" #Complétez\n",
" return h"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__E iv)__ Avec les fonctions que vous venez de définir, estimez les vitesses et épaisseurs pour un modèle à 2 couches selon la méthode des distances de croisement et des temps d'intercepte. Votre cellule devrait imprimer les valeurs (vitesses, épaisseurs) obtenues selon les deux méthodes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Répondre ici"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__E v)__ Refaites l'analyse pour un modèle à 3 couches."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Répondre ici"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__E vi)__ Enfin, refaites l'analyse pour un modèle à 4 couches."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Répondre ici"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Étape 6 Interprétation des résultats (0.5 points)\n",
"\n",
"Quel est le meilleur modèle selon vous (2,3,4 couches) ? Est-ce que le nombre de couches influence les vitesses et profondeurs obtenues. Selon votre meilleur modèle, interprétez les différentes couches obtenues en justifiant votre analyse."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Réponse : \n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Étape 7 Synthèse (0.25 points)\n",
"\n",
"Faites un retour sur les objectifs du levé. Ont-ils été atteints ? Votre analyse comporte-t-elle des limitations et incertitudes. Avez-vous des propositions pour des travaux futurs qui permettraient de valider vos résultats ?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Réponse : \n",
""
]
}
],
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