From 40b3e4606c98f64602e6d799d2f0ffbdb84792a8 Mon Sep 17 00:00:00 2001 From: Altay Sansal Date: Fri, 1 Sep 2023 14:36:39 -0500 Subject: [PATCH 1/4] adding metadata --- widess-1973/README.md | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) create mode 100644 widess-1973/README.md diff --git a/widess-1973/README.md b/widess-1973/README.md new file mode 100644 index 0000000..35fb9ec --- /dev/null +++ b/widess-1973/README.md @@ -0,0 +1,22 @@ +# How Thin is a Thin Bed? + +This paper has been reproduced by [Altay Sansal](https://github.com/tasansal). + + +### Reference + +
+ +| | | +|-------------|-----------------------------------| +| Publication | GEOPHYSICS | +| Published | December 1973 | +| Authors | M. B. Widess | +| DOI | https://doi.org/10.1190/1.1440403 | + +
+ + +### Abstract + +Based on reflective properties, a thin bed may be conveniently defined as one whose thickness is less than about 𝜆𝑏/8 where 𝜆𝑏 is the (predominant) wavelength computed using the velocity of the bed. The amplitude of a reflection from a thin bed is to the first order of approximation equal to 4𝜋Ab/𝜆𝑏, where b is the thickness of the bed and A is the amplitude of the reflection if the bed were to be very thick. The equation shows that a bed as thin as 10 ft has, for typical frequency and velocity, considerably more reflective power than is usually attributed to it. \ No newline at end of file From 20ffb721eee424fc59cdbe392a93087c6c4141a8 Mon Sep 17 00:00:00 2001 From: Altay Sansal Date: Fri, 1 Sep 2023 14:42:50 -0500 Subject: [PATCH 2/4] add citation reference --- widess-1973/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/widess-1973/README.md b/widess-1973/README.md index 35fb9ec..bef8c1f 100644 --- a/widess-1973/README.md +++ b/widess-1973/README.md @@ -5,6 +5,8 @@ This paper has been reproduced by [Altay Sansal](https://github.com/tasansal). ### Reference +M. B. Widess, (1973), "HOW THIN IS A THIN BED?," GEOPHYSICS 38: 1176-1180. +
| | | From d9d2cb2130341923d0b176aebf0578b6758958c9 Mon Sep 17 00:00:00 2001 From: Altay Sansal Date: Fri, 1 Sep 2023 17:03:46 -0500 Subject: [PATCH 3/4] first figure --- widess-1973/environment.yaml | 110 +++++++++++++++ widess-1973/figures.py | 130 ++++++++++++++++++ widess-1973/how-thin-is-a-thin-bed-1973.ipynb | 119 ++++++++++++++++ 3 files changed, 359 insertions(+) create mode 100644 widess-1973/environment.yaml create mode 100644 widess-1973/figures.py create mode 100644 widess-1973/how-thin-is-a-thin-bed-1973.ipynb diff --git a/widess-1973/environment.yaml b/widess-1973/environment.yaml new file mode 100644 index 0000000..29b9701 --- /dev/null +++ b/widess-1973/environment.yaml @@ -0,0 +1,110 @@ +name: repro-zoo +channels: + - conda-forge +dependencies: + - appnope=0.1.3=pyhd8ed1ab_0 + - asttokens=2.2.1=pyhd8ed1ab_0 + - backcall=0.2.0=pyh9f0ad1d_0 + - backports=1.0=pyhd8ed1ab_3 + - backports.functools_lru_cache=1.6.5=pyhd8ed1ab_0 + - brotli=1.1.0=hb547adb_0 + - brotli-bin=1.1.0=hb547adb_0 + - 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xorg-libxau=1.0.11=hb547adb_0 + - xorg-libxdmcp=1.1.3=h27ca646_0 + - xz=5.2.6=h57fd34a_0 + - zeromq=4.3.4=hbdafb3b_1 + - zipp=3.16.2=pyhd8ed1ab_0 + - zstd=1.5.5=h4f39d0f_0 +prefix: /Users/Altay.Sansal/mambaforge/envs/repro-zoo diff --git a/widess-1973/figures.py b/widess-1973/figures.py new file mode 100644 index 0000000..025f8a1 --- /dev/null +++ b/widess-1973/figures.py @@ -0,0 +1,130 @@ +import matplotlib.pyplot as plt +import numpy as np +from numpy.typing import NDArray + + +def figure1( + times: NDArray, + amplitude_r1: NDArray, + amplitude_r2: NDArray, + amplitude_rd: NDArray, + shift: float, + dpi: int, +) -> None: + """Generate Figure 1 of Widess, 1973.""" + fig, (fig_1a, fig_1b, fig_1c) = plt.subplots(3, 1, dpi=dpi) + arrow_props = dict(facecolor="black", width=0.001, headwidth=5, headlength=5) + + # Panel a. + fig_1a.plot(times, amplitude_r1, "black") + fig_1a.plot(times, amplitude_r2, "black") + fig_1a.fill_between( + times, + amplitude_r1, + amplitude_r2, + facecolor="none", + edgecolor="black", + hatch="|||||", + ) + fig_1a.grid(True, "major", axis="x") + fig_1a.set_xticks(np.arange(shift / 2, 1, 0.165) - 0.5) + fig_1a.annotate( + text="$R_1$", + xy=(times[25], amplitude_r1[25]), + xytext=(-0.35, 0.5), + arrowprops=arrow_props, + ) + fig_1a.annotate( + text="$-R_2$", + xy=(times[25], amplitude_r2[25]), + xytext=(-0.15, -0.5), + arrowprops=arrow_props, + ) + # Panel b. + fig_1b.plot(times, amplitude_rd, "black") + fig_1b.grid(True, "major") + fig_1b.set_xticks(np.arange(shift / 2, 1, 0.165) - 0.5) + fig_1b.set_yticks([0]) + fig_1b.annotate( + text="$R_d$", + xy=(times[40], amplitude_rd[40]), + xytext=(-0.25, -0.5), + arrowprops=arrow_props, + ) + + # Panel c (left side) + fig_1c.plot([-0.25, -0.25], [-0.5, 0.5], "black") + fig_1c.plot( + [-0.2, -0.2, -0.15, -0.15, -0.2, -0.2], + [-0.5, -0.25, -0.25, 0.25, 0.25, 0.5], + "black", + ) + fig_1c.annotate( + text="VELOCITY", + xy=(-0.1, 1), + xytext=(-0.35, 1), + arrowprops=arrow_props, + verticalalignment="center", + ) + fig_1c.annotate( + text="DEPTH", + xy=(-0.3, -0.5), + xytext=(-0.3, -0.1), + arrowprops=arrow_props, + horizontalalignment="center", + rotation=90, + ) + + fig_1c.text(x=-0.21, y=0.6, s="$V_1$") + fig_1c.text(x=-0.15, y=0.3, s="$V_2=V_b$") + fig_1c.text(x=-0.21, y=-0.75, s="$V_3=V_1$") + + fig_1c.text( + x=-0.225, + y=-1, + s="VELOCITY GRAPH", + horizontalalignment="center", + verticalalignment="center", + ) + + # Panel c (right side) + fig_1c.fill_between( + [0.1, 0.4], + [0.25, 0.25], + [-0.25, -0.25], + facecolor="none", + edgecolor="black", + hatch="//////", + ) + fig_1c.text(x=0.4, y=0.45, s="$V_1$", verticalalignment="center") + fig_1c.text( + x=0.37, + y=0.0, + s="$V_2=V_b$", + verticalalignment="center", + bbox=dict(facecolor="white", edgecolor="none", pad=0), + ) + fig_1c.text(x=0.4, y=-0.45, s="$V_3=V_1$", verticalalignment="center") + + fig_1c.arrow(0.2, 0.45, 0.05, -0.9, head_width=0.01, head_length=0.1, fc="black") + fig_1c.arrow(0.21, 0.25, 0.02, 0.4, head_width=0.01, head_length=0.1, fc="black") + fig_1c.arrow(0.24, -0.25, 0.044, 0.88, head_width=0.01, head_length=0.1, fc="black") + + fig_1c.text( + x=0.25, + y=-1, + s="REFLECTION RAY DIAGRAM", + horizontalalignment="center", + verticalalignment="center", + ) + + # Common plot formatting + for ax in [fig_1a, fig_1b, fig_1c]: + ax.set_xlim(-0.5, 0.5) + ax.set_ylim(-1, 1) + ax.set_xticklabels([]) + ax.set_yticklabels([]) + ax.tick_params(axis="both", which="both", length=0) + + for spine in ["left", "right", "top", "bottom"]: + ax.spines[spine].set_visible(False) diff --git a/widess-1973/how-thin-is-a-thin-bed-1973.ipynb b/widess-1973/how-thin-is-a-thin-bed-1973.ipynb new file mode 100644 index 0000000..ead5367 --- /dev/null +++ b/widess-1973/how-thin-is-a-thin-bed-1973.ipynb @@ -0,0 +1,119 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b62f3c3b-7e33-4707-b7b2-f8780e83b1e3", + "metadata": {}, + "source": [ + "# How Thin is a Thin Bed?\n", + "\n", + "This paper has been reproduced by [Altay Sansal](https://github.com/tasansal).\n", + "\n", + "\n", + "### Reference\n", + "\n", + "M. B. Widess, (1973), \"HOW THIN IS A THIN BED?,\" GEOPHYSICS 38: 1176-1180.\n", + "\n", + "
\n", + "\n", + "| | |\n", + "|-------------|-----------------------------------|\n", + "| Publication | GEOPHYSICS |\n", + "| Published | December 1973 |\n", + "| Authors | M. B. Widess |\n", + "| DOI | https://doi.org/10.1190/1.1440403 |\n", + "\n", + "
\n", + "\n", + "\n", + "### Abstract\n", + "\n", + "Based on reflective properties, a thin bed may be conveniently defined as one whose thickness is less than about 𝜆𝑏/8 where 𝜆𝑏 is the (predominant) wavelength computed using the velocity of the bed. The amplitude of a reflection from a thin bed is to the first order of approximation equal to 4𝜋Ab/𝜆𝑏, where b is the thickness of the bed and A is the amplitude of the reflection if the bed were to be very thick. The equation shows that a bed as thin as 10 ft has, for typical frequency and velocity, considerably more reflective power than is usually attributed to it." + ] + }, + { + "cell_type": "markdown", + "id": "f4ef6046-3629-40e7-9fdc-84097a08e72a", + "metadata": {}, + "source": [ + "# Figure 1\n", + "\n", + "Authors start by introducting a wavelet *R1*, its time-shifted variant *R2* in Figure 1.a. Then they show the difference *Rd* in Figure 1.b., and finally the ray paths for *R1* and *R2* in Figure 1.c.\n", + "\n", + "It is not explicitly specified what the wavelet is, however, it looks like a Gaussian modulated sinusoid wavelet. We will make the first figure with slight improvements in anootations; and roughly estimating some of the other things." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a6505db0-d499-4ae1-b64f-b3e0c6d6947c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from scipy.signal import gausspulse\n", + "\n", + "from figures import figure1\n", + "\n", + "shift = 0.025\n", + "\n", + "time_start = 0 # seconds\n", + "time_stop = 1 # seconds\n", + "time_step = 0.01 # seconds\n", + "\n", + "wavelet_freq = 3 # Hz\n", + "\n", + "# Make inclusive range [start, stop]\n", + "times = np.arange(time_start, time_stop + time_step, time_step) - 0.5\n", + "\n", + "# Make R_1 R_2 and R_d\n", + "amplitude_r1 = -1 * gausspulse(t=times, fc=wavelet_freq)\n", + "amplitude_r2 = -1 * gausspulse(t=times - shift, fc=wavelet_freq)\n", + "amplitude_rd = amplitude_r1 - amplitude_r2\n", + "\n", + "# Plot\n", + "figure1(times, amplitude_r1, amplitude_r2, amplitude_rd, shift, dpi=130)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "774d9ccb-451e-49ef-9dce-6be8916f78d7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "repro-zoo", + "language": "python", + "name": "repro-zoo" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From e85678b07a781b76bb4ae58e04e0be93ad39624d Mon Sep 17 00:00:00 2001 From: Altay Sansal Date: Fri, 1 Sep 2023 17:08:01 -0500 Subject: [PATCH 4/4] add instructions to run it --- widess-1973/README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/widess-1973/README.md b/widess-1973/README.md index bef8c1f..0c18a75 100644 --- a/widess-1973/README.md +++ b/widess-1973/README.md @@ -2,6 +2,11 @@ This paper has been reproduced by [Altay Sansal](https://github.com/tasansal). +### How to Run + +1. Create `conda` environment from `environment.yaml` file. +2. Install Jupyter or Jupyer Lab; or register environment kernel with global Jupyter Lab. +3. Run. ### Reference