{
"cells": [
{
"cell_type": "markdown",
"id": "mounted-order",
"metadata": {},
"source": [
"# Peak Finder\n",
"\n",
"This [Peak Finder](#Methodology) algorithm has been designed for the detection and grouping of time-domain electromagnetic (TEM) anomalies measured along flight lines.\n",
"Anomaly markers can be exported to Geoscience ANALYST, along with various metrics for characterization and targeting:\n",
"\n",
" \n",
" \n",
"- Amplitude\n",
"- Dip direction\n",
"- Mad Tau (TEM data only)\n",
"- Anomaly skewness\n",
"\n",
"While initially designed for TEM data, the same application can be used for the characterization of anomalies of mixed data types (e.g. magnetics, gravity, topography, etc.).\n",
"\n",
"\n",
" \n",
"\n",
"New user? Visit the [Getting Started](../installation.rst) page."
]
},
{
"cell_type": "markdown",
"id": "public-newman",
"metadata": {},
"source": [
"## Application\n",
"The following sections provide details on the different parameters controlling the application. Interactive widgets shown below are for demonstration purposes only."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "parliamentary-bookmark",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bb9b07536516492bbed4140aa458570a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(VBox(children=(Label(value='Workspace', style=DescriptionStyle(description_width='initial')), H…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from geoapps.peak_finder.application import PeakFinder\n",
"\n",
"app = PeakFinder(geoh5=r\"../../../assets/FlinFlon.geoh5\")\n",
"app()"
]
},
{
"cell_type": "markdown",
"id": "mexican-pennsylvania",
"metadata": {},
"source": [
"## Project Selection\n",
"\n",
"Select and connect to an existing **geoh5** project file containing data. \n",
"\n",
"OR \n",
"\n",
"Select a `*.ui.json` input file to re-load parameters from. See the [Input ui.json](#Input-ui.json) section for details."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "beautiful-mouse",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0edb5e9d9310413782d62e1d59d57a10",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(Label(value='Workspace', style=DescriptionStyle(description_width='initial')), HBox(children=(F…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"app.project_panel"
]
},
{
"cell_type": "markdown",
"id": "funny-press",
"metadata": {},
"source": [
"See the [Project Panel](base_application.ipynb#Project-Panel) page for more details."
]
},
{
"cell_type": "markdown",
"id": "curious-helicopter",
"metadata": {},
"source": [
"## Object/Data Selection\n",
"\n",
"List of `Curve` objects available containing line data. Multiple data channels and/or data groups can be selected. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "distinguished-lesbian",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0a4ac983784144e79510e9763b7a9eb4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(Dropdown(description='Object:', index=16, options=(['', None], ['Labels/Label3', UUID('39b2d390…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"app.data_panel"
]
},
{
"cell_type": "markdown",
"id": "vietnamese-princeton",
"metadata": {},
"source": [
"### Flip Y-axis\n",
"\n",
"Option to invert the data profile by a -1 multiplication. Useful for the detection of lows instead of highs."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "caring-drunk",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a3075d135e9246ce8d7adfd901462c00",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"ToggleButton(value=False, button_style='warning', description='Flip Y (-1x)', style=DescriptionStyle(descripti…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"app.flip_sign"
]
},
{
"cell_type": "markdown",
"id": "received-institution",
"metadata": {},
"source": [
"## TEM data\n",
"\n",
"Flag the selected data to be Time-Domain EM data. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "forty-supply",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c49edee77a8c4ce7a13c1519759e0c23",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(Checkbox(value=True, description='TEM Data', style=DescriptionStyle(description_width='initial'…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"app.tem_box"
]
},
{
"cell_type": "markdown",
"id": "dependent-nerve",
"metadata": {},
"source": [
"This option enables the [TEM System](#TEM-System), [Time Groups](#Channel-Groups) and [Decay Curve](#Decay-Curve) panels."
]
},
{
"cell_type": "markdown",
"id": "suffering-majority",
"metadata": {},
"source": [
"### TEM System\n",
"\n",
"List of available TEM systems.\n",
"The application will attempt to assign the *Survey Type* based on\n",
"known channel names (e.g. *Sf* => *VTEM*)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "arabic-member",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2ec5bade356b4e028c0c273aca196f03",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Time-Domain System:', index=20, options=('Airborne TEM Survey', 'AeroTEM (2007)', 'AeroT…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"app.system"
]
},
{
"cell_type": "markdown",
"id": "falling-quest",
"metadata": {},
"source": [
"Assigning the right time gates to each data channel is important for the calculation of the [Slanted Tau](#Slanted-Tau) parameter. "
]
},
{
"cell_type": "markdown",
"id": "sized-catalyst",
"metadata": {},
"source": [
"### Channel Groups\n",
"\n",
"The detection and grouping of anomalies is based on `property_groups`, or group of channels, found on the target curve object. These groups can be defined manually by the user in Geoscience ANALYST, or auto-generated from this application.\n",
"\n",
"See the [Methodology](#Methodology) section for details on how the grouping of anomalies is performed."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "altered-whale",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9ac7766920cd4e2dbb1341df1ef88356",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(VBox(children=(Dropdown(description='Group Name:', index=3, options=(['', None], ['-- Groups --…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"app.groups_panel"
]
},
{
"cell_type": "markdown",
"id": "254282fd",
"metadata": {},
"source": [
"#### Use-Create Defaults [Optional]\n",
"Create (6) pre-defined groups of data channels with corresponding default colors. Selected [Data channels](#Object/Data-Selection) are assigned to three based groups (`early`, `middle`, `late`), with three larger overlapping groups (e.g. anomalies found in both the `early` and `middle` channel groups would be labeled as `early+middle`). "
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "ff937b83",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e68ce17a879a4dceb0923fc7d334ccc5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"ToggleButton(value=False, button_style='success', description='Use/Create Default', style=DescriptionStyle(des…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"app.group_auto"
]
},
{
"cell_type": "markdown",
"id": "established-oxygen",
"metadata": {},
"source": [
"### Decay Curve\n",
"\n",
"Plot of peak values as a function of time for the nearest anomaly to the [Center](#Center) location."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "later-india",
"metadata": {},
"outputs": [
{
"data": {
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\n",
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