![\"drawing\"](\"Images/Geophysics.png\")
\n",
"\n",
"*Figure: (a) A cross-section illustrating surface geology with no subsurface information. (b) Model of physical parameter. The presence of a burial magnetic dipole cause the magnetic field anomaly observed on the surface. (c) Observed the magnetic anomaly along with a model of subsurface magnetic susceptibility that might cause such a change. The model agrees with the observed surface geology.*"
]
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"Borehole drilling is the only way to directly measure the physical properties of rocks. However such artifical, invasive procesures are expensive and damage the environment. Nevertheless, they are sometimes done to a depth of few hundred feet before construction of major projects (e.g. geothermal plant at Princeton) or for understanding regional stratigraphy. These barely scratch the skin of the Earth, which has an average radius of 6371 km. Kola Superdeep Borehole SG-3 retains the world record at 12.262 km (40,230 ft) reached in 1989 and is still the deepest artificial point on Earth. Borehole drilling has only been done at a few locations. Therefore, our vast understanding of the Earth's inner workings comes from geophysical measurements made from the Earth's surface or through remote sensing from orbiting satellites. We will focus on the geophysical measurements that can be made by human beings on land using different kinds of instruments (Table 1). A field survey is the method of collecting data for making inferences about the physical properties of the Earth's interior. "
]
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"|Instrument| LaCoste and Romberg Gravimeter | Superconducting Gravimeter | Accelerometer | Broadband Seismometer | Magnetometer | GPS | Radar |\n",
"|----------|--------------|---------------|-------------|--------------|-----|-------|---|\n",
"| Property Measured at the Earth's Surface | gravitational acceleration | gravitational acceleration | acceleration | ground motion | magnetic field intensity | position | electro-magnetic field |\n",
"| Property Investigated within Earth | density | density | seismic velocity, density, and attenuation | seismic velocity, density, and attenuation | magnetic susceptibility and remanent magnetization | density, viscosity | electrical conductivity | \n",
"| Governing Equation | Newton law of gravity | Newton law of gravity | Wave equation | Wave equation | Maxwell's Equations | General Relativity | Maxwell's Equations |\n",
"| Frequency Range | 0-1 Hz | 0-1 Hz | 0-100 Hz | 0.001-100 Hz | < 100 Hz | < 1 Hz | 25-1000 MHz |\n",
"\n",
"*Table 1: comparison of geophysical instruments on measured quantities, governing equation, and working frequency ranges. A frequency of 0 Hz means the instrument can measure a value at the steady-state.*"
]
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"Geophysical *signals* exist in a variety of forms, ranging from natural sources (e.g. mineral deposits) to anthropogenic noise (e.g. cars on a road), and manifest themselves as changes in various properties. These *signals* can vary either in their pattern or strength/amplitude depending on the time and location of measurement. For instance, the magnetic field at any location on Earth changes with time and we may want to record this temporal variation to understand the geodynamo in the core that creates this field. Alternatively, a buried drum or pipe may have a magnetic field that varies with location, which would be of interest to a civil and environmental engineer. Several categories of geophysical signals can indeed vary *both with time and location*. Seismic and ground penetrating radar signals fall into this category; these signals inform our understanding of both the current state and evolution of physical properties in the subsurface.\n",
"\n",
"An important aspect of the variation in geophysical *signals* pertains to its wavelength in time or space ($T$), and its inverse, frequency ($f$ = 1/$T$). As we shall see, various sources of signals manifest at different frequencies. For example, a shallow and small metallic object buried a few meters deep might give rise to high-frequency signals localized in space. On the other hand, a subducting slab that is a few hundreds of meters wide will give rise to signals that are longer in spatial wavelength (or lower in spatial frequency). One can also think of frequency range of geophysical *signals*; a transient vibration from a local earthquake will be high in its temporal frequency but deformation in the solid Earth due to tides will operate at lower frequencies. Depending on what the signal of interest, a decision about the primary geophysical instrument is made by the practitioner (Table 1). "
]
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"source": [
"## Part I: Geophysical Instruments"
]
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"Here is a summary of various intruments that are widely used in a geophysical survey. Gravimeters probe the gravity field, seismometers probe the ground movement, magnetometer probe the magnetic field, and ground penetrating radar can infer the burried structure.\n",
"\n",
"### Gravimeter\n",
"\n",
"A gravimeter is a sensitive instrument that can measure differences much less than a millionth of the surface gravity, $g$. This measurement can be done in three major ways. One way is to time the fall of an object, though this can be done very accurately in the laboratory but is rather inconvenient for measurements in the field. A second way is to use a pendulum, since its period depends upon the value of $g$ as well as its dimensions. This has been used widely in the past, but requires a bulky apparatus and takes considerable time. It has been largely superseded by the third way, which uses a spring balance, shown schematically in Figure 2. The pull of gravity on the mass, $m$, extends the spring, so that the spring has a slightly greater extension at places where $g$ is larger. A spring balance measures the *weight* of a body, the pull of gravity upon its mass, $m$, equal to $mg$. In contrast, a pair of scales compares *masses*, and if two masses are equal they will balance everywhere. Thus the mass of $m$ is constant but its weight varies from place to place, as we know from the near-weightlessness of objects in space.\n",
"\n",
"Modern gravity meters or **gravimeters** can detect changes in $g$ as small as a hundredth of a mGal or about 10$^{\\text{–8}} g$, and some extra-sensitive instruments can do considerably better. Such differences are much smaller than those measured in other branches of geophysics, so a gravimeter is an instrument of choice for many applications. To take a reading, the instrument is accurately levelled on a firm surface, and if it has a thermostat (to prevent the length of the spring varying due to internal temperature changes), the temperature is checked. Next, the mass is restored to a standard position by means of a screw and the number of turns of the screw is the reading. A practised operator can carry out the whole operation in a few minutes. Some instruments are fitted with electronic readouts that provide a number that can be fed into a computer, ready for data processing.\n",
"\n",
"![\"drawing\"](\"Images/Gravimeter.png\")
\n",
"\n",
"*Figure: Gravimeters need careful handling and are usually transported inside a protective box. Source: Looking Into the Earth: An Introduction to Geological Geophysics*"
]
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"#### LaCoste and Romberg Gravimeter\n",
"\n",
"LaCoste and Romberg gravimeters rely on a mass, supported by some kind of spring, responding to the pull of the Earth’s gravity. Because the changes to be measured are so small, the spring is arranged to amplify the effect (Figure below). Initially, the beam is at rest in position (i), with the pull of the spring balancing the pull of gravity on the mass. When the gravimeter is moved to where g is larger, the beam rotates to a new balance (ii), where the extra pull of the extended spring balances the extra pull of gravity.\n",
"\n",
"\n",
"![\"drawing\"](\"Images/LaCoste_Romberg_Simplified.png\")
\n",
"\n",
"*Figure: LaCoste and Romberg Gravimeter. Source: Looking Into the Earth: An Introduction to Geological Geophysics*"
]
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"source": [
"#### Superconducting Gravimeter\n",
"\n",
"Other relative gravimeters and seismometers are based on a test mass that is suspended by a spring attached to the instrument support. A change in gravity or motion of the ground moves the test mass and this motion generates a voltage that becomes the output signal (velocity or acceleration). Even in a thermally well-regulated environment the mechanical aspects of a spring suspension causes erratic drift that is difficult to remove by post processing. The superconducting gravimeter solves the drift problem by replacing the mechanical spring with the levitation of a test mass using a magnetic suspension.\n",
"\n",
"The levitation force is produced by the interaction between the magnetic field from the coils and the currents induced on the surface of the superconducting sphere. The current in the coils can be precisely adjusted to balance the force of gravity on the sphere at the center of the displacement transducer. As a result, a very small change in gravity (acceleration) gives a large displacement of the test mass. This allows for the instrument to achieve very high sensitivity. Because the levitation is magnetic, changes in the Earth’s magnetic field would seriously degrade its stability. A superconducting shield is used to exclude the earth’s magnetic field from entering the space where the sensor is housed.\n",
"\n",
"![\"drawing\"](\"Images/Superconducting_Gravimeter_Scheme.png\")
\n",
"\n",
"*Figure: The inner workings of a superconducting gravimeter. Source: [Operating Principles of the Superconducting Gravity Meter](https://www.gwrinstruments.com/pdf/principles-of-operation.pdf)*"
]
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"source": [
"### Seismometer\n",
"\n",
"Seismometers record ground motions ranging from large high-frequency accelerations near an earthquake to small ultra-long-period normal mode signals. Because no single seismograph can do this, different instruments have evolved to handle different *dynamic ranges* (amplitude) and *frequency ranges* of seismic waves."
]
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"#### Accelerometer\n",
"\n",
"An accelerometer is a type of inertial sensor which is configured to detect accelerations down to zero frequency. Unlike a gravimeter, they are typically configured to read peak accelerations on the order of 1 $g$ or larger without clipping and thus must have relatively low sensitivities. They can be used to detect static accelerations, for the purpose of integrating them to obtain position as in an inertial guidance system, or they can be used to detect strong seismic motions. Like a gravimeter they have a flat response to acceleration down to zero frequency, but the upper corner of their low-pass response will be at a much higher frequency.\n",
"\n",
"![](\"Images/Accelerometer_chip.jpeg\")
![](\"Images/Accelerometer_App.png\")
\n",
"\n",
"*Figure: (left) Accelerometer chip (right) An example of accelerometer application. Source: [Adafruit](https://www.adafruit.com/product/163) and Apple App Store.*"
]
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"#### Broadband seismometer\n",
"\n",
"A broadband seismometer can record a very broad frequency range (0.001-100 Hz) but not down to zero frequency of ground motion: displacement, velocity, or acceleration. It has a higher sensitivity and therefore measures much smaller motions than an accelerometer.\n",
"\n",
"![](\"Images/Broadband_Seismometer.jpeg\")
\n",
"\n",
"*Figure: a broadband seismometer. Source: [essearth.com](https://www.essearth.com/product/broadband-seismometer/)*"
]
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"### GPS\n",
"\n",
"GPS is fully operational and meets the criteria established in the 1960s for an optimum positioning system. The system provides accurate, continuous, world-wide, three-dimensional position and velocity information to users with the appropriate receiving equipment. GPS also disseminates a form of Coordinated Universal Time (UTC). The satellite constellation nominally consists of 24 satellites arranged in 6 orbital planes with 4 satellites per plane. The system utilizes the concept of one-way time of arrival (TOA) ranging. Satellite transmissions are referenced to highly accurate atomic frequency standards onboard the satellites, which are in synchronism with a GPS time base. This technique requires that the user receiver also contain a clock. Utilizing this technique to measure the receiver’s three-dimensional location requires that TOA ranging measurements be made to four satellites. If the receiver clock were synchronized with the satellite clocks, only three range measurements would be required. However, a crystal clock is usually employed in navigation receivers to minimize the cost, complexity, and size of the receiver. Thus, four measurements are required to determine user latitude, longitude, height, and receiver clock offset from internal system time. If either system time or height is accurately known, less than four satellites are required."
]
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"### Ground Penetrating Radar (GPR)\n",
"\n",
"Ground-penetrating radar, GPR, has by far the highest frequency of any electro-magnetic method and displays the wave aspect most clearly. It depends on reflection of pulses of waves, and so resembles seismic reflection. A pulse of electro-magnetic waves is transmitted downwards, reflects off interfaces, and is received back at the surface and the reflection time (two-way time, TWT) is used as a measure of the distance to the target. The transmitter has an antenna (or aerial), producing an extremely short pulse of waves, lasting only a few nanoseconds (a nanosecond, ns, is a thousandth of a millionth, or 10$^{–9}$, seconds). These pulses contain frequencies in the range 25 to 1000 MHz, and the shorter the pulse the higher its frequency. Such short pulses are needed because electro-magnetic waves travel so fast that the TWTs, for interfaces that may be less than a meter deep, are extremely small.\n",
"\n",
"GPR is increasingly used in situations where its high resolution is a great advantage, and its limited penetration is sufficient. Uses include hydrogeology (particularly for finding the position of the water table), environmental geophysical surveying (polluted ground), site engineering (faulting, fractures, cavities, and buried metal structures), mineral exploration, and archaeology. It can also be used for mapping Quaternary soils, such as showing the structure of fluvial sediments, or to investigate the detailed structures of oil reservoir rocks, including its use in boreholes."
]
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"source": [
"### Magnetometer\n",
"\n",
"Magnetometers used for magnetic surveying differ from the types used to measure the magnetisation of rock samples in the laboratory because they need to measure the strength of the field rather than the strength of magnetisation of a sample, and they have to be portable. There are two main types. The proton precession magnetometer, usually abbreviated to **proton magnetometer** measures the total strength of the magnetic field but not its direction and so shows a total field anomaly (also called a total intensity anomaly), while the **fluxgate magnetometer** measures the component of the field along the axis of the sensor; for land surveying the sensor is usually aligned vertically for this is easy to do, giving a vertical component anomaly, but in aircraft, ships, and satellites it is kept aligned along the field and so measures the total field and also gives its direction. The sensor of a proton precession magnetometer need not be oriented (except roughly), and its readings do not ‘drift’ with time, in contrast to that of a fluxgate magnetometer, which has to be kept aligned and does drift. However, proton precession magnetometers take a few seconds to make a reading and are best kept stationary while this is being done, whereas fluxgates give a continuous reading, which can result in quicker surveying when readings are taken at short intervals. Both types can measure the field to 1 nT or less, which is more than sufficient for most anomalies. In practice, proton magnetometers are used for most ground surveys, except that fluxgates are usually chosen for detailed gradiometer surveys and in boreholes, for a variety of reasons. Both types are used in aerial surveys. A third type, the **caesium vapor magnetometer**, is more expensive but is sometimes used when higher sensitivity is needed. Caesium vapor oscillates continuously in the presence of the ambient magnetic field. The frequency of oscillation is proportional to the ambient magnetic field. Modern magnetic processors have a resolution of 0.001 nT and read 10 times each second or faster.\n",
"\n",
"Magnetic surveying can be carried out on the ground, from aircraft, and from ships. Commonly used instruments are the proton (precession) magnetometer, which measures the total field, and the fluxgate magnetometer, which measures a component of the field; for ground surveys this component is usually the vertical one, but in ships and aircraft it is along the field and so effectively measures the total field and its direction. Gradiometers use a pair of sensors, usually fluxgates.\n",
"\n",
"![](\"Images/G-858_Cesium.png\")
\n",
"\n",
"*Figure: A caesium vapor magnetometer. Source: [Geometrics](https://www.geometrics.com/wp-content/uploads/2018/10/858Manual_D2.pdf)*"
]
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"![](\"Images/Acceleration_Sensitivity.png\")
\n",
"![](\"Images/Velocity_Sensitivity.png\")
\n",
"\n",
"*Figure: The frequency ranges depends on the sensitivity of the instruments at any given frequency. Source: [Principles of Broadband Seismometry, Encyclopedia of Earthquake Engineering](https://www.researchgate.net/profile/Nick_Ackerley/publication/273343086_Principles_of_Broadband_Seismometry/links/54fef5cf0cf2eaf210b4724c.pdf)*"
]
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"source": [
"**Question 1** Comment on which instrument(s) would be appropriate for each of these applications. Assume that there is no budgetary limitation.\n",
"\n",
"- Prospecting for gold\n",
"- Finding a skeleton\n",
"- Exploration of oil and gas\n",
"- Thickness of a glacier\n",
"- Monitor volcanic activity"
]
},
{
"cell_type": "markdown",
"id": "123525bb-6325-4541-ba88-55fc02006800",
"metadata": {},
"source": [
"**Answer**"
]
},
{
"cell_type": "markdown",
"id": "9c32fd60-8acb-48dc-a84d-6d065740d78a",
"metadata": {},
"source": [
"\n", "
" ] }, { "cell_type": "markdown", "id": "e3a5e9d0-db5a-40a7-b6d0-5d01b2d73e7b", "metadata": { "tags": [] }, "source": [ "----\n", "## Part III: Magnetometer Survey near Princeton\n", "\n", "The exercise will be a magnetometer survey along the towpath that follows the Delaware-Raritan canal (see map below). Somewhere north of the Blackwells Mills road parking lot is a *target* beneath the footpath. The goal of this survey is to find it, delineate its size, and to subsequently interpret the nature of the anomalous rock formation. The survey will be performed by teams of 2 people, each using a magnetometer. A key challenge of the survey will be designing the measurement line (i.e. how often to measure, where, etc), and in keeping careful records of locations and values of individual measurements. The latter part is essential for successful inverse modeling of the observed data." ] }, { "cell_type": "code", "execution_count": 1, "id": "7b0d4a09-cb23-4f68-bcaa-74da1487074a", "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "\n", "
![](\"Images/DR_Canal_1.jpeg\")
![](\"Images/DR_Canal_2.jpeg\")
![](\"Images/DR_Canal_3.jpeg\")
\n",
"\n",
"*Figure: Snapshots of the Delaware and Raritan Canal. Source: [New Jersey State Park Service](https://nj.gov/dep/parksandforests/parks/drcanalstatepark.html)*"
]
},
{
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"source": [
"#### TO DO\n",
"\n",
"**Question 3.1**: Write down all relevant details of this survey such as the time, team members, date, address and GPS coordinates where your survey began from the Gauges app."
]
},
{
"cell_type": "markdown",
"id": "0fccd403-4be2-4a8c-a5ad-249ca720ee6e",
"metadata": {},
"source": [
"**Answer**"
]
},
{
"cell_type": "markdown",
"id": "570cd673-bb45-4441-b84d-5d5d6bf9bae4",
"metadata": {},
"source": [
"\n", "
" ] }, { "cell_type": "markdown", "id": "72febdf3-62dc-4d1e-acb4-edfcb5d88f44", "metadata": {}, "source": [ "**Question 3.2**: List typical values of all gauges reported by the Gauges app (e.g. altimeter, barometer) while your *instrument* is standing still. These should be at the bottom of the app window and look like the snapshot below:\n", "\n", "
![](\"Images/Sample_All_Measure.jpeg\")
"
]
},
{
"cell_type": "markdown",
"id": "3f64ab82-14d2-4728-8a0c-bf8d128094ff",
"metadata": {},
"source": [
"**Answer**"
]
},
{
"cell_type": "markdown",
"id": "f0c93c75-e238-4b31-8f16-98696f790366",
"metadata": {},
"source": [
"\n", "
" ] }, { "cell_type": "markdown", "id": "643c2669-cd5c-4d5e-ae3c-e34eaf5950ad", "metadata": {}, "source": [ "**Question 3.3**: Describe in 1 or 2 lines each what property of the Earth or your surrounding each gauge on the Gauges app measures. " ] }, { "cell_type": "markdown", "id": "5bea7e61-1afd-4230-a926-84ec9df56d83", "metadata": {}, "source": [ "**Answer**" ] }, { "cell_type": "markdown", "id": "23483d10-33a4-46e2-8ca1-1f8ab1ed7474", "metadata": {}, "source": [ "
\n", "
" ] }, { "cell_type": "markdown", "id": "31683df3-34a8-4d27-b860-c470e667921b", "metadata": {}, "source": [ "**Question 3.4**: 1) Take any good photo for memory with your camera and 2) Make a map-view sketch of the target and survey region (if you discover one). Upload a scanned copy with this assignment and geotag both by reporting their GPS coordinates below. *Note*: You can wait till you have explored the survey area before sitting down to make a sketch!" ] }, { "cell_type": "markdown", "id": "bb645e69-baa1-4e86-9147-181abcb5b7d8", "metadata": {}, "source": [ "**Answer**" ] }, { "cell_type": "markdown", "id": "0dc86d20-1f1a-41df-9f83-de639edbf12e", "metadata": {}, "source": [ "
\n", "
" ] }, { "cell_type": "markdown", "id": "863281bf-9605-436f-973f-826a300a4be5", "metadata": { "tags": [] }, "source": [ "### Phase I: Noise Calibration\n", "- Upon arrival at the field site carry your *magnetometer* onto the towpath, and walk with it around 50 m North of the entry gate.\n", "- Mark the position where the survey will begin with GPS, reporting in in Question 3.1 above. An alternative strategy is sometimes to find a good landmark that may be tagged to a Google Earth image at a later time.\n", "- Change the settings on the Gauges app to have an *update interval* of 100 ms (i.e. 10 samples every second). This is our default sampling interval for our time domain signal ($\\Delta t$). We might need to adjust this later based on your survey design!\n", "- Press the record button on the Gauges app, walk along the tow path slowly with your *magnetometer* (1 step every second) for a few meters in front of you (longer the better).\n", "- Stop recording, open the file folder on the Gauges app and transfer the file to your computer. Rename the file as **calibration-CASING-PUID.csv** where CASING is the *casing* is either *truck* or *hockey*. \n", "- Redo this recording for both types of *casing* and perform the following calculations. Make sure to walk at a similar pace when doing both types, in order to facilitate comparisons." ] }, { "cell_type": "markdown", "id": "7eae7228-e8f3-42a0-9a8c-dd2ea90741cb", "metadata": {}, "source": [ "#### TO DO\n", "\n", "**Question 4**: 1) Report the Nyquist frequency of your time domain signal. 2) We will treat the results from this phase where are simply calibrating our *magnetometer* at the beginning of the survey as representing the noise level. Report the noise level of your two types of *casing* for magnetometers in dB. Which *casing* is noisier? Speculate on the possible reasons. " ] }, { "cell_type": "markdown", "id": "9e5b62d2-b359-4d78-a4e5-da0462e91357", "metadata": {}, "source": [ "**Answer**" ] }, { "cell_type": "markdown", "id": "59becb60-dacc-419c-a569-403ddba87e3f", "metadata": {}, "source": [ "
\n", "
" ] }, { "cell_type": "markdown", "id": "5b54a365-769a-451c-863c-ff9ea07bb14a", "metadata": { "tags": [] }, "source": [ "### Phase II: Prior Information\n", "\n", "When geophysicists head out to the field, they are never completely in the dark about the target! There are often prior observations or historical knowledge that inform the survey design such as the choice of instruments. Two major sources of information are available for this area of Central New Jersey. \n", "\n", "#### Typical Rocks and Geological Maps\n", "\n", "Princeton and adjoining areas are part of the Piedmont physiographic province and the Newark basin. The rocks of the Piedmont are of Late Triassic and Early Jurassic age (230 to 190 Ma). They rest on a large, elongate crustal block\n", "that dropped downward in the initial stages of the opening of the Atlantic\n", "Ocean, one of a series of such blocks in eastern North America. These downdropped blocks formed valleys known as rift basins. Sediment eroded from adjacent uplands was deposited along rivers and in lakes within the basins.\n", "These sediments became compacted and cemented to form conglomerate,\n", "sandstone, siltstone, and shale. \n", "\n", "In the course of rifting, the rock layers of the Piedmont became tilted\n", "northwestward, gently folded, and cut by several major faults. The Newark basin is perhaps the most extensively studied of the early Mesozoic basins of eastern North America that formed during the breakup of Pangea. The basin is around 190 km long and a maximum of 50 km wide. Volcanic\n", "activity was also associated with the rifting, as indicated by the basalt and\n", "diabase interlayered with sandstone and shale. Diabase is a rock formed by\n", "the cooling of magma at some depth in the crust; basalt is formed by cooling\n", "of an identical magma that has been extruded onto the surface as lava. \n", "\n", "
![](\"Images/psnjmap.png\")
\n"
]
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"#### Magnetic Properties of Rocks\n",
"\n",
"Before considering magnetic anomalies as a way to detect a target, we consider the topic of magnetic susceptibility and to review several other considerations\n",
"relevant to computation of magnetic intensities. Recall that we elected\n",
"to use the cgs system of units in this chapter. Although susceptibility is dimensionless,\n",
"values normally are expressed as cgs or emu (electromagnetic units) to call\n",
"attention to the fact that cgs units are in use rather than SI units. The susceptibility\n",
"values given here can be converted to SI units by multiplying by 4$\\pi$.\n",
"Although several familiar minerals have high susceptibilities (magnetite,\n",
"ilmenite, and pyrrhotite), magnetite is by far the most common. Rock susceptibility\n",
"almost always is directly related to the percentage of magnetite present.\n",
"The true susceptibility of magnetite varies from 0.1 to 1.0 emu depending on\n",
"grain size, shape, and impurities. Average susceptibility as quoted in several\n",
"sources ranges from 0.2 to 0. 5 emu, so we will adopt a value of k = 0.35 emu for\n",
"our computation. Representative values for a selection\n",
"of common rocks and minerals are presented in Telford, Geldart, and Sheriff\n",
"(1990, 74). For our discussions of anomaly interpretation, it is enough to concentrate on average\n",
"susceptibilities for common rock groups."
]
},
{
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"id": "731405fa-278d-4c27-8baa-e4f7536782e5",
"metadata": {
"jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
"| Rock type | Susceptibility (emu=SI/4$\\pi$) |\n",
"|----------|--------------|\n",
"|Sedimentary rocks | 0.00005 emu| \n",
"|Metamorphic rocks |0.0003 emu|\n",
"|Granites and rhyolites |0.0005 emu|\n",
"|Gabbros and basalts |0.006 emu|\n",
"|Ultrabasic rocks |0.012 emu|"
]
},
{
"cell_type": "markdown",
"id": "199e24fa-0068-4f41-a508-1a428024612e",
"metadata": {
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"source": [
"#### TO DO\n",
"\n",
"**Question 5**: Based on the two types of prior information outlined above, what are the major types and ages of rocks in our field survey area? Based on the magnetic susceptibilities, what kind of common rock type as targets will create strong magnetic *anomalies*?"
]
},
{
"cell_type": "markdown",
"id": "33b8a20e-ee70-4a23-a801-35735450a374",
"metadata": {},
"source": [
"**Answer**"
]
},
{
"cell_type": "markdown",
"id": "1252a62f-ad36-4999-91e8-5866353ee59a",
"metadata": {},
"source": [
"\n", "
" ] }, { "cell_type": "markdown", "id": "2345378d-c25b-4880-a1b9-860f9870b7f8", "metadata": {}, "source": [ "### Phase III: Prospecting" ] }, { "cell_type": "markdown", "id": "12551a6b-4a96-4715-a0b6-ae2943d2c797", "metadata": {}, "source": [ "Start walking at a slow pace along the towpath northwards, recording your measurements on the Gauges app. You are welcome to go as far as you want from the starting point for this first pass of prospecting for the target. However, a distance of 500 m should be appropriate to start. At the end of this first pass, download the Gauges data to your computer and name the file as **Gauges_Prospecting-PUID.csv**, which should be similar to [Magnetic_reading_example.csv](Files/Magnetic_reading_example.csv)." ] }, { "cell_type": "markdown", "id": "05f7f206-b2eb-4a70-b42b-2bb3b7ca6828", "metadata": { "tags": [] }, "source": [ "#### TO DO\n", "\n", "**Question 6**: Make a profile of the magnetic field intensity in nanoTeslas (nT) as a function of time elapsed along your prospecting profile. Do you see any signal that could correspond to a target of interest? If yes, note down the range in time where it is detected. What is the shape of this signal and how large is the signal amplitude in nT? You may modify the code provided below." ] }, { "cell_type": "markdown", "id": "80d4411b-de2e-429f-80b5-d35a670dc382", "metadata": {}, "source": [ "**Answer**" ] }, { "cell_type": "markdown", "id": "d3a8f352-68bf-40fc-a9e9-39cc997ec96b", "metadata": {}, "source": [ "
\n", "
" ] }, { "cell_type": "markdown", "id": "9bfe4afa-f3c1-4e1a-aacd-b4375c380868", "metadata": {}, "source": [ "**Modify the code below**" ] }, { "cell_type": "code", "execution_count": 19, "id": "734a3eeb-f298-4875-8097-aabbf8ea5e68", "metadata": {}, "outputs": [], "source": [ "############## USER DEFINED ###################\n", "gauges_file = 'Files/Magnetic_reading_example.csv'\n", "####################################################" ] }, { "cell_type": "code", "execution_count": 20, "id": "023c59b5-6133-481a-aa56-ae7652c35016", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from scipy.interpolate import interp1d\n", "from scipy.signal import detrend\n", "from pathlib import Path" ] }, { "cell_type": "code", "execution_count": 21, "id": "d83de20f-9300-4f04-a4cc-dd733468589c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Date', 'Speed', 'Altitude', 'Pressure', 'X', 'Y', 'Z', 'G', 'Latitude',\n", " 'Longitude', 'Heading', 'Magnetic Field', 'Sound Level', 'Luminance',\n", " 'Heart Rate'],\n", " dtype='object')" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_forward = pd.read_csv(gauges_file)\n", "data_forward.columns" ] }, { "cell_type": "code", "execution_count": 22, "id": "ba0c0808-558c-43b6-a8a8-f35bd7a47554", "metadata": {}, "outputs": [], "source": [ "data_forward['Date'] = pd.to_datetime(data_forward['Date'])\n", "data_forward['Difference'] = data_forward['Date']-data_forward['Date'][0]\n", "Time_seconds = []\n", "for val in data_forward['Difference']: Time_seconds.append(val.total_seconds())\n", "data_forward['Time_seconds'] = Time_seconds\n", "data_forward['Magnetic_Field_nT'] = data_forward['Magnetic Field']*1000" ] }, { "cell_type": "code", "execution_count": 23, "id": "6ae06f68-7500-40f4-a68a-88a1508edba0", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", "
" ] }, { "cell_type": "markdown", "id": "8b2527d7-d6e8-4aaf-9874-ac331c501b2e", "metadata": { "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ "Modify the code below" ] }, { "cell_type": "code", "execution_count": 25, "id": "0170ee75-666c-49ee-8731-adba6b15c0dc", "metadata": {}, "outputs": [], "source": [ "############## USER DEFINED ###################\n", "stake_fname = 'Files/Stakes_example.csv'\n", "reading_fname = 'Files/Magnetic_reading_example.csv'\n", "sampling_interval = 1 # sampling interval in m\n", "####################################################\n", "\n", "df_reading = read_gauges_data(reading_fname)\n", "df_stake = pd.read_csv(stake_fname)\n", "\n", "# resampling for uniform sampling interval\n", "x_uniform, B_uniform = get_uniform_profile(df_reading, df_stake, sampling_interval)" ] }, { "cell_type": "code", "execution_count": 33, "id": "e731ad75-e305-453d-8c98-adf4ed5c58ab", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, { "cell_type": "markdown", "id": "57087b31-edbf-4833-b6e6-784a306ca93a", "metadata": { "tags": [] }, "source": [ "**Question 8.2**: Calculate the signal power of the target in dB for a single profile, moving average and stacking of multiple runs. What is the SNR for each case if noise dB is the value from Question 4. " ] }, { "cell_type": "markdown", "id": "ffbcacb4-0c2a-4de8-9761-bbb7a2dbd821", "metadata": {}, "source": [ "**Answer**" ] }, { "cell_type": "markdown", "id": "6c747689-dbc7-4d99-b209-7e004a7eabc2", "metadata": {}, "source": [ "
\n", "
" ] }, { "cell_type": "markdown", "id": "ef9d501b-237e-46b1-a60b-3d5e5421398a", "metadata": {}, "source": [ "**Moving average**" ] }, { "cell_type": "code", "execution_count": 34, "id": "0cb5287a-31a4-45c6-a116-7f08ef5bb0f1", "metadata": {}, "outputs": [], "source": [ "############## USER DEFINED ###################\n", "length_average = 5 # number of samples to average in a running window\n", "#####################################################\n", "v = np.ones(length_average) / length_average\n", "B_uniform_moving = np.convolve(B_uniform, v, 'same')" ] }, { "cell_type": "code", "execution_count": 35, "id": "81a0bd7b-5d55-49dc-a5c4-fa89178d4d41", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
![](\"Images/HargravesMag.png\")
![](\"Images/Rathouse06122011.png\")
\n",
"\n",
"**Correction 2: Subtracting the global field.** The magnetic field varies a lot over the Earth’s\n",
"surface, being roughly twice as strong at the Earth’s\n",
"magnetic poles as the equator. This variation does\n",
"not matter for surveys of small areas, but is corrected\n",
"during regional survey. The field subtracted is\n",
"the IGRF – International Geomagnetic Reference\n",
"Field, which is revised every 5 years. This field expressed as sum of spherical harmonic series (a.k.a. 2-dimensional sinusoids), and matches the actual field measured at\n",
"observatories around the Earth by the fields of a\n",
"series of fictitious magnets at the Earth’s centre. A decent\n",
"fit is provided by a single dipole arising from the geodynamo, but\n",
"progressively smaller magnets with various orientations\n",
"are used to improve the match to about 1 nT on average.\n",
"\n",
"\n",
"**Finding the depth of the body** As a general rule, shallower anomalies tend to have sharper anomalies and shorter wavelengths, and this can be used to deduce the depth of the target. However, the shapes of anomalies also depend on the direction of magnetisation,\n",
"and often have both positive and negative parts. We will use Peter’s\n",
"half-slope method for quick calculations of anomaly depth in the field. The slope of the steepest part of the anomaly is drawn; then a\n",
"line half as steep is drawn. Next, positions\n",
"A and B, either side of the steepest part, are\n",
"found with the slope of line 2, and their horizontal\n",
"separation, S, measured. The depth, d, to the top\n",
"of the body is given approximately by depth,\n",
"d ~ 1.6 S. This relationship works best for dykes\n",
"or sheets aligned N–S (near-symmetric anomalies),\n",
"and at high latitudes. \n",
"\n",
"![](\"Images/Peters_half_slope.png\")
\n",
"\n",
"*Figure: Peter’s half-slope method for estimating approximate depth of a targent.*"
]
},
{
"cell_type": "markdown",
"id": "b725acbb-63d6-45d3-ad4d-7776e50cb0b7",
"metadata": {
"tags": []
},
"source": [
"#### TO DO\n",
"\n",
"**Question 9**: Deduce the depth of your target. Speculate on its orientation based on the anomaly shape."
]
},
{
"cell_type": "markdown",
"id": "15e96bca-eb29-4f0f-992d-24046870aaff",
"metadata": {},
"source": [
"**Answer**"
]
},
{
"cell_type": "markdown",
"id": "7eb93d3c-d244-437b-862f-f5dc05b5863f",
"metadata": {},
"source": [
"\n", "
" ] }, { "cell_type": "markdown", "id": "a1c5224d-00f7-4c9e-9009-91a427b96044", "metadata": {}, "source": [ "### Phase VII: Geological interpretation" ] }, { "cell_type": "markdown", "id": "5881d2ae-9c8c-40fc-9a7c-64bedc097341", "metadata": {}, "source": [ "A database of geologic units and structural features in New Jersey, with lithology, age, data structure, and format written and arranged just like the other states is available from the [United States Geological Survey](https://mrdata.usgs.gov/geology/state/state.php?state=NJ). The information is provided in terms of shapefiles, a commonly used format for sharing geometry data. Geometry of a given feature is stored as a set of vector coordinates. We first probe the columns and types of polygons representing various types of rock formations in New Jersey. Here is an example of what the shapefile [NJ_geol_poly.shp](Files/NJ/NJ_geol_poly.shp) contains. Next, we choose the polygons that outline the general type of rock type called 'Igneous, intrusive' and plot the outline of these rock formations." ] }, { "cell_type": "code", "execution_count": 38, "id": "d3c19970-b318-4b52-8ab1-64e2bf81a7e0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Columns are : Index(['STATE', 'ORIG_LABEL', 'SGMC_LABEL', 'UNIT_LINK', 'REF_ID',\n", " 'GENERALIZE', 'SRC_URL', 'URL', 'geometry'],\n", " dtype='object')\n", "General types are : ['Metamorphic, sedimentary clastic' 'Sedimentary, carbonate'\n", " 'Metamorphic, undifferentiated' 'Metamorphic, serpentinite'\n", " 'Sedimentary, undifferentiated' 'Sedimentary, clastic'\n", " 'Igneous, intrusive' 'Igneous, volcanic'\n", " 'Unconsolidated, undifferentiated'\n", " 'Metamorphic and Sedimentary, undifferentiated' 'Water'\n", " 'Metamorphic, amphibolite' 'Metamorphic, gneiss' 'Metamorphic, carbonate']\n" ] } ], "source": [ "import geopandas as gpd\n", "import json\n", "\n", "geojson = 'Files/NJ/NJ_geol_poly_instrusive.json'\n", "\n", "NJ_geol_poly = gpd.read_file('Files/NJ/NJ_geol_poly.shp')\n", "print('Columns are : ',NJ_geol_poly.columns)\n", "print('General types are : ',NJ_geol_poly.GENERALIZE.unique())\n", "\n", "# Choose intrusive igenous formations\n", "NJ_geol_poly[NJ_geol_poly.GENERALIZE=='Igneous, intrusive'].to_file(geojson, driver = \"GeoJSON\")\n", "with open(geojson) as geofile: j_file = json.load(geofile)" ] }, { "cell_type": "code", "execution_count": 39, "id": "5164804f-775a-4ea1-8355-01f90bed9f63", "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "colorscale": [ [ 0, "#636efa" ], [ 1, "#636efa" ] ], "geojson": { "bbox": [ -75.20043847882211, 40.22707818077836, -73.90358101414003, 41.275338325112656 ], "features": [ { 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