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All the academic papers I write.

What is a well log?

A well log, also known as a petrophysical log is similar to a cardiogram, which records electrical activity of your heart. An electrocardiogram (EKG or ECG) does not provide answers to hearth problems. It provides an outlook into the state of the heart. The EKG is interpreted by a medical doctor to provide solutions to heart problems.

A well log records physical, chemical and other petrophysical properties of Continue reading What is a well log?

Estimation of Q Factor

Estimation of Q Factor in Seismic Evaluations

Sanuja Senanayake
Geology Undergraduate Student: Winter 2015, University of Calgary.

Summary

The quality of seismic images varies with the several parameters. Fundamentally, the signal strength plays a major role in the clarity of seismic images. By analyzing the signal quality quantitatively as opposed to qualitatively, we can correct the loss of signal strength over a distance and time. The Q factor is a mathematical representation of signal degeneration. It can be used to evaluate the original seismic wavelet from a distorted wavelet. There are several methods to derive the value of Q. But currently there is no consensus among the geophysicists on which method is more accurate. In this particular study (Lupiancci, Andriano, Oliveira, 2015), researchers evaluated three methods; amplitude decay versus time decay, spectral ratio-based and Wang’s method. After several iterations of the data, they found the Wang method to be more accurate and provided the most consistent dataset. However, it should be highlighted the spectral ratio-based method also provided very accurate results.

Introduction

The seismic interpretations depend on both the knowledge and experience of the interpreter and the quality of seismic dataset itself. The seismic images are derived by collecting signal data from either seismic surveys or by measuring natural earthquakes. However, the measured data is almost always not the original seismic signal. As waves travel through a medium, they interact with the grains and fluids causing the waves to lose energy. Hence, the attributes of the original seismic signal is not detected by the geophones.

To obtain the original seismic signal from a distorted dataset, the rate or amount of energy loss should be quantified. This can be achieved by assigning a mathematical value to the quality of seismic images. The Q factor is one such mathematical parameter that can be used to quantify the energy loss of the seismic signal.

In physics, attenuation is described as the reduction in signal strength or the energy loss of signals over a distance or time. As the signal travels across a medium, it interacts with the particles within the medium. These interactions result in transfer of energy out of the signal. The Q factor measures how much of the original energy remains at the time of signal detection. Hence the Q factor is inversely proportional to the attenuation.

In this particular study, researchers focused on different methods of deriving the value of Q (Lupiancci, Andriano, Oliveira, 2015). They evaluated the errors in the Q factor obtained through amplitude decay versus time decay, spectral ratio-based and newly suggested Wang’s method.

Theory and Methodology

In order to analyze the seismic data, the signals must be decomposed using appropriate algorithms. Spectral decomposition is a commonly used method in which the change in frequency is analyzed. However, it is a non-unique process where depending on the algorithm used, the value of Q may vary widely for same dataset. Some possible methods for spectral decomposition includes, but not limited to, continuous wavelet transform, Gabor transform and S transform.

The seismic theory suggests that a wave propagating through an inelastic medium will result in exponential decay of amplitude. This relationship can be used to derive estimations for Q factor by regression applied to the decay function itself. However, the decay function itself is not clearly detected in both real world and in synthetic data. Decay function detection and analysis is especially difficult to obtain in short time windows due to less data values. In this study, researchers calculated Q factors in various time windows and verified the results with inverse Q filtering of a seismic section (Lupiancci, Andriano, Oliveira, 2015).

There are two parts to methodology: the modeling of seismic trace in a dissipative medium and the Q factor estimation approaches. The modelling was done using fundamental principles of wave propagation (1). The U0(ω) is the Fourier transform of the seismic pulse, ω is the angular frequency. V(ω) is the complex phase velocity and x and τ are the travel distance and time, respectively.

est_q_01

For weekly dispersive media, the Q factor is much higher than one (Q >> 1), hence the approximation can be reached using the following (Cravenly, 2001):

est_q_02

in which, VR(ω) is the real phase velocity and the Q(ω) is the medium Q factor. Hence V(ω) term in the first equation (1) can be replaced with the second equation (2). This new equation (3) is used to model the synthetic seismic trace for Q factor estimations.

est_q_03

In the above equation (3), the first exponent deals with the propagation and dispersion while the second exponent is responsible for the amplitude decay. Next, the equation (3) can be rewritten as:

est_q_04

In equation (4), τ = x/VR(ω)

The synthetic seismic data is modeled using frequency domain using the following relationship.

est_q_05

The equation (5) represents the Fourier transform of the trace and the wavelet with respect to the frequency domain. This function was derived using previous studies by numerous geophysics and physicists (Lupiancci, Andriano, Oliveira, 2015). Finally, by assuming transmission effect of the wave is negligible, finally the equation (6) was derived, which was used for calculating the Q factor for the nth layer.

est_q_06

The next part of this study is the analysis of Q factor itself. To obtain the values for Q, the researchers (Lupiancci, Andriano, Oliveira, 2015) relied on Gabor transform, a type of Fourier transform that uses a Gaussian window function to generate a time-frequency amplitude spectrum of a seismic trace.

Alternative methods of Q Estimation

Three alternative methods were briefly considered in the study. They all involve the amplitude spectrum of the time-frequency transform of the seismic trace. The first method was the amplitude decay versus time method. It is based on the amplitude curve of the time-frequency amplitude spectrum picked for a constant frequency value. For example, in this particular study it was 30 Hz (Lupiancci, Andriano, Oliveira, 2015).

The second method was the spectral ratio-based method. It is based on the measurement of the exponential decay along the frequency axes for a constant time.

The third method was coined as the “Wang’s method” by the authors because it was first introduced by Yanghua Wang in 2004. This involves measurement of the amplitude decay along the compound variable x = ωτ, where the variable x depends on the signal-to-noise ratio of the data. In this particular study researchers picked amplitudes along the curves defined by the equation, x = ωτ in the time-frequency domain. Then the average of the amplitudes was taken to define the exponential decay value s(x) for the following function (equation 7). (Lupiancci, Andriano, Oliveira, 2015)

est_q_07

Using the above three methods, the modelled seismic trace was used to evaluate the Q factor estimation approaches considering a medium formed by random spikes Lupiancci, Andriano, Oliveira, 2015). The estimations were measured at set time intervals; 0.5, 1.0, 2.0 and 3.0 seconds for the all three methods (Table 1-REMOVED due to Copyright).

Synthetic Data Results

The synthetic data were obtained through the use of equation (5) and (6). Three above mentioned Q estimation methods were then tested for the time windows of 0.5, 1.0, 2.0 and 3.0 seconds. For the spectral ratio-based method, the frequency band 5 to 70 Hz was used. For the Wang’s method, Xa = 23 and Xb = 180 were used for all time windows; equation (7).

The Q factor estimation reading were repeated for Q = 60 and Q = 90 and the results are analyzed (Table 1). The researchers observed that the Q factor estimation become more accurate and precise as the length of the analysis time window increase. Hence the time frame of the analysis directly related to the accuracy and precision of the Q factor. Longer the analysis time window, higher the accuracy of the Q factor. Another conclusion was that the exponential decay function also becomes more coherent as for longer time windows than shorter ones. This was expected by the researchers (Lupiancci, Andriano, Oliveira, 2015) because as the wave propagates through a medium, over time and distance the attenuation also increases. Attenuation modifies the signals through the loss of energy during the interaction between the propagating wave and the particles or atoms of the medium. The amplitude spectrum of the time-frequency transform of the signal is controlled by peaks and valleys due to interference from the reflected pluses. These resulted in oscillations that mask the exponential decay trend. This was highly reflected in analysis performed on short time windows. It should be highlighted that this issue occurred in all methods. However, the Wang’s method was the least affected by this problem. The amplitude decay versus time method showed fluctuations in the estimated Q value based on the chosen frequency. Therefore researchers have used a range of frequencies to reduce errors in estimating the value of Q.

Real World Data Results

The real world data were obtained using a seismic section from deep water Pelotas Basin, Brazil. The samples were processed using basic seismic processing methods. The processing flow can be summarized as: geometrical spread correction, deconvolution, velocity analysis, parabolic Radon demultiple, dip-moveout correction (DMO), common offset prestack migration (Stolt) and bandpass filter (5 – 55 Hz).

Once the data was processed, the Q factor estimations were obtained through all three (above mention) methods. The amplitude decay versus time method shows a progressive loss of energy through the degradation of the amplitude over a period of time. Compared to the synthetic data, it was found to have higher impedance. The researchers ( Lupiancci, Andriano, Oliveira, 2015) suggest that this is most likely caused by shallow gas within the formation. As the density of the material decreases, the attenuation also increases. The spectral ratio-based method on real world data made it almost impossible to detect the linear relationships between variables. The amplitude decay versus time method on the real world dataset also resulted in data without clear linear relationships. However, the Wang’s method produced a very clear data output for the real world dataset with very strong linear trend. This was predicted in both this study (Lupiancci, Andriano, Oliveira, 2015) as well as previous studies by Wang (2004).

Outcome of the Research

It was found that the amplitude versus time and spectral ratio-based methods did not work well in real world data. To obtained reasonable results, the spectral ratio-based method requires a careful choice of frequency intervals where the logarithm spectral amplitude ratio versus frequency curve is near liner. Additionally, this method is not robust for the estimation of Q factor due to its sensitivity to the size of the analysis window.

The amplitude decay versus time method performed well in the noise-free synthetic data, but the performance was poor in the real world dataset. This was most likely caused by the fact that this method requires a very good time-varying amplitude gain control in seismic processing. The processing should only correct the geometrical spread effect while preserving the exponential decay caused by attenuation. Hence it is difficult to achieve. Another problem is the presence of stronger reflections with anomalous amplitudes causing erroneous regression.

The Wang’s method was robust for real world datasets. The trace by trace analysis of Q estimation suggested by Wang (2005) provided the most consistent and statistically accurate Q factor estimations. In fact, the standard deviation was small in the two analysis window sizes (6.55 for the 0 – 2.0s window and 9.25 for the 1.0 – 3.0 window). The main reason for the effectiveness of the Wang’s method is that it effectively explores the time-frequency domain. This method shows that Q factor increased as the depth of the real world dataset increases. This is consistent with the petrophysics because once the wave propagates into deeper rock layers, which are more compact, the wave favors less seismic attenuation. Hence a higher Q factor in deeper and more compacted layers.

Other Studies

There are several different studies have been done on estimation of Q factor. In one particular study by Vassil Davidov (2012) on the earthquake seismology found that the Q factor estimation is very difficult to achieve in the real world scenarios. This is because the geologic materials in which the seismic waves propagate are almost always antistrophic, inelastic and heterogeneous. This results in variation in Q factor in almost every direction as the wave propagates through the medium. The data obtained through the geophones ground stations could not often properly calculate the Q factor due to these complications in physical geology itself.

Conclusions

While there are no set international standards on obtaining the value of Q factor, the estimations can be derived from variety of methods. In this particular study, researchers found that the Wang’s method proposed in 2004 to be the most accurate out of the three methods studied. Other studies on real world data such as the earthquake study by Davido (2012) also highlighted the need to alternative methods for obtaining Q factor estimations for real world datasets.

References

Lupinacci, Wagner Moreira, and Sérgio Adriano Moura Oliveira. “Q factor estimation from the amplitude spectrum of the time-frequency transform of stacked reflection seismic data.” Journal of Applied Geophysics 114 (2015): 202-209.

Wang, Y., 2004. Q analysis on reflection seismic data. Geophys. Res. Lett. 31, L17606.

Davidov, Vassil. Seismic quality factor (Q) of the mid-continental crust from regional earthquake seismograms. Diss. Northern Illinois University, 2012.

The research is done by respective authors of the listed papers under refrences. The resarch is not belong to Sanuja Senanayake. This is an online publication of the final Term Paper written by Sanuja Senanayake at the University of Calgary for Geophysics 559: Geophysical Interpretation in Winter 2015. The text is copyrighted to Sanuja Senanayake. You may follow the guidelines posted under the general Site Copyright Notice. Figures used in the original paper have been removed due to copyright laws.

No part of this publication may be reproduced or redistributed in any form or format without prior permission from the authors mentioned above. Please contact Sanuja Senanayake for requests for permission to reproduce and redistribution.

Careers Petroleum Geology

The terms “Petroleum Geology” and “Petroleum Engineering” have been around since the boom in the oil and gas industry. But you should ask yourself, is there any difference between a Geologist and a Petroleum Geologist? Can a Professional Geologist perform the same tasks as a specialized Professional Petroleum Geologist? To answer these questions, we need to understand the basics of petroleum geology and why it is important to our modern day energy needs.

Multidisciplinary Subject Matter

If you search the term “geology” you will come across several different definitions. One of which would be; Geology a science that studies the solid Earth and its life forms though past and current processes recorded in minerals and rocks. The key term in this particular definition is the processes. The idea of studying processes has been around even before the growth in petroleum industry. At least a basic understanding of geological and geophysical processes is very important to petroleum industry. Such information is valuable in predicting petroleum fluid accumulation zones which have economic significance. As natural resources become scarce, we need better methods for exploration and exploitation of sought after resources. We realized geology itself cannot find answers to these scientific and economic challenges. Hence to reduce costs associated with exploration and extraction of natural resources, specialization of Petroleum Geology was born. Specialization does not mean a student only learn one aspect of geology. For example, the petroleum geology program at the University of Calgary encompasses structural geology, geochemistry, physics, biology and many other disciplines with emphasis on their relation to petroleum industry.

Important Concepts

The petroleum Geologists are responsible for understanding the processes of petroleum source rock formation, fluid migration patterns, reservoir characteristics, hydrocarbon traps and structural or stratigraphic trap mechanisms. It is the job of the petroleum geologist to use such knowledge to advice oil and gas companies on where to find reservoirs and how to extract hydrocarbons economically.

Tools and Tricks of the Trade

There are many tools and tricks currently used by geoscientists (the term “geoscientist” is used as an umbrella for geologists, geophysicists, engineers, earth scientists, environmental scientists, etc) to understand the Earth.

The most fundamental knowledge comes from previous studies. Typically oil and gas companies keep large databases of previously published research projects, textbooks and other materials available to their geoscientists. The Government of Canada and the Government of Alberta also keep records of core samples and well logs from projects across the country. These resources are utmost important petroleum geologist because use of such information can minimize the possibility of making incorrect decisions.

Education, past experiences and the quality of past experiences also play a major role in success of a petroleum geologist. A geologist who is capable of understanding the basics of math, physics, chemistry, biology and structural systems trend to outshine his/her peers. This is because hydrocarbon reservoirs are dynamic in nature. Not only a petroleum geologist has to successfully predict deposit locations, but also has to be able to provide information on dynamic factors such as fluid migration during a hydrocarbon extraction project. You cannot simply find the reservoir and then walk out of it and if it is that the case, most petroleum geologists would be out of work in few years. This is why the specialization Petroleum Geology is important. While a typical geologist would be able to perform the same tasks, the specialization may help companies expedite the decision making process.

Technologically we use variety of tools to achieve our goals. Petroleum geologists and geophysics probably use more technologies than most other specializations in geosciences. For example, petrophyscial techniques on wireline well logs are used to interpret lithologies and fluid types in subsurface using software programs such as PowerLog, Petrel, AccuMap, etc. These software are also used for producing geological maps with interpreted data for exploration and development. Companies typically hire technical school graduates for software operations and maintenance. However, for advanced interpretations it is necessary to hire geology (or geoscience) graduates. One of the major problems with software use is that they often over or under predict multiple parameters resulting in naturally imposable interpretations. These can result in completely incorrect or incoherent simulations of reservoirs. This is where the education and experience of a professional geologist can make a difference. A professional geologist should be able to rectify such issues before it is too late by identifying these anomalies during the early stage of petroleum exploration and development.

We need Petroleum Geologists

Yes, a good geologist with no additional petroleum training may be able to perform the same task, but when you are working on a multimillion dollar project, you would want to hire geologists with specialized training and experience. This is because one mistake could cost a company millions in revenue loss. A petroleum geologist who has specialized training and/or experience in the oil and gas industry could make the difference between a successful profitable project and a complete disaster.

How would you become a Petroleum Geologist?

The term “Petroleum Geology” is very board. Anyone can identify themselves as petroleum geologist including those with engineering and other related degrees, certifications and professional experience. With proper training and experience and engineer or a technician can act as a petroleum geologist. However, there are several universities such as the University of Calgary currently offers petroleum geology classes and specialization degrees. In a competitive job market, having such courses and degrees definitely help you get into the industry much quicker than those who do not have such backgrounds.

It is important to highlight that having a degree in petroleum geology or by taking petroleum classes does not make a person a proficient professional petroleum geologist. Success comes down to the competency, professionalism and experience.

Late Heavy Bombardment

The Late Heavy Bombardment and its Impact on the Terrestrial Planets

Sanuja Senanayake1, Jenna Sie1, Brendan Visser1 and Cassie Vocke1
1Geology Undergraduate Students: Fall 2014, University of Calgary.

Abstract

The Late Heavy Bombardment (LHB) is a hypothetical astrophysical event which occurred in our Solar System 4.1 to 3.8 billion years ago. At this time, an increased flux of impacting materials hit the Earth, Moon and other terrestrial planets of the inner Solar System. This has been suggested as the source for the increased number of crater impacts seen on the lunar surface, Venus, and Mars, and inferred to have struck all the inner terrestrial planets; preserved evidence has yet to be discovered on Earth. Several theories have been proposed to explain the crater formations, however the focus will be on the two most accepted theories: the Nice Model and Planet V Hypothesis. The preservation of craters from the LHB is best seen on the Moon due to the lack of plate tectonics, minimal erosion and deposition. Analyzing the surface of the Moon can help us understand the impact that the LHB had on the inner solar system. A lunar timescale is currently being modified, and when calibrated with radiometric dates from Martian samples, a timescale for Mars and other planetary bodies could be developed to verify if the LHB was a synchronous event. The LHB was early in Earth’s evolution and the contribution of extraterrestrial material to the planet is thought to have affected it in different ways; this includes the development of the atmosphere, biosphere and hydrosphere. The LHB is important not only to explain the sudden increase in crater evidence but also to help confirm the current geochemical properties of the terrestrial planetary system as seen today.

Introduction

The increased frequency in crater formation observed on the Moon, Mars and Mercury is thought to be a result of the mass bombardment of the inner Solar System 4.1 to 3.8 Ga. Several computer models have been proposed based on available data such as lunar samples from the Apollo missions. Relative ages of the crater formations were examined based on superposition and degradation of lunar craters and the geochronology on lunar samples. Lunar data has the most comprehensive record, thus the basin formation frequency on Mars and Mercury were correlated with the lunar crater count distribution to correlate the age of the LHB. Further understanding of the effects on the other terrestrial planets provides insight into the impact of LHB on Earth.

Hypotheses and Models

There is no universally accepted explanation for the Late Heavy Bombardment (Levison et al., 2001). Researchers have suggested several theories with the majority of them were based on the hypothesis that LHB was a cataclysmic event. Popular theories include the Nice Model, Terrestrial Planet V Hypothesis and Late Uranus-Neptune formation; the Nice Model is currently the most accepted.

The Nice Model was proposed by Gomes et al. in 2005, and states that the LHB was caused by the migration of the giant gas planets. According to the model, the inner most planets, Jupiter, Saturn, Uranus and Neptune were stable with highly compact orbital configurations of 5.5 to 17 astronomical units (AU) at the early stage of the Solar System (Gomes et al., 2005). Jupiter and Saturn interacted with the surrounding planetesimals while the gaseous circumsolar nebula dissipated. As a result, the two planets crossed the 1:2 Mean Motion Resonance (MMR) 700 Myr after the Solar System’s formation. This event triggered a rapid migration of the giant planets (Gomes et al., 2005), and resulted in the expansion of their orbital configurations (Fig. 1). During the migration of Jupiter and Saturn, their respective planetesimal disks were destabilized and resulted in a scattering of materials from their originally stable position. This scattering resulted in a spike of crater formation on the inner terrestrial planets, known as the LHB (Gomes et al., 2005).

The rapid migration of Jupiter and Saturn facilitated Neptune and Uranus to exchange positions to their current state. This also created small concentrations of planetary bodies, now known as Jupiter’s Trojans and Neptune’s Kuiper belts (Gomes et al., 2005). Computer simulation on four giant gas planets indicated that at the beginning of LHB, 879 Myr after the formation of the Solar System, there was a high abundance of planetesimal “particles” around each orbital axis. However, 200 Myr later, only 3% of the initial mass of the disk was left (Fig. 1).

Gomes et al., (2005) stated that the limited available data and complex mathematical extrapolation limited their ability to predict every aspect of the LHB. They were only able to demonstrate main characteristics such as the 700Myr delay between the terrestrial planet formation and the LHB. However, they did not specify the shortcomings of their model. The model was recently improved by Levison et al. (2011), known as the Nice II Model. This model postulates that the inner edge of the planetesimal disk was several AUs further beyond the orbit of the outer most planets than stated by the original researchers (Gomes et al., 2005). Levison et al. (2011) also proved that energy can be exchanged between the planets and the planetesimals without close interactions. This means that planetesimals might have been destabilized during LHB without a physical interaction (Levison et al., 2011).

The terrestrial Planet V hypothesis was introduced by NASA Scientists as an explanation for radiometric and geochemical data obtained from the Apollo Mission samples (Chambers and Lissauer, 2002). Brasser and Morbidelli (2011) further suggested the LHB was caused by instability and subsequent migration of an arbitrary planet known as Planet V. As the Planet V migrated across the asteroid and comet belts, it scattered particles within the two belts across the Solar System (Chambers and Lissauer, 2002). Because the two belts were highly populated at the time, the intensity of the scattering resulted in cataclysmic event. Brasser and Morbidelli (2011) had done a detailed analysis of the original location of these astrophysical particles, and suggested that the Planet V could only have migrated across the inner Earth-Venus (EV) belt, but not the Earth-Mars (EM) belt. Thus, the Planet V hypothesis was used to mathematically model and identify the specific location for the materials contributed to the LHB (Brasser and Morbidelli, 2011).

Another theory suggests that LHB was caused by the late formation of Uranus and Neptune (Levison et al. 2001), which destabilized Jupiter and Saturn. Subsequent migration of Jupiter and Saturn would have caused interactions with objects within populated regions Jovian Trojan swarms, and the asteroid belt (Levison et al., 2001). After being dislodged, these objects might have triggered the LHB. However, the numerical simulation suggested that objects within asteroid belt were most likely the primary source, which also agrees with the Planet V hypothesis (Brasser and Morbidelli, 2011). This is the least accepted hypothesis for the Late Heavy Bombardment.

Researchers had difficulties in developing models for LHB primarily due to limited information on lunar impact melts, which is our best evidence for the frequency in crater formations (Brasser and Morbidelli, 2011). The complexity of models increased with the large number of unknowns and variables. This problem has been compounded by the limitations of technology, which computer simulations may require 4 to 6 months to generate viable results (Brasser and Morbidelli, 2011). Brasser and Morbidelli (2011) suggested that this problem can be dramatically improved if the available data from the lunar samples and other sources increased.

Lunar Cataclysm

The Late Heavy Bombardment had a large effect on the Moon from 4.1-3.8 Ga. This event is also known as the Lunar Cataclysm when specifically talking about the Moon. The most obvious evidence of the effects from the LHB is seen by the abundance of craters on the Moon’s surface. Impact craters are found on nearly every solid body in the solar system, however, the Moon happens to have the most complete and clear impact history available (Kring and Cohen, 2002). This is because of the Moon’s exceptional surface preservation due to the lack of plate tectonics, water and atmosphere. Dating of the craters on the Moon’s surface can help to understand the timing of the event and the frequency and mass of impacting material (Ryder, 2002). Further evidence has been preserved in isotopic systems of rocks and impact melts on the Moon (Tera et al., 1974). Analyzing isotopic data of these lunar samples has shown that widespread isotopic disturbances have occurred between 4.0 – 3.85 Ga (Tera et al., 1974). Proper interpretation of impact craters and isotopic disturbances can help to further understand the LHB and its effects on the Moon and the other terrestrial planets.

The flux of material impacting the Moon has varied significantly over time. As seen in Figure 2, the majority of impacting material was before 3.8 Ga with a large spike during the LHB (Tera et al., 1974; Ryder, 2002). The mass of impacting material during a specific time period is calculated by examining the characteristics of craters with known relative age. As most of the largest impact craters have been radiometrically dated from impact melt ejecta, we can determine the relative ages of the craters on these surfaces (Ryder, 2002). Projectile masses can then be determined by the dimensions of the crater, estimations on how much energy it took to produce, and how this energy relates to the mass, velocity and angle of the impacting meteorite (Ryder, 2002). These factors are not well known and therefore analysis of smaller, more recent and well-known craters were necessary. It has been calculated that approximately 2 – 6 x 1018 kg of material from over 1700 impacts struck the Moon during the LHB (Cohen et al., 2000; Ryder, 2002; Gomes et al., 2005).

The largest impacts during the LHB that have formed craters greater than 300km in diameter have been classified as impact basins. Dating of impact basins by superposition, crater counting and radiometric dating of impact melts has shown that the majority of these basins were formed between 4.6-3.7 Ga (Ryder, 1990; Stoffler et al., 2006). As seen in Figure 2, many of these impact basins formed during the LHB. Basin forming impacts are thought to be the cause for the widespread metamorphism and element redistribution seen in lunar samples during the LHB (Tera et al., 1974).

The impacts that formed extensive basins on the Moon would have caused widespread disturbances in lunar samples. Shock metamorphism and element redistribution are some of the disturbances seen in lunar samples due to the large amount of heat generated by the impact events (Tera et al., 1974). Over 800Lbs of lunar rock has been brought back from Apollo and Luna missions for further analysis. Many of these rocks are from the ancient anorthositic lunar highlands, which are greater than 4 Ga and are considered the oldest rocks on the Moon (Dalrymple and Ryder, 1993). Radiometric dating of U-Th-Pb, K-Ar, and 40Ar-39Ar isotopic systems within lunar samples was the main method used to determine the age of the lunar highland rocks (Tera et al., 1974). Due to their age, the lunar highlands have been heavily cratered with a majority of cratering before 3.8 Ga. Disturbances can be seen in the lunar highland rocks when looking at isochrons of U-Pb and Rb-Sr systems (Tera et al., 1974). Total rock analysis showed a U-Pb isochron with a metamorphic age of ~3.9 Ga and an Rb-Sr isochron showed a metamorphic age between 4.0-3.85 Ga (Tera et al., 1974). These ages infer an event or series of events within a narrow time interval of ~200 Ma (Tera et al., 1974). The LHB is the event responsible for the disturbances seen in the lunar samples due to the amount of impacting material that hit the Moon in such a short interval of time.

Another source of evidence on the Moon that supports the theory of the LHB is present in the abundance of impact melts younger than 4.0 Ga (Ryder, 1990; Cohen et al., 2000; Stoffler et al., 2006). Impact melts are materials that have been melted from the intense amount of heat generated during an impact event. This material is then ejected onto the surface of the Moon where it is rapidly cooled to form a glass-like volcanic substance. Radiometric dating of impact melts from the Moon can help to determine the age of formation of craters (Ryder, 1990; Stoffler et al., 2006). No impact melt material has been dated older than 4.0 Ga with many of the samples dated between 4.0-3.8 Ga (Ryder, 1990; Stoffler et al., 2006). This leads to the assumption that either the evidence has not been preserved or that there was little to no impact activity before 4.0 Ga (Ryder, 1990; Cohen et al., 2000). The second assumption is more widely accepted and overall impact melt data strongly suggests a spike of impacts on the Moon during the LHB.

The lunar samples from Apollo missions were only collected from the near side of the Moon because of limitations on radio and visual contact with the astronauts. Therefore, the evidence seen in these rocks can only be conclusive for about half of the Moon (Cohen et al., 2000). To determine if the same disturbances in the rock record have occurred globally, lunar meteorites found on Earth were analyzed. These meteorites have been ejected from the Moon by large impact events and after travelling approximately 1 Ma in space they have impacted the Earth’s surface (Cohen et al., 2000). The lunar meteorites have been found in deserts and in Antarctica where alteration due to weathering is minimal and preservation of the sample is exceptional. Based on the composition of the lunar meteorites and the interpretation of satellite photos, it has been predicted that some of the meteorites have originated from the unexplored far side of the Moon (Cohen et al., 2000). Analysis of U-Pb and Rb-Sr isochrons as well as cosmic ray exposure (CRE) dating has determined that the disturbances in the lunar samples due to the LHB are in fact a global occurrence (Cohen et al., 2000).

Many different authors have introduced two conflicting ideas for the rates of lunar cataclysm. ‘Terminal lunar cataclysm’ involves the sudden spike of impacting material within a short 200 Ma interval and is generally supported by most recent papers (Tera et al., 1974; Ryder, 1990 & 2002). However, other research papers by Hartmann et al. (2007) and Baldwin (2006) have suggested that the lunar cataclysm was a gradually decreasing event. These authors introduce the theory that the Moon has been bombarded fairly consistently since its formation 4.6 Ga. The argument for gradual bombardment mentions that radiogenic impact melt data is not present before 4.0 Ga because it was constantly reset by impacts (Hartmann et al., 2007). However, recent papers have disregarded this theory because extensive bombardment would not be able to reset all impact melt data before 4.0 Ga without leaving any evidence (Ryder, 2002).

Evidence on Mars and Mercury

Correlating evidence of LHB from the terrestrial planets of Mercury and Mars can help to piece together the events which took place in the Solar System over 3 Ga. Venus will not be discussed as its surface does not have good preservation of the LHB due to its intense resurfacing processes and much denser atmosphere. Like the Moon, Mercury and Mars show a history of intense cratering impacts on their surface in the form of basins that are kilometers in diameter (Fassett et al., 2013). However, the Moon has the best preserved cratering record with the most complete and accurate time scale. This is because the surface of Mercury and Mars are much more complex than the Moon when it comes to resurfacing processes. Thanks to MESSENGER and Viking data, Greeley et al. (1981) found that there is strong evidence that LHB triggered widespread volcanism on both terrestrial planets. This can be seen through mare ridges and flow lobes which are characteristic of volcanic plains (Greeley et al., 1981). These plains have partially to completely buried impact craters which has constrained dating methods used for the terrestrial planets.

Another limit to dating methods is the lack of samples from the planets. An exception to this lack of data is the SNC group of Martian meteorites (Shergottites, Nakhlites, Chassignites). These meteorites are known to be ejected from Mars because of their correspondence with Martian atmospheric compositions available from Viking data (Geiss et al., 2013). Through K/Ar dating methods the ages of these meteorites are determined to be significantly young, ranging from 1.3-0.58 Ga. The oldest among the Martian meteorites, ALH 84001, which is not classified as an SNC meteorite due to its composition, is 4.1 Ga (Geiss et al., 2013). These meteorite dates along with references from the lunar crater count time scale are what scientists are currently using to create a timescale for Mars, which in turn will aid in the understanding of the LHB. In Figure 3, the two curves on the time scale are representative of measured dates and hypothesized dates. The hypothesized dates come from the lunar chronology curve which is calibrated to Mars through the difference in crater production rates on Mars and the Moon. The time scale at this point is incomplete; there is no data beyond 4 Ga, and has some error, shown by the shaded areas under the curves. This is where the dates hypothesized don’t correlate to measured dates. In the future with samples returned from the planet, a more accurate timescale can be constructed using radiomateric dating and cosmic ray exposure ages.

Dating on Mercury is based on models of impact rate along with global mosaic images taken by Mariner 10 and MESSENGER (Marchi et al., 2013). The surface has a less distinct record of cratering than the Moon which is related to the more intensive resurfacing processes it underwent (Marchi et al., 2013). Processes such as volcanism and erosion modified or even completely erased the ancient features (Geiss et al., 2013). It is assumed that in general the larger the crater, the less it was affected by resurfacing. Hence, large and undisturbed basins are used for the derivation of absolute ages from the measurement of crater size frequency distributions. These distributions may be used in conjunction with lunar chronology if they are calibrated to Mercury based on differences in factors affecting impact basins (Marchi et al., 2013). Presently, there are no available samples from Mercury, so dating of the terrestrial planet relies on the lunar crater count time scale and crater frequency distributions.

When dating the surface of Mercury and the Moon, basins on a global-wide scale could be taken into account. Alternatively, when dating the surface of Mars, only the highlands which make up close to half of the surface could be considered (Werner, 2014). The lowlands are thought to have been reworked by volcanism, therefore the highlands are a more reliable source of basins to date. Overall, the Moon is thought to have the oldest surface at 4.3 ± 0.05 Ga, followed by Mercury at 4.1 ± 0.05 Ga, and Mars at 4.1 ± 0.01 Ga (Werner, 2014). As seen in Figure 4, the frequency of impact varies for each planet suggesting that LHB may have not been a synchronous event. Further evidence will be needed to confirm that bombardment of the terrestrial planets may have varied, but researchers have come up with some hypothesis to explain this phenomena. One hypothesis discussed the possibility that the Moons formation process led to a cratering record different than the terrestrial planets (Werner, 2014). Some scientists suggest hindered global surface solidifcation, while others argue that widespread volcanism could have created gaps in the cratering record (Werner, 2014). Although it is difficult to accurately constrain the dates of impact basins on Mercury and Mars, extrapolation of lunar cratering along with Martian meteorites has led scientists to believe that the terrestrial planets were all subject to an intense spike in bombardment 4.1 to 3.8 Ga (Fassett et al., 2013). A more complete understanding of how the LHB affected Mercury and Mars is important because it may lead scientists to an improved understanding of what has not been preserved on Earth.

Implications for Earth

The Late Heavy Bombardment (LHB) has no direct preserved evidence on Earth due to recycling of the crust by plate tectonics. Data extrapolated from lunar craters suggests there was an average of ~2 x 1020 kg (De Niem et al., 2012; Abramov et al., 2013) accreted material added between 4.1 – 3.8 Ga to the planet during this time. The crater diameters left by impactors were up to 1000 km (Glikson, 2001), however cumulative number of craters decreased with increased diameter (Fig. 5). As diameter of impactor increased, so did the long lasting physical effects on Earth due to greater sterilization volumes and longer cooling periods. Basin counting on the Moon suggests that the Earth collected 1.3-1.5 times more objects of the same mass per unit area than the Moon (Grieve, 1980). The material was added to Earth resulted in physical and chemical alterations of the hydrosphere, biosphere, atmosphere, and lithosphere.

The hydrosphere was affected to variable degrees, depending on the diameter of the impactor to strike. Valley (2005) provides evidence of pre-bombardment liquid water 4.4-4.0 Ga, due to δ18O values in zircons. This initial water, as explained by Frey (1980) and Izawa et al. (2010), arose as a result of the degassing of Earth’s interior from a magma ocean. Impactors with a crater diameter of >500 km had the potential to boil the ocean; however, the majority of impactors boiled only the surface layer (Zahnle et al., 2007). The increased temperature of liquid water established hydrothermal vents, which had the potential to shelter pre-bombardment life or host post-bombardment life.

All necessary conditions for life were present on Earth before the LHB, including continental crust, liquid water, and a primitive atmosphere (Martin et al., 2006). During periods of extreme heating, microbes located on the bottom of the ocean and deep within the subsurface were in good positions to survive (Houtkooper, 2011). This was possible as 1.2 – 2.5 vol% of the upper 20 km crust was melted and 0.3 – 1.5 vol% in a molten state at any given time, according to the models proposed by Abramov et al. (2013). The overall calculated total habitable volume of the crust pre-bombardment is proposed to be 2.1 x 109 km3, and 1.7 x 109 km3 at the end of the LHB (Abramov and Mojzsis, 2009).

Primitive life forms could have survived extreme environmental conditions in the subsurface and hydrothermal vents; thermophiles thrive in 50-80 oC environments, hyperthermophiles in 80-110 oC (Abramov and Mojzsis, 2009). As denoted by Figure 6, the percentage of habitable volume for hyperthermophiles increased during the period of bombardment, indicating better survival rate as temperature of hydrothermal environments increased. This does not necessarily suggest that life originated under hot conditions, only that the survival of hyperthermophiles created a bottleneck effect leading to the diversification of species today (Zahnle et al., 2007), with hyperthermophiles near the roots of the Tree of Life. This life may have originated on its own in pre-bombardment conditions, later to thrive in the hydrothermal systems created by the LHB. Or, perhaps the energy of the impactors and the post-bombardment conditions established were ideal environments to sustain life. Evidence of life is observed soon after the LHB, at 3.465 Ga, as filamentous microbes preserved in the Apex Chert of Western Australia (Schopf, 1993). Thus, it has been suggested that the impactors brought extraterrestrial life to seed Earth upon impact (Houtkooper, 2011), assuming the microbes capability of surviving transport and impact (Sheldon and Hoover, 2007).

Earth’s atmosphere endured both loss and gain processes, which altered the pressure and composition of the atmosphere. Impact erosion, as described by Hamano and Abe (2010), is the process in which energetic expansion of a vapor cloud blows off a fraction of a planet’s atmosphere due to rock ejecta from impact events. Atmospheric erosion due to impact was minor, however the atmospheric pressure increased during the course of the bombardment, mainly due to buildup of CO and CO2 (De Niem et al., 2012). The pressure built up as a result of the impact-produced rock and water vapor build up in the atmosphere, which helped slow down further impactors, and retain more of the vapor-plume (De Niem et al., 2012). Elemental depletion was a result of hydrodynamic escape within the hydrogen rich primordial atmosphere of Earth. As impactors enter the atmosphere, hydrogen escaped and drew heavier constituents out with it, such as Xenon (Pepin, 2006). However, while impacts stripped away parts of the atmosphere, they also contributed volatiles, including cometary water (Pepin, 2006). Comets can contain up to 50% ice, and up to 10% ice in carbonaceous chondrites (Marty and Yokochi, 2006). Thus, the competition between volatile supply by retention of the vapor cloud, and atmospheric loss via vapor expansion, would ultimately affect the volatile budget of Earth’s atmosphere (Hamano and Abe, 2010).

Earth’s lithosphere would have endured the most physical effects brought upon by the LHB through the formation of basins. When an impactor strikes, excavation of the target area ejects rock into the surrounding area, creating topographic dichotomy 3-4 km high (Grieve 1980). Partial melting of the mantle released basaltic magma into the basin floor from decompression melting (Frey, 1980). At impact sites >1000 km, the lithosphere may have been penetrated resulting in the uplift of the asthenosphere and the distortion of the geothermal gradient, which has been suggested to be steeper in the Archean than the present day, up to 90o C/km (Grieve, 1980). These thermal gradients could be steepened further by 20% from increases in impactors, which would have stirred up convection by up to 25% in the thinned and badly fractured lithosphere (Frey, 1980). Figure 7 summarizes these processes as the formation of a multi-ring structure. The large impact basins provided sinks for early oceans to localize, which suggests dry continental masses before bombardment was complete (Frey, 1980).

Conclusion

The evidence from the Moon and terrestrial planets suggests there was an increase in crater formation between 4.1 to 3.8 Ga. Even with the lunar evidence, it has been a difficult task to postulate a suitable mechanism for the causes of the LHB. While several theories have been suggested, the Nice Model is the most widely accepted. The exceptional surface preservation of craters on the Moon holds the most clear and complete impact history for the inner Solar System. Radiometric dating of lunar rocks and impact melts has provided a time frame and magnitude for the LHB that can then be interpreted for other terrestrial planets. The evidence found on Moon can be correlated with Mars and Mercury suggesting the LHB affected the entire inner Solar System. The peak impact time varies slightly between these bodies with the Moon recording the oldest peak time followed by Mercury and Mars. This suggests that the LHB may not have been a synchronous event. The LHB also had a significant impact on the Earth, which may have had long lasting implications on the Earth’s systems. This may have established the current environment that host the unique life. Although the LHB remains hypothetical as no model has yet been proven, it is likely the best explanation for the sudden increase in cratering of the terrestrial planets.

References

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This is an online publication of the final Term Paper written by four students at the University of Calgary for Geology 535: Early Earth Evolution in Fall 2014. The text is copyrighted to all four authors. You may follow the guidelines posted under the general Site Copyright Notice. Figures used in the original paper have been removed due to copyright laws.

No part of this publication may be reproduced or redistributed in any form or format without prior permission from the authors mentioned above. Please contact Sanuja Senanayake for requests for permission to reproduce and redistribution.

Thin Section Sketches

Thin section sketches are drawings that represent what you observed. But most of us (students, researchers and professors) are not artistically inclined. Even if you are good at drawing diagrams, you still have to empathize key features when drawing a thin section sketch. Here are some tips and tricks for making a good (if not perfect) thin section sketch.

Kyanite - PPL
Photomicrograph of Kyanite – PPL

I. Microscope scale and polarization

Rarely a thin section is drawn with both PPL (plane polarized light) and XPL (cross polarized light). Thin section sketches should be drawn in one type of polarization. Depending on the mineral assemblages in the sample, choose the polarization that shows the best detail and most features. Generally the sketches are made in XPL. But this is a rule or may not work for every sample. For example, if you have a thin section with very strong differences in relief between the minerals in PPL but similar features (for example, all isotropic) under XPL, it is best to use a PPL sketch.

Do not switch between powers (lenses) on the microscope during the sketch. Choose the power that best suited for the size of minerals and features and record it beside the sketch. You should also include a scale measuring from one end of the diagram to the other. When drawing by hand I found that it is a waste of time to reduce the scale bar. I rather draw the scale bar across the entire sketch.

Key points:

1. Draw the thin sections in either PPL or XPL, not both.

2. Choose a magnification that is best suited for the size of minerals and petrologic features.

II. Drawing

It is almost impossible for a mineral to grow by itself. Minerals are attached to either the matrix or other neighboring minerals. So if you are making a sketch of a specific grain, make sure you also draw the surrounding (even if it is just glass).

In order to show relief try the following; if the grain has a strong relief compared to the surrounding, thicken the border of that grain by drawing the grain boundary over and over. If the grain has a low relief use less pressure on your pencil when drawing the grain. Use common sense to draw everything in between the highest and the lowest relief. Remember, you can have two grains with very high relief next to each other. But in order to identify that you must have lower relief minerals surrounding those grains.

Yes, you may cheat a bit on your drawing. While you should always draw only what you can see under a particular field of view, sometimes not all features can be captured in a single view. In this case you have two options; drawing two different diagrams or adding features to the diagram you have that you found in other areas of the slide. To do this, you need experience to make and educated decision so that you do not fabricate any textures or features.

Key points:

1. Draw what you see! Nature has it’s own rules such as mineral by itself in a thin section is rare.

2. Pay attention to detail such as relief.

3. Sometimes you have to add addition features from other parts of the slide to your sketches in order to show all the features in one diagram. Use educated decisions on such cases to avoid fabrication of information.

4. Use good pencils and erasers. I have seen some students just smudges their paper trying to correct diagrams.

III. Labeling

To make it neat, place the labels to one side or limit writing the labels on all sides. If your scale is for the entire field of view, makes sure the scale bar do not cut across your labels/ It is also better to use conventional abbreviations for minerals. For example, Hbl – Hornblende, Qtz – Quartz, Grt/Gt – Garnet, Cum – Cummingtonite etc. For a complete list, please consult British Geological Survey mineral list document.

Avoid labeling the same mineral more than once except when a different form of optical properties are shown. For example, you may label Hornblende both at extinction and without extinction. But do not label Hornblende in one form (non-extinct for example) more than once.

Instead of using arrow, use lines. This will avoid obscuring the what mineral or features you are highlighting.

IV. Presentation

Finally, what ever you draw make sure that the next person who look at your sketch can intemperate what you are trying communicate. There is no point of drawing a thin section sketch if no one can understand what you are drawing. If there are additional information on the sample (such as location, series number, etc) write that on the corner of your sketch. This will not only help others, but also will help you for future references.