24/08.05 - gemsa
GEMS: the opportunity for stress-forecasting all damaging earthquakes worldwide Stuart Crampin1,2, Sergei V. Zatsepin1, Chris W. A. Browitt1, Vladimir I. KeilisBorok3, Kiyoshi Suyehiro4, Yuan Gao5, and Larry Walter6 1
School of GeoSciences, University of Edinburgh, Edinburgh, Scotland UK;
[email protected];
[email protected];
[email protected]. 2 also at Edinburgh Anisotropy Project, British Geological Survey, Edinburgh, Scotland UK. 3 Institute of Geophysics and Planetary Physics, University of California, Los Angeles, California USA;
[email protected] 4 JAMSTEC, Yokosuka, Japan;
[email protected]. 5 Institute of Earthquake Science, China Earthquake Administration, Beijing, China;
[email protected]. 6 Geospace Engineering Research International, Texas, USA,
[email protected].
Abstract A new understanding of rock deformation allows the accumulation of stress before earthquakes to be monitored by seismic shear-wave splitting. Such accumulations have been recognized with hindsight before over a dozen earthquakes worldwide using swarms of small earthquakes as the source of shear waves. On one occasion the time, magnitude, and faultbreak of a M 5 earthquake was successfully stress-forecast. However, suitable swarms of small earthquakes are very uncommon, and routine forecasting requires controlled-source observations in bore-hole Stress-Monitoring Sites (SMSs). A preliminary SMS confirmed that both science and technology are effective for monitoring stress changes before earthquakes. This means that a network of SMSs, on a 400 km-grid, say, could stress-forecast all M = 5 earthquakes (that is all damaging earthquakes) within the grid. This paper suggests that GEMS, a global earthquake monitoring system, could forecast all damaging earthquakes in both developing and developed countries worldwide. Strange as it may seem, we understand the distribution of matter in the interior of the sun far better than we understand the interior of the earth (Richard Feynman, “Six easy pieces”, 1985)
1 Introduction Despite over 200 years of geology and 100 years of geophysics, we know remarkably little about how rocks deform a few meters beneath our feet. This is because rocks at depth are extraordinarily remote. We cannot access them directly without destroying their in situ conditions by the severe trauma of de-stressing, cooling, and fluid disruption. This lack of understanding has serious consequences. The 1995, M 7.2, Kobe Earthquake in Japan killed 6,000 people and caused an estimated $250 billion damage. Earthquakes such as Kobe; 1999, Izmit, Turkey, 17,000 dead; 2001, Bhuj, India, 40,000 dead; and 2003, Bam, Iran, 34,000 dead; occur frequently, indiscriminately, and at all sorts of scales, causing incalculable suffering and loss. These four earthquakes were all close to magnitude M 7. The largest M 8 earthquakes, release probably thirty times more energy, but are fortunately an order of magnitude less frequent. Currently we cannot predict earthquakes and are sometimes fatally surprised. We suggest that such surprises are unacceptable in a modern civilized scientifically-advanced world. In the 21st century, with massive scientific progress projected, we ought to be able to go to bed in Beijing, Tokyo, San Francisco, Bhuj, or Bam, confident that neither we nor our children will be buried by earthquakes overnight. Is such assurance possible? Recent advances in understanding and modeling fluid-rock interaction suggest that it is. GEMS, a proposed global-network of earthquake stress-monitoring sites, would be able to stress-forecast the times and magnitudes of all M = 5 earthquakes (that is all damaging earthquakes) and also provide a means for mitigating their effects. Moreover, GEMS
would provide other significant advantages (see Sections 8 – 11, below), and would be a stimulating new research tool for investigating the dynamic evolution of the complex heterogeneous Earth on which our lives and future depend, but whose behavior we currently have little understanding or control, as witness the earthquake death toll. A combination of three recent developments provides the opportunity for GEMS. The first is the recognition that almost all in situ rocks (certainly in the crust, and most probably in the uppermost 450 km of the mantle) are pervaded by self-organized scale-invariant systems of fractures ranging from open fluid-saturated grain-boundary cracks and preferentially-oriented pores at millimeter or sub-millimeter scale to closed plate-boundaries at scales of thousands of kilometers. (The fluid is water-based salt solutions and sometimes hydrocarbons in the crust, and intergranular films of hydrologized melt in the upper mantle.) The criticality, particularly of the small-scale microcracks, is the underlying reason for rocks’ sensitivity to small disturbances. It is the “butterfly's wings” sensitivity typical of all critical systems. The second development is that we now know that details of stress-induced low-level evolution of crack distributions can be monitored by orthogonally polarized seismic shear-wave splitting. This means that the accumulation of stress before earthquakes can be monitored and the release of stress in earthquakes “stress-forecast”. Finally, and crucially important, recent advances in borehole instrumentation and technology allow polarized shear-waves to be monitored by repeated crosshole seismic-transmission measurements at Stress-Monitoring Sites. Note that three papers (Aster et al., 1990; Seher and Main, 2004; Liu et al., 2004) have criticized various aspects of these developments, and are sometimes cited as suggesting that the geophysical and observational developments are controversial. We discuss these ideas in the Appendix and show that the ‘controversies’ are unfounded.
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Shear-wave splitting and critical systems of fluid-saturated cracks
Stress-aligned shear-wave splitting, caused by propagation through the stress-aligned fluidsaturated 'microcracks', is observed at all depths in almost all in situ rocks (Crampin, 1994). The cracks are grain-boundary cracks in crystalline rocks, and preferentially-aligned pores and pore throats in sedimentary rocks. When stressed, fluid-saturated microcracks tend to take up alignments, like hydraulic fractures in the oil-industry, perpendicular to the direction of minimum horizontal stress, as illustrated schematically in Figure 1, leading to the approximately stressaligned shear-wave polarizations observed at the surface above small earthquakes worldwide. Stress-aligned microcracks are the only explanation for the stress-aligned shear-wave splitting almost universally observed in almost all in situ rocks (Crampin, 1993). Other compelling evidence for microcracks is that, as we shall see below, the anisotropic effects change with time and the only source of anisotropy with short-term compliance is fluid-saturated microcracks. As a result, shear-wave splitting is a unique diagnostic phenomenon that provides direct information about the orientations, crack densities, and aspect ratios of the distributions of in situ microcracks along the ray path. Such stress-aligned shear-wave splitting is observed with remarkably similar characteristics throughout the crust. This demonstrates that most rocks contain fluid-saturated microcracks that are aligned by the stress-field into, typically, near-parallel nearvertical orientations normal to the direction of horizontal minimum stress (Crampin, 1993, 1994, 1999). There are only a few well-understood exceptions. In the upper crust, the pore-fluids in microcracks are typically water-based salt solutions but sometimes oil or gas. These are supercritical fluids in the lower crust, and water-saturated melt in the mantle (Crampin et al., 1986). The direction of polarization of the faster split shear-waves typically indicates the prevailing direction of maximum horizontal stress (Figure 1). The universally-observed range of shear-wave velocity anisotropy in ostensibly-intact rock (1.5% to 4.5%) shows that the fluid-saturated microcracks are so closely-spaced that they verge on fracture criticality (Crampin, 1994). At fracture criticality, cracking is so extensive that the shear strength cannot support homogeneous deformation, and fracturing occurs at ever increasing scales. This leads to the observed selforganized self-similar fracture populations observed by Heffer and Bevan (1990). Such critical systems verging on criticality are said to possess self-organized criticality (Crampin, 1999). The best known example of self-organized phenomena in geophysics is the Gutenberg-Richter magnitude-frequency relationship, where the log of the number of earthquakes greater than
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magnitude M is self-similar to earthquake magnitude for seven or eight orders of magnitude. The criticality of the fluid-saturated rocks at the microcrack scale provides the necessary (but previously-unexplained) physical phenomenon underlying the Gutenberg-Richter relationship (Crampin, 1999).
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Anisotropic poro-elasticity (APE) modeling of rock mass evolution
The response of stress-aligned fluid-saturated microcrack distributions to low-level variations in stress field, pore-fluid pressure, fluid chemistry, and temperature, before fracturing takes place can be modeled by Anisotropic Poro-Elasticity (APE) (Zatsepin and Crampin, 1997; Crampin and Zatsepin, 1997). The mechanism is fluid movement by flow or dispersion along pressure gradients between neighboring microcracks at different orientations to the stress-field (Figure 2). The effect of low-level deformation (pre-fracturing deformation) is to change the angular distribution of cracks and crack aspect-ratios as indicated schematically in Figure 2. APE is a relatively simple model that provides a quantitative relationship between small stress variations and shear-wave splitting (Zatsepin and Crampin, 1997) under minimal assumptions and minimal model parameters. Below a few metros, the environment of rocks rapidly becomes hostile and inaccessible. With increasing depth, rock is subjected to high pressures and temperatures in toxic environments, which makes it impossible to physically examine undisturbed rock at depth. Thus our understanding of the behavior of in situ rocks is poor (witness the typical 30% recovered oil, despite enormous investments and investigations by oil companies). Consequently, direct calibration of APE in situ is difficult. Numerically modeling with APE approximately matches an ever-increasing number of phenomena (and innumerable individual observations) some of which are listed in Table 1 (Crampin, 1999, 2003). We suggest that the underlying reason why the simple nearly-parameterless APE approximately matches the behavior of the immensely complicated heterogeneous Earth is that the compliant fluid-saturated cracked rocks are critical systems and have the universality that implies (Crampin and Chastin, 2003; Crampin and Peacock, 2004).
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Two successful applications of this new understanding
The first successful application was in oil field operations. Angerer et al. (2002) matched changes in shear-wave anisotropy induced by CO2-injection at Vacuum Oil Field, New Mexico, by modeling the response of rock by inserting the injected pressures into APE. There were two field experiments injecting both high- and low-pressure CO2. In both cases, the match of observed to calculated effects was almost exact and is the best in situ quantitative test of APE to date. The second application is that the build up of stress before earthquakes has been recognized by observations of increasing time-delays between split shear-waves along those particular ray paths sensitive to the changes in aspect ratio in Figure 2 (Crampin, 1999). The difficulty is finding sufficiently-persistent swarms of source earthquakes. First seen, with hindsight, before the 1986, M 6, North Palm Springs Earthquake in California (Peacock et al., 1988; Crampin et al., 1990, 1991; and comments by Aster et al., 1990, discussed in the Appendix) and a few places elsewhere (Crampin, 1999), the breakthrough came when shear-wave splitting was routinely examined in Iceland. Iceland has persistent seismicity associated with transform faults of the Mid-Atlantic Ridge which, unusually, run onshore. Using swarms of small earthquakes as the source of shearwaves, changes in time-delays between shear-waves have now been seen (with hindsight) before some eight further earthquakes (Volti and Crampin, 2003b; Gao and Crampin, 2004; Crampin and Gao, 2004) ranging in magnitude from M 1.7 to M 7.7. On one occasion the increase was recognized before the earthquake occurred and the time and magnitude of an M 5 earthquake in SW Iceland was successfully stress-forecast (Crampin et al., 1999a). Figure 3 shows the observed behavior of shear-wave time-delays, and an outline of the arguments. The caption also shows the final exchange of emails between the University of Edinburgh and the Iceland Meteorological Office, which successfully stress-forecast the time and magnitude of the earthquake in a comparative narrow time/magnitude window (Crampin et al., 1999a). Time of earthquake occurrence is estimated as the time the increasing time-delays are projected to reach a critical level for the particular locality, when cracks are so pervasive that shear-strength is lost and rock fractures. The duration of this increase is proportional to the magnitude of the event. Rock is so weak to shear stress that the strain energy for a large
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earthquake has to accumulate over enormous volumes of rock: hundreds of millions to billions of cubic kilometers before the largest earthquakes. Consequently, stress accumulation can be recognized almost anywhere within a large volume from changes in shear-wave splitting, but the potential epicenter, the location of the impending earthquake, cannot be estimated from shearwave splitting alone. However, knowledge that a larger earthquake is approaching enables other precursory evidence to be interpreted realistically. In the case of the successful stress-forecast, local seismicity correctly identified the fault break, so time, magnitude, and location were successfully predicted (Crampin et al., 1999a). This is probably the first successfully predicted earthquake based on deterministic geophysics as opposed to probabilistic statistical data. However, such stress-forecasting requires a swarm of small earthquakes to provide a source of shear-waves, and sufficiently persistent swarms are rare. The largest recent earthquake in Iceland for several decades (Ms 6.6, June, 2000) was close to a monitored swarm. However, the earthquake was not stress-forecast, because the source swarm was quiescent for the first two months of the seven month’s increase. Consequently, the increase was not recognized and the stress-forecast was not made (Volti and Crampin, 2003b).
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Stress-monitoring sites (SMSs)
The unreliability of swarms of earthquakes, even when swarms are available, means that reliable earthquake forecasting requires some form of controlled-source crosshole-seismics between deep wells to avoid the heterogeneity of the near surface. The optimum geometry of a StressMonitoring Site (SMS) designed to radiate shear-waves along the particular ray paths most sensitive to increasing crack aspect-ratios and increasing stress is shown in Figure 4. The first SMS was developed by the European Commission funded SMSITES Project in Iceland, using wells previously drilled for geothermal purposes adjacent to the Húsavík-Flatey Transform Fault of the Mid-Atlantic Ridge where it runs onshore at Húsavík in northern Iceland (Crampin et al., 2003). The well geometry was not ideal for a SMS and signals were restricted to horizontal propagation at 500 m-depth between wells 315 m-apart. However, despite non-optimal geometry the records were spectacularly sensitive to small disturbances of stress. In what was intended to be a source calibration test of the Downhole Orbital Vibrator borehole source, the DOV was pulsed every 12 to 20 seconds for 24 hours for 13 days, yielding over 40,000 records at each of four downhole three-component geophones 1 m-apart. Hundred-fold stacking gave travel-time accuracies of ±0.02 ms. Fortuitously, the recordings coincided with a burst of low-level seismicity, 70 km NNW of SMSITES on the Grímsey Lineament, and the remarkable anomalies in Figure 5 were recorded. The variations in seismic travel-times between the two wells also correlated with NS and EW GPS (Global Positioning System) variations, and with changes in water level in a well on Flatey Island immediately above the fault. This sensitivity to low-level seismicity, with equivalent energy to one small M ˜ 3.5 earthquake at 70 km distance and at hundreds of times the conventional source dimensions, is far greater than would be expected in the conventional brittle-elastic crust and is another demonstration of the crack-critical crust. These observations confirm both the science and technology of SMSs for monitoring changes of stress in the Earth’s crust and stress-forecasting the times and magnitudes of earthquakes. Although the experiment was designed to monitor small changes, we were surprised by the sensitivity actually observed. Well-level changes and GPS variations have previously been observed by several authors at substantial distances from earthquake epicenters, but this is the first time that four seismic, vector GPS, and well-level changes have been observed simultaneously.
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Advances in borehole technology
Operation and recording permanent installations of seismic receivers and energy sources within deep boreholes is now well-established in oil industry surveys. Borehole seismic recorders can routinely operate at several kilometers depth at temperatures up to 150ºC. The most significant advance has been in controlling the DOV source and understanding the behavior and characteristics of the polarized seismic signals it generates. New developments of this seismic source provide the means to reliably control the source, record observations, and process signal measurements by satellite technology. This means that the whole shear-wave monitoring operation can be controlled and processed remotely both onshore and offshore, so that a global network (GEMS) of such sites could be managed economically on a continuous real-time basis.
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The GEMS global network of SMSs
The concept of Stress-Monitoring Sites (SMSs) is believed to be a significant advance. For the first time there is the opportunity for controlled-source operations by non-invasive seismic techniques that can monitor stress-induced changes to microcrack geometry in in situ rock. The power of a single SMS (Figure 4) is that it can monitor very subtle changes in behavior by timelapse techniques where, in very quiet conditions preferably at or below 1000 m-depth, records of the highly-repeatable DOV signals can be differenced to monitor the effects of very small changes in rock mass conditions. The measurements allow exceptional accuracy to ±0.02 ms (20 µs) over 315 m, which would be difficult to achieve in conventional laboratory conditions (Crampin et al., 2003). Note that although not specifically addressed by the discussions in this paper, the accuracy of SMSs would also be valuable for investigating the frequency dependence of seismic velocities. Such dispersion is currently of interest to the oil industry as a means of investigating the dimensions of the cracks that cause shear-wave splitting in hydrocarbon reservoirs. The seismic measurements in Figures 5a, b, and c are clearly not at the limit of their range. Simple conservative extrapolation suggests that a single SMS would be able to monitor changes induced by M ˜ 3.5 earthquakes to more than 70 km, and correspondingly M 5 to ~400 km; M 6 to ~1000 km; M 7 to ~3000 km; and M 8 earthquakes to the scale of tectonic plates, if not worldwide. This means that a global network of SMSs (GEMS), on a 400km grid, say, should be able recognize stress accumulation and stress-forecast the times and magnitudes of all earthquakes with magnitudes greater than M 5 (and many M 4 events) worldwide. In particular, what would be guaranteed is that the accumulation of stress before all damaging earthquakes would always be recognized. In particular, no change would indicate no impending large earthquake and hence security. However, if changes were observed, the estimate of the time of occurrence would depend on the rate of the tectonic stress accumulation which may vary from place to place, and possibly from time to time. The suggested GEMS network of a 400km grid would lead to some 1500 SMS, after adjusting distributions for stable and unstable regions. (There are large stable areas both onshore, such as the Canadian Shield, and particularly offshore as in oceanic basins, which are believed to be almost completely aseismic and would probably show little variation in stress, although this would be open to confirmation.) Note that routine drilling of deep wells offshore is only now becoming feasible as the Riser Drillship 'Chikyu' of the Integrated Ocean Drilling Program (IODP) becomes available in 2006. (Riser technology allows deeper and more easily re-entered wells to be drilled offshore.) Indeed, networks of borehole seismometers across ocean floors have been proposed to record and analyze earthquake data (Suyehiro, 2002). A 1000km grid was suggested, filling in the largest gaps in the worldwide network of seismic stations, and would be passive, monitoring earthquakes as they occurred.
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Lowering the potential for large earthquakes
As the accumulation of stress is so extensive, any increase in stress or, more generally, any largescale changes in stress pattern, can be recognized at substantial distances from the eventual earthquake epicenter. Consequently, if accumulating stress is believed to be threatening a large city or other vulnerable location, in principle, the accumulating stress can be released almost anywhere within the larger stressed volume and the potential for a city-threatening earthquake reduced. The most direct way to release stress would be by massive hydraulic pumping operations in non-vulnerable areas nearby, within 100 to 300km of the threatened city. (Hydro-fracturing is a routine Oil-Company operation.) Stress release by hydraulic fracturing could be sited in areas of low population and infrastructure such as mountains or deserts, or even offshore, with suitable allowances for tsunamis. However, this is an untested procedure and the effects are currently not known. The great advantage of GEMS is that the effects of the hydraulic pumping would be monitored so that the results could be optimized. The intention would be to release stress by exciting smaller earthquakes in areas within the larger stressed volume where earthquakes would be less
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destructive. The seismic (acoustic) events as oil reservoirs are depleted demonstrate this is possible. Such hydraulic fracturing operations would be massive, extensive, and very costly. However, the 1995 Kobe earthquake has been estimated as costing 250 billion dollars U.S. Had the accumulation of stress been recognized by GEMS, a (very reasonable) premium of 0.5% would provide $1.25 billion for hydraulic fracturing if a city such as Kobe was shown to be threatened by a large earthquake. This would not be a blind investment. GEMS would allow the effects to be monitored and the stress release optimized. This would mean that if the hydraulic fracturing at one location was not proving effective it could be relocated within the stressed volume until an effective relaxation regime had been instigated. Note that lowering the risk of large earthquakes on specific faults by hydraulic fracturing on the actual fault was suggested many years ago (Raleigh et al., 1976). At that time, this had the major disadvantage that such operations might excite the very event they are designed to prevent. The advance here is the recognition that the stress accumulation is so extensive that the hydraulicfracture induced events could be triggered at substantial distances from any vulnerable location, and that any such changes would be monitored by GEMS.
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A stress-forecasting service
Monitoring stress changes and directions at a single SMS is analogous to a single weather station, where the principal measurements are changes in air pressure, and wind speed and direction. These can be used to estimate, particularly the stability of the weather, and the likelihood of storms. (One of us finds it a useful guide to look at a barometer each day before stepping into Scottish weather!) The power of weather forecasting comes from networks of such weather stations, where recognizing areal and temporal patterns of behavior allow relatively reliable weather forecasting. However, weather forecasting does have all the uncertainties of another critical-system of heterogeneous complicated interactive phenomena. It is expected that identifying previously unrecognized patterns of behavior would allow a universal stress forecasting service analogous to weather forecasting. Such stress-forecasting should provide some predictive capability for the longer-term, possibly five to ten year, estimation of earthquake scenarios, so that long term preparations for earthquake hazard could be instituted. Currently, such questions are not even raised by the scientific community, because there is no means of acquiring such estimates of changes in stress. Stress-forecasting with GEMS would open this new capability.
10 The gems network of boreholes for other passive observations We have justified a Global SMS network on the basis of the immediate practical advantages in reducing earthquake hazards. The GEMS network of ~1 km-deep boreholes would also provide sites for passive monitoring of other geophysical parameters such as broadband seismics, gravity, resistivity, magnetism, amongst others. (This opportunity would also provide the incentive to develop suitable borehole instrumentation.) The exceptionally quiet environment and multiple stacking would allow time-lapse identification of extremely small changes (such as the seismic measurements in Figure 5) that would be quite impossible to resolve from noisy near-surface observations. This would be a stimulating new research tool for monitoring the dynamic evolution of the Earth, about which we currently have almost no knowledge or understanding.
11 GEMS as a new tool for monitoring earth evolution in the 21st century Apart from the earth tides, ocean tides, and other astro-geophysical influences, the major driving force of Earth evolution is expected to be the generation, spreading, and subduction of tectonic plates. We do not know and currently have no means of assessing the dynamics of plate motion and the way stress is distributed, except by modeling based on inadequate information. The two year relaxation stress implied from the decrease in time-delays following the 1996 Vatnajökull eruption in Iceland (Volti and Crampin, 2003b) suggests that these movements are highly episodic. It is well known that earthquake occurrence is fractal and varies over scales from minutes to millions of years. The reasons for this are likely to be the interaction of the dynamics of the core with movements of the mantle and movements of oceanic plates, but we currently have minimal information. Consequently, there are many comparatively simple questions such as whether plates
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are pushed by ridges or pulled by subduction zones which need to be answered. But the major questions are what drives the plates and how and why they vary with time. Currently we have no means of acquiring such information which is crucial for understanding the evolution of the Earth. GEMS by providing the stress deformation over the surface of the Earth would provide for the first time the means of investigating the dynamic evolution of the deep interior of the crack-critical Earth. It is perhaps worth noting that over 70% of the surface of the Earth is water beneath which lie approximately 70% of all earthquakes. This means that wholly satellite-based Synthetic Aperture Radar, Global Positioning System (displacement), or other similar measurements which are confined to observations of the solid surface can only monitor about 30% of all earthquakes. Only a borehole-based system such as GEMS can monitor the approach of all earthquakes.
12 Conclusions The effects of changes of stress on shear-wave splitting are comparatively subtle and easily overlooked or misunderstand. Consequently, interpretations of temporal changes in shear-wave splitting are sometimes claimed to be controversial (as discussed in the Appendix). We suggest that the evidence supporting APE modeling and temporal changes is vast (Table 1) and is confirmed by the unique observations from the SMS in Iceland (Figure 5). The observed sensitivity to remote seismicity is remarkable and marks a new property of the in situ rock mass. Despite not knowing exactly how stress behaves before earthquakes (how can we know without the information from GEMS!), the new geophysics, the new sensitivity, and the state-of-the-art technology are all proven attributes, although not wholly understood. GEMS would have the capability of monitoring stress changes and stress-forecasting earthquakes. There are no unknowns. The benefits of Global networks of SMS are summarized in Table 2. GEMS at the suggested grid size of 400km would stress-forecast the times and magnitudes of all damaging earthquakes (M = 5) worldwide. (The greatest advantage may be peace of mind. The absence of change would mean there could not be a large earthquake overnight.) Secondly, GEMS would provide monitoring for deterministic control for lessening the potential for a large earthquake by mitigation methods such as hydraulic fracturing. These would be practical advantages for understanding and mitigating earthquake hazard and would place mankind for the first time in some control of damaging earthquakes. Thirdly, GEMS would provide the data for a stress-forecasting service, similar to the familiar weather forecasting, for the longer-term estimation of stress and earthquake occurrence. Fourthly, GEMS would provide a network of borehole instruments for passive geophysical monitoring where very quiet locations would allow time-lapse monitoring of other geophysical phenomena and open up a whole new range of geophysical investigations. Finally, providing a tool for investigating the dynamic evolution of the Earth on which our lives depend would provide an enormous intellectual stimulus for understanding the Earth in the 21st century. GEMS, estimated as a five-ten billion dollar development, is matched in Earth Science only by the scale of oil industry investments. However, multi-billion dollar decisions need to be made. For example, the question of whether new buildings in the New Madrid Seismic Zone, USA (which has occasionally suffered very large earthquakes) should have the same earthquake resistant designs as coastal California (which has many more slightly smaller earthquakes). The argument between Frankel (2003) (Project Chief for the U.S. Geological Survey, seismic hazard maps) for different designs and Stein et al. (2003) for the same designs, has multi-billion dollar implications for the cost of new buildings. In the absence of real information, the answers depend on '…essentially philosophical differences about how to forecast and prepare for future natural hazard about which much is not well understood' (Stein et al., 2003). GEMS would eventually (it would need several years of data accumulation) provide factual information on which to base such costly decisions. Similar decisions are frequently needed. Beijing is in a belt of moderate to medium seismicity and GEMS stress-forecasting would have been important for estimating seismic hazards for the 2008 Olympic Games. Similarly the (projected 300m high) Three Gorges Dam in China is in a region of similar moderate seismicity where seismically active faults run close to the dam
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(Crampin et al., 2004b). These are both multi-billion dollar investments with multi-billion dollar implications. Currently, with inadequate information, we essentially have to guess the geophysical response. In contrast, a few billion dollar investment in GEMS would, for the first time, place man in some control of earthquake hazards, as well as providing the intellectual stimulus for investigating a system on which we are totally dependent every day of our lives. GEMS would provide the basic factual information for informed decisions about the re of the stressed crack-critical Earth.
Acknowledgements SC was partly supported by the European Commission SMSITES and PREPARED Projects, contract numbers: EVR1-CT1999-40002 and EVG1-CT2002-00073, respectively.
APPENDIX: Three claims of controversy are unfounded Three papers (Aster et al., 1990; Seher and Main, 2004; Liu et al., 2004) have commented on various results, and these are sometimes cited as suggesting that the concepts underlying GEMS are controversial. The 'controversies' in all papers are unfounded. 1) Aster et al. (1990) applied automatic techniques for measuring the parameters of shearwave splitting to the waveforms of same seismograms where Peacock et al. (1988) and Crampin et al. (1990) had observed temporal changes by visual techniques on three-dimensional cross-sections of the particle motion. Crampin et al. (1991) showed, by rotating seismograms into the faster and slower shear-wave polarisations, that the errors in automatically measured time-delays between split shear-waves of Aster et al. (1990) were in error by factors of up to 2.5 and 4. These substantial errors meant that claims of Aster et al. (1990) that the seismograms showed no temporal variations had no factual basis and were unfounded. The only viable explanation for the almost universally observed temporal and spatial variations in stress-aligned shear-wave splitting listed in Table 1 is fluid-saturated microcracks as modeled by APE (Zatsepin and Crampin, 1997). Such fluid-saturated microcracks are highly compliant and temporal stress-induced changes must be expected. Such changes have been observed in oil fields (Angerer et al., 2002), before earthquakes and volcanic eruptions (Crampin et al., 1999; Volti and Crampin, 2003b; Crampin and Gao, 2004), and are confirmed by the remarkable sensitivity of the SMSITE observations in Iceland (Figure 5, and Crampin et al., 2003). 2) Seher and Main (2004) made a statistical evaluation of the time-delay data on which the successful stress-forecast of Crampin et al. (1999) was based. Analyzing the two-year data set of time-delays as a continuous time-series Seher and Main implied that the conclusions of Crampin et al. (1999) were unsubstantiated. Crampin et al. (1999, 2004c) showed that no statistical techniques for analyzing discontinuous elements of a continuous time-series have yet been developed, and that the discontinuous increases in time-delays analyzed by Crampin et al. (1999) were statistically valid. The time-delay increases monitor the individual build-up of stress before individual earthquakes, and the continuous time series analyzed by Seher and Main (2004) was inappropriate. What happens between the occurrence of one earthquake and the beginning of the build up of stress before the next earthquake has no known statistical bearing on time and magnitude of the next earthquake. Although only one earthquake has been successfully stress-forecast in real time to date (Crampin et al., 1999a), the effects have been seen with hindsight before some 12 earthquakes. In particular, there are no known examples where temporal changes in shear-wave splitting would be expected and have not been observed. 3) Liu et al. (2004) made systematic measures of shear-wave splitting in aftershocks of the 1999 M 7.7 Chi-Chi earthquake in Taiwan and claimed to find no evidence of temporal changes. Aftershocks are the results of rapid stress variations, and patterns in shear-wave splitting in aftershock sequences have not yet been recognized, so their absence at Chi-Chi is perhaps not surprising. However, Figure 14a of Liu et al. (2004) showed changes in shear-wave splitting timedelays for 1000 days before the Chi-Chi earthquake. The data of this figure showed both the typical increase of time-delays believed to monitor the build up of stress, and the precursory decrease believed to monitor stress relaxation, as observed elsewhere. The duration and magnitude relationships of both the increase of stress and the stress relaxation had similar self-
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similar relationships to those observed elsewhere. The temporal variations at Chi-Chi were for the largest magnitude (M 7.7) where systematic changes have yet been observed.
References Angerer, E., S. Crampin, X.-Y. Li, and T. L. Davis (2002). Processing, modelling, and predicting time-lapse effects of overpressured fluid-injection in a fractured reservoir, Geophys. J. Int. 149, 267-280. Aster, R., P. Shearer, and J. Berger (1990). Qualitative measurements of shear wave polarizations at the Anza seismic network, southern California, J. Geophys. Res. 95, 12,449-12,473. Booth, D.C., S. Crampin, J. H. Lovell, and J.-M. Chiu (1990). Temporal changes in shear wave splitting during an earthquake swarm in Arkansas, J. Geophys. Res. 95, 11,151-11,164. Chapman, M., S. V. Zatsepin, and S. Crampin (1998). Anisotropic dispersion in stress-sensitive poroelasticity, 60th Conf. Eur. Ass. Geophys. Eng, Leipzig, Extended Abstracts 1, 10-10. Chapman, M., S. V. Zatsepin, and S. Crampin (2000). Incorporating stress-sensitivity into dynamic poroelasticity, 70th Ann. Mtg Soc. Explor. Geophys., Calgary, Expanded Abstracts 2, 1536-1539. Crampin, S. (1993). Arguments for EDA, Can. J. Explor. Geophys. 29, 18-30. Crampin, S. (1994). The fracture criticality of crustal rocks, Geophys. J. Int. 118, 428-438. Crampin, S. (1997). Going APE: I - Modeling the inherent anisotropy of intact rock, 67th 70th Ann. Mtg Soc. Explor. Geophys., Dallas, Expanded Abstracts 1, 952-955; see also 956-959, 9 21-924. Crampin, S. (1999). Calculable fluid-rock interactions, J. Geol. Soc. 156, 501-514. Crampin, S. (2001). Developing stress-monitoring sites using cross-hole seismology to stress-forecast the times and magnitudes of future earthquakes, Tectonophysics, 338, 233-245. Crampin, S. (2003). The New Geophysics: shear-wave splitting provides a window into the crack-critical rock mass, The Leading Edge, 22, 536-549. Crampin, S., and D. C. Booth (1989). Shear-wave splitting showing hydraulic dilatation of pre-existing joints in granite, Sci. Drilling 1, 21-26. Crampin, S. and S. Chastin (2003). A review of shear-wave splitting in the crack-critical crust, Geophys. J. Int. 155, 221-240. Crampin, S., and Y. Gao (2004). Comment on "Systematic Analysis of Shear-Wave Splitting in the Aftershock Zone of the 1999 Chi-Chi, Taiwan, Earthquake: Shallow Crustal Anisotropy and Lack of Precursory Changes," by Liu, Teng, and Ben-Zion, Bull. Seism. Soc. Am., in press. Crampin, S., and S. Peacock (2004). A review of shear-wave splitting in the compliant crack-critical anisotropic Earth, Wave Motion 41, 59-77. Crampin, S., and S. V. Zatsepin (1997). Modelling the compliance of crustal rock: II - response to temporal changes before earthquakes, Geophys. J. Int. 129, 495-506. Crampin, S., R. McGonigle, and M. Ando (1986) Extensive-dilatancy anisotropy beneath Mount Hood, Oregon, and the effect of aspect ratio on seismic velocities through aligned cracks, J. Geophys. Res. 91, 12,703-12,710. Crampin, S., D. C. Booth, R. Evans, S. Peacock, and J. B. Fletcher (1990). Changes in shear wave splitting at Anza near the time of the North Palm Springs Earthquake, J. Geophys. Res. 95, 11,197-11,212; Crampin, S., D. C. Booth, R. Evans, S. Peacock, and J. B. Fletcher (1991). Comment on "Quantitative Measurements of Shear Wave Polarizations at the Anza Seismic Network, Southern California: Implications for Shear Wave Splitting and Earthquake Prediction" by R. C. Aster, P. M. Shearer and J. Berger, J. Geophys. Res. 96, 6403-6414. Crampin, S., S. V. Zatsepin, C Slater, and L. Y. Brodov (1996). Abnormal shear-wave polarizations as indicators of pressures and over pressures, 58th Conf. Eur. Ass. Geophys. Eng., Amsterdam, Extended Abstracts, O38. Crampin, S., H. J. Rowlands, S. V. Zatsepin, B. J. Smart, K. Edlmann, and B. Crawford (1997). Predicting the response to effective stress of cores with different pore fluids, 59th Conf. Eur. Ass. Geophys. Eng., Geneva, Extended Abstracts 2, CO22. Crampin, S., T. Volti, and R. Stefánsson (1999a). A successfully stress-forecast earthquake, Geophys. J. Int. 138, F1-F5. Crampin, S., S. V. Zatsepin, H. J. Rowlands, B. J. Smart, and J. M. Somerville (1999b). APE-modelling of fluid/rock deformation of sandstone cores in laboratory stress-cells, 61th Conf. Eur. Ass. Geophys. Eng., Helsinki, Extended Abstracts 1, 2-08. Crampin, S., Volti, T., Chastin, S., and Gudmundsson, A., 2002. Indication of high pore-fluid pressures in a seismically-active fault zone, Geophys. J. Int. 151, F1-F5. Crampin, S., S. Chastin, and Y. Gao (2003). Shear-wave splitting in a critical crust: III - preliminary report of multi-variable measurements in active tectonics, J. Appl. Geophys. 54, 265-277. Crampin, S., S. Peacock, Y. Gao, and S. Chastin (2004a). The scatter of time-delays in shear-wave splitting above small earthquakes, Geophys. J. Int. 156, 39-44. Crampin, S., Y. Gao, and S. Peacock (2004b). Seismic hazard at the Three Gorges Dam, Terra Nova, submitted.
9
Crampin, S., T. Volti, and R. Stefánsson (2004c). Response to “A statistical evaluation of a ‘stress-forecast’ earthquake” by T. Seher and I. G. Main, Geophys. J. Int. 157, 194-199. Davis, T. L., R. D. Benson, S. L. Roche, and D. Talley (1997). 4-D 3-C seismology and dynamic reservoir characterization - a geophysical renaissance, 67th Ann. Mtg Soc. Explor. Geophys., Dallas, Expanded Abstracts 1, 880-882; see also 883-885, 886-889. Frankel, A. D. (2003). Comment of 'Should Memphis build on California's earthquakes', by S., Stein, J. Tomasello and A. Newman, and Reply, EOS, 84 (29), 271-273. Gao, Y., and S. Crampin (2004). Observations of stress relaxation before earthquakes, Geophys. J. Int. 157, 578-582. Gao, Y., P. Wang, S. Zheng, M. Wang, and Y.-T. Chen (1998). Temporal changes in shear-wave splitting at an isolated swarm of small earthquakes in 1992 near Dongfang, Hainan Island, Southern China, Geophys. J. Int. 135, 102-112. Heffer, K. J., and T. G. Bevan (1990). Scaling relationships in natural fractures, Soc. Pet. Eng, Paper 20981. King, M. S., N. A. Chaudhry, and S. Ahmed (1994). Experimental ultrasonic velocities and permeability of sandstones with aligned cracks, 61th Conf. Eur. Ass. Geophys. Eng., Vienna, Extended Abstracts P113. Liu, Y., S. Crampin, and I. Main (1997). Shear-wave anisotropy: spatial and temporal variations in time delays at Parkfield, Central California, Geophys. J. Int. 130, 771-785. Liu, Y., T.-L. Teng, and Y. Ben Zion (2004). Systematic analysis of shear-wave splitting in the aftershock zone of the 1999 Chi-Chi, Taiwan, earthquake: shallow crustal anisotropy and lack of precursory variations, Bull. Seism. Soc. Am., in press. Peacock, S., S. Crampin, D. C. Booth, and J. B. Fletcher (1988). Shear-wave splitting in the Anza seismic gap, Southern California: temporal variations as possible precursors, J. Geophys. Res. 93, 3339-3356. Raleigh, C. B., J. D. Healy, and J. D. Bredehoeft (1976). An experiment in earthquake control of Rangely, Colorado, Science 191, 1230-1237. Seher, T., and I. G. Main (2004). A statistical evaluation of a 'stress-forecast' earthquake, Geophys. J. Int. 157, 187-193. Sothcott, J., S. G. O'Hara, J. Khazanehdari, and C. McCann (2000a). From sonic to ultrasonic - the acoustic properties of reservoir sandstones, 61th Conf. Eur. Ass. Geophys. Eng., Glasgow, Extended Abstracts D39. Sothcott, J., C. McCann, and S. G. O'Hara (2000b). The influence of two different pore fluids on the acoustic properties of reservoir sandstones at sonic and ultrasonic frequencies, 70th Ann. Mtg Soc. Explor. Geophys., Calgary, Expanded Abstracts 2, 1883-1886. Stein, S., J. Tomasello, and A. Newman (2003). Should Memphis build on California's earthquakes, EOS, 84, 19, 177, 184-185. Suyehiro, K. (2002). Illuminating Earth's mantle and core: a new challenge for ODO, in Achievements and opportunities of Scientific Ocean Drilling, K. Becker (Editor), Joides J., Special Issue 28, 55-60. Volti, T., and S. Crampin (2003a). A four-year study of shear-wave splitting in Iceland: 1. Background and preliminary analysis, in New insights into structural interpretation and modelling, D.A. Nieuwland (Editor), Geol. Soc., Spec. Pub. 212, Geological Society, London 117-133; Volti, T., and S. Crampin (2003b). A four-year study of shear-wave splitting in Iceland: 2. Temporal changes before earthquakes and volcanic eruptions, in New insights into structural interpretation and modelling, D.A. Nieuwland (Editor), Geol. Soc., Spec. Pub. 212, Geological Society, London 135-149. Zatsepin, S. V., and S. Crampin (1996). Stress-induced coupling between anisotropic permeability and shearwave splitting, 58th Conf. Eur. Ass. Geophys. Eng., Amsterdam, Extended Abstracts C030. Zatsepin, S. V., and S. Crampin (1997). Modelling the compliance of crustal rock: I - response of shear-wave splitting to differential stress, Geophys. J. Int. 129, 477-494.
10
Table 1
Match of APE*-modeling to observations STATIC EFFECTS Field observations of SWVA‡ (below 500 to 1000 m-depth) 1) SWVA in all rocks independent of porosity, geology, and tectonics. 2) Minimum SWVA in intact rock: observed ≈ 1.5%; APE-modeled ≈ 1.0%. 3) Maximum SWVA in intact rock: observed ≈ 4.5%; APE-modeled ≈ 5.5% . 4) Narrow range of crack density: 0.025 ≤ e ≤ 0.045. 5) Proximity of fracture-criticality (at percolation threshold) ≈ 5.5%. Other field observations 6) Fracture-criticality limit specifies crack distributions with a range of dimensions of about 9orders of magnitude. 7) π/2 shear-wave polarization changes (90º-flips) in high-pressured reservoirs and in seismicallyactive fault zones.
Ref.
Ref.
(Obs.)
(APE)
[1] [1] [1] [1] [1]
[2] [2] [2] [2] [2]
[3]
[4]
[5,6]
[2,5,6]
[8] [6,9]
£ [6]
[10,11] [12]
[2] £
[11]
£
[13]
[14]
[15]
[15]
[16]
[17]
DYNAMIC EFFECTS Temporal changes in SWVA during production procedures 8) Changes before and after pumping tests. 9) Changes before and after high pressure CO2-flood in carbonate reservoir Temporal changes in SWTD† before earthquakes 10) Variations of time-delays before earthquakes (with hindsight). 11) Successful forecast of time and magnitude of an M=5 earthquake in SW Iceland. Temporal changes in SWTD before volcanic eruption 12) Variations in SWTD for some 5 months before 30th Sept., 1996, Vatnajökull eruption, Iceland, at distances of: 230 km WSW; 170 km SW; and 240 km, N. Variations of shear waves in laboratory experiments 13) Variations of SWVA and permeability in uniaxial stress cell. 14) Variations of (isotropic) shear-wave velocities to changes in confining pressure and pore-fluid pressure for oil-, water-, and gas- (dry) saturations in stress cells of sandstone cores. 15) Variations of velocity and attenuation from sonic (transducers) to seismic (resonant bar) frequencies. After Crampin and Chastin (2003). *APE - anisotropic poro-elasticity; ‡SWVA - shear-wave velocity-anisotropy;
£Effects compatible with APE; †SWTD - shear-wave time-delays;
[1] [2] [3] [4] [5] [6] [7] [8] [9]
[10] Booth et al. (1990), Crampin et al. (1990, 1991), Liu et al. (1997), Gao et al. (1998); [11] Volti and Crampin (2003a, 2003b); [12] Crampin et al. (1999a); [13] King et al. (1994); [14] Zatsepin and Crampin (1996); [15] Crampin et al. (1997, 1999b); [16] Sothcott et al. (2000a, 2000b); [17] Chapman et al. (1998, 2000).
Crampin (1994); Crampin and Zatsepin (1997); Heffer and Bevan (1990); Crampin (1997, 1999); Crampin et al. (1996); Angerer et al. (2002); Crampin et al. (2002); Crampin and Booth (1989); Davis et al. (1997);
11
Table 2
The benefits of GEMS Based on a global network of Stress-Monitoring Sites with a 400km-grid, GEMS would: 1) Provide data to stress-forecast of times and magnitudes of all damaging earthquakes worldwide with magnitudes greater or equal to M 5, and many greater than M 4. 2) Monitor the effects of massive hydraulic fracturing to optimize stress release to mitigate earthquake hazards threatening vulnerable locations. 3) Provide data for a stress-forecasting service, analogous to weather forecasting, which would provide longerterm estimates of earthquake occurrence and hazard. 4) Provide a network of deep boreholes for passive monitoring of broadband seismics, gravity, resistivity, magnetism, etc., in exceptionally-quiet environments for time-lapse monitoring of the dynamics of Earth evolution. 5) Provide a new controlled-source tool for monitoring the evolution of the crack-critical Earth to stimulate geoscience at the beginning of the 21st century.
12
FIGURE CAPTIONS Figure 1.
Stress-aligned seismic shear-wave splitting. Transversely polarized seismic shear-waves propagating through elastic anisotropic solids split into two almost orthogonal phases. This is similar to the birefringence of light in crystals, except that in solids, the velocities of the two split shear-waves are sufficiently different that they write characteristic easily-recognizable signatures into the three-dimensional wave trains, as illustrated schematically. Such stress-aligned shear-wave splitting is observed in almost all in situ rocks in the Earth’s crust where the faster split shear-wave vibrates parallel to the direction of maximum horizontal stress. As suggested in the figure, this is caused by the pervasive distributions of approximatelyvertical approximately-parallel fluid-saturated microcracks. These cracks are aligned (like hydraulic fractures in the oil industry) perpendicular to the direction of minimum compressional stress, s h. Since once below near-surface stress release anomalies, the minimum stress in the Earth is usually horizontal, the cracks tend to be aligned parallel and vertical, so that the faster split shear-wave is polarized parallel to the direction of maximum horizontal stress, s H. These microcracks are typically grain-boundary cracks in crystalline igneous and metamorphic rocks, and preferentially-oriented pores or pore throats in sedimentary rocks. Several features of the observations are important: 1) As crack density e is approximately equal to one hundredth of the percentage of the maximum shear-wave velocity anisotropy, it is comparatively easy to estimate crack density from observations of shear-wave splitting. Crack density e is a dimensionless parameter (Crampin, 1994) approximately equal to Na3/v, where N is the number of cracks of radius a in volume v. This parameter determines the effect of cracks on the effective elastic tensor of an anisotropic microcracked solid. 2) The observed shear-wave velocity anisotropy of 1.5% to 4.5% indicates a narrow range 0.015 = e = 0.045 of the inferred crack density in ostensibly intact rock. The values appear to be more-or-less independent of porosity, geology, and tectonic history, and are observed to have similar values in all sedimentary, igneous, and metamorphic rocks, with only a few well-understood exceptions (Crampin, 1994, 1999). In interpreting the observed percentages in terms of effective crack densities, since there is a factor less than two in average crack radius, between the minimum observed, and the maximum at fracture criticality, almost all in situ rocks are so heavily microcracked that shear strength is lost and fracturing occurs in the presence of any increase in differential stress. 3) Fracture criticality is directly associated with the percolation threshold ep = 0.055 for a distribution of stress-aligned parallel cracks (Crampin and Zatsepin, 1997). 4) The limited range of crack density means that the cracks in all in situ rock are critical systems verging on criticality and failure and are said to exhibit self-organized criticality (Crampin, 1999).
Figure 2. The Anisotropic Poro-Elastic (APE) model of rock deformation. APE models the response of seismic velocities (P-waves and orthogonally polarized shear-waves) to small changes of stress in fractured and porous rocks. The coupling mechanism between seismic waves and varying triaxial stress field is provided by a population of fluid-saturated cracks of arbitrary size. Stress-induced variations to microcrack geometry are driven by pore-fluid movement along pressure-gradients between neighboring cracks at different orientations to the stress field (Zatsepin and Crampin, 1997). Although APE modeling is fully 3D for all orientations of cracks, the figure is a simple schematic (but numerically accurate) illustration of the effects of APE on a 2D vertical distribution of cracks with a constant porosity of f = 6%, where f is percentage by volume. 1) Top-left is a cross-section of a small selection of randomly oriented vertical cracks (hexagons are elastically isotropic). Under zero differential stress sH = sh = 0 and all cracks have equal aspect ratios (equal width), and there is no shear-wave velocity anisotropy in the horizontal plane. 2) Top-right shows the effects of a small increase in differential stress, sH. Some fluid adjusts to the change of stress by changing crack aspect-ratios, but all cracks remain open and there is still no shear-wave velocity anisotropy. 3) Bottom-left shows the response when the critical stress sH (normalized to one) is sufficient to close cracks normal to the stress. The shear-wave velocity anisotropy jumps from zero to approximately the 1.5% minimum actually observed in the Earth (Figure 1) (Crampin, 1994). 4) Bottom-right shows that as stress increases, cracks begin to line up with an average normal in the direction of minimum principal stress. Eventually at fracture-criticality (equivalent to the percolation threshold at about e = 0.055, when there are through-going fractures), shear strength is lost and the rock fractures. This comparatively simple model is theoretically independent of porosity, and observationally similar effects are seen in 1% porosity granites, as in 20% porosity sandstones.
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Figure 3. The first successfully stress-forecast earthquake. The figure shows normalized time-delays at Station BJA in SW Iceland for four years 1996-1999 (Volti and Crampin, 2003a, 2003b). The curves are nine-point moving averages summarizing the variations. The lower diagram is time-delays in Band-1 raypath directions which are sensitive to crack aspect-ratios (and hence to increasing stress, see APE-modeling in Figure 2). (Band-1 directions are 15º - 45º either side of the average crack plane, that are sensitive to changes in aspect-ratio. Band-2 are directions 15º either side of the crack plane are sensitive to crack density, Crampin, 1999.) The straight lines in Band-1 are least-square fits to increasing time-delays, starting just before a minimum of the moving average and ending at a larger earthquake (or eruption). Note that the cause of the large scatter in time delays is 90º-flips in shear-wave polarizations (Crampin et al., 2002) induced by high pore-fluid pressures on all seismically active fault plane (Crampin et al., 2004a.) The arrows below indicate magnitudes and distances of larger earthquakes and eruptions. The first increase is the effect of the build-up of stress before the large Vatnajökull (Gjàlp) eruption of 1996. This is followed by a two year (2ms/km/year) decrease, with the duration marked by a horizontal line in Band-1, which is interpreted as stress relaxation as the Mid-Atlantic Ridge responds to the injection of magma in the fissure eruption (Volti and Crampin, 2003a, 2003b). By October 1998, towards the end of this two year decrease, four increases of time-delays before earthquakes, with magnitudes from M 3.8 to M 5.1, had been recognized with hindsight. Each earthquake occurs, as the increasing time-delays reach levels of fracture criticality, varying from about 14 ms/km in 1996 to about 10 ms/km in 1998. The duration of each least-squares line is approximately proportional, and the rate of increase inversely proportional, to the magnitude of the eventual earthquake. In October 1998, we recognized that the slope of the increase was similar to the slope of the increase before the M 5.1 earthquake five months earlier and were able to issue the following (successful) stress-forecast: Text of final exchange of email messages (in italics) between University of Edinburgh (EU) and Iceland Meteorological Office (IMO) (Crampin et al., 1999a). 10th Nov. 1998 EU to IMO: '…the last plot…is already very close to 10 ms/km. This means that an event could occur any time between now (M = 5) and end of February (M = 6).' 13th Nov. 1998 IMO to EU: '…there was a magnitude 5 earthquake just near BJA (preliminary epicenter 2 km west of BJA) this morning 10 38 GMT.'
Figure 4. Optimum geometry for Stress-Monitoring Sites (SMSs). A Downhole Orbital Vibrator (DOV*) source at depths between X+300m and X+1000m in the central source well, radiates SH- and SV-waves in Band-1 directions to three-component geophones in preferably two receiver wells. The geophones are at X m-depth at 300m-offset in azimuths ±30º either side of the direction of minimum horizontal stress (here assumed to be North) (Crampin, 2001). X needs to be greater than the critical depth, typically between 500 to 1000 m, where the vertical stress, s V, equals the minimum horizontal stress, s h. Thus at X m-depth, s V = s H = s h, and the microcracks are typically aligned vertically and perpendicular to the minimum horizontal stress, s h, just like hydraulic fractures in the oil industry. Theoretically, Band-1 directions are most sensitive to small changes of crack aspect-ratio in distributions of parallel vertical cracks (Figure 2), and hence are most sensitive indicators of small changes of stress (Crampin, 1999). The advantage of SMSs is that by differencing repeated shear-wave signals recorded by identical sourcereceiver geometry (known as time-lapse seismics), SMSs are extremely sensitive to nearly negligible changes in stress and other parameters. *The DOV is a highly-repeatable borehole source of both P-waves and shear-waves manufactured by Geospace Engineering Research International, Houston.
14
Figure 5. Observations at the first SMS: the SMSITES Project in Iceland (Crampin et al., 2003). Observations from 8th to 24th August, 2001, at the SMS near Húsavík in northern Iceland adjacent to the Húsavík-Flatey Fault (HFF) of the Mid-Atlantic Ridge: (a), (b), and (c) are travel times in ms of: P-waves; SV-waves and SH-waves; and SV-SH, respectively, at 500 m-depth between boreholes 315 m-apart. The directions are parallel to and at a distance of about 100 m from the HFF. Shear-waves are polarized vertically SV and horizontally SH as they are propagating in a vertical symmetry direction of the stress field. Also shown are (d) north-south and east-west (GPS) displacements in mm; and (e) water-pressure measurements in bars in a well on Flatey Island immediately over the HFF (the depth of the 'pulse' is ~1 m, and the ~40 cm oscillations are oceanic tides). All observations, (a) to (e), correlate with (f), a histogram of small scale seismicity on a parallel fault, the Grímsey Lineament, 70 km NNW of the SMS. Since the seismicity consists of 106 earthquakes where the largest is M 2.8, the total energy is approximately equivalent to one earthquake of magnitude M 3.5. A M 3.5 earthquake would be a comparatively small event with a fault diameter of a few hundred meters, at most. Thus, the observations show remarkable sensitivity to small disturbances at a distance of several hundred times the source diameter. This shows that time-lapse seismic SMS data has the ability to monitor extremely small changes in rock mass conditions. This is believed to be a unique data set. Variations in GPS measurements and changes in well levels have been observed previously and have been associated with seismicity and earthquakes. The remarkable observations of this data set are the seismic travel-times where the highly-repeatable DOV source has allowed recording accuracy better than ±0.02 ms. We are attempting to model the observations. With one exception, the various variations are broadly compatible with each other: shear-wave velocities and anisotropy, GPS, and well-level changes appear to be consistent, and can be modeled as microcracks opening and closing with seismicity-induced stress changes. The major puzzle is that the P-wave travel-times which, following the start of the seismicity, relax linearly over about nine days as cracks close, whereas the shear-waves relax (in classic S-shaped relaxation curves) over about five days. Since the P-waves and shear-wave travel times appear to be measured along similar ray paths, this anomaly may convey important information about how rocks respond to changes of stress.
15
sh sh
sH sH
O O
sV sV
Figure 1.
16
Figure 2.
17
I_______________________________I
I Figure 3.
18
Figure 4.
19
Figure 5.
20
