On claimed ULF seismogenic fractal signatures in the geomagnetic field

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115,, doi: /2010ja015311, 2010 On claimed ULF seismogenic fractal signatures in the geomagnetic field Fabrizio Masci 1 Received 26 January 2010; revised 14 July 2010; accepted 23 July 2010; published 19 October [1] During the last ten years, fractal analysis of ultra low frequency (ULF) geomagnetic field components has been proposed as one of the most promising tools to highlight magnetic precursory signals possibly generated by the preparation processes of earthquakes. Several papers claim seismogenic changes in the fractal features of the geomagnetic field some months before earthquakes occur. The target of the present paper is to put forth a qualitative investigation on the fractal characteristics of ULF magnetic signatures that previous authors have claimed to be related without doubt to strong earthquakes. This analysis takes into account both the temporal evolution of the geomagnetic field fractal parameters reported in previous researches and the temporal evolution of global geomagnetic activity. Running averages of the geomagnetic indices SK p and A p are plotted into the original figures from the previous publications. This simple analysis shows that the fractal features of the ULF geomagnetic field are closely related to the geomagnetic activity both before and after the earthquake occurs. The correlation between the geomagnetic field fractal parameters and geomagnetic activity is clearly shown over both long and short time scales. In light of this, the present paper shows that fractal behaviors of previously claimed seismogenic ULF magnetic signatures depend mainly on geomagnetic activity due to solar terrestrial interaction. Therefore, previously reported association with the preparation process of the earthquake is dubious. Citation: Masci, F. (2010), On claimed ULF seismogenic fractal signatures in the geomagnetic field, J. Geophys. Res., 115,, doi: /2010ja Istituto Nazionale di Geofisica e Vulcanologia, L Aquila, Italy. Copyright 2010 by the American Geophysical Union /10/2010JA Introduction [2] Research on earthquake precursors has been conducted for about a century. In the past, many geophysical and geochemical observations have been considered as possible earthquake precursory signatures. The motivation for this research is to realize short term deterministic earthquake prediction. If earthquake prediction were to be carried out correctly, this could reduce both the number of victims and damages due to seismic events. However, successful earthquake prediction requires reproducible preseismic signatures which provide relevant information in real time and specifics regarding intensity, location and time of the predicted earthquake. The possibility of developing short term earthquake predictions has been the subject of several scientific debates [see Wyss et al., 1997] (I. Main, Is reliable earthquake prediction of individual earthquakes a realistic scientific goal?, 1999, available at earthquake/equake_frameset.html), but at present the topic remains controversial. Therefore, in attempting to resolve this problem, a closer inspection of whether claimed earthquake precursors exist in geophysical data sets is required. Many scientists doubt observations of claimed seismogenic electromagnetic precursors. They emphasize the lack of validated preseismic signals and have published their concerns [see Pham and Geller, 2002; Reichhardt, 2003]. Other researchers have asserted that claimed preseismic signals could be caused by random noise or are chance events [Kagan, 1997], and seriously questioned the ability to develop short term earthquake prediction capabilities [see Geller, 1997; Main, 1997; Jordan, 2006]. The practice of retrospective validation of earthquake precursors has also been criticized [see Geller, 1991; Geller et al., 1997]. Furthermore, the Parkfield Earthquake Prediction Experiment [Bakun and Lindh, 1985] on the San Andres fault, California, showed clear evidence of coseismic electric and magnetic signals but no electromagnetic precursory signals before the 28 September 2004 M w = 6 Parkfield earthquake [Johnston et al., 2006; Park et al., 2007]. These results discouraged the American researchers to further develop short term earthquake prediction capability. [3] On the other hand, many reviews and monographs show magnetic, electric and electromagnetic phenomena prior to, during and after a tectonic event [Johnston, 1997; Johnston and Parrot, 1998; Mueller and Johnston, 1998; Hayakawa and Molchanov, 2002; Varotsos, 2005; Molchanov and Hayakawa, 2008]. Some researchers have accepted that the preparation processes of strong earthquakes can include both seismic and electromagnetic emission events. They are rather optimistic about the realization of short term 1of10

2 earthquake prediction based on seismogenic electromagnetic precursors. Other researchers suggest a multidisciplinary approach to overturn the skepticism of the scientific community [Uyeda et al., 2009]. In any case, it is quite difficult to demonstrate that any claimed electromagnetic precursory signal is related to seismic activity. Moreover, sometimes precursory signals do not occur before earthquakes, or precursory signals are claimed to occur with no corresponding coseismic signals. [4] Some authors have suggested investigating ULF ( Hz) magnetic emissions as a promising way to highlight seismogenic signals [see Hayakawa et al., 2007, and references therein]. ULF waves possibly generated near the earthquake focus may propagate in the lithosphere up to the Earth s surface with a small attenuation over long distance, whereas higher frequency waves undergo larger attenuation in propagation through the Earth s crust. Fraser Smith et al. [1990] reported for the first time ULF magnetic field anomalies that occurred before the 18 October 1989 Ms = 7.1 Loma Prieta, California, earthquake. After this paper, possibly ULF seismogenic emissions were claimed to occur in other seismic events such as the 7 December 1988 Ms = 6.9 Spitak, Armenia, earthquake [Molchanov et al., 1992], and the 8 August Ms = Guam, Mariana archipelago, earthquake [Hayakawa et al., 1996]. Moreover, some papers asserted the observation of seismogenic ULF signals up to hundreds of kilometres away from the epicenters of strong earthquakes [e.g., Saroso et al., 2009]. Several physical mechanisms have been proposed to explain the generation of the seismogenetic ULF emissions. These include the electro kinetic effect [Fenoglio et al., 1995], charge separation by micro fracturing in the hypocentral region due to stress increase before the earthquake [Molchanov and Hayakawa, 1995], and the magnetohydrodynamic effect [Draganov et al., 1991]. However, serious problems, which some researchers have pointed out, are the lack of reproducibility of these signals and the presence of ULF magnetic noise preceding the earthquakes without expected coseismic related larger signals [Johnston et al., 2006]. Other researchers have shown that claimed ULF seismogenic anomalies could actually be due to both instrumentation malfunction [Thomas et al., 2009a] and to normal geomagnetic activity [Campbell, 2009; Thomas et al., 2009b]. [5] ULF magnetic signals are caused by superposition of different signals including natural signals from solar terrestrial interaction; man made noise; signals possibly generated in the Earth s interiors. In any case, the majority of ULF emissions have a magnetospheric origin, whereas any signals associated with crustal activity should be very weak. Therefore the problem is to discriminate crustal signals from other signals. Different methods of analysis have been employed to isolate these low ULF signals. These include polarization ratio analysis [Hayakawa et al., 1996]; principal component analysis and singular spectral analysis [Serita et al., 2005]; transfer function approach [Harada et al., 2004]; direction finding method [Ismaguilov et al., 2003]; monofractal and multifractal analyses [Hayakawa et al., 1999; Ida et al., 2005]. Using these methods the various authors have claimed evidence for ULF signatures in the geomagnetic field related to the occurrence of earthquakes. [6] Resolving the problem of clearly identifying earthquake precursory signatures is one of the principal questions in the scientific community: What are the reliable electromagnetic precursors of earthquakes? In section 2, I will discuss the fractal analysis of the ULF geomagnetic field recorded around the same times as of well know earthquakes, questioning the origin of signatures that previous papers have associated with these seismic events. 2. Comments on Seismogenic Fractal Signatures in the Geomagnetic Field [7] Modern theories describe earthquakes as chaotic phenomena [Bak and Tang, 1989]. They suggest that the Earth s crust behaves as a self organized dynamical system that naturally evolves into a critical stationary state with power low spatial and temporal correlation functions [Bak et al., 1988]. That is, when the Earth s crust is heavily stressed by the pressure caused by tectonic motion, its evolution toward the final rupture, the earthquake, is characterized by a self organized critical (SOC) dynamic. In SOC state the earthquake focal system is extremely sensitive to any small disturbance that could alter the whole system toward an avalanche dynamic. That is, any small shock could degenerate into a strong event. Since the principal characteristic of SOC systems is the fractal organization, fractal analysis could be a useful tool to investigate its evolution. [8] In geophysics, fractal methods are employed to extract quantitative and qualitative dynamics from irregular geomagnetic time series. Several authors applied fractal analysis on the ULF geomagnetic field measured in seismic active regions to retrospectively investigate the focal system evolution toward the earthquake hazard. They suggest that the evolution of the Earth s crust toward the SOC state can occur not only in a seismological sense, but in an electromagnetic sense as well. To be more precise, these authors maintain that the fractal features of the earthquake preparation process should also be found in the electromagnetic waves which could possibly be generated by the seismic activity [Hayakawa et al., 1999; Hayakawa et al., 2000; Smirnova et al., 2001, 2004; Gotoh et al., 2004; Ida et al., 2005, 2007; Ida and Hayakawa, 2006; Smirnova and Hayakawa, 2007]. In light of this, they maintain that the investigation of the fractal seismogenic magnetic ULF signatures could provide information on the preparation process of the earthquake. [9] ULF spectrum S of the geomagnetic field shows the power law behavior S( f ) / f b, where f is the frequency. This is a typical behavior of SOC systems. The spectral exponent b describes the statistical features of different processes like white noise (b = 0), flicker noise (b = 1), Brownian motion (b = 2). Previous authors claim that the variations of b should also provide information about the state of the geomagnetic field that can change from chaotic to critical, just before the earthquake. The simplest way to obtain b exponent is to calculate the spectrum slope using the so called slope method, also called FFT or PSD method. According to this method, b is obtained by calculating the slope of the best fit straight line of the ULF power spectral density in log log form. The corresponding fractal dimension D is calculated using Berry s equation D =(5 b)/2. Other methods, such as 2of10

3 Burlaga Klein [Burlaga and Klein, 1986] and Higuchi [Higuchi, 1988] approaches have been used in several papers to calculate the fractal dimension of ULF geomagnetic field time series. According to several authors, the temporal evolution of the fractal dimensions calculated by these methods show similar behavior even if the absolute values could be quite different [e.g., Smirnova et al., 2001]. Moreover, the Higuchi method provides the most reliable results [e.g., Gotoh et al., 2004]. In any case, these authors maintain that all these methods show a pronounced tendency of the geomagnetic field spectrum slope b to decrease (toward b = 1), and consequently the fractal dimension increases (toward D = 2), some months before the date of occurrence of earthquakes. The authors attribute this type of dynamic of the fractal parameters to the preparation process of earthquakes [e.g., Smirnova and Hayakawa, 2007]. [10] In the next paragraphs several fractal signatures on the geomagnetic field that previous papers have claimed to be related to four strong seismic events are discussed. The earthquakes considered are Guam 1993, Biak 1996, Izu swarm 2000, and Sumatra Due to the fact that the majority of magnetic ULF emissions have a magnetospheric origin, the simplest way to verify the real presence of magnetic seismogenic signatures on the geomagnetic field is to take into account the temporal evolution of the geomagnetic activity. Therefore, in this paper the fractal characteristics of claimed seismogenic ULF magnetic signatures are compared with the geomagnetic indices SK p and A p. The two geomagnetic indices are taken as representative of the geomagnetic field average disturbances over planetary scale due to magnetosphere solar wind interaction, that is the main source of ULF magnetic signals. The two running averages of ±5 and ±15 days of the geomagnetic indices have been calculated to take into account the geomagnetic activity behavior both over relative short time scale (about 1 month) and over relative long time scale (several months). Finally, the running averages of the geomagnetic indices are plotted into the original figures from previous publications. I would like to underline that the choice between the two geomagnetic indices is founded only on the index reported in the original figure. In any case, the conclusions of the present paper do not depend on this choice. Here the geomagnetic indices from Kyoto World Data Center for Geomagnetism (available at u.ac.jp/) are used Guam Earthquake 1993 [11] On 8 August 1993 a strong earthquake (M w = 7.7, depth = 60 km) occurred offshore the island of Guam. Hayakawa et al. [1999] applied for the first time fractal analysis on the geomagnetic field data in the hope of finding the possible presence of ULF seismogenic fractal signatures. They investigated the fractal dimension temporal evolution of the geomagnetic field H (NS) component measured at Guam observatory located 65 km away from the earthquake epicenter. Figure 1 shows the temporal evolution of the spectral exponent b as reported both by Hayakawa et al. [1999] and later by Smirnova et al. [2001] and Smirnova and Hayakawa [2007] as well. To calculate b, the authors used the slope method. The temporal evolution of the geomagnetic index SK p is also reported in Figure 1 (bottom). b and SK p ± 5 days running averages are shown as well. The authors underline that b gradually decreases toward b = 1 when the earthquake date approaches. They consider the decrease of b as an indicator of a SOC state of the geomagnetic field. Such a decrease starts a few months before the earthquake takes place. Therefore, the authors attribute the behavior of b to the earthquake preparation process, and consider it as a possible seismogenic precursory signature. Moreover, the authors note a quasiperiodical variation, with a period of 27 days, superimposed on the slow decrease of b. Since the temporal evolution of SK p behaves in a similar manner, and the period of the modulation is close to the period of the Sun s rotation around its axis, the authors associate this modulation to the solar activity. Finally, the authors conclude that the preparation process of the earthquake has undoubtedly influenced the fractal properties of the ULF geomagnetic field. They maintain that the gradual decrease of b is related to the ULF emissions caused by the gradual alteration of the Earth s crust conductivity near the observation point, and that the 27 day modulation is superimposed on this decrease. As a matter of fact, Smirnova and Hayakawa [2007] report that the correlation between the fractal characteristics of magnetic ULF signals and geomagnetic activity truly exists. According to Smirnova and Hayakawa [2007] the highest correlation is observed near local midnight, while the lowest correlation is observed near noon. They conclude that noon is the most appropriate period to investigate seismogenic emissions, whereas night hours are preferable when investigating magnetospheric processes. Consider that the spectral exponent b shown in Figure 1 is calculated using geomagnetic data measured during local day time (12:00 13:00 LT), which is according Smirnova and Hayakawa [2007] the right period to investigate lithospheric processes. [12] Figure 2 shows Figure 1 (top). In Figure 2, ±5 and ±15 days running averages of the geomagnetic index SK p are superimposed on b curves. Consider that SK p vertical axis is descending as b vertical axis. Figure 2 clearly shows a close positive correlation over short time scale between SK p and b. The overlap between the ±5 days running averages of SK p and b is very satisfactory; only a few small differences are present. As a matter of fact, SK p ± 15 days running average shows that there is also a positive correlation between the geomagnetic activity and the spectral exponent over long time scale: on average b increases (decreases) when SK p increases (decreases). Since both SK p and b ± 5 days running averages show the same behavior, it is obvious that SK p and b when performing a ±15 days running average should show the same behavior. In light of this, it is clearly evident that the gradual decrease which b shows prior to the Guam earthquake could be attributed mainly to the normal geomagnetic activity. Therefore, the decrease of b does not seem related to the preparation process of the earthquake. In any case, a more in depth quantitative analysis could quantify how much variability in the data could be attributed to the geomagnetic global activity and how much to the possible seismogenic ULF emissions. I wish highlight that the choice of SK p has been made in agreement with Hayakawa et al. [1999]. Similar results, not reported here, have been obtained comparing the behavior of b with the 3h K p temporal evolution. [13] Ida and Hayakawa [2006] investigated the fractal dimension temporal evolution of the Guam geomagnetic field H component using the Higuchi method. Fractal analysis was performed using local day time data (14:00 15:00 LT). The authors noted a significant increase of the fractal dimension 3of10

4 Figure 1. Temporal evolution of the geomagnetic field H component spectral exponent b at Guam observatory, and temporal evolution of the geomagnetic index SK p as reported both by Hayakawa et al.[1999] and by Smirnova and Hayakawa [2007] (a reproduction of Smirnova and Hayakawa [2007, Figure 1]). Figure 2. Same as Figure 1 (top) (a reproduction of Smirnova and Hayakawa [2007, Figure 1]). Both ±5 days and ±15 days running averages of the geomagnetic index SK p are superimposed on the original b curves (daily values and ±5 days running average). Consider that SK p vertical axis is descending as the b vertical axis. See text for comments. 4of10

5 Figure 3. Temporal evolution of the geomagnetic field H component Higuchi fractal dimension D at Guam observatory, and temporal evolution of the geomagnetic index Ap during1993asreportedby [Ida and Hayakawa, 2006] (a reproduction of Ida and Hayakawa [2006, Figure 2]). Both ±5 days and ±15 days running averages of the geomagnetic index A p are superimposed on the original figure. which initiated a few months before the earthquake date. Figure 3 shows both the temporal evolution of fractal dimension D and the temporal evolution of A p index, as reported by Ida and Hayakawa [2006]. A p ± 5 and ±15 days running averages are superimposed on the original figure in Figure 3 (top) and 3 (bottom), respectively. Consider that, in Figure 3 (bottom), the A p running average vertical axis is descending contrary to D vertical axis. As expected, the two ±5 days running averages show a negative correlation between D and A p over short time scale. Moreover, A p ± 15 days running average shows that the negative correlation is evident over long time scale as well: on average, before the Guam earthquake, the gradual increase of D corresponds to the gradual decrease of A p index. This close negative correlation between D and A p is also confirmed by Figure 4. Figure 4 shows the temporal evolution of fractal dimension of Guam geomagnetic field H component in a longer period than the period reported in Figure 3. Figure 4 is a partial reproduction from Varlamov and Smirnova [2008, Figure 3]. In Figure 4 both ±5 and ±15 days A p running averages are plotted into the original figure. Temporal evolution of A p index is reported as well. Despite the bad resolution of the original figure, Figure 4 shows a close negative correlation between D and A p, over both long and short time scales. I want to underline that Figure 4 shows that the increase of D before the Guam earthquake is quite similar to the increase of D after March 1994, but, the authors do not associate this second increase to any earthquake. Therefore, Figure 4. (top) Temporal evolution of the geomagnetic index A p in the period October 1992 September (bottom) Temporal evolution of the geomagnetic field H component fractal dimension D at Guam observatory as reported by Varlamov and Smirnova [2008] (a reproduction of Varlamov and Smirnova, [2008, Figure 3]). Both ±5 days and ±15 days running averages of A p are superimposed on the original figure. 5of10

6 Figure 5. (a) Temporal evolution of the geomagnetic index SK p in the period November 1995 to March The ±15 days running average of SK p is reported as well. (b) Temporal evolution of the spectral exponent b at Biak and Darwin as reported by Hayakawa et al. [2000] (a reproduction of Hayakawa et al. [2000, Figure 4]). also in this case, it is evident that the claimed seismogenic anomaly on the geomagnetic field which occurs before the Guam earthquake is mainly caused by geomagnetic activity. [14] Thomas et al. [2009b] reach a similar conclusion analyzing the results of Hayakawa et al. [1996]. In the latter paper the authors claim seismogenic an increase of ULF magnetic polarization ratio (the ratio of vertical to horizontal field components) which occurs before the Guam earthquake. Thomas et al. [2009b] conclude that this increase in the polarization ratio was part of normal global magnetic field changes due to solar terrestrial interaction and thus it was not related to the earthquake Biak Earthquake 1996 [15] On 17 February 1996 a strong earthquake (M w = 8.2, depth = 20 km) struck Biak Island, Indonesia. Hayakawa et al. [2000] analyze the ULF magnetic field measured in two observatories: Biak and Darwin, Australia, located about 100 km and 1200 km, respectively, away from the earthquake epicenter. The authors report increases in Biak ULF polarization ratio before the earthquake date. As confirmation of this result, they calculate the spectral exponent b by the slope method, using noon data (12:00 13:00 LT). However, they do not specify the geomagnetic field component which they use in the fractal analysis. Figure 5b shows the temporal evolution of b for both the stations as reported by Hayakawa et al. [2000]. The authors underline that at Biak b approaches to the unity about two months before the earthquake. According to the authors, the behavior of b is due to a SOC state of the Biak lithosphere that can be attributed to the preparation process of the coming earthquake. [16] In Figure 5a, the temporal evolution of the geomagnetic SK p index has been added to the original figure. SK p ± 15 days running average is shown as well. The running average shows that, after a slight decrease during November 1995, SK p gradually increases in the remaining period of time. However, Figure 5 shows that b, both at Biak and Darwin observatories, has the same behavior of SKp. Therefore, Figure 5 once again shows a close relation between the fractal characteristics of the geomagnetic field and the geomagnetic activity on long time scale. As a matter of fact, the two spectral exponents similarly decrease toward 1; the only difference is that the Biak spectral exponent approaches the value b 1, whereas the Darwin spectral exponent approaches the value b 1.2. This difference could be associated to the method used in the fractal analysis. As previously reported, the slope method is the least reliable of the methods used in previous researches. This method could 6of10

7 Figure 6. (a) Temporal evolution of the geomagnetic index SK p in the period February 2000 to February (b) Temporal evolution of the Higuchi fractal dimension at Izu stations (red, blue and black lines indicate the Kamo, Seikoshi and Mochikoshi stations, respectively), Boso stations (red, blue and black lines indicate respectively the Uchiura, Kiyosumi and Unobe stations), Kakioka (red line) and Memambetsu (blue line) stations as reported by Gotoh et al. [2004] (a reproduction of Gotoh et al. [2004, Figure 6]). In the original figure, the four vertical full lines refer to the M > 6 earthquakes occurred on 1, 8, and 15 July and on 18 August, whereas the vertical dashed line has been added in correspondence of the 30 July earthquake. The ±1 day running average (green line) of the geomagnetic index SK p is superimposed on Izu panel. introduce appreciable errors on b [see Smirnova et al., 2001]. Moreover Smirnova et al. [2001] maintain that the absolute values of fractal parameters may not be totally reliable because different methods of analysis could produce substantially different values even if their temporal behaviors are similar. Taking this into account, the difference between Biak and Darwin spectral exponents cannot be truly attributed to the preparation process of the earthquake. On the contrary, Figure 5 shows that a positive correlation between SK p and b really exists on long time scale. Therefore, also in this case, the behaviors of both Biak and Darwin spectral exponents seem to be mainly related to the geomagnetic activity Izu Swarm 2000 [17] In the period June August 2000 a seismic swarm occurred offshore the Japanese peninsula of Izu. Five strong M > 6 earthquakes happened on 1, 8, 15 and 30 July and on 18 August [Gotoh et al., 2003]. Gotoh et al. [2004] applied the Higuchi method to the geomagnetic field H component measured during local nighttime (02:00 03:00 LT) in 8 Japanese stations. The geomagnetic stations are: 3 Izu stations, 3 Boso stations, Kakjoka station and Memabetsu station located about 80km, 130km, 160km, 1160km, respectively, away from the epicenters. Figure 6b shows the fractal dimension temporal evolution of the geomagnetic field as reported by Gotoh et al. [2004]. In Figure 6b, the four vertical solid lines represent the earthquakes which occurred on 1, 9, and 15 July and on 18 August. The authors forgot to report the 30 July earthquake, so in correspondence of this event a vertical dashed line has been added to Figure 6b. The authors note that the fractal dimension D exhibits a sharply increase before the first earthquake at Izu and Boso stations. Later, D goes down and then rises again before the 8 July earthquake. D does not increase before the 15 July earthquake but shows a significant increase before the 18 August earthquake. The authors justify the lack of increase in D before the third seismic event assuming that the generation mechanism of this earthquake has different characteristics. Finally, they claim that the D increases at Izu and Boso stations are clearly related to the earthquake swarm because the fractal dimension temporal evolution coming from Kakioka and Memabetsu stations, which were further from the epicenters, do not show similar anomalies. [18] In Figure 6a SK p temporal evolution is added to the original figure, whereas in Figure 6b SK p ± 1 day running average is superimposed on the Izu fractal dimension temporal evolution as well. Gotoh et al. [2004] do not specify if the values of D are reported as raw data or average data, so SK p ± 1 day running average has been plotted in Figure 6 because it shows similar features of D curve. Figure 6 clearly shows that the increases in D, which are claimed to 7of10

8 Figure 7. Temporal evolution of Higuchi fractal dimension as reported by [Saroso et al. 2009] (a reproduction of Saroso et al. [2009, Figure 4]). (top) SK p temporal evolution is superimposed together with the Dst index which is reported in the original figure. (bottom) The tin and full black lines represent respectively the FD daily values and the FD ±5 days running average, whereas the red line refer to SK p ±5 days running average. be seismogenic, are negative correlated with SKp running average. Lack of D increases before the 15 and the 30 July earthquakes can be explained with lack of decreases in geomagnetic index. In light of this, the fractal dimension changes which occurred during the Izu swarm seem to be once again mainly due to the geomagnetic activity. However, the different behaviors of the fractal dimension both at Kakioka and Memambetsu respect to the fractal dimension behaviors at Boso and Izu stations remain unresolved Sumatra Earthquakes [19] Between the end of 2004 and the beginning of 2005 two very powerful earthquakes occurred in Indonesia: Sumatra Andamanearthquakeon26December2004(M w =9, depth = 30km), and Nias earthquake on 28 March 2005 (M w = 8.7, depth = 30km). In the hope of finding possible earthquakes precursory signals, Saroso et al. [2009] analyzed ULF geomagnetic field data measured at Kototabang station. The station is located 600 km away from the earthquake epicenter. Fractal analysis has been performed applying the Higuchi method to local night data (15:00 20:00 UT). As a matter of fact, in previous papers, the fractal characteristics of the geomagnetic H component are investigated because, according to the authors, this component demonstrates the most significant dynamics in fractal parameters even if Z (vertical) and D (EW) components show similar results [Ida et al., 2007; Smirnova and Hayakawa, 2007]. On the contrary, Saroso et al. [2009] investigate the fractal features of Z component because this component is more sensitive to the crustal activity. Figure 7 shows the fractal dimension (FD) temporal evolution as reported by Saroso et al. [2009]. Both daily values and ±5 days running average of FD are shown. According to the authors, FD shows a clear decrease in November and December 2004 just before the first earthquake. Later, FD again shows small values and it increases just before the second earthquake. Last, FD recovers to ordinary values, around 1.5, just after the second earthquake. As a matter of fact, the authors examining the Dst index behavior note that the FD minimum which occurred during the period 6 11 November could be caused by the geomagnetic activity because it is correlated to a large geomagnetic storm. In any case, they conclude that the FD decrease occurred between one month to few weeks before the first earthquake could be an occurrence of a selforganized process in the lithosphere. This conclusion is rather strange, because usually in previous papers a fractal dimension increase, and not a decrease, is claimed as an occurrence of a SOC state of the Earth s crust. [20] Anyway, the temporal evolution of the geomagnetic index SK p has been superimposed in the top plot of the original figure, whereas SK p ± 5 days running average has been superimposed on the FD temporal evolution shown in the bottom panel. Figure 7 once again shows a close negative correlation between D and SK p over the whole period. Therefore, both the FD decrease which occurred before the first earthquake and the subsequent recovery seem to be clearly related to the geomagnetic activity, and therefore their seismogenic origin is rather dubious. 3. Conclusions [21] ULF magnetic fractal signatures which previous authors have claimed to be related to the preparation processes of four strong earthquakes (Guam 1993, Biak 1996, Izu swarm 2000, and Sumatra ) are put into question by a simple qualitative investigation. This analysis takes into account both the temporal evolution of the geomagnetic indices SK p and A p and the behavior of the geomagnetic field fractal parameters, D and b, reported in previous papers. The investigation shows that the fractal 8of10

9 behavior of the geomagnetic field and the global geomagnetic activity are closely related both before and after earthquake occurs. Moreover, this correlation is clearly shown over both long and short time scales. In light of this, the changes in fractal features of the geomagnetic field which are claimed to be related to the SOC state of the Earth s crust seem to depend mainly on the level of the geomagnetic activity related to the solar terrestrial interaction. Thus, previous association made with the seismic activity is quite dubious. As a matter of fact, this paper shows that the oversimplified conclusion, reported in previous papers, concerning the existence of seismogenic fractal signatures on the geomagnetic field is not totally correct if the global geomagnetic activity is not taken into account in an appropriate way. More precisely, correlation between geomagnetic field fractal characteristics and geomagnetic activity must be investigated by averaging procedures using the same time window. Due to the fact that the qualitative analysis reported here is a visual analysis because of the lack of the original measurements, I suggest performing a more in depth quantitative analyses to carry out a better investigation of the possible presence of seismogenic fractal signatures. These new investigations could be useful in quantifying how much variability in the data can be attributed to global trends and how much to the possible ULF precursors. In any case, if seismogenic ULF magnetic signals had a real influence on the geomagnetic field, the lack of evident fractal signatures before the earthquakes might be due to the large distance existing between the magnetic observatories and the hypocentral region of the earthquakes, or to the small contribution of the lithospheric emissions to the ULF signals. These slow seismogenic signals can be screened by the natural environmental background. This simple task would be a little help attempting to resolve the problem of possibly developing reliable short term earthquake predictions based on electromagnetic precursors. [22] Acknowledgments. A special thanks to Roberta Giangiuliani for her invaluable help. I am also grateful to the Editor and three anonymous reviewers for constructive comments and heavy criticism that in any case were useful to improve the manuscript. I thank also Natalia A. Smirnova, AGU, Elsevier and Copernicus Publications for the permission to reproduce their figures. [23] Philippa Browning thanks Colin Waters, Joachim Vogt, and another reviewer for their assistance in evaluating this paper. References Bak, P., and C. Tang (1989), Earthquakes as self organized critical phenomenon, J. Geophys. Res., 94(B11), 15,635 15,637, doi: / JB094iB11p Bak, P., C. Tang, and K. Wiesenfeld (1988), Self organized criticality, Phys. 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