“…The combination of a “W”‐shaped SP profile and a shallow conductor near the summit has also been observed at other volcanoes. At Miyake‐jima, a shallow conductor was reported in the summit region [ Zlotnicki et al , 2003] where the potential is slightly above the reference value near the shoreline and ∼600–800 mV higher than the minimum values observed in the area surrounding the summit [ Sasai et al , 1997]. At Piton de la Fournaise (Réunion), SP decreases by ∼3 V (compared to the base station near the eastern shoreline) with a 1500 m increase in elevation, but approaching the central cone more closely the potential begins to increase and exhibits a maximum of more than 1 V [ Lénat et al , 2000], which might be brought about by the rise of the relatively conductive layer near the central cone.…”
Section: Discussionmentioning
confidence: 99%
“…Numerous self‐potential (SP) surveys of active volcanoes have been carried out during the last thirty years; e.g., Kilauea [ Zablocki , 1976], Akita Yake‐yama [ Ishido et al , 1989], Unzen [ Hashimoto and Tanaka , 1995], Miyake‐jima [e.g., Sasai et al , 1997], Izu‐Oshima [ Ishido et al , 1997] and La Fournaise [e.g., Michel and Zlotnicki , 1998]. Obvious positive‐polarity SP anomalies have often been observed around volcanic craters or vents associated with fumarole activity.…”
Numerical simulations of electrokinetic potentials were performed to investigate the mechanisms responsible for the generation of the “W”‐shaped self‐potential (SP) profiles observed on volcanoes. The primary cause of the SP pattern is shown to be the drag current associated with downward liquid flows in the unsaturated and underlying saturated fresh‐water layers within the volcanic body, which make the shallow and deep regions low and high in electric potential respectively. If a conductive structure extending to deep levels is present below the summit area, SP around the summit is substantially increased, resulting in the characteristic “W”‐shaped SP profile.
“…The combination of a “W”‐shaped SP profile and a shallow conductor near the summit has also been observed at other volcanoes. At Miyake‐jima, a shallow conductor was reported in the summit region [ Zlotnicki et al , 2003] where the potential is slightly above the reference value near the shoreline and ∼600–800 mV higher than the minimum values observed in the area surrounding the summit [ Sasai et al , 1997]. At Piton de la Fournaise (Réunion), SP decreases by ∼3 V (compared to the base station near the eastern shoreline) with a 1500 m increase in elevation, but approaching the central cone more closely the potential begins to increase and exhibits a maximum of more than 1 V [ Lénat et al , 2000], which might be brought about by the rise of the relatively conductive layer near the central cone.…”
Section: Discussionmentioning
confidence: 99%
“…Numerous self‐potential (SP) surveys of active volcanoes have been carried out during the last thirty years; e.g., Kilauea [ Zablocki , 1976], Akita Yake‐yama [ Ishido et al , 1989], Unzen [ Hashimoto and Tanaka , 1995], Miyake‐jima [e.g., Sasai et al , 1997], Izu‐Oshima [ Ishido et al , 1997] and La Fournaise [e.g., Michel and Zlotnicki , 1998]. Obvious positive‐polarity SP anomalies have often been observed around volcanic craters or vents associated with fumarole activity.…”
Numerical simulations of electrokinetic potentials were performed to investigate the mechanisms responsible for the generation of the “W”‐shaped self‐potential (SP) profiles observed on volcanoes. The primary cause of the SP pattern is shown to be the drag current associated with downward liquid flows in the unsaturated and underlying saturated fresh‐water layers within the volcanic body, which make the shallow and deep regions low and high in electric potential respectively. If a conductive structure extending to deep levels is present below the summit area, SP around the summit is substantially increased, resulting in the characteristic “W”‐shaped SP profile.
“…With the exception of the presence of ore and graphite mineralization, the self‐potential anomalies observed in geothermal field and active volcanoes are mainly associated with the flow of the groundwater [e.g., Corwin and Hoover , 1979; Goldstein et al , 1989; Zlotnicki and Nishida , 2003; Finizola et al , 2003; Jardani et al , 2008]. A vigorous flow of groundwater can generate self‐potential anomalies of the order of several hundreds of millivolts [e.g., Sasai et al , 1997; Ishido et al , 1997; Lénat et al , 1998; Lewicki et al , 2003; Aizawa et al , 2005; Finizola et al , 2006] and sometimes of several volts [ Finizola et al , 2004]. Therefore, a number of researches have been conducted to model the self‐potential anomalies in terms of groundwater flow and thermohydromechanical disturbances [ Corwin and Hoover , 1979; Revil and Pezard , 1998; Revil et al , 2004, 2005; Wilkinson et al , 2005; Jardani et al , 2006; Crespy et al , 2008; Revil et al , 2008].…”
[1] The Syabru-Bensi hot springs are located at the Main Central Thrust (MCT) zone in central Nepal. High carbon dioxide and radon exhalation fluxes (reaching 19 kg m À2 d
À1and 5 Bq m À2 s
À1, respectively) are associated with these hot springs, making this site a promising case to study the relationship between self-potential and fluids (gas and water) exhalation along a fault zone. A high-resolution self-potential map, covering an area of 100 m by 150 m that surrounds the main gas and water discharge spots, exhibits a dipolar self-potential anomaly with a negative peak reaching À180 mV at the main gas discharge spot. The positive lobe of the anomaly reaching 120 mV is located along the terraces above the main gas and water discharge spots. Several electrical resistivity tomograms were performed in this area. The resistivity tomogram crossing the degassing area shows a dipping resistive channel interpreted as a fracture zone channeling the gas and the hot water. We propose a numerical finite difference model to simulate the flow pattern in this area with the constraints imposed by the electrical resistivity tomograms, the self-potential data, the position of the gas vents, and hot water discharge area. This study provides insights on the generation of electrical currents associated with geothermal circulation in a geodynamically active area, a necessary prerequisite to study, using self-potentials, a possible modulation of the geothermal circulation by tectonic activity.
“…Utada et al (2000) recently suggested that the crustal field locally shows annual and semi‐annual variations due to meteorological temperature variation. If a volcano is located near the coast, electric currents induced by sea water motion are also known to generate the total intensity changes (Sasai et al 1997).…”
SUMMARY
We report the development of numerical procedures for extracting long‐term geomagnetic field variations caused by volcanic activity from an observed geomagnetic field by using statistical methods. The newly developed procedures are to estimate the trend from the observed data, as well as variations of non‐volcanic origin such as periodic components, components related to external geomagnetic variations and observational noise. We also aim at referring to data obtained at a remote standard geomagnetic observatory rather than using a temporarily installed reference site for reasons of data quality.
Two different approaches—a Bayesian statistical method and a Kalman filter method—are applied to decompose the geomagnetic field data into four components for comparison. The number of filter coefficients and the degree of condition realizations are optimized on the basis of minimization of the information criteria.
The two procedures were evaluated by using a synthetic data set. Generally, the results of both methods are equally sufficient. Subtle differences are seen at the first, several data points due to arbitrarily selected initial values in the case of the Kalman filter method and at the smaller residual for the Bayesian statistical method. The largest differences are in computation time and memory size. The Kalman filter method runs a thousand times faster on a testing workstation and requires less memory than the Bayesian method.
The Kalman filter method was applied to the total intensity data at Kuchi‐erabu‐jima volcano. The result suggests that the procedure works reasonably well.
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