A fractal approach to probabilistic seismic hazard assessment

The seismicity in the territory of China, a seismotectonically complicated region, has been examined by using three complete samples of earthquakes which occurred during the last two centuries . The b value of the Gutenberg-Richter relation was estimated by using this data sample. Taking into account the fact that the b value is spatially more stable than the a value, the b values were calculated at the nodes of a normal grid superposing on the entire area studied, and their distribution was examined. The results showed that the b value increases smoothly from 0.4 in inner-Mongolia to 0.8 in the east, south and southwest of China with higher values (b \0.8) in the Taiwan region. Furthermore, keeping fixed the obtained b values, the a value distribution was also examined. In order to display more detailed information about the seismicity, smaller cell surface (10,000 km 2 ) for the calculation of the a values was chosen. The mean return periods for different cutoff magnitudes were also calculated for each of these small cells. It was observed that the mean return periods are the shortest ones in China, which are 10 and 50 years for the magnitude larger than or equal to 6.0 and 7.0, respectively.

Section: Spatial Distribution Of a Valuesmentioning

confidence: 99%

“…The value of the a parameter characterizes the general level of seismicity in a region (RUNDLE, 1989;TURCOTTE, 1989;TURCOTTE, 1992). In contrast to the b value, the a value can change sharply spatially since it depends more on the seismicity than on the tectonic structure (PAPAZACHOS, 1997).…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution of Time-independent Seismicity in China

Qin¹,

Papadimitriou²,

Papazachos³

et al. 1999

“…Therefore, the constants of the relation are expected to remain unchanged across the magnitude range. The constant a characterizes the general level of seismicity in the examined region (GUTENBERG and RICHTER, 1954;RUNDLE, 1989;TURCOTTE, 1989TURCOTTE, , 1992PACHECO et al, 1992). The larger the a value, the higher the seismicity, whereas the b value is proven to describe the tectonic environment (MOGI, 1967;SCHOLZ, 1968).…”

Section: Introductionmentioning

confidence: 99%

The Thickness of Seismogenic Layer Under the Areas Surrounding the Ordos Block, Northern China

Qin¹

2002

A seismic approach was used to calculate the thickness distribution of the seismogenic layer under the block Ordos in northern China. This block was chosen because of its stability, the complicated tectonics around its boundaries and the completeness of the data set. Several strong earthquakes occurred in this area in the 20 th century, with the largest one (Haiyuan, M ¼ 8.4, 1920) in the southwest of the Ordos. Most of the large faults around the Ordos are strike-slip ones. The breaking point (i.e., the saturation of earthquake magnitudes M c ) of the self-similarity from small to large events based on the Gutenberg-Richter relation is calculated. Under the assumption of the L model which expresses that the offset is proportional to the length of the fault (SCHOLZ, 1982(SCHOLZ, , 1990(SCHOLZ, , 1994, the thickness (W c ) of the seismogenic layer is directly related to M c . To display smoothly the change of the thickness of the seismogenic layer and to ensure the reliability of the results, a moving cell of 150 events, with a moving step of 0.1 degree, was adopted. To take advantage of the existing data set, the ''mean value method,'' which is based on the latest complete data information to make the existing data set a 'complete' one, was used to extrapolate it so that the data covers the whole time period. The results show that the average thickness of the seismogenic layer in the southwest, in the northwest and in the east of the Ordos is around 30, 9 and 17 km, respectively. The thickness generally decreases from the southwest to the northeast. This kind of spatial variation is in agreement with the maximum depth distribution of large amounts of microearthquakes, with the correlation coefficient to be about 0.88, and the Moho depth.

“…Fractal analysis has been used to characterize spatial distribution of sources, to evaluate global scaling properties exhibited by various experimental data sets and to investigate modifications of these properties in the course of evolution of a rockmass failure process. The latter gives rise to new proposals of seismic hazard assessment (e.g., TURCOTTE, 1989;COUGHLIN and KRANZ, 1991;XIE and PARISEAU, 1992;GRASSO and VOLANT, 1994;MORTIMER and LASOCKI, 1996).…”

Section: Introductionmentioning

confidence: 99%

“…of side r occupied by point sources. If the set of sources has a statistical fractal character, the log N(r) vs. log(1/r) relation can be approximated with the straight line in a certain range of r. The slope of the line assigns the value of capacity dimension (TURCOTTE, 1989;PREUSS, 1995); -Number-radius method which is used to evaluate the cluster dimension D b . Let (x i , y i ) denote the position of epicenter i.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo Studies of Relations between Fractal Dimensions in Monofractal Data Sets

Lasocki

Luca

1998

Within the fractal approach to studying the distribution of seismic event locations, different fractal dimension definitions and estimation algorithms are in use. Although one expects that for the same data set, values of different dimensions will be different, it is usually anticipated that the direction of fractal dimension changes among different data sets will be the same for every fractal dimension.Mutual relations between the three most popular fractal dimensions, namely: the capacity, cluster and correlation dimensions, have been investigated in the present work. The studies were performed on the Monte Carlo generated data sets. The analysis has shown that dependence of the fractal dimensions on epicenter distribution, and relations among the fractal dimensions, are complex and variable. Neither values nor even inequalities among dimension estimates are preserved when different fractal dimensions are used. The correlation and the capacity dimensions seem to be good tools to trace collinear tendencies of eipicenters while the cluster dimension is more appropriate to studying uniform clustering of points.