Moment-tensor statistics of the 1997 Long Valley microearthquake swarm

Abstract. A simple, fast, moment-tensor inversion method using bandpass-filtered P-amplitudes was used to study the moment-tensor statistics of Long Valley caldera microearthquakes. The events were recorded in the summer of 1997, during a swarm in the caldera. The swarm was associated with geodetic extension, uplift, and subsequent moderate earthquake activity. The moment tensor solutions for 1,993 events were calculated using the new method. The majority of the resulting focal mechanisms appear to be explained in terms of double couple mechanisms. Since some events did exhibit considerable deviation from double-couples, the moment data were studied for their statistical significance. The moments of the actual data were compared to the moments of synthetic data with varying degrees of random noise in their spectra. The results of this study suggested that unless data from more than 20 stations are used and the earthquake epicenter is located inside or very close to the network area, moment-tensor inversion does not correctly resolve the non-double-couple components of microearthquakes. Analysis of the inversion residuals shows that the average noise in the P-wave spectra was close to 20%. The fluctuations of the volumetric components of the moment-tensor are in good agreement with those of the synthetic pure double-couples with 20% of added noise. Thus the moment-tensor statistics suggests that little if any volume change is required to explain the observed seismic energy release in the swarm. However, the statistics do show that a significant compensated-linear-vector-dipole component maybe present in the bulk of the seismicity. Given the network used in this study, such a component could not be precisely resolved for individual earthquakes. This possibility deserves further investigation because of its bearing on the nature of fluid-fault-earthquake processes in swarms.     

Multiparametric Traveltime Inversion

In conventional seismic processing, the classical algorithm of Hubral and Krey is routinely applied to extract an initial macrovelocity model that consists of a stack of homogeneous layers bounded by curved interfaces. Input for the algorithm are identified primary reflections together with normal moveout (NMO) velocities, as derived from a previous velocity analysis conducted on common midpoint (CMP) data. This work presents a modified version of the Hubral and Krey algorithm that is designed to extend the original version in two ways, namely (a) it makes an advantageous use of previously obtained common-reflection-surface (CRS) attributes as its input and (b) it also allows for gradient layer velocities in depth. A new strategy to recover interfaces as optimized cubic splines is also proposed. Some synthetic examples are provided to illustrate and explain the implementation of the method.     

Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping

Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach.     

近、现代黄河三角洲地貌形态反演

1855年黄河改夺大清河河道后于山东利津县东北入海。至今黄河尾癌河道历经10次大的改道变迁，塑造了近、现代黄河三角洲，形成了7个主要的亚三洲堆积体。该文利用黄河尾癌河道改道资料，近、现代河口流路变迁图，近、现代黄河三角洲体系图以及1985－1986年的水文工程地质钻孔资料，在研究黄河三角洲沉积模式的基础上，对工程地质钻孔数据进行沉积层序分析，将今论古，反推近、现代黄河三角洲沉积地貌，研究结果近似展示了近、现代黄河三角洲二维平面的发育演变和其时其地的地貌形态。