Lopez Presents ‘Spatial Analysis and Directional Statistics Applied to the Correlation of Geochemical and Geophysical Anomalies and Fault Zones’

Dr. Dina Lopez, Professor and Chair of Geological Sciences at Ohio University, presented “Spatial Analysis and Directional Statistics Applied to the Correlation of Geochemical and Geophysical Anomalies and Fault Zones” in October at the annual meeting of the Geological Society of America in Vancouver, British Columbia.

Dr. Dina Lopez

Her co-authors were David Grimaldi, an Ohio University graduate student; Stanley Williams of Arizona State University; and  Angelica Sanchez of the Universidad Nacional de Colombia, Bogotá, Colombia.

Abstract: Geochemical and geophysical anomalies often are associated to fault zones. However, the association is usually established by visual observation instead quantified statistically. In this paper, a simple statistical approach to investigate and quantify the statistical correlation between the trend of anomalies or springs and the trace of a fault is presented. The anisotropy of the spatial distribution of anomalies is investigated for a linear trend using cluster analysis and Rayleigh test. Then the angular confidence interval for the angle of the trend of anomalies (95% confidence level) is found. If the fault trace is within the interval then a similar orientation for both can be assumed. Alternative, if the fault does not have a unique direction but slightly changes or there is a group of similar faults probably directing the fluids, then an F test for the equality of two directions is applied. The method has been used in several geothermal fields including Nevado del Ruiz Geothermal system to determine the main faults transferring fluid in the field; at Ahuachapán Geothermal field, helium anomalies in soil gases were used to statistically relate them with the EW faults of the Central American graben; and in Siena Basin and in Crati graben in Italy to determine the correlation of radon anomalies and fault traces. All these examples show that the application of statistical methods strengthens the conclusions and gives a better insight into the correlation between anomalies and faults.