Analysis of the Goose Point area near Lacombe, Louisiana, Validates New Geophysical Data Type—Natural Sourced Electromagnetism (NSEM)—for Detection of Lineaments Associated with Faults and Sedimentary Features

Abstract

This newly patented natural sourced electromagnetism (NSEM) associated with lightning strike databases was used to reexamine an area previously interpreted by only conventional geological datasets. The available existing data was explored and integrated into a 3D framework of resistivity and permittivity data on a Landmark DecisionSpaceTM workstation. This lightning data integration project resulted in the re-interpretation of mapped faults and the introduction of several new possible faults by correlating indicative patterns of resistivity and permittivity through the data cube. The Goose Point lightning data study area covers a 110 sq. mi (285 sq. km) near Lacombe, Louisiana. The surface of the study area encompasses the Pleistocene Prairie Allow Group, Holocene marsh, and a northeast segment of Lake Pontchartrain.

Based on the years of lightning data available from projects in Texas, Louisiana, North Dakota, Michigan, and Florida, we have learned that lightning strikes are not uniformly distributed and tend to cluster. Lightning strike locations and their associated attributes are primarily controlled by shallow geological modifications of the earth's terralevis (shallow) currents. These electrical currents are influenced by lateral geological inhomogeneity caused by faults, fractures, lithology, mineralization, gas, pore-fluids, and salinity variations. Though still in its infancy, lightning data is progressing towards becoming an effective reconnaissance tool in petroleum and mineral exploration as well as geo-hazard and environmental studies.

This study area was chosen because it exhibits all of the same land change and marsh break-up characteristics as observed across much of coastal Louisiana. However, Goose Point does not possess any of the same anthropogenic influences typically assigned as causing land loss/land change. Active faults associated with the Baton Rouge fault system, crustal down-warp, and sea level rise constitute the probable natural drivers for subsidence and land change in this area.

Existing datasets utilized in this study include: light detection and ranging (LIDAR), a geologic map of the region, high resolution sparker data, several shallow cores, three types of NSEM attribute data, and resistivity and permittivity data. Rise time, rate of rise time, and strike-density were used to identify and interpret lineaments and patterns related to known faults, transforms, and channel features.

The high resolution sparker data were used to tie surface fault interpretations to 3D lightning resistivity and permittivity volumes. Collectively, these ties and comparisons resulted in a re-interpretation of the existing fault data resulting in the identification of faults not previously mapped. While each region's geology is different, we now have more insight into the role and benefits of lightning strike data as a platform for linking sparse surface and shallow data types into an improved, more coherent interpretation of the subsurface.

AAPG Datapages/Search and Discovery Article #90219 © 2015 GCAGS, Houston, Texas, September 20-22, 2015