Changes

==Sample density==

==Sample density==

Hydrocarbon microseepage data, whether soil gas or microbial or other geochemical measurements, are inherently noisy and require adequate sample density to distinguish between anomalous and background areas. Matthews<ref name=ch18r26 />) reviews the importance of sampling design and sampling density in target recognition. He states that undersampling is probably the major cause of ambiguity and interpretation failures involving surface geochemical studies.

Hydrocarbon microseepage data, whether soil gas or microbial or other geochemical measurements, are inherently noisy and require adequate sample density to distinguish between anomalous and background areas. Matthews<ref name=ch18r26 />) reviews the importance of sampling design and sampling density in target recognition. He states that undersampling is probably the major cause of ambiguity and interpretation failures involving surface geochemical studies.

==Recognizing anomalies==

==Recognizing anomalies==

==Example==

==Example==

The recognition of surface geochemical anomalies improves by increasing sample number and reducing sample spacing. The example below from Oklahoma illustrates the value of geochemical grids over geochemical traverses for anomaly recognition.

The recognition of surface geochemical anomalies improves by increasing sample number and reducing sample spacing. [[:file:surface-geochemical-exploration-for-petroleum_fig18-4.png|Figure 1]], from Oklahoma, illustrates the value of geochemical grids over geochemical traverses for anomaly recognition.

 

[[file:surface-geochemical-exploration-for-petroleum_fig18-4.png|thumb|{{figure number|18-4}}. Copyright: Geo-Microbial Technologies, Inc.]]

 

==See also==

==See also==

* [[Designing surface geochemical surveys]]

* [[Designing surface geochemical surveys]]