What techniques can you use to identify outliers and anomalies in exploration data?

2 Statistical methods

Another technique to identify outliers and anomalies is to use statistical methods, such as standard deviation, z-scores, or interquartile range. These methods measure how far a data point is from the mean or median of the data, and assign a threshold or criterion to define outliers. For example, you can use the rule of thumb that any data point that is more than three standard deviations away from the mean is an outlier, or you can use the 1.5 times interquartile range rule for boxplots. Statistical methods can provide a more objective and quantitative way to identify outliers, but they can also be sensitive to the assumptions and parameters of the methods, and may not capture all types of outliers or anomalies.

3 Machine learning methods

A more advanced technique to identify outliers and anomalies is to use machine learning methods, such as clustering, classification, or anomaly detection. These methods use algorithms to learn from the data and group or label the data points based on their similarity or dissimilarity. For example, you can use clustering methods, such as k-means or DBSCAN, to partition the data into clusters, and then identify any data points that do not belong to any cluster as outliers or anomalies. You can also use classification methods, such as decision trees or random forests, to train a model to classify the data into normal or anomalous classes, based on some features or criteria. Machine learning methods can offer a more flexible and powerful way to identify outliers and anomalies, but they can also be complex, computationally intensive, and require careful tuning and validation.

4 Data cleaning and validation

Once you have identified outliers and anomalies in your exploration data, you need to decide how to handle them. One option is to remove or filter out the outliers or anomalies, if they are caused by errors, noise, or irrelevant factors. This can improve the quality and accuracy of your data and reduce the distortion or bias of your analysis. However, you need to be careful not to discard any valuable or meaningful information, and document your data cleaning and validation process. Another option is to keep or investigate the outliers or anomalies, if they are caused by real or interesting phenomena, such as mineralization, alteration, or geophysical anomalies. This can enhance your knowledge and insight of your exploration target and area, and lead to new discoveries or opportunities. However, you need to be aware of the uncertainties and limitations of your data and analysis, and verify your findings with other sources of evidence.

5 Data transformation and normalization

Another option to handle outliers and anomalies in your exploration data is to transform or normalize the data, so that the outliers or anomalies become less extreme or more consistent with the rest of the data. This can make the data more suitable for further analysis, such as statistical or machine learning methods, that require certain assumptions or conditions of the data, such as normality, linearity, or homogeneity. For example, you can use log, square root, or power transformations to reduce the skewness or variance of the data, or you can use min-max, z-score, or decimal scaling to rescale the data to a common range or scale. Data transformation and normalization can improve the performance and robustness of your analysis, but they can also alter the original meaning or distribution of the data, and introduce errors or artifacts.

6 Data exploration and interpretation

The final technique to handle outliers and anomalies in your exploration data is to explore and interpret the data, using your domain knowledge, geological understanding, and analytical skills. You can use various tools and methods, such as descriptive statistics, geostatistics, geospatial analysis, or data mining, to summarize, visualize, model, and extract information from the data. You can also use multiple sources and types of data, such as geochemical, geophysical, geological, or remote sensing data, to cross-validate, integrate, and correlate the data. Data exploration and interpretation can help you explain the causes and effects of the outliers and anomalies, and generate hypotheses and insights for your exploration project. However, you need to be critical and cautious of your assumptions and conclusions, and test them with further data collection and analysis.