Perbandingan Hasil Estimasi Inverse distance square dan Ordinari Kriging

Summary

In this video, Rudi presents a comparison of two estimation methods, focusing on the accuracy of resource estimation. The analysis covers data validation, the role of statistical analysis, and the importance of cross-validation in ensuring the reliability of results. Key concepts such as skewness, histogram distribution, and spatial statistics are explored, along with the application of these methods to different geological zones. Rudi emphasizes the need for a comprehensive approach to estimation, showcasing how the chosen method can impact the final results, ensuring the best method is applied for precise resource estimation.

Takeaways

• 😀 The presentation compares two resource estimation methods to assess which one provides more accurate results in geological exploration.
• 😀 Data validation is a crucial process in ensuring the accuracy of resource estimates, involving several classification categories such as geological, geophysical, and survey data.
• 😀 Statistical analysis of the data helps understand the distribution of the values, with skewness analysis determining whether the data is positively or negatively skewed.
• 😀 Cross-validation is used to evaluate the accuracy of the estimation methods. A higher R-value indicates better alignment with actual data, signifying a more accurate method.
• 😀 The data comes from various departments, including exploration and laboratory testing, and requires careful validation before it is used in estimation.
• 😀 Visualizations such as histograms and graphs are used to demonstrate the results of data analysis, showing distributions and comparing estimation results across methods.
• 😀 The presentation highlights the importance of using multiple estimation methods to cross-check results and improve overall accuracy in resource estimation.
• 😀 The mean, minimum, and maximum values are calculated for both methods, helping to assess the consistency and reliability of the estimations.
• 😀 Based on the results of the analysis, the estimation method that more closely follows the patterns of actual borehole data is preferred as the more accurate method.
• 😀 The findings suggest that one of the methods (likely 'ids') performs better in terms of accuracy and should be implemented for future estimation projects.
• 😀 The overall conclusion stresses the importance of using validated and cross-checked data to improve the quality of resource estimation in geological surveys.

Q & A

What is the primary objective of the presentation?

The primary objective of the presentation is to compare different estimation methods for resource estimation and to analyze their accuracy in order to determine which method should be applied.

Why is it important to use more than one estimation method?

It is important to use multiple estimation methods because relying on just one method may not always provide accurate results, and comparing them helps determine which one yields the best outcomes.

What are the two estimation methods used in the analysis?

The two estimation methods used in the analysis are not explicitly named in the transcript, but it is mentioned that different estimation techniques were compared to assess their accuracy.

What is the purpose of the validation process in the estimation?

The purpose of the validation process is to verify whether the data aligns with the expected results and to ensure the accuracy of the estimation by identifying any discrepancies.

How is the data validated during the exploration process?

During the exploration process, data is validated by categorizing it into four types: geological, survey, lithological, and coordinate data, each being checked for consistency and accuracy.

What role does statistical analysis play in this estimation process?

Statistical analysis is crucial for evaluating the mean, minimum, and maximum values, as well as the distribution (skewness) of the data, helping to assess the reliability and characteristics of the estimated resource.

What does the skewness value indicate in the data analysis?

Skewness measures the asymmetry of the data distribution. A positive skewness indicates that the data leans to the left, while a negative skewness suggests that the data leans to the right.

How is cross-validation used to assess the accuracy of the estimation methods?

Cross-validation is used by comparing the estimated values with actual bore data to check the accuracy of the estimation methods. A higher R-value (close to 1) indicates better accuracy.

What is the significance of the comparison between bore data and estimation results?

The comparison between bore data and estimation results helps identify which estimation method best aligns with the actual observed data, allowing for the selection of the most reliable method.

What is the main conclusion regarding the estimation methods based on the analysis?

The main conclusion is that the estimation method that produces results closest to the bore data is considered the most accurate and should be applied for further resource estimation.