Resource evaluation drilling is usually on a regular pattern; the pattern spacing should be optimised to maximise profit from mining, accounting for the cost of drilling and the value of additional information from increased density of drilling, and also to reduce risk in mined ore tonnes and grades to an acceptable level. Conditional simulation methods for determining optimum drilling spacing are more powerful than traditional methods, and simulation methods can take into account local variability in grade. Mining profit functions can consider profit and density of evaluation drilling so that profit can be maximised.

Detailed spatial models generated by conditional simulation provide a powerful tool for case-specific optimization of sampling designs. The entire process of sampling, estimation, and decision can be simulated on such a model by a Monte-Carlo approach. Optimization can be based on economic functions or on decision quality constraints rather than simple minimization of estimation variance.

A thorough program of quality assurance/quality control (QA/QC) will enable collection of meaningful and scientifically credible samples. Quality assurance (QA) includes a range of management and technical practices designed to guarantee that the delivered product is commensurate with the intended use. For environmental- or discharge-related studies, QA ensures that the data are of adequate scientific credibility to permit statistical interpretations that lead to resource-use management decisions. 

Once the decision to drill a hole has been made it is the geologist's responsibility to insure the site is properly located in the field. The collar should then be clearly marked with 1.5 - 2.0 m high wooden marker that is spray painted, labelled and marked with flagging tape.

The Global Optimiser used by Whittle Consulting has gone through three major versions to date. The first was based on the Milawa optimisation algorithm; it worked, but had many shortcomings. The second, known internally as ProberA, had a different approach to optimisation in that it used a series of random starting points and found the nearest local NPV maximum to each.

Я.В. Самойлова Результаты рабогь по геологическому наследовании залежей фосфоритовъ въ 1915 году
А.М. Жирмунский. Геологическое изследование залежей фосфоритов въ Слободскомъ уезде Вятской губернии
Д.И. Соколова. Геологическое изелйдоваше залежей фосфоритовъ въ южной части Ореибургскаго и смежных Уральского и Актюбинском уездовъ
А.Н. Розановъ и Б.М. Даньшинъ. Геологическое изследование залежей фосфоритовъ въ Сергачскомъ уезд Нижегородской губ
А.Н. Замятин. Отчеть о геологическом изследовании фосфоритов въ восточной части Темирскаго уезда Уральской области въ 1915 г
Г.Ф. Мирчинкъ. Изследования фосфоритовых залежей Могилевской губершп
A.Н. Семихатовъ и Б.Н. Семиххатов ъ. Геологпческия изслдования фосфоритовыхъ залежей Петровскаго и Вольского уйздовъ Саратовской губернии
С.А. Добровъ. Геологическое строение и фосфориты бассейна р. Вороны (Тамбовская губ.)

1. Кандагачское фосфоритное месторождение

2. Геолого-поисковые работы в Аккемирском районе Казахской АССР в 1929 г.

1. Машинное обучение для геохимических исследований (Классификация металлогенического плодородия в дуговых магмах и понимание формирования медно-порфировых месторождений)

2. Машинное обучение для структурных построений (Структурный контроль минерализации меди, Восточный Китай)

3. Машинное обучение для прогнозирования цели поиска (на основе сети выборочного переноса)